+++ /dev/null
-#!/bin/sh
-cd `dirname $0`/..
-
-ANDROID_DIR=`pwd`
-
-rm -rf package
-mkdir -p package
-cd package
-
-PRG_DIR=`pwd`
-mkdir opencv
-
-# neon-enabled build
-#cd $PRG_DIR
-#mkdir build-neon
-#cd build-neon
-
-#cmake -DANDROID_ABI="armeabi-v7a with NEON" -DBUILD_DOCS=OFF -DBUILD_TESTS=OFF -DBUILD_EXAMPLES=OFF -DBUILD_ANDROID_EXAMPLES=OFF -DCMAKE_TOOLCHAIN_FILE="$ANDROID_DIR/android.toolchain.cmake" -DCMAKE_INSTALL_PREFIX="$PRG_DIR/opencv" "$ANDROID_DIR/.." || exit 1
-#make -j8 install/strip || exit 1
-
-#cd "$PRG_DIR/opencv"
-#rm -rf doc include src .classpath .project AndroidManifest.xml default.properties share/OpenCV/haarcascades share/OpenCV/lbpcascades share/OpenCV/*.cmake share/OpenCV/OpenCV.mk
-#mv libs/armeabi-v7a libs/armeabi-v7a-neon
-#mv share/OpenCV/3rdparty/libs/armeabi-v7a share/OpenCV/3rdparty/libs/armeabi-v7a-neon
-
-
-# armeabi-v7a build
-cd "$PRG_DIR"
-mkdir build
-cd build
-
-cmake -DANDROID_ABI="armeabi-v7a" -DBUILD_DOCS=OFF -DBUILD_TESTS=ON -DBUILD_EXAMPLES=OFF -DBUILD_ANDROID_EXAMPLES=ON -DCMAKE_TOOLCHAIN_FILE="$ANDROID_DIR/android.toolchain.cmake" -DCMAKE_INSTALL_PREFIX="$PRG_DIR/opencv" "$ANDROID_DIR/.." || exit 1
-make -j8 install/strip || exit 1
-
-cd "$PRG_DIR/opencv"
-rm -rf doc include src .classpath .project AndroidManifest.xml default.properties project.properties share/OpenCV/haarcascades share/OpenCV/lbpcascades share/OpenCV/*.cmake share/OpenCV/OpenCV.mk
-
-
-# armeabi build
-cd "$PRG_DIR/build"
-rm -rf CMakeCache.txt
-
-cmake -DANDROID_ABI="armeabi" -DBUILD_DOCS=ON -DBUILD_TESTS=ON -DBUILD_EXAMPLES=OFF -DBUILD_ANDROID_EXAMPLES=ON -DINSTALL_ANDROID_EXAMPLES=ON -DCMAKE_TOOLCHAIN_FILE="$ANDROID_DIR/android.toolchain.cmake" -DCMAKE_INSTALL_PREFIX="$PRG_DIR/opencv" "$ANDROID_DIR/.." || exit 1
-make -j8 install/strip docs || exit 1
-
-find doc -name "*.pdf" -exec cp {} $PRG_DIR/opencv/doc \;
-
-cd $PRG_DIR
-rm -rf opencv/doc/CMakeLists.txt
-cp "$ANDROID_DIR/README.android" opencv/
-cp "$ANDROID_DIR/../README" opencv/
-
-
-# get opencv version
-CV_VERSION=`grep -o "[0-9]\+\.[0-9]\+\.[0-9]\+" opencv/share/OpenCV/OpenCVConfig-version.cmake`
-OPENCV_NAME=OpenCV-$CV_VERSION
-mv opencv $OPENCV_NAME
-
-#samples
-cp -r "$ANDROID_DIR/../samples/android" "$PRG_DIR/samples"
-cd "$PRG_DIR/samples"
-
-#enable for loops over items with spaces in their name
-IFS="
-"
-for dir in `ls -1`
-do
- if [ -f "$dir/default.properties" ]
- then
- HAS_REFERENCE=`cat "$dir/project.properties" | grep -c android.library.reference.1`
- if [ $HAS_REFERENCE = 1 ]
- then
- echo -n > "$dir/project.properties"
- android update project --name "$dir" --target "android-8" --library "../../$OPENCV_NAME" --path "$dir"
- #echo 'android update project --name "$dir" --target "android-8" --library "../opencv$CV_VERSION" --path "$dir"'
- fi
- else
- if [ -f "$dir/default.properties" ]
- then
- HAS_REFERENCE=`cat "$dir/default.properties" | grep -c android.library.reference.1`
- if [ $HAS_REFERENCE = 1 ]
- then
- echo -n > "$dir/default.properties"
- android update project --name "$dir" --target "android-8" --library "../../$OPENCV_NAME" --path "$dir"
- #echo 'android update project --name "$dir" --target "android-8" --library "../opencv$CV_VERSION" --path "$dir"'
- fi
- else
- rm -rf "$dir"
- fi
- fi
-done
-
-echo "OPENCV_MK_PATH:=../../$OPENCV_NAME/share/OpenCV/OpenCV.mk" > includeOpenCV.mk
-
-
-#clean samples
-cd "$PRG_DIR/samples"
-#remove ignored files/folders
-svn status --no-ignore | grep ^I | cut -c9- | xargs -d \\n rm -rf
-#remove unversioned files/folders
-svn status | grep ^\? | cut -c9- | xargs -d \\n rm -rf
-
-
-#generate "gen" folders to eliminate eclipse warnings
-cd "$PRG_DIR/samples"
-for dir in `ls -1`
-do
- if [ -d "$dir" ]
- then
- mkdir "$dir/gen"
- fi
-done
-
-
-#generate folders "gen" and "res" for opencv (dummy eclipse stiff)
-cd $PRG_DIR
-mkdir "$OPENCV_NAME/gen"
-mkdir "$OPENCV_NAME/res"
-
-# pack all files
-cd $PRG_DIR
-PRG_NAME=OpenCV-$CV_VERSION-tp-android-bin.tar.bz2
-tar cjpf $PRG_NAME --exclude-vcs $OPENCV_NAME samples || exit -1
-echo
-echo "Package $PRG_NAME is successfully created"
if(ENABLE_SOLUTION_FOLDERS)
set_target_properties(uninstall PROPERTIES FOLDER "CMakeTargets")
endif()
-
+
# ----------------------------------------------------------------------------
# Source package, for "make package_source"
set(TAR_TRANSFORM "\"s,^,${TARBALL_NAME}/,\"")
add_custom_target(package_source
#TODO: maybe we should not remove dll's
- COMMAND ${TAR_CMD} --transform ${TAR_TRANSFORM} -cjpf ${CMAKE_CURRENT_BINARY_DIR}/${TARBALL_NAME}.tar.bz2 --exclude=".svn" --exclude="*.pyc" --exclude="*.vcproj" --exclude="*/lib/*" --exclude="*.dll" ./
+ COMMAND ${TAR_CMD} --transform ${TAR_TRANSFORM} -cjpf ${CMAKE_CURRENT_BINARY_DIR}/${TARBALL_NAME}.tar.bz2 --exclude=".svn" --exclude=".git" --exclude="*.pyc" --exclude="*.vcproj" --exclude="*/lib/*" --exclude="*.dll" ./
WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})
else()
add_custom_target(package_source
- COMMAND zip -9 -r ${CMAKE_CURRENT_BINARY_DIR}/${TARBALL_NAME}.zip . -x '*/.svn/*' '*.vcproj' '*.pyc'
+ COMMAND zip -9 -r ${CMAKE_CURRENT_BINARY_DIR}/${TARBALL_NAME}.zip . -x '*/.svn/*' '*/.git/*' '*.vcproj' '*.pyc'
WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})
endif()
if(ENABLE_SOLUTION_FOLDERS)
%
% creating matrices
% from scratch
-% from previously allocated data: plain arrays, vectors
+% from previously allocated data: plain arrays, vectors
% converting to/from old-style structures
%
% element access, iteration through matrix elements
% color space transformations
% histograms & back projections
% contours
-%
+%
% i/o:
% displaying images
% saving/loading to/from file (XML/YAML & image file formats)
% findcontours, bounding box, convex hull, min area rect,
% transformations, to/from homogeneous coordinates
% matching point sets: homography, fundamental matrix, rigid transforms
-%
+%
% 3d:
% camera calibration, pose estimation.
% uncalibrated case
% stereo: rectification, running stereo correspondence, obtaining the depth.
-%
+%
% feature detection:
% features2d toolbox
-%
+%
% object detection:
% using a classifier running on a sliding window: cascadeclassifier + hog.
% using salient point features: features2d -> matching
-%
+%
% statistical data processing:
% clustering (k-means),
% classification + regression (SVM, boosting, k-nearest),
%\texttt{\href{http://www.ros.org/wiki/Stack Manifest}{stack manifest}} & Description of a ROS stack.
%\end{tabular}
-\emph{The OpenCV C++ reference manual is here: \url{http://opencv.itseez.com}. Use \textbf{Quick Search} to find descriptions of the particular functions and classes}
+\emph{The OpenCV C++ reference manual is here: \url{http://docs.opencv.org}. Use \textbf{Quick Search} to find descriptions of the particular functions and classes}
\section{Key OpenCV Classes}
\begin{tabular}{@{}p{\the\MyLen}%
@{}p{\linewidth-\the\MyLen}@{}}
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/basic_structures.html\#Point_}{Point\_}} & Template 2D point class \\
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/basic_structures.html\#Point3_}{Point3\_}} & Template 3D point class \\
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/basic_structures.html\#Size_}{Size\_}} & Template size (width, height) class \\
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/basic_structures.html\#Vec}{Vec}} & Template short vector class \\
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/basic_structures.html\#Matx}{Matx}} & Template small matrix class \\
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/basic_structures.html\#Scalar_}{Scalar}} & 4-element vector \\
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/basic_structures.html\#Rect_}{Rect}} & Rectangle \\
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/basic_structures.html\#Range}{Range}} & Integer value range \\
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/basic_structures.html\#Mat}{Mat}} & 2D or multi-dimensional dense array (can be used to store matrices, images, histograms, feature descriptors, voxel volumes etc.)\\
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/basic_structures.html\#sparsemat}{SparseMat}} & Multi-dimensional sparse array \\
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/basic_structures.html\#Ptr}{Ptr}} & Template smart pointer class
+\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#Point_}{Point\_}} & Template 2D point class \\
+\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#Point3_}{Point3\_}} & Template 3D point class \\
+\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#Size_}{Size\_}} & Template size (width, height) class \\
+\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#Vec}{Vec}} & Template short vector class \\
+\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#Matx}{Matx}} & Template small matrix class \\
+\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#Scalar_}{Scalar}} & 4-element vector \\
+\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#Rect_}{Rect}} & Rectangle \\
+\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#Range}{Range}} & Integer value range \\
+\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#Mat}{Mat}} & 2D or multi-dimensional dense array (can be used to store matrices, images, histograms, feature descriptors, voxel volumes etc.)\\
+\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#sparsemat}{SparseMat}} & Multi-dimensional sparse array \\
+\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#Ptr}{Ptr}} & Template smart pointer class
\end{tabular}
\section{Matrix Basics}
\> \texttt{Mat image(240, 320, CV\_8UC3);} \\
\textbf{[Re]allocate a pre-declared matrix}\\
-\> \texttt{image.\href{http://opencv.itseez.com/modules/core/doc/basic_structures.html\#mat-create}{create}(480, 640, CV\_8UC3);}\\
+\> \texttt{image.\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#mat-create}{create}(480, 640, CV\_8UC3);}\\
\textbf{Create a matrix initialized with a constant}\\
\> \texttt{Mat A33(3, 3, CV\_32F, Scalar(5));} \\
\> \texttt{Mat B22 = Mat(2, 2, CV\_32F, B22data).clone();}\\
\textbf{Initialize a random matrix}\\
-\> \texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#randu}{randu}(image, Scalar(0), Scalar(256)); }\textit{// uniform dist}\\
-\> \texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#randn}{randn}(image, Scalar(128), Scalar(10)); }\textit{// Gaussian dist}\\
+\> \texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#randu}{randu}(image, Scalar(0), Scalar(256)); }\textit{// uniform dist}\\
+\> \texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#randn}{randn}(image, Scalar(128), Scalar(10)); }\textit{// Gaussian dist}\\
\textbf{Convert matrix to/from other structures}\\
\>\textbf{(without copying the data)}\\
\section{Matrix Manipulations: Copying, Shuffling, Part Access}
\begin{tabular}{@{}p{\the\MyLen}%
@{}p{\linewidth-\the\MyLen}@{}}
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/basic_structures.html\#mat-copyto}{src.copyTo(dst)}} & Copy matrix to another one \\
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/basic_structures.html\#mat-convertto}{src.convertTo(dst,type,scale,shift)}} & \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ Scale and convert to another datatype \\
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/basic_structures.html\#mat-clone}{m.clone()}} & Make deep copy of a matrix \\
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/basic_structures.html\#mat-reshape}{m.reshape(nch,nrows)}} & Change matrix dimensions and/or number of channels without copying data \\
+\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#mat-copyto}{src.copyTo(dst)}} & Copy matrix to another one \\
+\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#mat-convertto}{src.convertTo(dst,type,scale,shift)}} & \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ Scale and convert to another datatype \\
+\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#mat-clone}{m.clone()}} & Make deep copy of a matrix \\
+\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#mat-reshape}{m.reshape(nch,nrows)}} & Change matrix dimensions and/or number of channels without copying data \\
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/basic_structures.html\#mat-row}{m.row(i)}},
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/basic_structures.html\#mat-col}{m.col(i)}} & Take a matrix row/column \\
+\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#mat-row}{m.row(i)}},
+\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#mat-col}{m.col(i)}} & Take a matrix row/column \\
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/basic_structures.html\#mat-rowrange}{m.rowRange(Range(i1,i2))}}
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/basic_structures.html\#mat-colrange}{m.colRange(Range(j1,j2))}} & \ \ \ \ \ \ \ Take a matrix row/column span \\
+\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#mat-rowrange}{m.rowRange(Range(i1,i2))}}
+\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#mat-colrange}{m.colRange(Range(j1,j2))}} & \ \ \ \ \ \ \ Take a matrix row/column span \\
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/basic_structures.html\#mat-diag}{m.diag(i)}} & Take a matrix diagonal \\
+\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#mat-diag}{m.diag(i)}} & Take a matrix diagonal \\
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/basic_structures.html\#Mat}{m(Range(i1,i2),Range(j1,j2)), m(roi)}} & \ \ \ \ \ \ \ \ \ \ \ \ \ Take a submatrix \\
+\texttt{\href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#Mat}{m(Range(i1,i2),Range(j1,j2)), m(roi)}} & \ \ \ \ \ \ \ \ \ \ \ \ \ Take a submatrix \\
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#repeat}{m.repeat(ny,nx)}} & Make a bigger matrix from a smaller one \\
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#repeat}{m.repeat(ny,nx)}} & Make a bigger matrix from a smaller one \\
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#flip}{flip(src,dst,dir)}} & Reverse the order of matrix rows and/or columns \\
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#flip}{flip(src,dst,dir)}} & Reverse the order of matrix rows and/or columns \\
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#split}{split(...)}} & Split multi-channel matrix into separate channels \\
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#split}{split(...)}} & Split multi-channel matrix into separate channels \\
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#merge}{merge(...)}} & Make a multi-channel matrix out of the separate channels \\
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#merge}{merge(...)}} & Make a multi-channel matrix out of the separate channels \\
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#mixchannels}{mixChannels(...)}} & Generalized form of split() and merge() \\
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#mixchannels}{mixChannels(...)}} & Generalized form of split() and merge() \\
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#randshuffle}{randShuffle(...)}} & Randomly shuffle matrix elements \\
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#randshuffle}{randShuffle(...)}} & Randomly shuffle matrix elements \\
\end{tabular}
\begin{itemize}
\item
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#add}{add()}},
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#subtract}{subtract()}},
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#multiply}{multiply()}},
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#divide}{divide()}},
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#absdiff}{absdiff()}},
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#bitwise-and}{bitwise\_and()}},
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#bitwise-or}{bitwise\_or()}},
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#bitwise-xor}{bitwise\_xor()}},
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#max}{max()}},
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#min}{min()}},
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#compare}{compare()}}
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#add}{add()}},
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#subtract}{subtract()}},
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#multiply}{multiply()}},
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#divide}{divide()}},
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#absdiff}{absdiff()}},
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#bitwise-and}{bitwise\_and()}},
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#bitwise-or}{bitwise\_or()}},
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#bitwise-xor}{bitwise\_xor()}},
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#max}{max()}},
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#min}{min()}},
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#compare}{compare()}}
-- correspondingly, addition, subtraction, element-wise multiplication ... comparison of two matrices or a matrix and a scalar.
\item
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#sum}{sum()}},
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#mean}{mean()}},
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#meanstddev}{meanStdDev()}},
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#norm}{norm()}},
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#countnonzero}{countNonZero()}},
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#minmaxloc}{minMaxLoc()}},
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#sum}{sum()}},
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#mean}{mean()}},
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#meanstddev}{meanStdDev()}},
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#norm}{norm()}},
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#countnonzero}{countNonZero()}},
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#minmaxloc}{minMaxLoc()}},
-- various statistics of matrix elements.
\item
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#exp}{exp()}},
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#log}{log()}},
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#pow}{pow()}},
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#sqrt}{sqrt()}},
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#carttopolar}{cartToPolar()}},
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#polartocart}{polarToCart()}}
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#exp}{exp()}},
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#log}{log()}},
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#pow}{pow()}},
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#sqrt}{sqrt()}},
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#carttopolar}{cartToPolar()}},
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#polartocart}{polarToCart()}}
-- the classical math functions.
\item
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#scaleadd}{scaleAdd()}},
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#transpose}{transpose()}},
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#gemm}{gemm()}},
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#invert}{invert()}},
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#solve}{solve()}},
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#determinant}{determinant()}},
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#trace}{trace()}},
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#eigen}{eigen()}},
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#SVD}{SVD}},
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#scaleadd}{scaleAdd()}},
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#transpose}{transpose()}},
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#gemm}{gemm()}},
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#invert}{invert()}},
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#solve}{solve()}},
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#determinant}{determinant()}},
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#trace}{trace()}},
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#eigen}{eigen()}},
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#SVD}{SVD}},
-- the algebraic functions + SVD class.
\item
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#dft}{dft()}},
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#idft}{idft()}},
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#dct}{dct()}},
-\texttt{\href{http://opencv.itseez.com/modules/core/doc/operations_on_arrays.html\#idct}{idct()}},
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#dft}{dft()}},
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#idft}{idft()}},
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#dct}{dct()}},
+\texttt{\href{http://docs.opencv.org/modules/core/doc/operations_on_arrays.html\#idct}{idct()}},
--- discrete Fourier and cosine transformations
+-- discrete Fourier and cosine transformations
\end{itemize}
-For some operations a more convenient \href{http://opencv.itseez.com/modules/core/doc/basic_structures.html\#matrix-expressions}{algebraic notation} can be used, for example:
+For some operations a more convenient \href{http://docs.opencv.org/modules/core/doc/basic_structures.html\#matrix-expressions}{algebraic notation} can be used, for example:
\begin{tabbing}
\texttt{Mat}\={} \texttt{delta = (J.t()*J + lambda*}\\
\>\texttt{Mat::eye(J.cols, J.cols, J.type()))}\\
\begin{tabular}{@{}p{\the\MyLen}%
@{}p{\linewidth-\the\MyLen}@{}}
-\texttt{\href{http://opencv.itseez.com/modules/imgproc/doc/filtering.html\#filter2d}{filter2D()}} & Non-separable linear filter \\
+\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/filtering.html\#filter2d}{filter2D()}} & Non-separable linear filter \\
-\texttt{\href{http://opencv.itseez.com/modules/imgproc/doc/filtering.html\#sepfilter2d}{sepFilter2D()}} & Separable linear filter \\
+\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/filtering.html\#sepfilter2d}{sepFilter2D()}} & Separable linear filter \\
-\texttt{\href{http://opencv.itseez.com/modules/imgproc/doc/filtering.html\#blur}{boxFilter()}}, \texttt{\href{http://opencv.itseez.com/modules/imgproc/doc/filtering.html\#gaussianblur}{GaussianBlur()}},
-\texttt{\href{http://opencv.itseez.com/modules/imgproc/doc/filtering.html\#medianblur}{medianBlur()}},
-\texttt{\href{http://opencv.itseez.com/modules/imgproc/doc/filtering.html\#bilateralfilter}{bilateralFilter()}}
+\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/filtering.html\#blur}{boxFilter()}}, \texttt{\href{http://docs.opencv.org/modules/imgproc/doc/filtering.html\#gaussianblur}{GaussianBlur()}},
+\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/filtering.html\#medianblur}{medianBlur()}},
+\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/filtering.html\#bilateralfilter}{bilateralFilter()}}
& Smooth the image with one of the linear or non-linear filters \\
-\texttt{\href{http://opencv.itseez.com/modules/imgproc/doc/filtering.html\#sobel}{Sobel()}}, \texttt{\href{http://opencv.itseez.com/modules/imgproc/doc/filtering.html\#scharr}{Scharr()}}
+\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/filtering.html\#sobel}{Sobel()}}, \texttt{\href{http://docs.opencv.org/modules/imgproc/doc/filtering.html\#scharr}{Scharr()}}
& Compute the spatial image derivatives \\
-\texttt{\href{http://opencv.itseez.com/modules/imgproc/doc/filtering.html\#laplacian}{Laplacian()}} & compute Laplacian: $\Delta I = \frac{\partial ^ 2 I}{\partial x^2} + \frac{\partial ^ 2 I}{\partial y^2}$ \\
+\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/filtering.html\#laplacian}{Laplacian()}} & compute Laplacian: $\Delta I = \frac{\partial ^ 2 I}{\partial x^2} + \frac{\partial ^ 2 I}{\partial y^2}$ \\
-\texttt{\href{http://opencv.itseez.com/modules/imgproc/doc/filtering.html\#erode}{erode()}}, \texttt{\href{http://opencv.itseez.com/modules/imgproc/doc/filtering.html\#dilate}{dilate()}} & Morphological operations \\
+\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/filtering.html\#erode}{erode()}}, \texttt{\href{http://docs.opencv.org/modules/imgproc/doc/filtering.html\#dilate}{dilate()}} & Morphological operations \\
\end{tabular}
\begin{tabular}{@{}p{\the\MyLen}%
@{}p{\linewidth-\the\MyLen}@{}}
-\texttt{\href{http://opencv.itseez.com/modules/imgproc/doc/geometric_transformations.html\#resize}{resize()}} & Resize image \\
+\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/geometric_transformations.html\#resize}{resize()}} & Resize image \\
-\texttt{\href{http://opencv.itseez.com/modules/imgproc/doc/geometric_transformations.html\#getrectsubpix}{getRectSubPix()}} & Extract an image patch \\
+\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/geometric_transformations.html\#getrectsubpix}{getRectSubPix()}} & Extract an image patch \\
-\texttt{\href{http://opencv.itseez.com/modules/imgproc/doc/geometric_transformations.html\#warpaffine}{warpAffine()}} & Warp image affinely\\
+\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/geometric_transformations.html\#warpaffine}{warpAffine()}} & Warp image affinely\\
-\texttt{\href{http://opencv.itseez.com/modules/imgproc/doc/geometric_transformations.html\#warpperspective}{warpPerspective()}} & Warp image perspectively\\
+\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/geometric_transformations.html\#warpperspective}{warpPerspective()}} & Warp image perspectively\\
-\texttt{\href{http://opencv.itseez.com/modules/imgproc/doc/geometric_transformations.html\#remap}{remap()}} & Generic image warping\\
+\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/geometric_transformations.html\#remap}{remap()}} & Generic image warping\\
-\texttt{\href{http://opencv.itseez.com/modules/imgproc/doc/geometric_transformations.html\#convertmaps}{convertMaps()}} & Optimize maps for a faster remap() execution\\
+\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/geometric_transformations.html\#convertmaps}{convertMaps()}} & Optimize maps for a faster remap() execution\\
\end{tabular}
\begin{tabular}{@{}p{\the\MyLen}%
@{}p{\linewidth-\the\MyLen}@{}}
-\texttt{\href{http://opencv.itseez.com/modules/imgproc/doc/miscellaneous_transformations.html\#cvtcolor}{cvtColor()}} & Convert image from one color space to another \\
+\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/miscellaneous_transformations.html\#cvtcolor}{cvtColor()}} & Convert image from one color space to another \\
-\texttt{\href{http://opencv.itseez.com/modules/imgproc/doc/miscellaneous_transformations.html\#threshold}{threshold()}}, \texttt{\href{http://opencv.itseez.com/modules/imgproc/doc/miscellaneous_transformations.html\#adaptivethreshold}{adaptivethreshold()}} & Convert grayscale image to binary image using a fixed or a variable threshold \\
+\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/miscellaneous_transformations.html\#threshold}{threshold()}}, \texttt{\href{http://docs.opencv.org/modules/imgproc/doc/miscellaneous_transformations.html\#adaptivethreshold}{adaptivethreshold()}} & Convert grayscale image to binary image using a fixed or a variable threshold \\
-\texttt{\href{http://opencv.itseez.com/modules/imgproc/doc/miscellaneous_transformations.html\#floodfill}{floodFill()}} & Find a connected component using region growing algorithm\\
+\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/miscellaneous_transformations.html\#floodfill}{floodFill()}} & Find a connected component using region growing algorithm\\
-\texttt{\href{http://opencv.itseez.com/modules/imgproc/doc/miscellaneous_transformations.html\#integral}{integral()}} & Compute integral image \\
+\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/miscellaneous_transformations.html\#integral}{integral()}} & Compute integral image \\
-\texttt{\href{http://opencv.itseez.com/modules/imgproc/doc/miscellaneous_transformations.html\#distancetransform}{distanceTransform()}}
+\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/miscellaneous_transformations.html\#distancetransform}{distanceTransform()}}
& build distance map or discrete Voronoi diagram for a binary image. \\
-\texttt{\href{http://opencv.itseez.com/modules/imgproc/doc/miscellaneous_transformations.html\#watershed}{watershed()}},
-\texttt{\href{http://opencv.itseez.com/modules/imgproc/doc/miscellaneous_transformations.html\#grabcut}{grabCut()}}
+\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/miscellaneous_transformations.html\#watershed}{watershed()}},
+\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/miscellaneous_transformations.html\#grabcut}{grabCut()}}
& marker-based image segmentation algorithms.
- See the samples \texttt{\href{http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/watershed.cpp}{watershed.cpp}} and \texttt{\href{http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/grabcut.cpp}{grabcut.cpp}}.
+ See the samples \texttt{\href{http://code.opencv.org/projects/opencv/repository/revisions/master/entry/samples/cpp/watershed.cpp}{watershed.cpp}} and \texttt{\href{http://code.opencv.org/projects/opencv/repository/revisions/master/entry/samples/cpp/grabcut.cpp}{grabcut.cpp}}.
\end{tabular}
\begin{tabular}{@{}p{\the\MyLen}%
@{}p{\linewidth-\the\MyLen}@{}}
-\texttt{\href{http://opencv.itseez.com/modules/imgproc/doc/histograms.html\#calchist}{calcHist()}} & Compute image(s) histogram \\
+\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/histograms.html\#calchist}{calcHist()}} & Compute image(s) histogram \\
-\texttt{\href{http://opencv.itseez.com/modules/imgproc/doc/histograms.html\#calcbackproject}{calcBackProject()}} & Back-project the histogram \\
+\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/histograms.html\#calcbackproject}{calcBackProject()}} & Back-project the histogram \\
-\texttt{\href{http://opencv.itseez.com/modules/imgproc/doc/histograms.html\#equalizehist}{equalizeHist()}} & Normalize image brightness and contrast\\
+\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/histograms.html\#equalizehist}{equalizeHist()}} & Normalize image brightness and contrast\\
-\texttt{\href{http://opencv.itseez.com/modules/imgproc/doc/histograms.html\#comparehist}{compareHist()}} & Compare two histograms\\
+\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/histograms.html\#comparehist}{compareHist()}} & Compare two histograms\\
\end{tabular}
\end{tabbing}
\subsection{Contours}
-See \texttt{\href{http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/contours2.cpp}{contours2.cpp}} and \texttt{\href{http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/squares.cpp}{squares.cpp}}
+See \texttt{\href{http://code.opencv.org/projects/opencv/repository/revisions/master/entry/samples/cpp/contours2.cpp}{contours2.cpp}} and \texttt{\href{http://code.opencv.org/projects/opencv/repository/revisions/master/entry/samples/cpp/squares.cpp}{squares.cpp}}
samples on what are the contours and how to use them.
\section{Data I/O}
-\href{http://opencv.itseez.com/modules/core/doc/xml_yaml_persistence.html\#xml-yaml-file-storages-writing-to-a-file-storage}{XML/YAML storages} are collections (possibly nested) of scalar values, structures and heterogeneous lists.
+\href{http://docs.opencv.org/modules/core/doc/xml_yaml_persistence.html\#xml-yaml-file-storages-writing-to-a-file-storage}{XML/YAML storages} are collections (possibly nested) of scalar values, structures and heterogeneous lists.
\begin{tabbing}
\textbf{Wr}\=\textbf{iting data to YAML (or XML)}\\
\texttt{Rect r; r.x = (int)tm["x"], r.y = (int)tm["y"];}\\
\texttt{r.width = (int)tm["width"], r.height = (int)tm["height"];}\\
-
+
\texttt{int lbp\_val = 0;}\\
\texttt{FileNodeIterator it = tm["lbp"].begin();}\\
\begin{tabbing}
\textbf{Wr}\=\textbf{iting and reading raster images}\\
-\texttt{\href{http://opencv.itseez.com/modules/highgui/doc/reading_and_writing_images_and_video.html\#imwrite}{imwrite}("myimage.jpg", image);}\\
-\texttt{Mat image\_color\_copy = \href{http://opencv.itseez.com/modules/highgui/doc/reading_and_writing_images_and_video.html\#imread}{imread}("myimage.jpg", 1);}\\
-\texttt{Mat image\_grayscale\_copy = \href{http://opencv.itseez.com/modules/highgui/doc/reading_and_writing_images_and_video.html\#imread}{imread}("myimage.jpg", 0);}\\
+\texttt{\href{http://docs.opencv.org/modules/highgui/doc/reading_and_writing_images_and_video.html\#imwrite}{imwrite}("myimage.jpg", image);}\\
+\texttt{Mat image\_color\_copy = \href{http://docs.opencv.org/modules/highgui/doc/reading_and_writing_images_and_video.html\#imread}{imread}("myimage.jpg", 1);}\\
+\texttt{Mat image\_grayscale\_copy = \href{http://docs.opencv.org/modules/highgui/doc/reading_and_writing_images_and_video.html\#imread}{imread}("myimage.jpg", 0);}\\
\end{tabbing}
\emph{The functions can read/write images in the following formats: \textbf{BMP (.bmp), JPEG (.jpg, .jpeg), TIFF (.tif, .tiff), PNG (.png), PBM/PGM/PPM (.p?m), Sun Raster (.sr), JPEG 2000 (.jp2)}. Every format supports 8-bit, 1- or 3-channel images. Some formats (PNG, JPEG 2000) support 16 bits per channel.}
\begin{tabular}{@{}p{\the\MyLen}%
@{}p{\linewidth-\the\MyLen}@{}}
-\texttt{\href{http://opencv.itseez.com/modules/highgui/doc/user_interface.html\#namedwindow}{namedWindow(winname,flags)}} & \ \ \ \ \ \ \ \ \ \ Create named highgui window \\
+\texttt{\href{http://docs.opencv.org/modules/highgui/doc/user_interface.html\#namedwindow}{namedWindow(winname,flags)}} & \ \ \ \ \ \ \ \ \ \ Create named highgui window \\
-\texttt{\href{http://opencv.itseez.com/modules/highgui/doc/user_interface.html\#destroywindow}{destroyWindow(winname)}} & \ \ \ Destroy the specified window \\
+\texttt{\href{http://docs.opencv.org/modules/highgui/doc/user_interface.html\#destroywindow}{destroyWindow(winname)}} & \ \ \ Destroy the specified window \\
-\texttt{\href{http://opencv.itseez.com/modules/highgui/doc/user_interface.html\#imshow}{imshow(winname, mtx)}} & Show image in the window \\
+\texttt{\href{http://docs.opencv.org/modules/highgui/doc/user_interface.html\#imshow}{imshow(winname, mtx)}} & Show image in the window \\
-\texttt{\href{http://opencv.itseez.com/modules/highgui/doc/user_interface.html\#waitkey}{waitKey(delay)}} & Wait for a key press during the specified time interval (or forever). Process events while waiting. \emph{Do not forget to call this function several times a second in your code.} \\
+\texttt{\href{http://docs.opencv.org/modules/highgui/doc/user_interface.html\#waitkey}{waitKey(delay)}} & Wait for a key press during the specified time interval (or forever). Process events while waiting. \emph{Do not forget to call this function several times a second in your code.} \\
-\texttt{\href{http://opencv.itseez.com/modules/highgui/doc/user_interface.html\#createtrackbar}{createTrackbar(...)}} & Add trackbar (slider) to the specified window \\
+\texttt{\href{http://docs.opencv.org/modules/highgui/doc/user_interface.html\#createtrackbar}{createTrackbar(...)}} & Add trackbar (slider) to the specified window \\
-\texttt{\href{http://opencv.itseez.com/modules/highgui/doc/user_interface.html\#setmousecallback}{setMouseCallback(...)}} & \ \ Set the callback on mouse clicks and movements in the specified window \\
+\texttt{\href{http://docs.opencv.org/modules/highgui/doc/user_interface.html\#setmousecallback}{setMouseCallback(...)}} & \ \ Set the callback on mouse clicks and movements in the specified window \\
\end{tabular}
-See \texttt{\href{http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/camshiftdemo.cpp}{camshiftdemo.cpp}} and other \href{http://code.opencv.org/svn/opencv/trunk/opencv/samples/}{OpenCV samples} on how to use the GUI functions.
+See \texttt{\href{http://code.opencv.org/projects/opencv/repository/revisions/master/entry/samples/cpp/camshiftdemo.cpp}{camshiftdemo.cpp}} and other \href{http://code.opencv.org/projects/opencv/repository/revisions/master/entry/samples/}{OpenCV samples} on how to use the GUI functions.
\section{Camera Calibration, Pose Estimation and Depth Estimation}
\begin{tabular}{@{}p{\the\MyLen}%
@{}p{\linewidth-\the\MyLen}@{}}
-\texttt{\href{http://opencv.itseez.com/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html\#calibratecamera}{calibrateCamera()}} & Calibrate camera from several views of a calibration pattern. \\
+\texttt{\href{http://docs.opencv.org/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html\#calibratecamera}{calibrateCamera()}} & Calibrate camera from several views of a calibration pattern. \\
-\texttt{\href{http://opencv.itseez.com/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html\#findchessboardcorners}{findChessboardCorners()}} & \ \ \ \ \ \ Find feature points on the checkerboard calibration pattern. \\
+\texttt{\href{http://docs.opencv.org/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html\#findchessboardcorners}{findChessboardCorners()}} & \ \ \ \ \ \ Find feature points on the checkerboard calibration pattern. \\
-\texttt{\href{http://opencv.itseez.com/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html\#solvepnp}{solvePnP()}} & Find the object pose from the known projections of its feature points. \\
+\texttt{\href{http://docs.opencv.org/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html\#solvepnp}{solvePnP()}} & Find the object pose from the known projections of its feature points. \\
-\texttt{\href{http://opencv.itseez.com/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html\#stereocalibrate}{stereoCalibrate()}} & Calibrate stereo camera. \\
+\texttt{\href{http://docs.opencv.org/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html\#stereocalibrate}{stereoCalibrate()}} & Calibrate stereo camera. \\
-\texttt{\href{http://opencv.itseez.com/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html\#stereorectify}{stereoRectify()}} & Compute the rectification transforms for a calibrated stereo camera.\\
+\texttt{\href{http://docs.opencv.org/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html\#stereorectify}{stereoRectify()}} & Compute the rectification transforms for a calibrated stereo camera.\\
-\texttt{\href{http://opencv.itseez.com/modules/imgproc/doc/geometric_transformations.html\#initundistortrectifymap}{initUndistortRectifyMap()}} & \ \ \ \ \ \ Compute rectification map (for \texttt{remap()}) for each stereo camera head.\\
+\texttt{\href{http://docs.opencv.org/modules/imgproc/doc/geometric_transformations.html\#initundistortrectifymap}{initUndistortRectifyMap()}} & \ \ \ \ \ \ Compute rectification map (for \texttt{remap()}) for each stereo camera head.\\
-\texttt{\href{http://opencv.itseez.com/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html\#StereoBM}{StereoBM}}, \texttt{\href{http://opencv.itseez.com/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html\#StereoSGBM}{StereoSGBM}} & The stereo correspondence engines to be run on rectified stereo pairs.\\
+\texttt{\href{http://docs.opencv.org/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html\#StereoBM}{StereoBM}}, \texttt{\href{http://docs.opencv.org/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html\#StereoSGBM}{StereoSGBM}} & The stereo correspondence engines to be run on rectified stereo pairs.\\
-\texttt{\href{http://opencv.itseez.com/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html\#reprojectimageto3d}{reprojectImageTo3D()}} & Convert disparity map to 3D point cloud.\\
+\texttt{\href{http://docs.opencv.org/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html\#reprojectimageto3d}{reprojectImageTo3D()}} & Convert disparity map to 3D point cloud.\\
-\texttt{\href{http://opencv.itseez.com/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html\#findhomography}{findHomography()}} & Find best-fit perspective transformation between two 2D point sets. \\
+\texttt{\href{http://docs.opencv.org/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html\#findhomography}{findHomography()}} & Find best-fit perspective transformation between two 2D point sets. \\
\end{tabular}
-To calibrate a camera, you can use \texttt{\href{http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/calibration.cpp}{calibration.cpp}} or
-\texttt{\href{http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/stereo\_calib.cpp}{stereo\_calib.cpp}} samples.
+To calibrate a camera, you can use \texttt{\href{http://code.opencv.org/projects/opencv/repository/revisions/master/entry/samples/cpp/calibration.cpp}{calibration.cpp}} or
+\texttt{\href{http://code.opencv.org/projects/opencv/repository/revisions/master/entry/samples/cpp/stereo\_calib.cpp}{stereo\_calib.cpp}} samples.
To get the disparity maps and the point clouds, use
-\texttt{\href{http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/stereo\_match.cpp}{stereo\_match.cpp}} sample.
+\texttt{\href{http://code.opencv.org/projects/opencv/repository/revisions/master/entry/samples/cpp/stereo\_match.cpp}{stereo\_match.cpp}} sample.
\section{Object Detection}
\begin{tabular}{@{}p{\the\MyLen}%
@{}p{\linewidth-\the\MyLen}@{}}
- \texttt{\href{http://opencv.itseez.com/modules/imgproc/doc/object_detection.html\#matchtemplate}{matchTemplate}} & Compute proximity map for given template.\\
+ \texttt{\href{http://docs.opencv.org/modules/imgproc/doc/object_detection.html\#matchtemplate}{matchTemplate}} & Compute proximity map for given template.\\
-\texttt{\href{http://opencv.itseez.com/modules/objdetect/doc/cascade_classification.html\#cascadeclassifier}{CascadeClassifier}} & Viola's Cascade of Boosted classifiers using Haar or LBP features. Suits for detecting faces, facial features and some other objects without diverse textures. See \texttt{\href{http://code.opencv.org/svn/opencv/trunk/opencv/samples/c/facedetect.cpp}{facedetect.cpp}}\\
+\texttt{\href{http://docs.opencv.org/modules/objdetect/doc/cascade_classification.html\#cascadeclassifier}{CascadeClassifier}} & Viola's Cascade of Boosted classifiers using Haar or LBP features. Suits for detecting faces, facial features and some other objects without diverse textures. See \texttt{\href{http://code.opencv.org/projects/opencv/repository/revisions/master/entry/samples/c/facedetect.cpp}{facedetect.cpp}}\\
-\texttt{{HOGDescriptor}} & N. Dalal's object detector using Histogram-of-Oriented-Gradients (HOG) features. Suits for detecting people, cars and other objects with well-defined silhouettes. See \texttt{\href{http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/peopledetect.cpp}{peopledetect.cpp}}\\
+\texttt{{HOGDescriptor}} & N. Dalal's object detector using Histogram-of-Oriented-Gradients (HOG) features. Suits for detecting people, cars and other objects with well-defined silhouettes. See \texttt{\href{http://code.opencv.org/projects/opencv/repository/revisions/master/entry/samples/cpp/peopledetect.cpp}{peopledetect.cpp}}\\
\end{tabular}
-%
+%
% feature detection:
% features2d toolbox
-%
+%
% object detection:
% using a classifier running on a sliding window: cascadeclassifier + hog.
% using salient point features: features2d -> matching
-%
+%
% statistical data processing:
% clustering (k-means),
% classification + regression (SVM, boosting, k-nearest),
.. container:: enumeratevisibleitemswithsquare
- * Use :point:`Point <>` to define 2D points in an image.
+ * Use :point:`Point <>` to define 2D points in an image.
* Use :scalar:`Scalar <>` and why it is useful
* Draw a **line** by using the OpenCV function :line:`line <>`
* Draw an **ellipse** by using the OpenCV function :ellipse:`ellipse <>`
It represents a 2D point, specified by its image coordinates :math:`x` and :math:`y`. We can define it as:
.. code-block:: cpp
-
+
Point pt;
- pt.x = 10;
+ pt.x = 10;
pt.y = 8;
or
.. code-block:: cpp
-
+
Point pt = Point(10, 8);
Scalar
* Let's see an example, if we are asked for a color argument and we give:
.. code-block:: cpp
-
+
Scalar( a, b, c )
We would be defining a RGB color such as: *Red = c*, *Green = b* and *Blue = a*
Code
=====
-* This code is in your OpenCV sample folder. Otherwise you can grab it from `here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/core/Matrix/Drawing_1.cpp>`_
+* This code is in your OpenCV sample folder. Otherwise you can grab it from `here <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/core/Matrix/Drawing_1.cpp>`_
Explanation
=============
-#. Since we plan to draw two examples (an atom and a rook), we have to create 02 images and two windows to display them.
+#. Since we plan to draw two examples (an atom and a rook), we have to create 02 images and two windows to display them.
.. code-block:: cpp
char atom_window[] = "Drawing 1: Atom";
char rook_window[] = "Drawing 2: Rook";
- /// Create black empty images
+ /// Create black empty images
Mat atom_image = Mat::zeros( w, w, CV_8UC3 );
Mat rook_image = Mat::zeros( w, w, CV_8UC3 );
/// 1. Draw a simple atom:
- /// 1.a. Creating ellipses
+ /// 1.a. Creating ellipses
MyEllipse( atom_image, 90 );
MyEllipse( atom_image, 0 );
MyEllipse( atom_image, 45 );
-1,
8 );
- /// 2.c. Create a few lines
+ /// 2.c. Create a few lines
MyLine( rook_image, Point( 0, 15*w/16 ), Point( w, 15*w/16 ) );
MyLine( rook_image, Point( w/4, 7*w/8 ), Point( w/4, w ) );
MyLine( rook_image, Point( w/2, 7*w/8 ), Point( w/2, w ) );
#. Let's check what is inside each of these functions:
- * *MyLine*
-
- .. code-block:: cpp
+ * *MyLine*
+
+ .. code-block:: cpp
void MyLine( Mat img, Point start, Point end )
{
int thickness = 2;
int lineType = 8;
- line( img,
+ line( img,
start,
end,
Scalar( 0, 0, 0 ),
* Draw a line from Point **start** to Point **end**
* The line is displayed in the image **img**
* The line color is defined by **Scalar( 0, 0, 0)** which is the RGB value correspondent to **Black**
- * The line thickness is set to **thickness** (in this case 2)
+ * The line thickness is set to **thickness** (in this case 2)
* The line is a 8-connected one (**lineType** = 8)
* *MyEllipse*
- .. code-block:: cpp
+ .. code-block:: cpp
void MyEllipse( Mat img, double angle )
{
Point( w/2.0, w/2.0 ),
Size( w/4.0, w/16.0 ),
angle,
- 0,
+ 0,
360,
Scalar( 255, 0, 0 ),
thickness,
- lineType );
+ lineType );
}
From the code above, we can observe that the function :ellipse:`ellipse <>` draws an ellipse such that:
-
+
.. container:: enumeratevisibleitemswithsquare
* The ellipse is displayed in the image **img**
* The ellipse extends an arc between **0** and **360** degrees
* The color of the figure will be **Scalar( 255, 255, 0)** which means blue in RGB value.
* The ellipse's **thickness** is 2.
-
+
* *MyFilledCircle*
int thickness = -1;
int lineType = 8;
- circle( img,
+ circle( img,
center,
w/32.0,
Scalar( 0, 0, 255 ),
- thickness,
+ thickness,
lineType );
}
.. container:: enumeratevisibleitemswithsquare
* The image where the circle will be displayed (**img**)
- * The center of the circle denoted as the Point **center**
+ * The center of the circle denoted as the Point **center**
* The radius of the circle: **w/32.0**
- * The color of the circle: **Scalar(0, 0, 255)** which means *Red* in BGR
+ * The color of the circle: **Scalar(0, 0, 255)** which means *Red* in BGR
* Since **thickness** = -1, the circle will be drawn filled.
* *MyPolygon*
npt,
1,
Scalar( 255, 255, 255 ),
- lineType );
+ lineType );
}
To draw a filled polygon we use the function :fill_poly:`fillPoly <>`. We note that:
-
+
.. container:: enumeratevisibleitemswithsquare
* The polygon will be drawn on **img**
* The vertices of the polygon are the set of points in **ppt**
* The total number of vertices to be drawn are **npt**
* The number of polygons to be drawn is only **1**
- * The color of the polygon is defined by **Scalar( 255, 255, 255)**, which is the BGR value for *white*
+ * The color of the polygon is defined by **Scalar( 255, 255, 255)**, which is the BGR value for *white*
* *rectangle*
.. image:: images/Drawing_1_Tutorial_Result_0.png
:alt: Drawing Tutorial 1 - Final Result
- :align: center
+ :align: center
.. container:: enumeratevisibleitemswithsquare
* In the previous tutorial (:ref:`Drawing_1`) we drew diverse geometric figures, giving as input parameters such as coordinates (in the form of :point:`Points <>`), color, thickness, etc. You might have noticed that we gave specific values for these arguments.
-
+
* In this tutorial, we intend to use *random* values for the drawing parameters. Also, we intend to populate our image with a big number of geometric figures. Since we will be initializing them in a random fashion, this process will be automatic and made by using *loops* .
- * This code is in your OpenCV sample folder. Otherwise you can grab it from `here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/core/Matrix/Drawing_2.cpp>`_ .
+ * This code is in your OpenCV sample folder. Otherwise you can grab it from `here <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/core/Matrix/Drawing_2.cpp>`_ .
Explanation
============
Mat image = Mat::zeros( window_height, window_width, CV_8UC3 );
/// Show it in a window during DELAY ms
- imshow( window_name, image );
+ imshow( window_name, image );
#. Then we proceed to draw crazy stuff. After taking a look at the code, you can see that it is mainly divided in 8 sections, defined as functions:
* The *for* loop will repeat **NUMBER** times. Since the function :line:`line <>` is inside this loop, that means that **NUMBER** lines will be generated.
* The line extremes are given by *pt1* and *pt2*. For *pt1* we can see that:
-
+
.. code-block:: cpp
-
- pt1.x = rng.uniform( x_1, x_2 );
+
+ pt1.x = rng.uniform( x_1, x_2 );
pt1.y = rng.uniform( y_1, y_2 );
- * We know that **rng** is a *Random number generator* object. In the code above we are calling **rng.uniform(a,b)**. This generates a radombly uniformed distribution between the values **a** and **b** (inclusive in **a**, exclusive in **b**).
+ * We know that **rng** is a *Random number generator* object. In the code above we are calling **rng.uniform(a,b)**. This generates a radombly uniformed distribution between the values **a** and **b** (inclusive in **a**, exclusive in **b**).
* From the explanation above, we deduce that the extremes *pt1* and *pt2* will be random values, so the lines positions will be quite impredictable, giving a nice visual effect (check out the Result section below).
* As another observation, we notice that in the :line:`line <>` arguments, for the *color* input we enter:
.. code-block:: cpp
-
- randomColor(rng)
-
+
+ randomColor(rng)
+
Let's check the function implementation:
.. code-block:: cpp
As we can see, the return value is an *Scalar* with 3 randomly initialized values, which are used as the *R*, *G* and *B* parameters for the line color. Hence, the color of the lines will be random too!
-#. The explanation above applies for the other functions generating circles, ellipses, polygones, etc. The parameters such as *center* and *vertices* are also generated randomly.
+#. The explanation above applies for the other functions generating circles, ellipses, polygones, etc. The parameters such as *center* and *vertices* are also generated randomly.
#. Before finishing, we also should take a look at the functions *Display_Random_Text* and *Displaying_Big_End*, since they both have a few interesting features:
putText( image, "Testing text rendering", org, rng.uniform(0,8),
rng.uniform(0,100)*0.05+0.1, randomColor(rng), rng.uniform(1, 10), lineType);
-
+
imshow( window_name, image );
if( waitKey(DELAY) >= 0 )
{ return -1; }
.. code-block:: cpp
putText( image, "Testing text rendering", org, rng.uniform(0,8),
- rng.uniform(0,100)*0.05+0.1, randomColor(rng), rng.uniform(1, 10), lineType);
+ rng.uniform(0,100)*0.05+0.1, randomColor(rng), rng.uniform(1, 10), lineType);
So, what does the function :put_text:`putText <>` do? In our example:
Size textsize = getTextSize("OpenCV forever!", CV_FONT_HERSHEY_COMPLEX, 3, 5, 0);
Point org((window_width - textsize.width)/2, (window_height - textsize.height)/2);
int lineType = 8;
-
+
Mat image2;
for( int i = 0; i < 255; i += 2 )
image2 = image - Scalar::all(i);
putText( image2, "OpenCV forever!", org, CV_FONT_HERSHEY_COMPLEX, 3,
Scalar(i, i, 255), 5, lineType );
-
+
imshow( window_name, image2 );
if( waitKey(DELAY) >= 0 )
{ return -1; }
So, **image2** is the substraction of **image** and **Scalar::all(i)**. In fact, what happens here is that every pixel of **image2** will be the result of substracting every pixel of **image** minus the value of **i** (remember that for each pixel we are considering three values such as R, G and B, so each of them will be affected)
- Also remember that the substraction operation *always* performs internally a **saturate** operation, which means that the result obtained will always be inside the allowed range (no negative and between 0 and 255 for our example).
-
+ Also remember that the substraction operation *always* performs internally a **saturate** operation, which means that the result obtained will always be inside the allowed range (no negative and between 0 and 255 for our example).
+
Result
========
.. image:: images/Drawing_2_Tutorial_Result_0.jpg
:alt: Drawing Tutorial 2 - Final Result 0
- :align: center
+ :align: center
#. Then, a new set of figures, these time *rectangles* will follow.
.. image:: images/Drawing_2_Tutorial_Result_2.jpg
:alt: Drawing Tutorial 2 - Final Result 2
- :align: center
+ :align: center
#. Now, *polylines* with 03 segments will appear on screen, again in random configurations.
.. image:: images/Drawing_2_Tutorial_Result_3.jpg
:alt: Drawing Tutorial 2 - Final Result 3
- :align: center
+ :align: center
#. Filled polygons (in this example triangles) will follow.
.. image:: images/Drawing_2_Tutorial_Result_5.jpg
:alt: Drawing Tutorial 2 - Final Result 5
- :align: center
+ :align: center
#. Near the end, the text *"Testing Text Rendering"* will appear in a variety of fonts, sizes, colors and positions.
.. image:: images/Drawing_2_Tutorial_Result_7.jpg
:alt: Drawing Tutorial 2 - Final Result 7
- :align: center
+ :align: center
* Use :surf_descriptor_extractor:`SurfDescriptorExtractor<>` and its function :descriptor_extractor:`compute<>` to perform the required calculations.
* Use a :brute_force_matcher:`BruteForceMatcher<>` to match the features vector
* Use the function :draw_matches:`drawMatches<>` to draw the detected matches.
-
+
Theory
======
Code
====
-This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/features2D/SURF_descriptor.cpp>`_
+This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/features2D/SURF_descriptor.cpp>`_
-.. code-block:: cpp
+.. code-block:: cpp
#include <stdio.h>
#include <iostream>
Mat img_1 = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );
Mat img_2 = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );
-
+
if( !img_1.data || !img_2.data )
{ return -1; }
//-- Draw matches
Mat img_matches;
- drawMatches( img_1, keypoints_1, img_2, keypoints_2, matches, img_matches );
+ drawMatches( img_1, keypoints_1, img_2, keypoints_2, matches, img_matches );
//-- Show detected matches
imshow("Matches", img_matches );
Result
======
-
+
#. Here is the result after applying the BruteForce matcher between the two original images:
-
+
.. image:: images/Feature_Description_BruteForce_Result.jpg
:align: center
:height: 200pt
* Use the :surf_feature_detector:`SurfFeatureDetector<>` and its function :feature_detector_detect:`detect<>` to perform the detection process
* Use the function :draw_keypoints:`drawKeypoints<>` to draw the detected keypoints
-
+
Theory
======
Code
====
-This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/features2D/SURF_detector.cpp>`_
+This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/features2D/SURF_detector.cpp>`_
-.. code-block:: cpp
+.. code-block:: cpp
#include <stdio.h>
#include <iostream>
#include "opencv2/core/core.hpp"
- #include "opencv2/features2d/features2d.hpp"
+ #include "opencv2/features2d/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
using namespace cv;
Mat img_1 = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );
Mat img_2 = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );
-
+
if( !img_1.data || !img_2.data )
{ std::cout<< " --(!) Error reading images " << std::endl; return -1; }
//-- Draw keypoints
Mat img_keypoints_1; Mat img_keypoints_2;
- drawKeypoints( img_1, keypoints_1, img_keypoints_1, Scalar::all(-1), DrawMatchesFlags::DEFAULT );
- drawKeypoints( img_2, keypoints_2, img_keypoints_2, Scalar::all(-1), DrawMatchesFlags::DEFAULT );
+ drawKeypoints( img_1, keypoints_1, img_keypoints_1, Scalar::all(-1), DrawMatchesFlags::DEFAULT );
+ drawKeypoints( img_2, keypoints_2, img_keypoints_2, Scalar::all(-1), DrawMatchesFlags::DEFAULT );
//-- Show detected (drawn) keypoints
imshow("Keypoints 1", img_keypoints_1 );
Result
======
-
+
#. Here is the result of the feature detection applied to the first image:
-
+
.. image:: images/Feature_Detection_Result_a.jpg
:align: center
:height: 125pt
#. And here is the result for the second image:
.. image:: images/Feature_Detection_Result_b.jpg
- :align: center
- :height: 200pt
+ :align: center
+ :height: 200pt
Code
====
-This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/features2D/SURF_FlannMatcher.cpp>`_
+This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/features2D/SURF_FlannMatcher.cpp>`_
-.. code-block:: cpp
+.. code-block:: cpp
#include <stdio.h>
#include <iostream>
Mat img_1 = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );
Mat img_2 = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );
-
+
if( !img_1.data || !img_2.data )
{ std::cout<< " --(!) Error reading images " << std::endl; return -1; }
printf("-- Max dist : %f \n", max_dist );
printf("-- Min dist : %f \n", min_dist );
-
+
//-- Draw only "good" matches (i.e. whose distance is less than 2*min_dist )
//-- PS.- radiusMatch can also be used here.
std::vector< DMatch > good_matches;
for( int i = 0; i < descriptors_1.rows; i++ )
{ if( matches[i].distance < 2*min_dist )
{ good_matches.push_back( matches[i]); }
- }
+ }
//-- Draw only "good" matches
Mat img_matches;
- drawMatches( img_1, keypoints_1, img_2, keypoints_2,
- good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
- vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
+ drawMatches( img_1, keypoints_1, img_2, keypoints_2,
+ good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
+ vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
//-- Show detected matches
imshow( "Good Matches", img_matches );
Result
======
-
+
#. Here is the result of the feature detection applied to the first image:
-
+
.. image:: images/Featur_FlannMatcher_Result.jpg
:align: center
:height: 250pt
* Use the function :find_homography:`findHomography<>` to find the transform between matched keypoints.
* Use the function :perspective_transform:`perspectiveTransform<>` to map the points.
-
+
Theory
======
Code
====
-This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/features2D/SURF_Homography.cpp>`_
+This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/features2D/SURF_Homography.cpp>`_
-.. code-block:: cpp
+.. code-block:: cpp
#include <stdio.h>
#include <iostream>
Mat img_object = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );
Mat img_scene = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );
-
+
if( !img_object.data || !img_scene.data )
{ std::cout<< " --(!) Error reading images " << std::endl; return -1; }
printf("-- Max dist : %f \n", max_dist );
printf("-- Min dist : %f \n", min_dist );
-
+
//-- Draw only "good" matches (i.e. whose distance is less than 3*min_dist )
std::vector< DMatch > good_matches;
for( int i = 0; i < descriptors_object.rows; i++ )
{ if( matches[i].distance < 3*min_dist )
{ good_matches.push_back( matches[i]); }
- }
+ }
Mat img_matches;
- drawMatches( img_object, keypoints_object, img_scene, keypoints_scene,
- good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
- vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
+ drawMatches( img_object, keypoints_object, img_scene, keypoints_scene,
+ good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
+ vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
- //-- Localize the object
+ //-- Localize the object
std::vector<Point2f> obj;
std::vector<Point2f> scene;
{
//-- Get the keypoints from the good matches
obj.push_back( keypoints_object[ good_matches[i].queryIdx ].pt );
- scene.push_back( keypoints_scene[ good_matches[i].trainIdx ].pt );
+ scene.push_back( keypoints_scene[ good_matches[i].trainIdx ].pt );
}
Mat H = findHomography( obj, scene, CV_RANSAC );
#. And here is the result for the detected object (highlighted in green)
.. image:: images/Feature_Homography_Result.jpg
- :align: center
- :height: 200pt
+ :align: center
+ :height: 200pt
Code
====
-This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/TrackingMotion/cornerSubPix_Demo.cpp>`_
+This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/TrackingMotion/cornerSubPix_Demo.cpp>`_
-.. code-block:: cpp
+.. code-block:: cpp
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
namedWindow( source_window, CV_WINDOW_AUTOSIZE );
/// Create Trackbar to set the number of corners
- createTrackbar( "Max corners:", source_window, &maxCorners, maxTrackbar, goodFeaturesToTrack_Demo);
+ createTrackbar( "Max corners:", source_window, &maxCorners, maxTrackbar, goodFeaturesToTrack_Demo);
imshow( source_window, src );
void goodFeaturesToTrack_Demo( int, void* )
{
if( maxCorners < 1 ) { maxCorners = 1; }
-
+
/// Parameters for Shi-Tomasi algorithm
vector<Point2f> corners;
double qualityLevel = 0.01;
copy = src.clone();
/// Apply corner detection
- goodFeaturesToTrack( src_gray,
+ goodFeaturesToTrack( src_gray,
corners,
maxCorners,
qualityLevel,
blockSize,
useHarrisDetector,
k );
-
+
/// Draw corners detected
cout<<"** Number of corners detected: "<<corners.size()<<endl;
int r = 4;
for( int i = 0; i < corners.size(); i++ )
- { circle( copy, corners[i], r, Scalar(rng.uniform(0,255), rng.uniform(0,255),
+ { circle( copy, corners[i], r, Scalar(rng.uniform(0,255), rng.uniform(0,255),
rng.uniform(0,255)), -1, 8, 0 ); }
/// Show what you got
namedWindow( source_window, CV_WINDOW_AUTOSIZE );
- imshow( source_window, copy );
+ imshow( source_window, copy );
/// Set the neeed parameters to find the refined corners
Size winSize = Size( 5, 5 );
/// Write them down
for( int i = 0; i < corners.size(); i++ )
- { cout<<" -- Refined Corner ["<<i<<"] ("<<corners[i].x<<","<<corners[i].y<<")"<<endl; }
+ { cout<<" -- Refined Corner ["<<i<<"] ("<<corners[i].x<<","<<corners[i].y<<")"<<endl; }
}
======
.. image:: images/Corner_Subpixeles_Original_Image.jpg
- :align: center
-
+ :align: center
+
Here is the result:
.. image:: images/Corner_Subpixeles_Result.jpg
- :align: center
+ :align: center
.. container:: enumeratevisibleitemswithsquare
* Use the OpenCV function :corner_eigenvals_and_vecs:`cornerEigenValsAndVecs <>` to find the eigenvalues and eigenvectors to determine if a pixel is a corner.
- * Use the OpenCV function :corner_min_eigenval:`cornerMinEigenVal <>` to find the minimum eigenvalues for corner detection.
+ * Use the OpenCV function :corner_min_eigenval:`cornerMinEigenVal <>` to find the minimum eigenvalues for corner detection.
* To implement our own version of the Harris detector as well as the Shi-Tomasi detector, by using the two functions above.
Theory
Code
====
-This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/TrackingMotion/cornerDetector_Demo.cpp>`_
+This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/TrackingMotion/cornerDetector_Demo.cpp>`_
-.. code-block:: cpp
+.. code-block:: cpp
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
using namespace std;
/// Global variables
- Mat src, src_gray;
+ Mat src, src_gray;
Mat myHarris_dst; Mat myHarris_copy; Mat Mc;
- Mat myShiTomasi_dst; Mat myShiTomasi_copy;
+ Mat myShiTomasi_dst; Mat myShiTomasi_copy;
int myShiTomasi_qualityLevel = 50;
int myHarris_qualityLevel = 50;
cornerEigenValsAndVecs( src_gray, myHarris_dst, blockSize, apertureSize, BORDER_DEFAULT );
- /* calculate Mc */
+ /* calculate Mc */
for( int j = 0; j < src_gray.rows; j++ )
{ for( int i = 0; i < src_gray.cols; i++ )
{
}
minMaxLoc( Mc, &myHarris_minVal, &myHarris_maxVal, 0, 0, Mat() );
-
+
/* Create Window and Trackbar */
namedWindow( myHarris_window, CV_WINDOW_AUTOSIZE );
- createTrackbar( " Quality Level:", myHarris_window, &myHarris_qualityLevel, max_qualityLevel,
- myHarris_function );
+ createTrackbar( " Quality Level:", myHarris_window, &myHarris_qualityLevel, max_qualityLevel,
+ myHarris_function );
myHarris_function( 0, 0 );
/// My Shi-Tomasi -- Using cornerMinEigenVal
- myShiTomasi_dst = Mat::zeros( src_gray.size(), CV_32FC1 );
+ myShiTomasi_dst = Mat::zeros( src_gray.size(), CV_32FC1 );
cornerMinEigenVal( src_gray, myShiTomasi_dst, blockSize, apertureSize, BORDER_DEFAULT );
minMaxLoc( myShiTomasi_dst, &myShiTomasi_minVal, &myShiTomasi_maxVal, 0, 0, Mat() );
/* Create Window and Trackbar */
- namedWindow( myShiTomasi_window, CV_WINDOW_AUTOSIZE );
- createTrackbar( " Quality Level:", myShiTomasi_window, &myShiTomasi_qualityLevel, max_qualityLevel,
- myShiTomasi_function );
+ namedWindow( myShiTomasi_window, CV_WINDOW_AUTOSIZE );
+ createTrackbar( " Quality Level:", myShiTomasi_window, &myShiTomasi_qualityLevel, max_qualityLevel,
+ myShiTomasi_function );
myShiTomasi_function( 0, 0 );
-
+
waitKey(0);
return(0);
}
for( int j = 0; j < src_gray.rows; j++ )
{ for( int i = 0; i < src_gray.cols; i++ )
{
- if( myShiTomasi_dst.at<float>(j,i) > myShiTomasi_minVal + ( myShiTomasi_maxVal -
+ if( myShiTomasi_dst.at<float>(j,i) > myShiTomasi_minVal + ( myShiTomasi_maxVal -
myShiTomasi_minVal )*myShiTomasi_qualityLevel/max_qualityLevel )
- { circle( myShiTomasi_copy, Point(i,j), 4, Scalar( rng.uniform(0,255),
+ { circle( myShiTomasi_copy, Point(i,j), 4, Scalar( rng.uniform(0,255),
rng.uniform(0,255), rng.uniform(0,255) ), -1, 8, 0 ); }
}
}
{
if( Mc.at<float>(j,i) > myHarris_minVal + ( myHarris_maxVal - myHarris_minVal )
*myHarris_qualityLevel/max_qualityLevel )
- { circle( myHarris_copy, Point(i,j), 4, Scalar( rng.uniform(0,255), rng.uniform(0,255),
+ { circle( myHarris_copy, Point(i,j), 4, Scalar( rng.uniform(0,255), rng.uniform(0,255),
rng.uniform(0,255) ), -1, 8, 0 ); }
- }
+ }
}
imshow( myHarris_window, myHarris_copy );
}
======
.. image:: images/My_Harris_corner_detector_Result.jpg
- :align: center
+ :align: center
.. image:: images/My_Shi_Tomasi_corner_detector_Result.jpg
- :align: center
+ :align: center
Code
====
-This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/TrackingMotion/goodFeaturesToTrack_Demo.cpp>`_
+This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/TrackingMotion/goodFeaturesToTrack_Demo.cpp>`_
-.. code-block:: cpp
+.. code-block:: cpp
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
namedWindow( source_window, CV_WINDOW_AUTOSIZE );
/// Create Trackbar to set the number of corners
- createTrackbar( "Max corners:", source_window, &maxCorners, maxTrackbar, goodFeaturesToTrack_Demo );
+ createTrackbar( "Max corners:", source_window, &maxCorners, maxTrackbar, goodFeaturesToTrack_Demo );
imshow( source_window, src );
* @function goodFeaturesToTrack_Demo.cpp
* @brief Apply Shi-Tomasi corner detector
*/
- void goodFeaturesToTrack_Demo( int, void* )
+ void goodFeaturesToTrack_Demo( int, void* )
{
if( maxCorners < 1 ) { maxCorners = 1; }
-
+
/// Parameters for Shi-Tomasi algorithm
vector<Point2f> corners;
double qualityLevel = 0.01;
copy = src.clone();
/// Apply corner detection
- goodFeaturesToTrack( src_gray,
+ goodFeaturesToTrack( src_gray,
corners,
maxCorners,
qualityLevel,
blockSize,
useHarrisDetector,
k );
-
+
/// Draw corners detected
cout<<"** Number of corners detected: "<<corners.size()<<endl;
int r = 4;
for( int i = 0; i < corners.size(); i++ )
- { circle( copy, corners[i], r, Scalar(rng.uniform(0,255), rng.uniform(0,255),
+ { circle( copy, corners[i], r, Scalar(rng.uniform(0,255), rng.uniform(0,255),
rng.uniform(0,255)), -1, 8, 0 ); }
/// Show what you got
namedWindow( source_window, CV_WINDOW_AUTOSIZE );
- imshow( source_window, copy );
+ imshow( source_window, copy );
}
Explanation
======
.. image:: images/Feature_Detection_Result_a.jpg
- :align: center
+ :align: center
.. container:: enumeratevisibleitemswithsquare
- * What features are and why they are important
+ * What features are and why they are important
* Use the function :corner_harris:`cornerHarris <>` to detect corners using the Harris-Stephens method.
Theory
.. container:: enumeratevisibleitemswithsquare
- * Let's look for corners. Since corners represents a variation in the gradient in the image, we will look for this "variation".
+ * Let's look for corners. Since corners represents a variation in the gradient in the image, we will look for this "variation".
* Consider a grayscale image :math:`I`. We are going to sweep a window :math:`w(x,y)` (with displacements :math:`u` in the x direction and :math:`v` in the right direction) :math:`I` and will calculate the variation of intensity.
where:
- * :math:`w(x,y)` is the window at position :math:`(x,y)`
+ * :math:`w(x,y)` is the window at position :math:`(x,y)`
* :math:`I(x,y)` is the intensity at :math:`(x,y)`
* :math:`I(x+u,y+v)` is the intensity at the moved window :math:`(x+u,y+v)`
-
+
* Since we are looking for windows with corners, we are looking for windows with a large variation in intensity. Hence, we have to maximize the equation above, specifically the term:
.. math::
.. math::
E(u,v) \approx \sum _{x,y} u^{2}I_{x}^{2} + 2uvI_{x}I_{y} + v^{2}I_{y}^{2}
-
+
* Which can be expressed in a matrix form as:
.. math::
E(u,v) \approx \begin{bmatrix}
- u & v
+ u & v
\end{bmatrix}
\left (
\displaystyle \sum_{x,y}
w(x,y)
\begin{bmatrix}
I_x^{2} & I_{x}I_{y} \\
- I_xI_{y} & I_{y}^{2}
+ I_xI_{y} & I_{y}^{2}
\end{bmatrix}
- \right )
+ \right )
\begin{bmatrix}
u \\
- v
- \end{bmatrix}
+ v
+ \end{bmatrix}
* Let's denote:
.. math::
M = \displaystyle \sum_{x,y}
- w(x,y)
+ w(x,y)
\begin{bmatrix}
I_x^{2} & I_{x}I_{y} \\
- I_xI_{y} & I_{y}^{2}
+ I_xI_{y} & I_{y}^{2}
\end{bmatrix}
* So, our equation now is:
.. math::
E(u,v) \approx \begin{bmatrix}
- u & v
+ u & v
\end{bmatrix}
M
\begin{bmatrix}
u \\
- v
- \end{bmatrix}
+ v
+ \end{bmatrix}
+
-
* A score is calculated for each window, to determine if it can possibly contain a corner:
.. math::
- R = det(M) - k(trace(M))^{2}
-
+ R = det(M) - k(trace(M))^{2}
+
where:
-
+
* det(M) = :math:`\lambda_{1}\lambda_{2}`
* trace(M) = :math:`\lambda_{1}+\lambda_{2}`
a window with a score :math:`R` greater than a certain value is considered a "corner"
-
+
Code
====
-This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/TrackingMotion/cornerHarris_Demo.cpp>`_
+This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/TrackingMotion/cornerHarris_Demo.cpp>`_
-.. code-block:: cpp
+.. code-block:: cpp
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <stdio.h>
#include <stdlib.h>
- using namespace cv;
+ using namespace cv;
using namespace std;
/// Global variables
namedWindow( source_window, CV_WINDOW_AUTOSIZE );
createTrackbar( "Threshold: ", source_window, &thresh, max_thresh, cornerHarris_demo );
imshow( source_window, src );
-
+
cornerHarris_demo( 0, 0 );
waitKey(0);
int blockSize = 2;
int apertureSize = 3;
double k = 0.04;
-
+
/// Detecting corners
cornerHarris( src_gray, dst, blockSize, apertureSize, k, BORDER_DEFAULT );
/// Normalizing
normalize( dst, dst_norm, 0, 255, NORM_MINMAX, CV_32FC1, Mat() );
- convertScaleAbs( dst_norm, dst_norm_scaled );
+ convertScaleAbs( dst_norm, dst_norm_scaled );
/// Drawing a circle around corners
for( int j = 0; j < dst_norm.rows ; j++ )
{ for( int i = 0; i < dst_norm.cols; i++ )
{
if( (int) dst_norm.at<float>(j,i) > thresh )
- {
- circle( dst_norm_scaled, Point( i, j ), 5, Scalar(0), 2, 8, 0 );
+ {
+ circle( dst_norm_scaled, Point( i, j ), 5, Scalar(0), 2, 8, 0 );
}
- }
- }
- /// Showing the result
+ }
+ }
+ /// Showing the result
namedWindow( corners_window, CV_WINDOW_AUTOSIZE );
imshow( corners_window, dst_norm_scaled );
}
The original image:
.. image:: images/Harris_Detector_Original_Image.jpg
- :align: center
+ :align: center
The detected corners are surrounded by a small black circle
.. image:: images/Harris_Detector_Result.jpg
- :align: center
+ :align: center
Morphological Operations
--------------------------
-* In short: A set of operations that process images based on shapes. Morphological operations apply a *structuring element* to an input image and generate an output image.
+* In short: A set of operations that process images based on shapes. Morphological operations apply a *structuring element* to an input image and generate an output image.
* The most basic morphological operations are two: Erosion and Dilation. They have a wide array of uses, i.e. :
.. image:: images/Morphology_1_Tutorial_Theory_Original_Image.png
:alt: Original image
- :align: center
+ :align: center
Dilation
^^^^^^^^^
.. image:: images/Morphology_1_Tutorial_Theory_Dilation.png
:alt: Dilation result - Theory example
- :align: center
+ :align: center
The background (bright) dilates around the black regions of the letter.
* This operation is the sister of dilation. What this does is to compute a local minimum over the area of the kernel.
-* As the kernel :math:`B` is scanned over the image, we compute the minimal pixel value overlapped by :math:`B` and replace the image pixel under the anchor point with that minimal value.
+* As the kernel :math:`B` is scanned over the image, we compute the minimal pixel value overlapped by :math:`B` and replace the image pixel under the anchor point with that minimal value.
* Analagously to the example for dilation, we can apply the erosion operator to the original image (shown above). You can see in the result below that the bright areas of the image (the background, apparently), get thinner, whereas the dark zones (the "writing"( gets bigger.
.. image:: images/Morphology_1_Tutorial_Theory_Erosion.png
:alt: Erosion result - Theory example
- :align: center
+ :align: center
Code
======
-This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/ImgProc/Morphology_1.cpp>`_
+This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/ImgProc/Morphology_1.cpp>`_
-.. code-block:: cpp
+.. code-block:: cpp
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"
if( !src.data )
{ return -1; }
-
+
/// Create windows
namedWindow( "Erosion Demo", CV_WINDOW_AUTOSIZE );
namedWindow( "Dilation Demo", CV_WINDOW_AUTOSIZE );
cvMoveWindow( "Dilation Demo", src.cols, 0 );
/// Create Erosion Trackbar
- createTrackbar( "Element:\n 0: Rect \n 1: Cross \n 2: Ellipse", "Erosion Demo",
- &erosion_elem, max_elem,
+ createTrackbar( "Element:\n 0: Rect \n 1: Cross \n 2: Ellipse", "Erosion Demo",
+ &erosion_elem, max_elem,
Erosion );
- createTrackbar( "Kernel size:\n 2n +1", "Erosion Demo",
+ createTrackbar( "Kernel size:\n 2n +1", "Erosion Demo",
&erosion_size, max_kernel_size,
Erosion );
/// Create Dilation Trackbar
- createTrackbar( "Element:\n 0: Rect \n 1: Cross \n 2: Ellipse", "Dilation Demo",
- &dilation_elem, max_elem,
+ createTrackbar( "Element:\n 0: Rect \n 1: Cross \n 2: Ellipse", "Dilation Demo",
+ &dilation_elem, max_elem,
Dilation );
- createTrackbar( "Kernel size:\n 2n +1", "Dilation Demo",
+ createTrackbar( "Kernel size:\n 2n +1", "Dilation Demo",
&dilation_size, max_kernel_size,
- Dilation );
+ Dilation );
/// Default start
Erosion( 0, 0 );
else if( erosion_elem == 1 ){ erosion_type = MORPH_CROSS; }
else if( erosion_elem == 2) { erosion_type = MORPH_ELLIPSE; }
- Mat element = getStructuringElement( erosion_type,
+ Mat element = getStructuringElement( erosion_type,
Size( 2*erosion_size + 1, 2*erosion_size+1 ),
- Point( erosion_size, erosion_size ) );
+ Point( erosion_size, erosion_size ) );
/// Apply the erosion operation
erode( src, erosion_dst, element );
- imshow( "Erosion Demo", erosion_dst );
+ imshow( "Erosion Demo", erosion_dst );
}
/** @function Dilation */
else if( dilation_elem == 1 ){ dilation_type = MORPH_CROSS; }
else if( dilation_elem == 2) { dilation_type = MORPH_ELLIPSE; }
- Mat element = getStructuringElement( dilation_type,
+ Mat element = getStructuringElement( dilation_type,
Size( 2*dilation_size + 1, 2*dilation_size+1 ),
- Point( dilation_size, dilation_size ) );
+ Point( dilation_size, dilation_size ) );
/// Apply the dilation operation
dilate( src, dilation_dst, element );
- imshow( "Dilation Demo", dilation_dst );
+ imshow( "Dilation Demo", dilation_dst );
}
* Create a set of 02 Trackbars for each operation:
* The first trackbar "Element" returns either **erosion_elem** or **dilation_elem**
- * The second trackbar "Kernel size" return **erosion_size** or **dilation_size** for the corresponding operation.
+ * The second trackbar "Kernel size" return **erosion_size** or **dilation_size** for the corresponding operation.
* Every time we move any slider, the user's function **Erosion** or **Dilation** will be called and it will update the output image based on the current trackbar values.
-
+
Let's analyze these two functions:
-
+
#. **erosion:**
.. code-block:: cpp
else if( erosion_elem == 1 ){ erosion_type = MORPH_CROSS; }
else if( erosion_elem == 2) { erosion_type = MORPH_ELLIPSE; }
- Mat element = getStructuringElement( erosion_type,
+ Mat element = getStructuringElement( erosion_type,
Size( 2*erosion_size + 1, 2*erosion_size+1 ),
- Point( erosion_size, erosion_size ) );
+ Point( erosion_size, erosion_size ) );
/// Apply the erosion operation
erode( src, erosion_dst, element );
- imshow( "Erosion Demo", erosion_dst );
+ imshow( "Erosion Demo", erosion_dst );
}
* The function that performs the *erosion* operation is :erode:`erode <>`. As we can see, it receives three arguments:
-
+
* *src*: The source image
* *erosion_dst*: The output image
* *element*: This is the kernel we will use to perform the operation. If we do not specify, the default is a simple :math:`3x3` matrix. Otherwise, we can specify its shape. For this, we need to use the function :get_structuring_element:`getStructuringElement <>`:
.. code-block:: cpp
- Mat element = getStructuringElement( erosion_type,
+ Mat element = getStructuringElement( erosion_type,
Size( 2*erosion_size + 1, 2*erosion_size+1 ),
- Point( erosion_size, erosion_size ) );
-
+ Point( erosion_size, erosion_size ) );
+
We can choose any of three shapes for our kernel:
.. container:: enumeratevisibleitemswithsquare
+ Rectangular box: MORPH_RECT
- + Cross: MORPH_CROSS
+ + Cross: MORPH_CROSS
+ Ellipse: MORPH_ELLIPSE
Then, we just have to specify the size of our kernel and the *anchor point*. If not specified, it is assumed to be in the center.
* That is all. We are ready to perform the erosion of our image.
.. note::
- Additionally, there is another parameter that allows you to perform multiple erosions (iterations) at once. We are not using it in this simple tutorial, though. You can check out the Reference for more details.
-
+ Additionally, there is another parameter that allows you to perform multiple erosions (iterations) at once. We are not using it in this simple tutorial, though. You can check out the Reference for more details.
+
#. **dilation:**
else if( dilation_elem == 1 ){ dilation_type = MORPH_CROSS; }
else if( dilation_elem == 2) { dilation_type = MORPH_ELLIPSE; }
- Mat element = getStructuringElement( dilation_type,
+ Mat element = getStructuringElement( dilation_type,
Size( 2*dilation_size + 1, 2*dilation_size+1 ),
- Point( dilation_size, dilation_size ) );
+ Point( dilation_size, dilation_size ) );
/// Apply the dilation operation
dilate( src, dilation_dst, element );
- imshow( "Dilation Demo", dilation_dst );
+ imshow( "Dilation Demo", dilation_dst );
}
.. image:: images/Morphology_1_Tutorial_Original_Image.jpg
:alt: Original image
- :align: center
+ :align: center
We get the results below. Varying the indices in the Trackbars give different output images, naturally. Try them out! You can even try to add a third Trackbar to control the number of iterations.
.. image:: images/Morphology_1_Tutorial_Cover.jpg
:alt: Dilation and Erosion application
- :align: center
+ :align: center
======
.. note::
- The explanation below belongs to the book `Computer Vision: Algorithms and Applications <http://szeliski.org/Book/>`_ by Richard Szeliski and to *LearningOpenCV*
+ The explanation below belongs to the book `Computer Vision: Algorithms and Applications <http://szeliski.org/Book/>`_ by Richard Szeliski and to *LearningOpenCV*
.. container:: enumeratevisibleitemswithsquare
- * *Smoothing*, also called *blurring*, is a simple and frequently used image processing operation.
+ * *Smoothing*, also called *blurring*, is a simple and frequently used image processing operation.
* There are many reasons for smoothing. In this tutorial we will focus on smoothing in order to reduce noise (other uses will be seen in the following tutorials).
g(i,j) = \sum_{k,l} f(i+k, j+l) h(k,l)
:math:`h(k,l)` is called the *kernel*, which is nothing more than the coefficients of the filter.
-
+
It helps to visualize a *filter* as a window of coefficients sliding across the image.
.. container:: enumeratevisibleitemswithsquare
- * This filter is the simplest of all! Each output pixel is the *mean* of its kernel neighbors ( all of them contribute with equal weights)
+ * This filter is the simplest of all! Each output pixel is the *mean* of its kernel neighbors ( all of them contribute with equal weights)
* The kernel is below:
.. math::
-
+
K = \dfrac{1}{K_{width} \cdot K_{height}} \begin{bmatrix}
1 & 1 & 1 & ... & 1 \\
1 & 1 & 1 & ... & 1 \\
. & . & . & ... & 1 \\
. & . & . & ... & 1 \\
1 & 1 & 1 & ... & 1
- \end{bmatrix}
+ \end{bmatrix}
Gaussian Filter
* Just to make the picture clearer, remember how a 1D Gaussian kernel look like?
.. image:: images/Smoothing_Tutorial_theory_gaussian_0.jpg
- :align: center
+ :align: center
- Assuming that an image is 1D, you can notice that the pixel located in the middle would have the biggest weight. The weight of its neighbors decreases as the spatial distance between them and the center pixel increases.
+ Assuming that an image is 1D, you can notice that the pixel located in the middle would have the biggest weight. The weight of its neighbors decreases as the spatial distance between them and the center pixel increases.
.. note::
Remember that a 2D Gaussian can be represented as :
-
+
.. math::
-
+
G_{0}(x, y) = A e^{ \dfrac{ -(x - \mu_{x})^{2} }{ 2\sigma^{2}_{x} } + \dfrac{ -(y - \mu_{y})^{2} }{ 2\sigma^{2}_{y} } }
where :math:`\mu` is the mean (the peak) and :math:`\sigma` represents the variance (per each of the variables :math:`x` and :math:`y`)
* So far, we have explained some filters which main goal is to *smooth* an input image. However, sometimes the filters do not only dissolve the noise, but also smooth away the *edges*. To avoid this (at certain extent at least), we can use a bilateral filter.
- * In an analogous way as the Gaussian filter, the bilateral filter also considers the neighboring pixels with weights assigned to each of them. These weights have two components, the first of which is the same weighting used by the Gaussian filter. The second component takes into account the difference in intensity between the neighboring pixels and the evaluated one.
+ * In an analogous way as the Gaussian filter, the bilateral filter also considers the neighboring pixels with weights assigned to each of them. These weights have two components, the first of which is the same weighting used by the Gaussian filter. The second component takes into account the difference in intensity between the neighboring pixels and the evaluated one.
- * For a more detailed explanation you can check `this link <http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/MANDUCHI1/Bilateral_Filtering.html>`_
+ * For a more detailed explanation you can check `this link <http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/MANDUCHI1/Bilateral_Filtering.html>`_
Code
.. container:: enumeratevisibleitemswithsquare
* **What does this program do?**
-
+
.. container:: enumeratevisibleitemswithsquare
* Loads an image
* Applies 4 different kinds of filters (explained in Theory) and show the filtered images sequentially
* **Downloadable code**:
- Click `here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/ImgProc/Smoothing.cpp>`_
+ Click `here <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/ImgProc/Smoothing.cpp>`_
* **Code at glance:**
int display_caption( char* caption );
int display_dst( int delay );
- /**
- * function main
+ /**
+ * function main
*/
- int main( int argc, char** argv )
+ int main( int argc, char** argv )
{
namedWindow( window_name, CV_WINDOW_AUTOSIZE );
/// Load the source image
- src = imread( "../images/lena.jpg", 1 );
+ src = imread( "../images/lena.jpg", 1 );
if( display_caption( "Original Image" ) != 0 ) { return 0; }
dst = src.clone();
if( display_dst( DELAY_CAPTION ) != 0 ) { return 0; }
- /// Applying Homogeneous blur
+ /// Applying Homogeneous blur
if( display_caption( "Homogeneous Blur" ) != 0 ) { return 0; }
for ( int i = 1; i < MAX_KERNEL_LENGTH; i = i + 2 )
{ blur( src, dst, Size( i, i ), Point(-1,-1) );
if( display_dst( DELAY_BLUR ) != 0 ) { return 0; } }
- /// Applying Gaussian blur
+ /// Applying Gaussian blur
if( display_caption( "Gaussian Blur" ) != 0 ) { return 0; }
for ( int i = 1; i < MAX_KERNEL_LENGTH; i = i + 2 )
int display_caption( char* caption )
{
dst = Mat::zeros( src.size(), src.type() );
- putText( dst, caption,
- Point( src.cols/4, src.rows/2),
+ putText( dst, caption,
+ Point( src.cols/4, src.rows/2),
CV_FONT_HERSHEY_COMPLEX, 1, Scalar(255, 255, 255) );
imshow( window_name, dst );
imshow( window_name, dst );
int c = waitKey ( delay );
if( c >= 0 ) { return -1; }
- return 0;
+ return 0;
}
Explanation
=============
-#. Let's check the OpenCV functions that involve only the smoothing procedure, since the rest is already known by now.
+#. Let's check the OpenCV functions that involve only the smoothing procedure, since the rest is already known by now.
#. **Normalized Block Filter:**
+ *dst*: Destination image
- + *Size( w,h )*: Defines the size of the kernel to be used ( of width *w* pixels and height *h* pixels)
+ + *Size( w,h )*: Defines the size of the kernel to be used ( of width *w* pixels and height *h* pixels)
+
+ + *Point(-1, -1)*: Indicates where the anchor point (the pixel evaluated) is located with respect to the neighborhood. If there is a negative value, then the center of the kernel is considered the anchor point.
- + *Point(-1, -1)*: Indicates where the anchor point (the pixel evaluated) is located with respect to the neighborhood. If there is a negative value, then the center of the kernel is considered the anchor point.
-
#. **Gaussian Filter:**
It is performed by the function :gaussian_blur:`GaussianBlur <>` :
+ *Size(w, h)*: The size of the kernel to be used (the neighbors to be considered). :math:`w` and :math:`h` have to be odd and positive numbers otherwise thi size will be calculated using the :math:`\sigma_{x}` and :math:`\sigma_{y}` arguments.
+ :math:`\sigma_{x}`: The standard deviation in x. Writing :math:`0` implies that :math:`\sigma_{x}` is calculated using kernel size.
-
+
+ :math:`\sigma_{y}`: The standard deviation in y. Writing :math:`0` implies that :math:`\sigma_{y}` is calculated using kernel size.
-
+
#. **Median Filter:**
+ *src*: Source image
+ *dst*: Destination image, must be the same type as *src*
-
- + *i*: Size of the kernel (only one because we use a square window). Must be odd.
+
+ + *i*: Size of the kernel (only one because we use a square window). Must be odd.
#. **Bilateral Filter**
-
+
Provided by OpenCV function :bilateral_filter:`bilateralFilter <>`
.. code-block:: cpp
for ( int i = 1; i < MAX_KERNEL_LENGTH; i = i + 2 )
{ bilateralFilter ( src, dst, i, i*2, i/2 );
if( display_dst( DELAY_BLUR ) != 0 ) { return 0; } }
-
+
We use 5 arguments:
.. container:: enumeratevisibleitemswithsquare
+ *dst*: Destination image
+ *d*: The diameter of each pixel neighborhood.
-
+
+ :math:`\sigma_{Color}`: Standard deviation in the color space.
-
+
+ :math:`\sigma_{Space}`: Standard deviation in the coordinate space (in pixel terms)
.. container:: enumeratevisibleitemswithsquare
- * The code opens an image (in this case *lena.jpg*) and display it under the effects of the 4 filters explained.
+ * The code opens an image (in this case *lena.jpg*) and display it under the effects of the 4 filters explained.
* Here is a snapshot of the image smoothed using *medianBlur*:
.. image:: images/Smoothing_Tutorial_Result_Median_Filter.jpg
:alt: Smoothing with a median filter
- :align: center
+ :align: center
* What is Back Projection and why it is useful
* How to use the OpenCV function :calc_back_project:`calcBackProject <>` to calculate Back Projection
-
+
* How to mix different channels of an image by using the OpenCV function :mix_channels:`mixChannels <>`
.. container:: enumeratevisibleitemswithsquare
* Back Projection is a way of recording how well the pixels of a given image fit the distribution of pixels in a histogram model.
-
- * To make it simpler: For Back Projection, you calculate the histogram model of a feature and then use it to find this feature in an image.
+
+ * To make it simpler: For Back Projection, you calculate the histogram model of a feature and then use it to find this feature in an image.
* Application example: If you have a histogram of flesh color (say, a Hue-Saturation histogram ), then you can use it to find flesh color areas in an image:
* Let's say you have gotten a skin histogram (Hue-Saturation) based on the image below. The histogram besides is going to be our *model histogram* (which we know represents a sample of skin tonality). You applied some mask to capture only the histogram of the skin area:
- ====== ======
- |T0| |T1|
- ====== ======
+ ====== ======
+ |T0| |T1|
+ ====== ======
.. |T0| image:: images/Back_Projection_Theory0.jpg
:align: middle
* Now, let's imagine that you get another hand image (Test Image) like the one below: (with its respective histogram):
- ====== ======
- |T2| |T3|
- ====== ======
+ ====== ======
+ |T2| |T3|
+ ====== ======
.. |T2| image:: images/Back_Projection_Theory2.jpg
:align: middle
a. In each pixel of our Test Image (i.e. :math:`p(i,j)` ), collect the data and find the correspondent bin location for that pixel (i.e. :math:`( h_{i,j}, s_{i,j} )` ).
- b. Lookup the *model histogram* in the correspondent bin - :math:`( h_{i,j}, s_{i,j} )` - and read the bin value.
+ b. Lookup the *model histogram* in the correspondent bin - :math:`( h_{i,j}, s_{i,j} )` - and read the bin value.
c. Store this bin value in a new image (*BackProjection*). Also, you may consider to normalize the *model histogram* first, so the output for the Test Image can be visible for you.
.. container:: enumeratevisibleitemswithsquare
* **What does this program do?**
-
+
.. container:: enumeratevisibleitemswithsquare
* Loads an image
* **Downloadable code**:
- a. Click `here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/Histograms_Matching/calcBackProject_Demo1.cpp>`_ for the basic version (explained in this tutorial).
- b. For stuff slightly fancier (using H-S histograms and floodFill to define a mask for the skin area) you can check the `improved demo <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/Histograms_Matching/calcBackProject_Demo2.cpp>`_
- c. ...or you can always check out the classical `camshiftdemo <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/camshiftdemo.cpp>`_ in samples.
+ a. Click `here <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/Histograms_Matching/calcBackProject_Demo1.cpp>`_ for the basic version (explained in this tutorial).
+ b. For stuff slightly fancier (using H-S histograms and floodFill to define a mask for the skin area) you can check the `improved demo <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/Histograms_Matching/calcBackProject_Demo2.cpp>`_
+ c. ...or you can always check out the classical `camshiftdemo <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/camshiftdemo.cpp>`_ in samples.
* **Code at glance:**
using namespace std;
/// Global Variables
- Mat src; Mat hsv; Mat hue;
+ Mat src; Mat hsv; Mat hue;
int bins = 25;
/// Function Headers
/// Use only the Hue value
hue.create( hsv.size(), hsv.depth() );
int ch[] = { 0, 0 };
- mixChannels( &hsv, 1, &hue, 1, ch, 1 );
+ mixChannels( &hsv, 1, &hue, 1, ch, 1 );
/// Create Trackbar to enter the number of bins
char* window_image = "Source image";
/// Wait until user exits the program
waitKey(0);
- return 0;
+ return 0;
}
void Hist_and_Backproj(int, void* )
{
MatND hist;
- int histSize = MAX( bins, 2 );
+ int histSize = MAX( bins, 2 );
float hue_range[] = { 0, 180 };
const float* ranges = { hue_range };
/// Get Backprojection
MatND backproj;
calcBackProject( &hue, 1, 0, hist, backproj, &ranges, 1, true );
-
+
/// Draw the backproj
imshow( "BackProj", backproj );
/// Draw the histogram
int w = 400; int h = 400;
- int bin_w = cvRound( (double) w / histSize );
+ int bin_w = cvRound( (double) w / histSize );
Mat histImg = Mat::zeros( w, h, CV_8UC3 );
- for( int i = 0; i < bins; i ++ )
+ for( int i = 0; i < bins; i ++ )
{ rectangle( histImg, Point( i*bin_w, h ), Point( (i+1)*bin_w, h - cvRound( hist.at<float>(i)*h/255.0 ) ), Scalar( 0, 0, 255 ), -1 ); }
imshow( "Histogram", histImg );
.. code-block:: cpp
- Mat src; Mat hsv; Mat hue;
+ Mat src; Mat hsv; Mat hue;
int bins = 25;
#. Read the input image and transform it to HSV format:
hue.create( hsv.size(), hsv.depth() );
int ch[] = { 0, 0 };
- mixChannels( &hsv, 1, &hue, 1, ch, 1 );
+ mixChannels( &hsv, 1, &hue, 1, ch, 1 );
as you see, we use the function :mix_channels:`mixChannels` to get only the channel 0 (Hue) from the hsv image. It gets the following parameters:
+ **&hsv:** The source array from which the channels will be copied
+ **1:** The number of source arrays
- + **&hue:** The destination array of the copied channels
+ + **&hue:** The destination array of the copied channels
+ **1:** The number of destination arrays
+ **ch[] = {0,0}:** The array of index pairs indicating how the channels are copied. In this case, the Hue(0) channel of &hsv is being copied to the 0 channel of &hue (1-channel)
- + **1:** Number of index pairs
-
+ + **1:** Number of index pairs
+
#. Create a Trackbar for the user to enter the bin values. Any change on the Trackbar means a call to the **Hist_and_Backproj** callback function.
.. code-block:: cpp
-
+
char* window_image = "Source image";
namedWindow( window_image, CV_WINDOW_AUTOSIZE );
createTrackbar("* Hue bins: ", window_image, &bins, 180, Hist_and_Backproj );
imshow( window_image, src );
waitKey(0);
- return 0;
+ return 0;
#. **Hist_and_Backproj function:** Initialize the arguments needed for :calc_hist:`calcHist <>`. The number of bins comes from the Trackbar:
void Hist_and_Backproj(int, void* )
{
MatND hist;
- int histSize = MAX( bins, 2 );
+ int histSize = MAX( bins, 2 );
float hue_range[] = { 0, 180 };
const float* ranges = { hue_range };
calcBackProject( &hue, 1, 0, hist, backproj, &ranges, 1, true );
all the arguments are known (the same as used to calculate the histogram), only we add the backproj matrix, which will store the backprojection of the source image (&hue)
-
+
#. Display backproj:
.. code-block:: cpp
.. code-block:: cpp
int w = 400; int h = 400;
- int bin_w = cvRound( (double) w / histSize );
+ int bin_w = cvRound( (double) w / histSize );
Mat histImg = Mat::zeros( w, h, CV_8UC3 );
- for( int i = 0; i < bins; i ++ )
+ for( int i = 0; i < bins; i ++ )
{ rectangle( histImg, Point( i*bin_w, h ), Point( (i+1)*bin_w, h - cvRound( hist.at<float>(i)*h/255.0 ) ), Scalar( 0, 0, 255 ), -1 ); }
imshow( "Histogram", histImg );
#. Here are the output by using a sample image ( guess what? Another hand ). You can play with the bin values and you will observe how it affects the results:
- ====== ====== ======
- |R0| |R1| |R2|
- ====== ====== ======
+ ====== ====== ======
+ |R0| |R1| |R2|
+ ====== ====== ======
.. |R0| image:: images/Back_Projection1_Source_Image.jpg
:align: middle
* Use the OpenCV function :split:`split <>` to divide an image into its correspondent planes.
* To calculate histograms of arrays of images by using the OpenCV function :calc_hist:`calcHist <>`
-
+
* To normalize an array by using the function :normalize:`normalize <>`
.. image:: images/Histogram_Calculation_Theory_Hist0.jpg
- :align: center
+ :align: center
* What happens if we want to *count* this data in an organized way? Since we know that the *range* of information value for this case is 256 values, we can segment our range in subparts (called **bins**) like:
\begin{array}{l}
[0, 255] = { [0, 15] \cup [16, 31] \cup ....\cup [240,255] } \\
range = { bin_{1} \cup bin_{2} \cup ....\cup bin_{n = 15} }
- \end{array}
+ \end{array}
and we can keep count of the number of pixels that fall in the range of each :math:`bin_{i}`. Applying this to the example above we get the image below ( axis x represents the bins and axis y the number of pixels in each of them).
-
+
.. image:: images/Histogram_Calculation_Theory_Hist1.jpg
- :align: center
+ :align: center
+
+ * This was just a simple example of how an histogram works and why it is useful. An histogram can keep count not only of color intensities, but of whatever image features that we want to measure (i.e. gradients, directions, etc).
- * This was just a simple example of how an histogram works and why it is useful. An histogram can keep count not only of color intensities, but of whatever image features that we want to measure (i.e. gradients, directions, etc).
-
* Let's identify some parts of the histogram:
a. **dims**: The number of parameters you want to collect data of. In our example, **dims = 1** because we are only counting the intensity values of each pixel (in a greyscale image).
b. **bins**: It is the number of **subdivisions** in each dim. In our example, **bins = 16**
- c. **range**: The limits for the values to be measured. In this case: **range = [0,255]**
-
+ c. **range**: The limits for the values to be measured. In this case: **range = [0,255]**
+
* What if you want to count two features? In this case your resulting histogram would be a 3D plot (in which x and y would be :math:`bin_{x}` and :math:`bin_{y}` for each feature and z would be the number of counts for each combination of :math:`(bin_{x}, bin_{y})`. The same would apply for more features (of course it gets trickier).
-----------------------
For simple purposes, OpenCV implements the function :calc_hist:`calcHist <>`, which calculates the histogram of a set of arrays (usually images or image planes). It can operate with up to 32 dimensions. We will see it in the code below!
-
+
Code
====
.. container:: enumeratevisibleitemswithsquare
* **What does this program do?**
-
+
.. container:: enumeratevisibleitemswithsquare
* Loads an image
* Plot the three histograms in a window
* **Downloadable code**:
- Click `here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/Histograms_Matching/calcHist_Demo.cpp>`_
+ Click `here <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/Histograms_Matching/calcHist_Demo.cpp>`_
* **Code at glance:**
if( !src.data )
{ return -1; }
-#. Separate the source image in its three R,G and B planes. For this we use the OpenCV function :split:`split <>`:
+#. Separate the source image in its three R,G and B planes. For this we use the OpenCV function :split:`split <>`:
.. code-block:: cpp
a. Establish number of bins (5, 10...):
.. code-block:: cpp
-
+
int histSize = 256; //from 0 to 255
b. Set the range of values (as we said, between 0 and 255 )
Mat b_hist, g_hist, r_hist;
e. We proceed to calculate the histograms by using the OpenCV function :calc_hist:`calcHist <>`:
-
+
.. code-block:: cpp
/// Compute the histograms:
calcHist( &bgr_planes[0], 1, 0, Mat(), b_hist, 1, &histSize, &histRange, uniform, accumulate );
calcHist( &bgr_planes[1], 1, 0, Mat(), g_hist, 1, &histSize, &histRange, uniform, accumulate );
calcHist( &bgr_planes[2], 1, 0, Mat(), r_hist, 1, &histSize, &histRange, uniform, accumulate );
-
+
where the arguments are:
.. container:: enumeratevisibleitemswithsquare
-
+
+ **&bgr_planes[0]:** The source array(s)
+ **1**: The number of source arrays (in this case we are using 1. We can enter here also a list of arrays )
+ **0**: The channel (*dim*) to be measured. In this case it is just the intensity (each array is single-channel) so we just write 0.
+ **Mat()**: A mask to be used on the source array ( zeros indicating pixels to be ignored ). If not defined it is not used
+ **b_hist**: The Mat object where the histogram will be stored
- + **1**: The histogram dimensionality.
- + **histSize:** The number of bins per each used dimension
+ + **1**: The histogram dimensionality.
+ + **histSize:** The number of bins per each used dimension
+ **histRange:** The range of values to be measured per each dimension
+ **uniform** and **accumulate**: The bin sizes are the same and the histogram is cleared at the beginning.
this function receives these arguments:
.. container:: enumeratevisibleitemswithsquare
-
+
+ **b_hist:** Input array
+ **b_hist:** Output normalized array (can be the same)
+ **0** and**histImage.rows**: For this example, they are the lower and upper limits to normalize the values of **r_hist**
}
- we use the expression:
+ we use the expression:
.. code-block:: cpp
waitKey(0);
return 0;
-
+
Result
======
#. Using as input argument an image like the shown below:
.. image:: images/Histogram_Calculation_Original_Image.jpg
- :align: center
+ :align: center
#. Produces the following histogram:
.. image:: images/Histogram_Calculation_Result.jpg
- :align: center
+ :align: center
a. **Correlation ( CV\_COMP\_CORREL )**
-
+
.. math::
-
- d(H_1,H_2) = \frac{\sum_I (H_1(I) - \bar{H_1}) (H_2(I) - \bar{H_2})}{\sqrt{\sum_I(H_1(I) - \bar{H_1})^2 \sum_I(H_2(I) - \bar{H_2})^2}}
-
+
+ d(H_1,H_2) = \frac{\sum_I (H_1(I) - \bar{H_1}) (H_2(I) - \bar{H_2})}{\sqrt{\sum_I(H_1(I) - \bar{H_1})^2 \sum_I(H_2(I) - \bar{H_2})^2}}
+
where
-
+
.. math::
-
- \bar{H_k} = \frac{1}{N} \sum _J H_k(J)
-
-
+
+ \bar{H_k} = \frac{1}{N} \sum _J H_k(J)
+
+
and :math:`N` is the total number of histogram bins.
-
-
+
+
b. **Chi-Square ( CV\_COMP\_CHISQR )**
-
+
.. math::
-
- d(H_1,H_2) = \sum _I \frac{\left(H_1(I)-H_2(I)\right)^2}{H_1(I)}
-
-
+
+ d(H_1,H_2) = \sum _I \frac{\left(H_1(I)-H_2(I)\right)^2}{H_1(I)}
+
+
c. **Intersection ( method=CV\_COMP\_INTERSECT )**
-
+
.. math::
-
- d(H_1,H_2) = \sum _I \min (H_1(I), H_2(I))
-
-
+
+ d(H_1,H_2) = \sum _I \min (H_1(I), H_2(I))
+
+
d. **Bhattacharyya distance ( CV\_COMP\_BHATTACHARYYA )**
-
+
.. math::
-
- d(H_1,H_2) = \sqrt{1 - \frac{1}{\sqrt{\bar{H_1} \bar{H_2} N^2}} \sum_I \sqrt{H_1(I) \cdot H_2(I)}}
-
-
+
+ d(H_1,H_2) = \sqrt{1 - \frac{1}{\sqrt{\bar{H_1} \bar{H_2} N^2}} \sum_I \sqrt{H_1(I) \cdot H_2(I)}}
+
+
Code
====
.. container:: enumeratevisibleitemswithsquare
* **What does this program do?**
-
+
.. container:: enumeratevisibleitemswithsquare
* Loads a *base image* and 2 *test images* to be compared with it.
* Compare the histogram of the *base image* with respect to the 2 test histograms, the histogram of the lower half base image and with the same base image histogram.
* Display the numerical matching parameters obtained.
- * **Downloadable code**:
- Click `here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/Histograms_Matching/compareHist_Demo.cpp>`_
+ * **Downloadable code**:
+ Click `here <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/Histograms_Matching/compareHist_Demo.cpp>`_
* **Code at glance:**
/// Load three images with different environment settings
if( argc < 4 )
{ printf("** Error. Usage: ./compareHist_Demo <image_settings0> <image_setting1> <image_settings2>\n");
- return -1;
+ return -1;
}
src_base = imread( argv[1], 1 );
cvtColor( src_test1, hsv_test1, CV_BGR2HSV );
cvtColor( src_test2, hsv_test2, CV_BGR2HSV );
- hsv_half_down = hsv_base( Range( hsv_base.rows/2, hsv_base.rows - 1 ), Range( 0, hsv_base.cols - 1 ) );
+ hsv_half_down = hsv_base( Range( hsv_base.rows/2, hsv_base.rows - 1 ), Range( 0, hsv_base.cols - 1 ) );
/// Using 30 bins for hue and 32 for saturation
int h_bins = 50; int s_bins = 60;
/// Apply the histogram comparison methods
for( int i = 0; i < 4; i++ )
- { int compare_method = i;
+ { int compare_method = i;
double base_base = compareHist( hist_base, hist_base, compare_method );
double base_half = compareHist( hist_base, hist_half_down, compare_method );
double base_test1 = compareHist( hist_base, hist_test1, compare_method );
double base_test2 = compareHist( hist_base, hist_test2, compare_method );
-
+
printf( " Method [%d] Perfect, Base-Half, Base-Test(1), Base-Test(2) : %f, %f, %f, %f \n", i, base_base, base_half , base_test1, base_test2 );
- }
+ }
printf( "Done \n" );
Explanation
===========
-#. Declare variables such as the matrices to store the base image and the two other images to compare ( RGB and HSV )
+#. Declare variables such as the matrices to store the base image and the two other images to compare ( RGB and HSV )
.. code-block:: cpp
if( argc < 4 )
{ printf("** Error. Usage: ./compareHist_Demo <image_settings0> <image_setting1> <image_settings2>\n");
- return -1;
+ return -1;
}
src_base = imread( argv[1], 1 );
.. code-block:: cpp
- hsv_half_down = hsv_base( Range( hsv_base.rows/2, hsv_base.rows - 1 ), Range( 0, hsv_base.cols - 1 ) );
+ hsv_half_down = hsv_base( Range( hsv_base.rows/2, hsv_base.rows - 1 ), Range( 0, hsv_base.cols - 1 ) );
#. Initialize the arguments to calculate the histograms (bins, ranges and channels H and S ).
#. Calculate the Histograms for the base image, the 2 test images and the half-down base image:
.. code-block:: cpp
-
+
calcHist( &hsv_base, 1, channels, Mat(), hist_base, 2, histSize, ranges, true, false );
normalize( hist_base, hist_base, 0, 1, NORM_MINMAX, -1, Mat() );
.. code-block:: cpp
for( int i = 0; i < 4; i++ )
- { int compare_method = i;
+ { int compare_method = i;
double base_base = compareHist( hist_base, hist_base, compare_method );
double base_half = compareHist( hist_base, hist_half_down, compare_method );
double base_test1 = compareHist( hist_base, hist_test1, compare_method );
double base_test2 = compareHist( hist_base, hist_test2, compare_method );
-
+
printf( " Method [%d] Perfect, Base-Half, Base-Test(1), Base-Test(2) : %f, %f, %f, %f \n", i, base_base, base_half , base_test1, base_test2 );
- }
+ }
+
-
Results
========
#. We use as input the following images:
- ============ ============ ============
+ ============ ============ ============
|Base_0| |Test_1| |Test_2|
- ============ ============ ============
+ ============ ============ ============
.. |Base_0| image:: images/Histogram_Comparison_Source_0.jpg
:align: middle
=============== =============== =============== =============== ===============
*Method* Base - Base Base - Half Base - Test 1 Base - Test 2
=============== =============== =============== =============== ===============
- *Correlation* 1.000000 0.930766 0.182073 0.120447
- *Chi-square* 0.000000 4.940466 21.184536 49.273437
- *Intersection* 24.391548 14.959809 3.889029 5.775088
- *Bhattacharyya* 0.000000 0.222609 0.646576 0.801869
+ *Correlation* 1.000000 0.930766 0.182073 0.120447
+ *Chi-square* 0.000000 4.940466 21.184536 49.273437
+ *Intersection* 24.391548 14.959809 3.889029 5.775088
+ *Bhattacharyya* 0.000000 0.222609 0.646576 0.801869
=============== =============== =============== =============== ===============
* What an image histogram is and why it is useful
- * To equalize histograms of images by using the OpenCV function:equalize_hist:`equalizeHist <>`
+ * To equalize histograms of images by using the OpenCV function:equalize_hist:`equalizeHist <>`
.. container:: enumeratevisibleitemswithsquare
- * It is a graphical representation of the intensity distribution of an image.
+ * It is a graphical representation of the intensity distribution of an image.
* It quantifies the number of pixels for each intensity value considered.
.. image:: images/Histogram_Equalization_Theory_0.jpg
- :align: center
+ :align: center
What is Histogram Equalization?
* To make it clearer, from the image above, you can see that the pixels seem clustered around the middle of the available range of intensities. What Histogram Equalization does is to *stretch out* this range. Take a look at the figure below: The green circles indicate the *underpopulated* intensities. After applying the equalization, we get an histogram like the figure in the center. The resulting image is shown in the picture at right.
.. image:: images/Histogram_Equalization_Theory_1.jpg
- :align: center
+ :align: center
How does it work?
-----------------
.. container:: enumeratevisibleitemswithsquare
- * Equalization implies *mapping* one distribution (the given histogram) to another distribution (a wider and more uniform distribution of intensity values) so the intensity values are spreaded over the whole range.
+ * Equalization implies *mapping* one distribution (the given histogram) to another distribution (a wider and more uniform distribution of intensity values) so the intensity values are spreaded over the whole range.
* To accomplish the equalization effect, the remapping should be the *cumulative distribution function (cdf)* (more details, refer to *Learning OpenCV*). For the histogram :math:`H(i)`, its *cumulative distribution* :math:`H^{'}(i)` is:
.. math::
- H^{'}(i) = \sum_{0 \le j < i} H(j)
+ H^{'}(i) = \sum_{0 \le j < i} H(j)
To use this as a remapping function, we have to normalize :math:`H^{'}(i)` such that the maximum value is 255 ( or the maximum value for the intensity of the image ). From the example above, the cumulative function is:
.. image:: images/Histogram_Equalization_Theory_2.jpg
- :align: center
+ :align: center
* Finally, we use a simple remapping procedure to obtain the intensity values of the equalized image:
.. math::
-
+
equalized( x, y ) = H^{'}( src(x,y) )
Code
.. container:: enumeratevisibleitemswithsquare
* **What does this program do?**
-
+
.. container:: enumeratevisibleitemswithsquare
* Loads an image
- * Convert the original image to grayscale
+ * Convert the original image to grayscale
* Equalize the Histogram by using the OpenCV function :equalize_hist:`EqualizeHist <>`
* Display the source and equalized images in a window.
* **Downloadable code**:
- Click `here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/Histograms_Matching/EqualizeHist_Demo.cpp>`_
+ Click `here <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/Histograms_Matching/EqualizeHist_Demo.cpp>`_
* **Code at glance:**
/// Apply Histogram Equalization
equalizeHist( src, dst );
-
+
/// Display results
namedWindow( source_window, CV_WINDOW_AUTOSIZE );
namedWindow( equalized_window, CV_WINDOW_AUTOSIZE );
imshow( source_window, src );
imshow( equalized_window, dst );
-
- /// Wait until user exits the program
+
+ /// Wait until user exits the program
waitKey(0);
return 0;
Explanation
===========
-#. Declare the source and destination images as well as the windows names:
+#. Declare the source and destination images as well as the windows names:
.. code-block:: cpp
char* equalized_window = "Equalized Image";
#. Load the source image:
-
+
.. code-block:: cpp
src = imread( argv[1], 1 );
.. code-block:: cpp
equalizeHist( src, dst );
-
+
As it can be easily seen, the only arguments are the original image and the output (equalized) image.
#. Display both images (original and equalized) :
imshow( source_window, src );
imshow( equalized_window, dst );
-
+
#. Wait until user exists the program
-
+
.. code-block:: cpp
waitKey(0);
#. To appreciate better the results of equalization, let's introduce an image with not much contrast, such as:
.. image:: images/Histogram_Equalization_Original_Image.jpg
- :align: center
+ :align: center
which, by the way, has this histogram:
.. image:: images/Histogram_Equalization_Original_Histogram.jpg
- :align: center
+ :align: center
notice that the pixels are clustered around the center of the histogram.
#. After applying the equalization with our program, we get this result:
.. image:: images/Histogram_Equalization_Equalized_Image.jpg
- :align: center
+ :align: center
this image has certainly more contrast. Check out its new histogram like this:
* We need two primary components:
- a. **Source image (I):** The image in which we expect to find a match to the template image
- b. **Template image (T):** The patch image which will be compared to the template image
+ a. **Source image (I):** The image in which we expect to find a match to the template image
+ b. **Template image (T):** The patch image which will be compared to the template image
our goal is to detect the highest matching area:
.. image:: images/Template_Matching_Template_Theory_Summary.jpg
- :align: center
+ :align: center
* To identify the matching area, we have to *compare* the template image against the source image by sliding it:
.. image:: images/Template_Matching_Template_Theory_Sliding.jpg
- :align: center
+ :align: center
* By **sliding**, we mean moving the patch one pixel at a time (left to right, up to down). At each location, a metric is calculated so it represents how "good" or "bad" the match at that location is (or how similar the patch is to that particular area of the source image).
* For each location of **T** over **I**, you *store* the metric in the *result matrix* **(R)**. Each location :math:`(x,y)` in **R** contains the match metric:
.. image:: images/Template_Matching_Template_Theory_Result.jpg
- :align: center
+ :align: center
- the image above is the result **R** of sliding the patch with a metric **TM_CCORR_NORMED**. The brightest locations indicate the highest matches. As you can see, the location marked by the red circle is probably the one with the highest value, so that location (the rectangle formed by that point as a corner and width and height equal to the patch image) is considered the match.
+ the image above is the result **R** of sliding the patch with a metric **TM_CCORR_NORMED**. The brightest locations indicate the highest matches. As you can see, the location marked by the red circle is probably the one with the highest value, so that location (the rectangle formed by that point as a corner and width and height equal to the patch image) is considered the match.
* In practice, we use the function :min_max_loc:`minMaxLoc <>` to locate the highest value (or lower, depending of the type of matching method) in the *R* matrix.
-
+
Which are the matching methods available in OpenCV?
----------------------------------------------------
Good question. OpenCV implements Template matching in the function :match_template:`matchTemplate <>`. The available methods are 6:
a. **method=CV\_TM\_SQDIFF**
-
+
.. math::
-
- R(x,y)= \sum _{x',y'} (T(x',y')-I(x+x',y+y'))^2
-
-
+
+ R(x,y)= \sum _{x',y'} (T(x',y')-I(x+x',y+y'))^2
+
+
b. **method=CV\_TM\_SQDIFF\_NORMED**
-
+
.. math::
-
- R(x,y)= \frac{\sum_{x',y'} (T(x',y')-I(x+x',y+y'))^2}{\sqrt{\sum_{x',y'}T(x',y')^2 \cdot \sum_{x',y'} I(x+x',y+y')^2}}
-
+
+ R(x,y)= \frac{\sum_{x',y'} (T(x',y')-I(x+x',y+y'))^2}{\sqrt{\sum_{x',y'}T(x',y')^2 \cdot \sum_{x',y'} I(x+x',y+y')^2}}
+
c. **method=CV\_TM\_CCORR**
-
+
.. math::
-
- R(x,y)= \sum _{x',y'} (T(x',y') \cdot I(x+x',y+y'))
-
-d. **method=CV\_TM\_CCORR\_NORMED**
-
+ R(x,y)= \sum _{x',y'} (T(x',y') \cdot I(x+x',y+y'))
+
+
+d. **method=CV\_TM\_CCORR\_NORMED**
+
.. math::
-
- R(x,y)= \frac{\sum_{x',y'} (T(x',y') \cdot I'(x+x',y+y'))}{\sqrt{\sum_{x',y'}T(x',y')^2 \cdot \sum_{x',y'} I(x+x',y+y')^2}}
-
+
+ R(x,y)= \frac{\sum_{x',y'} (T(x',y') \cdot I'(x+x',y+y'))}{\sqrt{\sum_{x',y'}T(x',y')^2 \cdot \sum_{x',y'} I(x+x',y+y')^2}}
+
e. **method=CV\_TM\_CCOEFF**
-
+
.. math::
-
- R(x,y)= \sum _{x',y'} (T'(x',y') \cdot I(x+x',y+y'))
-
+
+ R(x,y)= \sum _{x',y'} (T'(x',y') \cdot I(x+x',y+y'))
+
where
-
+
.. math::
-
- \begin{array}{l} T'(x',y')=T(x',y') - 1/(w \cdot h) \cdot \sum _{x'',y''} T(x'',y'') \\ I'(x+x',y+y')=I(x+x',y+y') - 1/(w \cdot h) \cdot \sum _{x'',y''} I(x+x'',y+y'') \end{array}
-
-
+
+ \begin{array}{l} T'(x',y')=T(x',y') - 1/(w \cdot h) \cdot \sum _{x'',y''} T(x'',y'') \\ I'(x+x',y+y')=I(x+x',y+y') - 1/(w \cdot h) \cdot \sum _{x'',y''} I(x+x'',y+y'') \end{array}
+
+
f. **method=CV\_TM\_CCOEFF\_NORMED**
-
+
.. math::
-
- R(x,y)= \frac{ \sum_{x',y'} (T'(x',y') \cdot I'(x+x',y+y')) }{ \sqrt{\sum_{x',y'}T'(x',y')^2 \cdot \sum_{x',y'} I'(x+x',y+y')^2} }
+
+ R(x,y)= \frac{ \sum_{x',y'} (T'(x',y') \cdot I'(x+x',y+y')) }{ \sqrt{\sum_{x',y'}T'(x',y')^2 \cdot \sum_{x',y'} I'(x+x',y+y')^2} }
Code
.. container:: enumeratevisibleitemswithsquare
* **What does this program do?**
-
+
.. container:: enumeratevisibleitemswithsquare
* Loads an input image and a image patch (*template*)
* Draw a rectangle around the area corresponding to the highest match
* **Downloadable code**:
- Click `here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/Histograms_Matching/MatchTemplate_Demo.cpp>`_
+ Click `here <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/Histograms_Matching/MatchTemplate_Demo.cpp>`_
* **Code at glance:**
.. code-block:: cpp
- #include "opencv2/highgui/highgui.hpp"
+ #include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <iostream>
#include <stdio.h>
/// Create windows
namedWindow( image_window, CV_WINDOW_AUTOSIZE );
namedWindow( result_window, CV_WINDOW_AUTOSIZE );
-
+
/// Create Trackbar
char* trackbar_label = "Method: \n 0: SQDIFF \n 1: SQDIFF NORMED \n 2: TM CCORR \n 3: TM CCORR NORMED \n 4: TM COEFF \n 5: TM COEFF NORMED";
createTrackbar( trackbar_label, image_window, &match_method, max_Trackbar, MatchingMethod );
/// Source image to display
Mat img_display;
img.copyTo( img_display );
-
+
/// Create the result matrix
int result_cols = img.cols - templ.cols + 1;
- int result_rows = img.rows - templ.rows + 1;
-
+ int result_rows = img.rows - templ.rows + 1;
+
result.create( result_cols, result_rows, CV_32FC1 );
/// Do the Matching and Normalize
/// Localizing the best match with minMaxLoc
double minVal; double maxVal; Point minLoc; Point maxLoc;
Point matchLoc;
-
+
minMaxLoc( result, &minVal, &maxVal, &minLoc, &maxLoc, Mat() );
/// For SQDIFF and SQDIFF_NORMED, the best matches are lower values. For all the other methods, the higher the better
if( match_method == CV_TM_SQDIFF || match_method == CV_TM_SQDIFF_NORMED )
{ matchLoc = minLoc; }
- else
+ else
{ matchLoc = maxLoc; }
/// Show me what you got
- rectangle( img_display, matchLoc, Point( matchLoc.x + templ.cols , matchLoc.y + templ.rows ), Scalar::all(0), 2, 8, 0 );
- rectangle( result, matchLoc, Point( matchLoc.x + templ.cols , matchLoc.y + templ.rows ), Scalar::all(0), 2, 8, 0 );
+ rectangle( img_display, matchLoc, Point( matchLoc.x + templ.cols , matchLoc.y + templ.rows ), Scalar::all(0), 2, 8, 0 );
+ rectangle( result, matchLoc, Point( matchLoc.x + templ.cols , matchLoc.y + templ.rows ), Scalar::all(0), 2, 8, 0 );
imshow( image_window, img_display );
imshow( result_window, result );
namedWindow( image_window, CV_WINDOW_AUTOSIZE );
namedWindow( result_window, CV_WINDOW_AUTOSIZE );
-
+
#. Create the Trackbar to enter the kind of matching method to be used. When a change is detected the callback function **MatchingMethod** is called.
.. code-block:: cpp
waitKey(0);
return 0;
-
+
#. Let's check out the callback function. First, it makes a copy of the source image:
.. code-block:: cpp
#. Next, it creates the result matrix that will store the matching results for each template location. Observe in detail the size of the result matrix (which matches all possible locations for it)
.. code-block:: cpp
-
+
int result_cols = img.cols - templ.cols + 1;
- int result_rows = img.rows - templ.rows + 1;
-
+ int result_rows = img.rows - templ.rows + 1;
+
result.create( result_cols, result_rows, CV_32FC1 );
-
+
#. Perform the template matching operation:
.. code-block:: cpp
normalize( result, result, 0, 1, NORM_MINMAX, -1, Mat() );
-#. We localize the minimum and maximum values in the result matrix **R** by using :min_max_loc:`minMaxLoc <>`.
+#. We localize the minimum and maximum values in the result matrix **R** by using :min_max_loc:`minMaxLoc <>`.
.. code-block:: cpp
double minVal; double maxVal; Point minLoc; Point maxLoc;
Point matchLoc;
-
+
minMaxLoc( result, &minVal, &maxVal, &minLoc, &maxLoc, Mat() );
-
+
the function calls as arguments:
- .. container:: enumeratevisibleitemswithsquare
+ .. container:: enumeratevisibleitemswithsquare
+ **result:** The source array
+ **&minVal** and **&maxVal:** Variables to save the minimum and maximum values in **result**
#. For the first two methods ( CV\_SQDIFF and CV\_SQDIFF\_NORMED ) the best match are the lowest values. For all the others, higher values represent better matches. So, we save the corresponding value in the **matchLoc** variable:
.. code-block:: cpp
-
+
if( match_method == CV_TM_SQDIFF || match_method == CV_TM_SQDIFF_NORMED )
{ matchLoc = minLoc; }
- else
+ else
{ matchLoc = maxLoc; }
#. Display the source image and the result matrix. Draw a rectangle around the highest possible matching area:
.. code-block:: cpp
- rectangle( img_display, matchLoc, Point( matchLoc.x + templ.cols , matchLoc.y + templ.rows ), Scalar::all(0), 2, 8, 0 );
- rectangle( result, matchLoc, Point( matchLoc.x + templ.cols , matchLoc.y + templ.rows ), Scalar::all(0), 2, 8, 0 );
+ rectangle( img_display, matchLoc, Point( matchLoc.x + templ.cols , matchLoc.y + templ.rows ), Scalar::all(0), 2, 8, 0 );
+ rectangle( result, matchLoc, Point( matchLoc.x + templ.cols , matchLoc.y + templ.rows ), Scalar::all(0), 2, 8, 0 );
imshow( image_window, img_display );
imshow( result_window, result );
.. image:: images/Template_Matching_Original_Image.jpg
:align: center
-
+
and a template image:
.. image:: images/Template_Matching_Template_Image.jpg
- :align: center
+ :align: center
#. Generate the following result matrices (first row are the standard methods SQDIFF, CCORR and CCOEFF, second row are the same methods in its normalized version). In the first column, the darkest is the better match, for the other two columns, the brighter a location, the higher the match.
- ============ ============ ============
+ ============ ============ ============
|Result_0| |Result_2| |Result_4|
- ============ ============ ============
+ ============ ============ ============
|Result_1| |Result_3| |Result_5|
- ============ ============ ============
+ ============ ============ ============
.. |Result_0| image:: images/Template_Matching_Correl_Result_0.jpg
:align: middle
* **Low error rate:** Meaning a good detection of only existent edges.
* **Good localization:** The distance between edge pixels detected and real edge pixels have to be minimized.
- * **Minimal response:** Only one detector response per edge.
+ * **Minimal response:** Only one detector response per edge.
Steps
------
#. Filter out any noise. The Gaussian filter is used for this purpose. An example of a Gaussian kernel of :math:`size = 5` that might be used is shown below:
.. math::
-
+
K = \dfrac{1}{159}\begin{bmatrix}
2 & 4 & 5 & 4 & 2 \\
4 & 9 & 12 & 9 & 4 \\
5 & 12 & 15 & 12 & 5 \\
4 & 9 & 12 & 9 & 4 \\
- 2 & 4 & 5 & 4 & 2
- \end{bmatrix}
+ 2 & 4 & 5 & 4 & 2
+ \end{bmatrix}
-#. Find the intensity gradient of the image. For this, we follow a procedure analogous to Sobel:
+#. Find the intensity gradient of the image. For this, we follow a procedure analogous to Sobel:
a. Apply a pair of convolution masks (in :math:`x` and :math:`y` directions:
.. math::
-
+
G_{x} = \begin{bmatrix}
-1 & 0 & +1 \\
-2 & 0 & +2 \\
- -1 & 0 & +1
+ -1 & 0 & +1
\end{bmatrix}
-
+
G_{y} = \begin{bmatrix}
-1 & -2 & -1 \\
0 & 0 & 0 \\
- +1 & +2 & +1
- \end{bmatrix}
+ +1 & +2 & +1
+ \end{bmatrix}
b. Find the gradient strength and direction with:
.. math::
\begin{array}{l}
- G = \sqrt{ G_{x}^{2} + G_{y}^{2} } \\
+ G = \sqrt{ G_{x}^{2} + G_{y}^{2} } \\
\theta = \arctan(\dfrac{ G_{y} }{ G_{x} })
\end{array}
a. If a pixel gradient is higher than the *upper* threshold, the pixel is accepted as an edge
b. If a pixel gradient value is below the *lower* threshold, then it is rejected.
- c. If the pixel gradient is between the two thresholds, then it will be accepted only if it is connected to a pixel that is above the *upper* threshold.
+ c. If the pixel gradient is between the two thresholds, then it will be accepted only if it is connected to a pixel that is above the *upper* threshold.
Canny recommended a *upper*:*lower* ratio between 2:1 and 3:1.
-
-#. For more details, you can always consult your favorite Computer Vision book.
+
+#. For more details, you can always consult your favorite Computer Vision book.
Code
=====
#. **What does this program do?**
-
+
* Asks the user to enter a numerical value to set the lower threshold for our *Canny Edge Detector* (by means of a Trackbar)
* Applies the *Canny Detector* and generates a **mask** (bright lines representing the edges on a black background).
* Applies the mask obtained on the original image and display it in a window.
-
-#. The tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/ImgTrans/CannyDetector_Demo.cpp>`_
+
+#. The tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/ImgTrans/CannyDetector_Demo.cpp>`_
.. code-block:: cpp
/// Using Canny's output as a mask, we display our result
dst = Scalar::all(0);
-
+
src.copyTo( dst, detected_edges);
imshow( window_name, dst );
}
{ return -1; }
#. Create a matrix of the same type and size of *src* (to be *dst*)
-
+
.. code-block:: cpp
dst.create( src.size(), src.type() );
.. code-block:: cpp
dst = Scalar::all(0);
-
-#. Finally, we will use the function :copy_to:`copyTo <>` to map only the areas of the image that are identified as edges (on a black background).
-
+
+#. Finally, we will use the function :copy_to:`copyTo <>` to map only the areas of the image that are identified as edges (on a black background).
+
.. code-block:: cpp
src.copyTo( dst, detected_edges);
:alt: Result after running Canny
:width: 200pt
:align: center
-
+
* Notice how the image is superposed to the black background on the edge regions.
-
+
.. container:: enumeratevisibleitemswithsquare
- * Use the OpenCV function :copy_make_border:`copyMakeBorder <>` to set the borders (extra padding to your image).
-
+ * Use the OpenCV function :copy_make_border:`copyMakeBorder <>` to set the borders (extra padding to your image).
+
Theory
========
The explanation below belongs to the book **Learning OpenCV** by Bradski and Kaehler.
-#. In our previous tutorial we learned to use convolution to operate on images. One problem that naturally arises is how to handle the boundaries. How can we convolve them if the evaluated points are at the edge of the image?
+#. In our previous tutorial we learned to use convolution to operate on images. One problem that naturally arises is how to handle the boundaries. How can we convolve them if the evaluated points are at the edge of the image?
#. What most of OpenCV functions do is to copy a given image onto another slightly larger image and then automatically pads the boundary (by any of the methods explained in the sample code just below). This way, the convolution can be performed over the needed pixels without problems (the extra padding is cut after the operation is done).
#. In this tutorial, we will briefly explore two ways of defining the extra padding (border) for an image:
a. **BORDER_CONSTANT**: Pad the image with a constant value (i.e. black or :math:`0`
-
+
b. **BORDER_REPLICATE**: The row or column at the very edge of the original is replicated to the extra border.
This will be seen more clearly in the Code section.
======
#. **What does this program do?**
-
- * Load an image
+
+ * Load an image
* Let the user choose what kind of padding use in the input image. There are two options:
-
- #. *Constant value border*: Applies a padding of a constant value for the whole border. This value will be updated randomly each 0.5 seconds.
+
+ #. *Constant value border*: Applies a padding of a constant value for the whole border. This value will be updated randomly each 0.5 seconds.
#. *Replicated border*: The border will be replicated from the pixel values at the edges of the original image.
The user chooses either option by pressing 'c' (constant) or 'r' (replicate)
* The program finishes when the user presses 'ESC'
-
-#. The tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/ImgTrans/copyMakeBorder_demo.cpp>`_
+#. The tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/ImgTrans/copyMakeBorder_demo.cpp>`_
-.. code-block:: cpp
+
+.. code-block:: cpp
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"
using namespace cv;
- /// Global Variables
+ /// Global Variables
Mat src, dst;
int top, bottom, left, right;
int borderType;
/// Load an image
src = imread( argv[1] );
-
+
if( !src.data )
{ return -1;
- printf(" No data entered, please enter the path to an image file \n");
+ printf(" No data entered, please enter the path to an image file \n");
}
/// Brief how-to for this program
namedWindow( window_name, CV_WINDOW_AUTOSIZE );
/// Initialize arguments for the filter
- top = (int) (0.05*src.rows); bottom = (int) (0.05*src.rows);
+ top = (int) (0.05*src.rows); bottom = (int) (0.05*src.rows);
left = (int) (0.05*src.cols); right = (int) (0.05*src.cols);
dst = src;
imshow( window_name, dst );
-
+
while( true )
{
c = waitKey(500);
.. code-block:: cpp
src = imread( argv[1] );
-
+
if( !src.data )
{ return -1;
- printf(" No data entered, please enter the path to an image file \n");
+ printf(" No data entered, please enter the path to an image file \n");
}
#. After giving a short intro of how to use the program, we create a window:
-
+
.. code-block:: cpp
namedWindow( window_name, CV_WINDOW_AUTOSIZE );
.. code-block:: cpp
- top = (int) (0.05*src.rows); bottom = (int) (0.05*src.rows);
+ top = (int) (0.05*src.rows); bottom = (int) (0.05*src.rows);
left = (int) (0.05*src.cols); right = (int) (0.05*src.cols);
#. The program begins a *while* loop. If the user presses 'c' or 'r', the *borderType* variable takes the value of *BORDER_CONSTANT* or *BORDER_REPLICATE* respectively:
.. code-block:: cpp
-
+
while( true )
{
c = waitKey(500);
#. Finally, we call the function :copy_make_border:`copyMakeBorder <>` to apply the respective padding:
.. code-block:: cpp
-
+
copyMakeBorder( src, dst, top, bottom, left, right, borderType, value );
The arguments are:
#. We display our output image in the image created previously
.. code-block:: cpp
-
+
imshow( window_name, dst );
.. container:: enumeratevisibleitemswithsquare
* By default, it begins with the border set to BORDER_CONSTANT. Hence, a succession of random colored borders will be shown.
- * If you press 'r', the border will become a replica of the edge pixels.
+ * If you press 'r', the border will become a replica of the edge pixels.
* If you press 'c', the random colored borders will appear again
* If you press 'ESC' the program will exit.
Below some screenshot showing how the border changes color and how the *BORDER_REPLICATE* option looks:
-
+
.. image:: images/CopyMakeBorder_Tutorial_Results.jpg
:alt: Final result after copyMakeBorder application
.. container:: enumeratevisibleitemswithsquare
- * Use the OpenCV function :filter2d:`filter2D <>` to create your own linear filters.
-
+ * Use the OpenCV function :filter2d:`filter2D <>` to create your own linear filters.
+
Theory
=======
Convolution
------------
-In a very general sense, convolution is an operation between every part of an image and an operator (kernel).
+In a very general sense, convolution is an operation between every part of an image and an operator (kernel).
What is a kernel?
------------------
-A kernel is essentially a fixed size array of numerical coefficeints along with an *anchor point* in that array, which is tipically located at the center.
+A kernel is essentially a fixed size array of numerical coefficeints along with an *anchor point* in that array, which is tipically located at the center.
.. image:: images/filter_2d_tutorial_kernel_theory.png
:alt: kernel example
- :align: center
+ :align: center
How does convolution with a kernel work?
-----------------------------------------
#. Place the kernel anchor on top of a determined pixel, with the rest of the kernel overlaying the corresponding local pixels in the image.
-#. Multiply the kernel coefficients by the corresponding image pixel values and sum the result.
+#. Multiply the kernel coefficients by the corresponding image pixel values and sum the result.
#. Place the result to the location of the *anchor* in the input image.
Expressing the procedure above in the form of an equation we would have:
.. math::
-
+
H(x,y) = \sum_{i=0}^{M_{i} - 1} \sum_{j=0}^{M_{j}-1} I(x+i - a_{i}, y + j - a_{j})K(i,j)
-Fortunately, OpenCV provides you with the function :filter2d:`filter2D <>` so you do not have to code all these operations.
+Fortunately, OpenCV provides you with the function :filter2d:`filter2D <>` so you do not have to code all these operations.
Code
======
#. **What does this program do?**
-
+
* Loads an image
* Performs a *normalized box filter*. For instance, for a kernel of size :math:`size = 3`, the kernel would be:
.. math::
-
+
K = \dfrac{1}{3 \cdot 3} \begin{bmatrix}
1 & 1 & 1 \\
1 & 1 & 1 \\
- 1 & 1 & 1
- \end{bmatrix}
+ 1 & 1 & 1
+ \end{bmatrix}
The program will perform the filter operation with kernels of sizes 3, 5, 7, 9 and 11.
* The filter output (with each kernel) will be shown during 500 milliseconds
-#. The tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/ImgTrans/filter2D_demo.cpp>`_
+#. The tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/ImgTrans/filter2D_demo.cpp>`_
-.. code-block:: cpp
+.. code-block:: cpp
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"
Mat kernel;
Point anchor;
double delta;
- int ddepth;
+ int ddepth;
int kernel_size;
char* window_name = "filter2D Demo";
/// Create window
namedWindow( window_name, CV_WINDOW_AUTOSIZE );
-
+
/// Initialize arguments for the filter
anchor = Point( -1, -1 );
delta = 0;
imshow( window_name, dst );
ind++;
}
-
+
return 0;
}
kernel_size = 3 + 2*( ind%5 );
kernel = Mat::ones( kernel_size, kernel_size, CV_32F )/ (float)(kernel_size*kernel_size);
- The first line is to update the *kernel_size* to odd values in the range: :math:`[3,11]`. The second line actually builds the kernel by setting its value to a matrix filled with :math:`1's` and normalizing it by dividing it between the number of elements.
+ The first line is to update the *kernel_size* to odd values in the range: :math:`[3,11]`. The second line actually builds the kernel by setting its value to a matrix filled with :math:`1's` and normalizing it by dividing it between the number of elements.
#. After setting the kernel, we can generate the filter by using the function :filter2d:`filter2D <>`:
.. code-block:: cpp
-
+
filter2D(src, dst, ddepth , kernel, anchor, delta, BORDER_DEFAULT );
The arguments denote:
\r
* The Hough Circle Transform works in a *roughly* analogous way to the Hough Line Transform explained in the previous tutorial.\r
* In the line detection case, a line was defined by two parameters :math:`(r, \theta)`. In the circle case, we need three parameters to define a circle:\r
- \r
+\r
.. math::\r
- \r
- C : ( x_{center}, y_{center}, r ) \r
+\r
+ C : ( x_{center}, y_{center}, r )\r
\r
where :math:`(x_{center}, y_{center})` define the center position (gree point) and :math:`r` is the radius, which allows us to completely define a circle, as it can be seen below:\r
\r
.. image:: images/Hough_Circle_Tutorial_Theory_0.jpg\r
:alt: Result of detecting circles with Hough Transform\r
- :align: center \r
+ :align: center\r
\r
* For sake of efficiency, OpenCV implements a detection method slightly trickier than the standard Hough Transform: *The Hough gradient method*. For more details, please check the book *Learning OpenCV* or your favorite Computer Vision bibliography\r
\r
======\r
\r
#. **What does this program do?**\r
- \r
+\r
* Loads an image and blur it to reduce the noise\r
- * Applies the *Hough Circle Transform* to the blurred image . \r
+ * Applies the *Hough Circle Transform* to the blurred image .\r
* Display the detected circle in a window.\r
\r
.. |TutorialHoughCirclesSimpleDownload| replace:: here\r
- .. _TutorialHoughCirclesSimpleDownload: http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/houghlines.cpp\r
+ .. _TutorialHoughCirclesSimpleDownload: http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/houghlines.cpp\r
.. |TutorialHoughCirclesFancyDownload| replace:: here\r
- .. _TutorialHoughCirclesFancyDownload: http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/ImgTrans/HoughCircle_Demo.cpp\r
+ .. _TutorialHoughCirclesFancyDownload: http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/ImgTrans/HoughCircle_Demo.cpp\r
\r
#. The sample code that we will explain can be downloaded from |TutorialHoughCirclesSimpleDownload|_. A slightly fancier version (which shows both Hough standard and probabilistic with trackbars for changing the threshold values) can be found |TutorialHoughCirclesFancyDownload|_.\r
\r
-.. code-block:: cpp \r
+.. code-block:: cpp\r
\r
#include "opencv2/highgui/highgui.hpp"\r
#include "opencv2/imgproc/imgproc.hpp"\r
if( !src.data )\r
{ return -1; }\r
\r
- /// Convert it to gray \r
+ /// Convert it to gray\r
cvtColor( src, src_gray, CV_BGR2GRAY );\r
\r
/// Reduce the noise so we avoid false circle detection\r
circle( src, center, radius, Scalar(0,0,255), 3, 8, 0 );\r
}\r
\r
- /// Show your results \r
+ /// Show your results\r
namedWindow( "Hough Circle Transform Demo", CV_WINDOW_AUTOSIZE );\r
imshow( "Hough Circle Transform Demo", src );\r
\r
cvtColor( src, src_gray, CV_BGR2GRAY );\r
\r
#. Apply a Gaussian blur to reduce noise and avoid false circle detection:\r
- \r
+\r
.. code-block:: cpp\r
\r
GaussianBlur( src_gray, src_gray, Size(9, 9), 2, 2 );\r
* *dp = 1*: The inverse ratio of resolution\r
* *min_dist = src_gray.rows/8*: Minimum distance between detected centers\r
* *param_1 = 200*: Upper threshold for the internal Canny edge detector\r
- * *param_2* = 100*: Threshold for center detection. \r
- * *min_radius = 0*: Minimum radio to be detected. If unknown, put zero as default. \r
+ * *param_2* = 100*: Threshold for center detection.\r
+ * *min_radius = 0*: Minimum radio to be detected. If unknown, put zero as default.\r
* *max_radius = 0*: Maximum radius to be detected. If unknown, put zero as default\r
- \r
+\r
#. Draw the detected circles:\r
\r
.. code-block:: cpp\r
circle( src, center, 3, Scalar(0,255,0), -1, 8, 0 );\r
// circle outline\r
circle( src, center, radius, Scalar(0,0,255), 3, 8, 0 );\r
- } \r
+ }\r
\r
You can see that we will draw the circle(s) on red and the center(s) with a small green dot\r
\r
#. Display the detected circle(s):\r
\r
.. code-block:: cpp\r
- \r
+\r
namedWindow( "Hough Circle Transform Demo", CV_WINDOW_AUTOSIZE );\r
imshow( "Hough Circle Transform Demo", src );\r
\r
Result\r
=======\r
\r
-The result of running the code above with a test image is shown below: \r
+The result of running the code above with a test image is shown below:\r
\r
.. image:: images/Hough_Circle_Tutorial_Result.jpg\r
:alt: Result of detecting circles with Hough Transform\r
- :align: center \r
+ :align: center\r
In this tutorial you will learn how to:
* Use the OpenCV functions :hough_lines:`HoughLines <>` and :hough_lines_p:`HoughLinesP <>` to detect lines in an image.
-
+
Theory
=======
Hough Line Transform
---------------------
-#. The Hough Line Transform is a transform used to detect straight lines.
+#. The Hough Line Transform is a transform used to detect straight lines.
#. To apply the Transform, first an edge detection pre-processing is desirable.
How does it work?
^^^^^^^^^^^^^^^^^^
#. As you know, a line in the image space can be expressed with two variables. For example:
-
+
a. In the **Cartesian coordinate system:** Parameters: :math:`(m,b)`.
b. In the **Polar coordinate system:** Parameters: :math:`(r,\theta)`
.. image:: images/Hough_Lines_Tutorial_Theory_0.jpg
:alt: Line variables
- :align: center
+ :align: center
- For Hough Transforms, we will express lines in the *Polar system*. Hence, a line equation can be written as:
+ For Hough Transforms, we will express lines in the *Polar system*. Hence, a line equation can be written as:
.. math::
- y = \left ( -\dfrac{\cos \theta}{\sin \theta} \right ) x + \left ( \dfrac{r}{\sin \theta} \right )
+ y = \left ( -\dfrac{\cos \theta}{\sin \theta} \right ) x + \left ( \dfrac{r}{\sin \theta} \right )
Arranging the terms: :math:`r = x \cos \theta + y \sin \theta`
#. In general for each point :math:`(x_{0}, y_{0})`, we can define the family of lines that goes through that point as:
.. math::
-
+
r_{\theta} = x_{0} \cdot \cos \theta + y_{0} \cdot \sin \theta
- Meaning that each pair :math:`(r_{\theta},\theta)` represents each line that passes by :math:`(x_{0}, y_{0})`.
+ Meaning that each pair :math:`(r_{\theta},\theta)` represents each line that passes by :math:`(x_{0}, y_{0})`.
#. If for a given :math:`(x_{0}, y_{0})` we plot the family of lines that goes through it, we get a sinusoid. For instance, for :math:`x_{0} = 8` and :math:`y_{0} = 6` we get the following plot (in a plane :math:`\theta` - :math:`r`):
.. image:: images/Hough_Lines_Tutorial_Theory_1.jpg
:alt: Polar plot of a the family of lines of a point
- :align: center
+ :align: center
- We consider only points such that :math:`r > 0` and :math:`0< \theta < 2 \pi`.
+ We consider only points such that :math:`r > 0` and :math:`0< \theta < 2 \pi`.
#. We can do the same operation above for all the points in an image. If the curves of two different points intersect in the plane :math:`\theta` - :math:`r`, that means that both points belong to a same line. For instance, following with the example above and drawing the plot for two more points: :math:`x_{1} = 9`, :math:`y_{1} = 4` and :math:`x_{2} = 12`, :math:`y_{2} = 3`, we get:
.. image:: images/Hough_Lines_Tutorial_Theory_2.jpg
:alt: Polar plot of the family of lines for three points
- :align: center
+ :align: center
- The three plots intersect in one single point :math:`(0.925, 9.6)`, these coordinates are the parameters (:math:`\theta, r`) or the line in which :math:`(x_{0}, y_{0})`, :math:`(x_{1}, y_{1})` and :math:`(x_{2}, y_{2})` lay.
+ The three plots intersect in one single point :math:`(0.925, 9.6)`, these coordinates are the parameters (:math:`\theta, r`) or the line in which :math:`(x_{0}, y_{0})`, :math:`(x_{1}, y_{1})` and :math:`(x_{2}, y_{2})` lay.
#. What does all the stuff above mean? It means that in general, a line can be *detected* by finding the number of intersections between curves.The more curves intersecting means that the line represented by that intersection have more points. In general, we can define a *threshold* of the minimum number of intersections needed to *detect* a line.
-
+
#. This is what the Hough Line Transform does. It keeps track of the intersection between curves of every point in the image. If the number of intersections is above some *threshold*, then it declares it as a line with the parameters :math:`(\theta, r_{\theta})` of the intersection point.
Standard and Probabilistic Hough Line Transform
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-OpenCV implements two kind of Hough Line Transforms:
+OpenCV implements two kind of Hough Line Transforms:
a. **The Standard Hough Transform**
Code
======
-.. |TutorialHoughLinesSimpleDownload| replace:: here\r
-.. _TutorialHoughLinesSimpleDownload: http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/houghlines.cpp\r
-.. |TutorialHoughLinesFancyDownload| replace:: here\r
-.. _TutorialHoughLinesFancyDownload: http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/ImgTrans/HoughLines_Demo.cpp\r
+.. |TutorialHoughLinesSimpleDownload| replace:: here
+.. _TutorialHoughLinesSimpleDownload: http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/houghlines.cpp
+.. |TutorialHoughLinesFancyDownload| replace:: here
+.. _TutorialHoughLinesFancyDownload: http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/ImgTrans/HoughLines_Demo.cpp
#. **What does this program do?**
-
+
* Loads an image
- * Applies either a *Standard Hough Line Transform* or a *Probabilistic Line Transform*.
+ * Applies either a *Standard Hough Line Transform* or a *Probabilistic Line Transform*.
* Display the original image and the detected line in two windows.
#. The sample code that we will explain can be downloaded from |TutorialHoughLinesSimpleDownload|_. A slightly fancier version (which shows both Hough standard and probabilistic with trackbars for changing the threshold values) can be found |TutorialHoughLinesFancyDownload|_.
-.. code-block:: cpp
+.. code-block:: cpp
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
* *rho* : The resolution of the parameter :math:`r` in pixels. We use **1** pixel.
* *theta*: The resolution of the parameter :math:`\theta` in radians. We use **1 degree** (CV_PI/180)
* *threshold*: The minimum number of intersections to "*detect*" a line
- * *srn* and *stn*: Default parameters to zero. Check OpenCV reference for more info.
+ * *srn* and *stn*: Default parameters to zero. Check OpenCV reference for more info.
- b. And then you display the result by drawing the lines.
+ b. And then you display the result by drawing the lines.
.. code-block:: cpp
HoughLinesP(dst, lines, 1, CV_PI/180, 50, 50, 10 );
with the arguments:
-
+
* *dst*: Output of the edge detector. It should be a grayscale image (although in fact it is a binary one)
* *lines*: A vector that will store the parameters :math:`(x_{start}, y_{start}, x_{end}, y_{end})` of the detected lines
* *rho* : The resolution of the parameter :math:`r` in pixels. We use **1** pixel.
* *theta*: The resolution of the parameter :math:`\theta` in radians. We use **1 degree** (CV_PI/180)
* *threshold*: The minimum number of intersections to "*detect*" a line
- * *minLinLength*: The minimum number of points that can form a line. Lines with less than this number of points are disregarded.
- * *maxLineGap*: The maximum gap between two points to be considered in the same line.
+ * *minLinLength*: The minimum number of points that can form a line. Lines with less than this number of points are disregarded.
+ * *maxLineGap*: The maximum gap between two points to be considered in the same line.
b. And then you display the result by drawing the lines.
}
-#. Display the original image and the detected lines:
+#. Display the original image and the detected lines:
.. code-block:: cpp
=======
.. note::
-
+
The results below are obtained using the slightly fancier version we mentioned in the *Code* section. It still implements the same stuff as above, only adding the Trackbar for the Threshold.
Using an input image such as:
.. image:: images/Hough_Lines_Tutorial_Original_Image.jpg
:alt: Result of detecting lines with Hough Transform
- :align: center
-
+ :align: center
+
We get the following result by using the Probabilistic Hough Line Transform:
.. image:: images/Hough_Lines_Tutorial_Result.jpg
:alt: Result of detecting lines with Hough Transform
- :align: center
+ :align: center
-You may observe that the number of lines detected vary while you change the *threshold*. The explanation is sort of evident: If you establish a higher threshold, fewer lines will be detected (since you will need more points to declare a line detected).
+You may observe that the number of lines detected vary while you change the *threshold*. The explanation is sort of evident: If you establish a higher threshold, fewer lines will be detected (since you will need more points to declare a line detected).
-------------------
#. From the explanation above, we deduce that the second derivative can be used to *detect edges*. Since images are "*2D*", we would need to take the derivative in both dimensions. Here, the Laplacian operator comes handy.
-
+
#. The *Laplacian operator* is defined by:
.. math::
======
#. **What does this program do?**
-
+
* Loads an image
- * Remove noise by applying a Gaussian blur and then convert the original image to grayscale
+ * Remove noise by applying a Gaussian blur and then convert the original image to grayscale
* Applies a Laplacian operator to the grayscale image and stores the output image
* Display the result in a window
-#. The tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/ImgTrans/Laplace_Demo.cpp>`_
+#. The tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/ImgTrans/Laplace_Demo.cpp>`_
.. code-block:: cpp
int main( int argc, char** argv )
{
Mat src, src_gray, dst;
- int kernel_size = 3;
+ int kernel_size = 3;
int scale = 1;
int delta = 0;
int ddepth = CV_16S;
.. code-block:: cpp
Mat src, src_gray, dst;
- int kernel_size = 3;
+ int kernel_size = 3;
int scale = 1;
int delta = 0;
int ddepth = CV_16S;
.. code-block:: cpp
GaussianBlur( src, src, Size(3,3), 0, 0, BORDER_DEFAULT );
-
+
#. Convert the image to grayscale using :cvt_color:`cvtColor <>`
.. code-block:: cpp
What is remapping?
------------------
-* It is the process of taking pixels from one place in the image and locating them in another position in a new image.
+* It is the process of taking pixels from one place in the image and locating them in another position in a new image.
* To accomplish the mapping process, it might be necessary to do some interpolation for non-integer pixel locations, since there will not always be a one-to-one-pixel correspondence between source and destination images.
* We can express the remap for every pixel location :math:`(x,y)` as:
.. math::
-
+
g(x,y) = f ( h(x,y) )
where :math:`g()` is the remapped image, :math:`f()` the source image and :math:`h(x,y)` is the mapping function that operates on :math:`(x,y)`.
h(x,y) = (I.cols - x, y )
- What would happen? It is easily seen that the image would flip in the :math:`x` direction. For instance, consider the input image:
+ What would happen? It is easily seen that the image would flip in the :math:`x` direction. For instance, consider the input image:
.. image:: images/Remap_Tutorial_Theory_0.jpg
:alt: Original test image
====
#. **What does this program do?**
-
+
* Loads an image
* Each second, apply 1 of 4 different remapping processes to the image and display them indefinitely in a window.
* Wait for the user to exit the program
-
-#. The tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/ImgTrans/Remap_Demo.cpp>`_
+
+#. The tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/ImgTrans/Remap_Demo.cpp>`_
.. code-block:: cpp
dst.create( src.size(), src.type() );
map_x.create( src.size(), CV_32FC1 );
map_y.create( src.size(), CV_32FC1 );
-
+
/// Create window
namedWindow( remap_window, CV_WINDOW_AUTOSIZE );
/// Update map_x & map_y. Then apply remap
update_map();
- remap( src, dst, map_x, map_y, CV_INTER_LINEAR, BORDER_CONSTANT, Scalar(0,0, 0) );
+ remap( src, dst, map_x, map_y, CV_INTER_LINEAR, BORDER_CONSTANT, Scalar(0,0, 0) );
/// Display results
imshow( remap_window, dst );
{ for( int i = 0; i < src.cols; i++ )
{
switch( ind )
- {
+ {
case 0:
if( i > src.cols*0.25 && i < src.cols*0.75 && j > src.rows*0.25 && j < src.rows*0.75 )
{
int ind = 0;
#. Load an image:
-
+
.. code-block:: cpp
src = imread( argv[1], 1 );
dst.create( src.size(), src.type() );
map_x.create( src.size(), CV_32FC1 );
map_y.create( src.size(), CV_32FC1 );
-
+
#. Create a window to display results
.. code-block:: cpp
namedWindow( remap_window, CV_WINDOW_AUTOSIZE );
#. Establish a loop. Each 1000 ms we update our mapping matrices (*mat_x* and *mat_y*) and apply them to our source image:
-
+
.. code-block:: cpp
while( true )
/// Update map_x & map_y. Then apply remap
update_map();
- remap( src, dst, map_x, map_y, CV_INTER_LINEAR, BORDER_CONSTANT, Scalar(0,0, 0) );
+ remap( src, dst, map_x, map_y, CV_INTER_LINEAR, BORDER_CONSTANT, Scalar(0,0, 0) );
/// Display results
imshow( remap_window, dst );
}
The function that applies the remapping is :remap:`remap <>`. We give the following arguments:
-
+
* **src**: Source image
* **dst**: Destination image of same size as *src*
* **map_x**: The mapping function in the x direction. It is equivalent to the first component of :math:`h(i,j)`
* **map_y**: Same as above, but in y direction. Note that *map_y* and *map_x* are both of the same size as *src*
- * **CV_INTER_LINEAR**: The type of interpolation to use for non-integer pixels. This is by default.
+ * **CV_INTER_LINEAR**: The type of interpolation to use for non-integer pixels. This is by default.
* **BORDER_CONSTANT**: Default
How do we update our mapping matrices *mat_x* and *mat_y*? Go on reading:
.. math::
- h(i,j) = ( 2*i - src.cols/2 + 0.5, 2*j - src.rows/2 + 0.5)
+ h(i,j) = ( 2*i - src.cols/2 + 0.5, 2*j - src.rows/2 + 0.5)
+
+ for all pairs :math:`(i,j)` such that: :math:`\dfrac{src.cols}{4}<i<\dfrac{3 \cdot src.cols}{4}` and :math:`\dfrac{src.rows}{4}<j<\dfrac{3 \cdot src.rows}{4}`
- for all pairs :math:`(i,j)` such that: :math:`\dfrac{src.cols}{4}<i<\dfrac{3 \cdot src.cols}{4}` and :math:`\dfrac{src.rows}{4}<j<\dfrac{3 \cdot src.rows}{4}`
-
b. Turn the image upside down: :math:`h( i, j ) = (i, src.rows - j)`
-
+
c. Reflect the image from left to right: :math:`h(i,j) = ( src.cols - i, j )`
d. Combination of b and c: :math:`h(i,j) = ( src.cols - i, src.rows - j )`
This is expressed in the following snippet. Here, *map_x* represents the first coordinate of *h(i,j)* and *map_y* the second coordinate.
-
+
.. code-block:: cpp
for( int j = 0; j < src.rows; j++ )
{ for( int i = 0; i < src.cols; i++ )
{
switch( ind )
- {
+ {
case 0:
if( i > src.cols*0.25 && i < src.cols*0.75 && j > src.rows*0.25 && j < src.rows*0.75 )
{
:align: center
#. Turning it upside down:
-
+
.. image:: images/Remap_Tutorial_Result_1.jpg
:alt: Result 0 for remapping
:width: 250pt
.. container:: enumeratevisibleitemswithsquare
* Use the OpenCV function :sobel:`Sobel <>` to calculate the derivatives from an image.
- * Use the OpenCV function :scharr:`Scharr <>` to calculate a more accurate derivative for a kernel of size :math:`3 \cdot 3`
-
+ * Use the OpenCV function :scharr:`Scharr <>` to calculate a more accurate derivative for a kernel of size :math:`3 \cdot 3`
+
Theory
========
.. image:: images/Sobel_Derivatives_Tutorial_Theory_0.jpg
:alt: How intensity changes in an edge
:align: center
-
- You can easily notice that in an *edge*, the pixel intensity *changes* in a notorious way. A good way to express *changes* is by using *derivatives*. A high change in gradient indicates a major change in the image.
+
+ You can easily notice that in an *edge*, the pixel intensity *changes* in a notorious way. A good way to express *changes* is by using *derivatives*. A high change in gradient indicates a major change in the image.
#. To be more graphical, let's assume we have a 1D-image. An edge is shown by the "jump" in intensity in the plot below:
Sobel Operator
---------------
-#. The Sobel Operator is a discrete differentiation operator. It computes an approximation of the gradient of an image intensity function.
+#. The Sobel Operator is a discrete differentiation operator. It computes an approximation of the gradient of an image intensity function.
-#. The Sobel Operator combines Gaussian smoothing and differentiation.
+#. The Sobel Operator combines Gaussian smoothing and differentiation.
Formulation
^^^^^^^^^^^^
a. **Horizontal changes**: This is computed by convolving :math:`I` with a kernel :math:`G_{x}` with odd size. For example for a kernel size of 3, :math:`G_{x}` would be computed as:
.. math::
-
+
G_{x} = \begin{bmatrix}
-1 & 0 & +1 \\
-2 & 0 & +2 \\
- -1 & 0 & +1
+ -1 & 0 & +1
\end{bmatrix} * I
b. **Vertical changes**: This is computed by convolving :math:`I` with a kernel :math:`G_{y}` with odd size. For example for a kernel size of 3, :math:`G_{y}` would be computed as:
.. math::
-
+
G_{y} = \begin{bmatrix}
-1 & -2 & -1 \\
0 & 0 & 0 \\
- +1 & +2 & +1
+ +1 & +2 & +1
\end{bmatrix} * I
#. At each point of the image we calculate an approximation of the *gradient* in that point by combining both results above:
Although sometimes the following simpler equation is used:
.. math::
-
+
G = |G_{x}| + |G_{y}|
G_{x} = \begin{bmatrix}
-3 & 0 & +3 \\
-10 & 0 & +10 \\
- -3 & 0 & +3
- \end{bmatrix}
-
+ -3 & 0 & +3
+ \end{bmatrix}
+
G_{y} = \begin{bmatrix}
-3 & -10 & -3 \\
0 & 0 & 0 \\
- +3 & +10 & +3
- \end{bmatrix}
+ +3 & +10 & +3
+ \end{bmatrix}
You can check out more information of this function in the OpenCV reference (:scharr:`Scharr <>`). Also, in the sample code below, you will notice that above the code for :sobel:`Sobel <>` function there is also code for the :scharr:`Scharr <>` function commented. Uncommenting it (and obviously commenting the Sobel stuff) should give you an idea of how this function works.
=====
#. **What does this program do?**
-
+
* Applies the *Sobel Operator* and generates as output an image with the detected *edges* bright on a darker background.
-
-#. The tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/ImgTrans/Sobel_Demo.cpp>`_
-.. code-block:: cpp
+#. The tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/ImgTrans/Sobel_Demo.cpp>`_
+
+.. code-block:: cpp
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"
{
Mat src, src_gray;
- Mat grad;
+ Mat grad;
char* window_name = "Sobel Demo - Simple Edge Detector";
int scale = 1;
int delta = 0;
/// Generate grad_x and grad_y
Mat grad_x, grad_y;
Mat abs_grad_x, abs_grad_y;
-
+
/// Gradient X
//Scharr( src_gray, grad_x, ddepth, 1, 0, scale, delta, BORDER_DEFAULT );
- Sobel( src_gray, grad_x, ddepth, 1, 0, 3, scale, delta, BORDER_DEFAULT );
+ Sobel( src_gray, grad_x, ddepth, 1, 0, 3, scale, delta, BORDER_DEFAULT );
convertScaleAbs( grad_x, abs_grad_x );
- /// Gradient Y
+ /// Gradient Y
//Scharr( src_gray, grad_y, ddepth, 0, 1, scale, delta, BORDER_DEFAULT );
- Sobel( src_gray, grad_y, ddepth, 0, 1, 3, scale, delta, BORDER_DEFAULT );
+ Sobel( src_gray, grad_y, ddepth, 0, 1, 3, scale, delta, BORDER_DEFAULT );
convertScaleAbs( grad_y, abs_grad_y );
/// Total Gradient (approximate)
.. code-block:: cpp
Mat src, src_gray;
- Mat grad;
+ Mat grad;
char* window_name = "Sobel Demo - Simple Edge Detector";
int scale = 1;
int delta = 0;
.. code-block:: cpp
src = imread( argv[1] );
-
+
if( !src.data )
{ return -1; }
#. First, we apply a :gaussian_blur:`GaussianBlur <>` to our image to reduce the noise ( kernel size = 3 )
-
+
.. code-block:: cpp
GaussianBlur( src, src, Size(3,3), 0, 0, BORDER_DEFAULT );
cvtColor( src, src_gray, CV_RGB2GRAY );
#. Second, we calculate the "*derivatives*" in *x* and *y* directions. For this, we use the function :sobel:`Sobel <>` as shown below:
-
+
.. code-block:: cpp
Mat grad_x, grad_y;
Mat abs_grad_x, abs_grad_y;
-
+
/// Gradient X
- Sobel( src_gray, grad_x, ddepth, 1, 0, 3, scale, delta, BORDER_DEFAULT );
- /// Gradient Y
- Sobel( src_gray, grad_y, ddepth, 0, 1, 3, scale, delta, BORDER_DEFAULT );
+ Sobel( src_gray, grad_x, ddepth, 1, 0, 3, scale, delta, BORDER_DEFAULT );
+ /// Gradient Y
+ Sobel( src_gray, grad_y, ddepth, 0, 1, 3, scale, delta, BORDER_DEFAULT );
The function takes the following arguments:
- * *src_gray*: In our example, the input image. Here it is *CV_8U*
- * *grad_x*/*grad_y*: The output image.
+ * *src_gray*: In our example, the input image. Here it is *CV_8U*
+ * *grad_x*/*grad_y*: The output image.
* *ddepth*: The depth of the output image. We set it to *CV_16S* to avoid overflow.
- * *x_order*: The order of the derivative in **x** direction.
- * *y_order*: The order of the derivative in **y** direction.
+ * *x_order*: The order of the derivative in **x** direction.
+ * *y_order*: The order of the derivative in **y** direction.
* *scale*, *delta* and *BORDER_DEFAULT*: We use default values.
- Notice that to calculate the gradient in *x* direction we use: :math:`x_{order}= 1` and :math:`y_{order} = 0`. We do analogously for the *y* direction.
+ Notice that to calculate the gradient in *x* direction we use: :math:`x_{order}= 1` and :math:`y_{order} = 0`. We do analogously for the *y* direction.
#. We convert our partial results back to *CV_8U*:
convertScaleAbs( grad_x, abs_grad_x );
convertScaleAbs( grad_y, abs_grad_y );
-
+
#. Finally, we try to approximate the *gradient* by adding both directional gradients (note that this is not an exact calculation at all! but it is good for our purposes).
========
#. Here is the output of applying our basic detector to *lena.jpg*:
-
+
.. image:: images/Sobel_Derivatives_Tutorial_Result.jpg
:alt: Result of applying Sobel operator to lena.jpg
What is an Affine Transformation?
----------------------------------
-#. It is any transformation that can be expressed in the form of a *matrix multiplication* (linear transformation) followed by a *vector addition* (translation).
+#. It is any transformation that can be expressed in the form of a *matrix multiplication* (linear transformation) followed by a *vector addition* (translation).
-#. From the above, We can use an Affine Transformation to express:
+#. From the above, We can use an Affine Transformation to express:
a. Rotations (linear transformation)
b. Translations (vector addition)
c. Scale operations (linear transformation)
- you can see that, in essence, an Affine Transformation represents a **relation** between two images.
-
-#. The usual way to represent an Affine Transform is by using a :math:`2 \times 3` matrix.
+ you can see that, in essence, an Affine Transformation represents a **relation** between two images.
- .. math::
+#. The usual way to represent an Affine Transform is by using a :math:`2 \times 3` matrix.
+
+ .. math::
A = \begin{bmatrix}
- a_{00} & a_{01} \\
+ a_{00} & a_{01} \\
a_{10} & a_{11}
\end{bmatrix}_{2 \times 2}
B = \begin{bmatrix}
- b_{00} \\
+ b_{00} \\
b_{10}
\end{bmatrix}_{2 \times 1}
-
+
M = \begin{bmatrix}
- A & B
+ A & B
\end{bmatrix}
- =
+ =
\begin{bmatrix}
- a_{00} & a_{01} & b_{00} \\
- a_{10} & a_{11} & b_{10}
+ a_{00} & a_{01} & b_{00} \\
+ a_{10} & a_{11} & b_{10}
\end{bmatrix}_{2 \times 3}
Considering that we want to transform a 2D vector :math:`X = \begin{bmatrix}x \\ y\end{bmatrix}` by using :math:`A` and :math:`B`, we can do it equivalently with:
-
+
:math:`T = A \cdot \begin{bmatrix}x \\ y\end{bmatrix} + B` or :math:`T = M \cdot [x, y, 1]^{T}`
.. math::
T = \begin{bmatrix}
- a_{00}x + a_{01}y + b_{00} \\
+ a_{00}x + a_{01}y + b_{00} \\
a_{10}x + a_{11}y + b_{10}
- \end{bmatrix}
+ \end{bmatrix}
How do we get an Affine Transformation?
:width: 350pt
:align: center
- the points 1, 2 and 3 (forming a triangle in image 1) are mapped into image 2, still forming a triangle, but now they have changed notoriously. If we find the Affine Transformation with these 3 points (you can choose them as you like), then we can apply this found relation to the whole pixels in the image.
-
+ the points 1, 2 and 3 (forming a triangle in image 1) are mapped into image 2, still forming a triangle, but now they have changed notoriously. If we find the Affine Transformation with these 3 points (you can choose them as you like), then we can apply this found relation to the whole pixels in the image.
+
Code
====
#. **What does this program do?**
-
+
* Loads an image
* Applies an Affine Transform to the image. This Transform is obtained from the relation between three points. We use the function :warp_affine:`warpAffine <>` for that purpose.
* Applies a Rotation to the image after being transformed. This rotation is with respect to the image center
* Waits until the user exits the program
-
-#. The tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/ImgTrans/Geometric_Transforms_Demo.cpp>`_
+
+#. The tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/ImgTrans/Geometric_Transforms_Demo.cpp>`_
.. code-block:: cpp
/// Load the image
src = imread( argv[1], 1 );
- /// Set the dst image the same type and size as src
+ /// Set the dst image the same type and size as src
warp_dst = Mat::zeros( src.rows, src.cols, src.type() );
/// Set your 3 points to calculate the Affine Transform
srcTri[0] = Point2f( 0,0 );
srcTri[1] = Point2f( src.cols - 1, 0 );
srcTri[2] = Point2f( 0, src.rows - 1 );
-
+
dstTri[0] = Point2f( src.cols*0.0, src.rows*0.33 );
dstTri[1] = Point2f( src.cols*0.85, src.rows*0.25 );
dstTri[2] = Point2f( src.cols*0.15, src.rows*0.7 );
/// Rotate the warped image
warpAffine( warp_dst, warp_rotate_dst, rot_mat, warp_dst.size() );
-
+
/// Show what you got
namedWindow( source_window, CV_WINDOW_AUTOSIZE );
imshow( source_window, src );
#. Initialize the destination image as having the same size and type as the source:
.. code-block:: cpp
-
+
warp_dst = Mat::zeros( src.rows, src.cols, src.type() );
#. **Affine Transform:** As we explained lines above, we need two sets of 3 points to derive the affine transform relation. Take a look:
srcTri[0] = Point2f( 0,0 );
srcTri[1] = Point2f( src.cols - 1, 0 );
srcTri[2] = Point2f( 0, src.rows - 1 );
-
+
dstTri[0] = Point2f( src.cols*0.0, src.rows*0.33 );
dstTri[1] = Point2f( src.cols*0.85, src.rows*0.25 );
dstTri[2] = Point2f( src.cols*0.15, src.rows*0.7 );
-
+
You may want to draw the points to make a better idea of how they change. Their locations are approximately the same as the ones depicted in the example figure (in the Theory section). You may note that the size and orientation of the triangle defined by the 3 points change.
#. Armed with both sets of points, we calculate the Affine Transform by using OpenCV function :get_affine_transform:`getAffineTransform <>`:
#. Finally, we display our results in two windows plus the original image for good measure:
.. code-block:: cpp
-
+
namedWindow( source_window, CV_WINDOW_AUTOSIZE );
imshow( source_window, src );
:alt: Original image
:width: 250pt
:align: center
-
+
after applying the first Affine Transform we obtain:
.. image:: images/Warp_Affine_Tutorial_Result_Warp.jpg
.. container:: enumeratevisibleitemswithsquare
* Use the OpenCV function :morphology_ex:`morphologyEx <>` to apply Morphological Transformation such as:
-
- + Opening
+
+ + Opening
+ Closing
+ Morphological Gradient
+ Top Hat
.. note::
The explanation below belongs to the book **Learning OpenCV** by Bradski and Kaehler.
-In the previous tutorial we covered two basic Morphology operations:
+In the previous tutorial we covered two basic Morphology operations:
.. container:: enumeratevisibleitemswithsquare
* Erosion
- * Dilation.
+ * Dilation.
Based on these two we can effectuate more sophisticated transformations to our images. Here we discuss briefly 05 operations offered by OpenCV:
* It is obtained by the erosion of an image followed by a dilation.
.. math::
-
+
dst = open( src, element) = dilate( erode( src, element ) )
* Useful for removing small objects (it is assumed that the objects are bright on a dark foreground)
.. image:: images/Morphology_2_Tutorial_Theory_Opening.png
:alt: Opening
- :align: center
+ :align: center
Closing
---------
* It is obtained by the dilation of an image followed by an erosion.
.. math::
-
+
dst = close( src, element ) = erode( dilate( src, element ) )
-* Useful to remove small holes (dark regions).
+* Useful to remove small holes (dark regions).
.. image:: images/Morphology_2_Tutorial_Theory_Closing.png
:alt: Closing example
- :align: center
+ :align: center
Morphological Gradient
.. image:: images/Morphology_2_Tutorial_Theory_Gradient.png
:alt: Gradient
- :align: center
+ :align: center
Top Hat
* It is the difference between an input image and its opening.
.. math::
-
+
dst = tophat( src, element ) = src - open( src, element )
.. image:: images/Morphology_2_Tutorial_Theory_TopHat.png
:alt: Top Hat
- :align: center
+ :align: center
Black Hat
----------
* It is the difference between the closing and its input image
.. math::
-
+
dst = blackhat( src, element ) = close( src, element ) - src
.. image:: images/Morphology_2_Tutorial_Theory_BlackHat.png
:alt: Black Hat
- :align: center
+ :align: center
Code
======
-This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/ImgProc/Morphology_2.cpp>`_
+This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/ImgProc/Morphology_2.cpp>`_
-.. code-block:: cpp
+.. code-block:: cpp
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"
if( !src.data )
{ return -1; }
-
+
/// Create window
namedWindow( window_name, CV_WINDOW_AUTOSIZE );
createTrackbar("Operator:\n 0: Opening - 1: Closing \n 2: Gradient - 3: Top Hat \n 4: Black Hat", window_name, &morph_operator, max_operator, Morphology_Operations );
/// Create Trackbar to select kernel type
- createTrackbar( "Element:\n 0: Rect - 1: Cross - 2: Ellipse", window_name,
- &morph_elem, max_elem,
+ createTrackbar( "Element:\n 0: Rect - 1: Cross - 2: Ellipse", window_name,
+ &morph_elem, max_elem,
Morphology_Operations );
/// Create Trackbar to choose kernel size
- createTrackbar( "Kernel size:\n 2n +1", window_name,
+ createTrackbar( "Kernel size:\n 2n +1", window_name,
&morph_size, max_kernel_size,
Morphology_Operations );
return 0;
}
- /**
+ /**
* @function Morphology_Operations
*/
void Morphology_Operations( int, void* )
// Since MORPH_X : 2,3,4,5 and 6
int operation = morph_operator + 2;
- Mat element = getStructuringElement( morph_elem, Size( 2*morph_size + 1, 2*morph_size+1 ), Point( morph_size, morph_size ) );
+ Mat element = getStructuringElement( morph_elem, Size( 2*morph_size + 1, 2*morph_size+1 ), Point( morph_size, morph_size ) );
/// Apply the specified morphology operation
morphologyEx( src, dst, operation, element );
- imshow( window_name, dst );
+ imshow( window_name, dst );
}
.. code-block:: cpp
- createTrackbar("Operator:\n 0: Opening - 1: Closing \n 2: Gradient - 3: Top Hat \n 4: Black Hat",
- window_name, &morph_operator, max_operator,
+ createTrackbar("Operator:\n 0: Opening - 1: Closing \n 2: Gradient - 3: Top Hat \n 4: Black Hat",
+ window_name, &morph_operator, max_operator,
Morphology_Operations );
- * The second trackbar **"Element"** returns **morph_elem**, which indicates what kind of structure our kernel is:
+ * The second trackbar **"Element"** returns **morph_elem**, which indicates what kind of structure our kernel is:
.. code-block:: cpp
- createTrackbar( "Element:\n 0: Rect - 1: Cross - 2: Ellipse", window_name,
- &morph_elem, max_elem,
+ createTrackbar( "Element:\n 0: Rect - 1: Cross - 2: Ellipse", window_name,
+ &morph_elem, max_elem,
Morphology_Operations );
* The final trackbar **"Kernel Size"** returns the size of the kernel to be used (**morph_size**)
.. code-block:: cpp
- createTrackbar( "Kernel size:\n 2n +1", window_name,
+ createTrackbar( "Kernel size:\n 2n +1", window_name,
&morph_size, max_kernel_size,
Morphology_Operations );
* Every time we move any slider, the user's function **Morphology_Operations** will be called to effectuate a new morphology operation and it will update the output image based on the current trackbar values.
-
+
.. code-block:: cpp
- /**
+ /**
* @function Morphology_Operations
*/
void Morphology_Operations( int, void* )
// Since MORPH_X : 2,3,4,5 and 6
int operation = morph_operator + 2;
- Mat element = getStructuringElement( morph_elem, Size( 2*morph_size + 1, 2*morph_size+1 ), Point( morph_size, morph_size ) );
+ Mat element = getStructuringElement( morph_elem, Size( 2*morph_size + 1, 2*morph_size+1 ), Point( morph_size, morph_size ) );
/// Apply the specified morphology operation
morphologyEx( src, dst, operation, element );
- imshow( window_name, dst );
+ imshow( window_name, dst );
}
.. code-block:: cpp
- int operation = morph_operator + 2;
+ int operation = morph_operator + 2;
* **element**: The kernel to be used. We use the function :get_structuring_element:`getStructuringElement <>` to define our own structure.
-
+
Results
========
.. image:: images/Morphology_2_Tutorial_Original_Image.jpg
:alt: Morphology 2: Original image
- :align: center
+ :align: center
* And here are two snapshots of the display window. The first picture shows the output after using the operator **Opening** with a cross kernel. The second picture (right side, shows the result of using a **Blackhat** operator with an ellipse kernel.
-
+
.. image:: images/Morphology_2_Tutorial_Cover.jpg
:alt: Morphology 2: Result sample
- :align: center
+ :align: center
.. container:: enumeratevisibleitemswithsquare
* Use the OpenCV functions :pyr_up:`pyrUp <>` and :pyr_down:`pyrDown <>` to downsample or upsample a given image.
-
+
Theory
=======
.. container:: enumeratevisibleitemswithsquare
* Usually we need to convert an image to a size different than its original. For this, there are two possible options:
-
- #. *Upsize* the image (zoom in) or
- #. *Downsize* it (zoom out).
+
+ #. *Upsize* the image (zoom in) or
+ #. *Downsize* it (zoom out).
* Although there is a *geometric transformation* function in OpenCV that -literally- resize an image (:resize:`resize <>`, which we will show in a future tutorial), in this section we analyze first the use of **Image Pyramids**, which are widely applied in a huge range of vision applications.
* **Gaussian pyramid:** Used to downsample images
- * **Laplacian pyramid:** Used to reconstruct an upsampled image from an image lower in the pyramid (with less resolution)
+ * **Laplacian pyramid:** Used to reconstruct an upsampled image from an image lower in the pyramid (with less resolution)
* In this tutorial we'll use the *Gaussian pyramid*.
Gaussian Pyramid
^^^^^^^^^^^^^^^^^
-* Imagine the pyramid as a set of layers in which the higher the layer, the smaller the size.
+* Imagine the pyramid as a set of layers in which the higher the layer, the smaller the size.
.. image:: images/Pyramids_Tutorial_Pyramid_Theory.png
:alt: Pyramid figure
- :align: center
+ :align: center
-* Every layer is numbered from bottom to top, so layer :math:`(i+1)` (denoted as :math:`G_{i+1}` is smaller than layer :math:`i` (:math:`G_{i}`).
+* Every layer is numbered from bottom to top, so layer :math:`(i+1)` (denoted as :math:`G_{i+1}` is smaller than layer :math:`i` (:math:`G_{i}`).
* To produce layer :math:`(i+1)` in the Gaussian pyramid, we do the following:
.. math::
- \frac{1}{16} \begin{bmatrix} 1 & 4 & 6 & 4 & 1 \\ 4 & 16 & 24 & 16 & 4 \\ 6 & 24 & 36 & 24 & 6 \\ 4 & 16 & 24 & 16 & 4 \\ 1 & 4 & 6 & 4 & 1 \end{bmatrix}
+ \frac{1}{16} \begin{bmatrix} 1 & 4 & 6 & 4 & 1 \\ 4 & 16 & 24 & 16 & 4 \\ 6 & 24 & 36 & 24 & 6 \\ 4 & 16 & 24 & 16 & 4 \\ 1 & 4 & 6 & 4 & 1 \end{bmatrix}
- * Remove every even-numbered row and column.
+ * Remove every even-numbered row and column.
* You can easily notice that the resulting image will be exactly one-quarter the area of its predecessor. Iterating this process on the input image :math:`G_{0}` (original image) produces the entire pyramid.
* Perform a convolution with the same kernel shown above (multiplied by 4) to approximate the values of the "missing pixels"
-* These two procedures (downsampling and upsampling as explained above) are implemented by the OpenCV functions :pyr_up:`pyrUp <>` and :pyr_down:`pyrDown <>`, as we will see in an example with the code below:
+* These two procedures (downsampling and upsampling as explained above) are implemented by the OpenCV functions :pyr_up:`pyrUp <>` and :pyr_down:`pyrDown <>`, as we will see in an example with the code below:
.. note::
When we reduce the size of an image, we are actually *losing* information of the image.
Code
======
-This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/ImgProc/Pyramids.cpp>`_
+This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/ImgProc/Pyramids.cpp>`_
-.. code-block:: cpp
+.. code-block:: cpp
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"
{ printf(" No data! -- Exiting the program \n");
return -1; }
- tmp = src;
+ tmp = src;
dst = tmp;
/// Create window
/// Loop
while( true )
- {
+ {
int c;
c = waitKey(10);
{ break; }
if( (char)c == 'u' )
{ pyrUp( tmp, dst, Size( tmp.cols*2, tmp.rows*2 ) );
- printf( "** Zoom In: Image x 2 \n" );
+ printf( "** Zoom In: Image x 2 \n" );
}
else if( (char)c == 'd' )
{ pyrDown( tmp, dst, Size( tmp.cols/2, tmp.rows/2 ) );
imshow( window_name, dst );
tmp = dst;
- }
+ }
return 0;
}
{ printf(" No data! -- Exiting the program \n");
return -1; }
- * Create a Mat object to store the result of the operations (*dst*) and one to save temporal results (*tmp*).
+ * Create a Mat object to store the result of the operations (*dst*) and one to save temporal results (*tmp*).
.. code-block:: cpp
-
+
Mat src, dst, tmp;
/* ... */
- tmp = src;
+ tmp = src;
dst = tmp;
.. code-block:: cpp
while( true )
- {
+ {
int c;
c = waitKey(10);
{ break; }
if( (char)c == 'u' )
{ pyrUp( tmp, dst, Size( tmp.cols*2, tmp.rows*2 ) );
- printf( "** Zoom In: Image x 2 \n" );
+ printf( "** Zoom In: Image x 2 \n" );
}
else if( (char)c == 'd' )
{ pyrDown( tmp, dst, Size( tmp.cols/2, tmp.rows/2 ) );
imshow( window_name, dst );
tmp = dst;
- }
-
+ }
+
Our program exits if the user presses *ESC*. Besides, it has two options:
-
- * **Perform upsampling (after pressing 'u')**
+
+ * **Perform upsampling (after pressing 'u')**
.. code-block:: cpp
* *dst*: The destination image (to be shown on screen, supposedly the double of the input image)
* *Size( tmp.cols*2, tmp.rows*2 )* : The destination size. Since we are upsampling, :pyr_up:`pyrUp <>` expects a size double than the input image (in this case *tmp*).
- * **Perform downsampling (after pressing 'd')**
+ * **Perform downsampling (after pressing 'd')**
.. code-block:: cpp
* Notice that it is important that the input image can be divided by a factor of two (in both dimensions). Otherwise, an error will be shown.
* Finally, we update the input image **tmp** with the current image displayed, so the subsequent operations are performed on it.
-
+
.. code-block:: cpp
tmp = dst;
* After compiling the code above we can test it. The program calls an image **chicky_512.jpg** that comes in the *tutorial_code/image* folder. Notice that this image is :math:`512 \times 512`, hence a downsample won't generate any error (:math:`512 = 2^{9}`). The original image is shown below:
.. image:: images/Pyramids_Tutorial_Original_Image.jpg
- :alt: Pyramids: Original image
- :align: center
+ :alt: Pyramids: Original image
+ :align: center
* First we apply two successive :pyr_down:`pyrDown <>` operations by pressing 'd'. Our output is:
-
+
.. image:: images/Pyramids_Tutorial_PyrDown_Result.jpg
:alt: Pyramids: PyrDown Result
- :align: center
+ :align: center
* Note that we should have lost some resolution due to the fact that we are diminishing the size of the image. This is evident after we apply :pyr_up:`pyrUp <>` twice (by pressing 'u'). Our output is now:
-
+
.. image:: images/Pyramids_Tutorial_PyrUp_Result.jpg
:alt: Pyramids: PyrUp Result
- :align: center
+ :align: center
.. container:: enumeratevisibleitemswithsquare
- * Use the OpenCV function :bounding_rect:`boundingRect <>`
+ * Use the OpenCV function :bounding_rect:`boundingRect <>`
* Use the OpenCV function :min_enclosing_circle:`minEnclosingCircle <>`
-
+
Theory
======
Code
====
-This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/ShapeDescriptors/generalContours_demo1.cpp>`_
+This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/ShapeDescriptors/generalContours_demo1.cpp>`_
-.. code-block:: cpp
+.. code-block:: cpp
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
/// Detect edges using Threshold
threshold( src_gray, threshold_output, thresh, 255, THRESH_BINARY );
- /// Find contours
+ /// Find contours
findContours( threshold_output, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0) );
/// Approximate contours to polygons + get bounding rects and circles
vector<float>radius( contours.size() );
for( int i = 0; i < contours.size(); i++ )
- { approxPolyDP( Mat(contours[i]), contours_poly[i], 3, true );
- boundRect[i] = boundingRect( Mat(contours_poly[i]) );
+ { approxPolyDP( Mat(contours[i]), contours_poly[i], 3, true );
+ boundRect[i] = boundingRect( Mat(contours_poly[i]) );
minEnclosingCircle( contours_poly[i], center[i], radius[i] );
- }
+ }
/// Draw polygonal contour + bonding rects + circles
Mat drawing = Mat::zeros( threshold_output.size(), CV_8UC3 );
for( int i = 0; i< contours.size(); i++ )
- {
+ {
Scalar color = Scalar( rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255) );
- drawContours( drawing, contours_poly, i, color, 1, 8, vector<Vec4i>(), 0, Point() );
+ drawContours( drawing, contours_poly, i, color, 1, 8, vector<Vec4i>(), 0, Point() );
rectangle( drawing, boundRect[i].tl(), boundRect[i].br(), color, 2, 8, 0 );
circle( drawing, center[i], (int)radius[i], color, 2, 8, 0 );
}
#. Here it is:
- ========== ==========
- |BRC_0| |BRC_1|
- ========== ==========
+ ========== ==========
+ |BRC_0| |BRC_1|
+ ========== ==========
.. |BRC_0| image:: images/Bounding_Rects_Circles_Source_Image.jpg
:align: middle
.. |BRC_1| image:: images/Bounding_Rects_Circles_Result.jpg
- :align: middle
+ :align: middle
.. container:: enumeratevisibleitemswithsquare
- * Use the OpenCV function :min_area_rect:`minAreaRect <>`
+ * Use the OpenCV function :min_area_rect:`minAreaRect <>`
* Use the OpenCV function :fit_ellipse:`fitEllipse <>`
-
+
Theory
======
Code
====
-This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/ShapeDescriptors/generalContours_demo2.cpp>`_
+This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/ShapeDescriptors/generalContours_demo2.cpp>`_
-.. code-block:: cpp
+.. code-block:: cpp
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
/// Detect edges using Threshold
threshold( src_gray, threshold_output, thresh, 255, THRESH_BINARY );
- /// Find contours
+ /// Find contours
findContours( threshold_output, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0) );
/// Find the rotated rectangles and ellipses for each contour
vector<RotatedRect> minEllipse( contours.size() );
for( int i = 0; i < contours.size(); i++ )
- { minRect[i] = minAreaRect( Mat(contours[i]) );
+ { minRect[i] = minAreaRect( Mat(contours[i]) );
if( contours[i].size() > 5 )
{ minEllipse[i] = fitEllipse( Mat(contours[i]) ); }
- }
+ }
/// Draw contours + rotated rects + ellipses
Mat drawing = Mat::zeros( threshold_output.size(), CV_8UC3 );
for( int i = 0; i< contours.size(); i++ )
- {
+ {
Scalar color = Scalar( rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255) );
// contour
- drawContours( drawing, contours, i, color, 1, 8, vector<Vec4i>(), 0, Point() );
+ drawContours( drawing, contours, i, color, 1, 8, vector<Vec4i>(), 0, Point() );
// ellipse
ellipse( drawing, minEllipse[i], color, 2, 8 );
- // rotated rectangle
+ // rotated rectangle
Point2f rect_points[4]; minRect[i].points( rect_points );
for( int j = 0; j < 4; j++ )
- line( drawing, rect_points[j], rect_points[(j+1)%4], color, 1, 8 );
+ line( drawing, rect_points[j], rect_points[(j+1)%4], color, 1, 8 );
}
/// Show in a window
namedWindow( "Contours", CV_WINDOW_AUTOSIZE );
- imshow( "Contours", drawing );
+ imshow( "Contours", drawing );
}
Explanation
#. Here it is:
- ========== ==========
- |BRE_0| |BRE_1|
- ========== ==========
+ ========== ==========
+ |BRE_0| |BRE_1|
+ ========== ==========
.. |BRE_0| image:: images/Bounding_Rotated_Ellipses_Source_Image.jpg
:align: middle
.. |BRE_1| image:: images/Bounding_Rotated_Ellipses_Result.jpg
- :align: middle
+ :align: middle
.. container:: enumeratevisibleitemswithsquare
- * Use the OpenCV function :find_contours:`findContours <>`
- * Use the OpenCV function :draw_contours:`drawContours <>`
+ * Use the OpenCV function :find_contours:`findContours <>`
+ * Use the OpenCV function :draw_contours:`drawContours <>`
Theory
======
Code
====
-This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/ShapeDescriptors/findContours_demo.cpp>`_
+This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/ShapeDescriptors/findContours_demo.cpp>`_
-.. code-block:: cpp
+.. code-block:: cpp
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
/// Detect edges using canny
Canny( src_gray, canny_output, thresh, thresh*2, 3 );
- /// Find contours
+ /// Find contours
findContours( canny_output, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0) );
/// Draw contours
Mat drawing = Mat::zeros( canny_output.size(), CV_8UC3 );
for( int i = 0; i< contours.size(); i++ )
- {
+ {
Scalar color = Scalar( rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255) );
- drawContours( drawing, contours, i, color, 2, 8, hierarchy, 0, Point() );
+ drawContours( drawing, contours, i, color, 2, 8, hierarchy, 0, Point() );
}
/// Show in a window
namedWindow( "Contours", CV_WINDOW_AUTOSIZE );
- imshow( "Contours", drawing );
+ imshow( "Contours", drawing );
}
Explanation
#. Here it is:
- ============= =============
- |contour_0| |contour_1|
- ============= =============
+ ============= =============
+ |contour_0| |contour_1|
+ ============= =============
.. |contour_0| image:: images/Find_Contours_Original_Image.jpg
:align: middle
.. |contour_1| image:: images/Find_Contours_Result.jpg
- :align: middle
+ :align: middle
.. container:: enumeratevisibleitemswithsquare
- * Use the OpenCV function :convex_hull:`convexHull <>`
+ * Use the OpenCV function :convex_hull:`convexHull <>`
Theory
Code
====
-This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/ShapeDescriptors/hull_demo.cpp>`_
+This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/ShapeDescriptors/hull_demo.cpp>`_
-.. code-block:: cpp
+.. code-block:: cpp
- #include "opencv2/highgui/highgui.hpp"
+ #include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <iostream>
#include <stdio.h>
using namespace std;
Mat src; Mat src_gray;
- int thresh = 100;
+ int thresh = 100;
int max_thresh = 255;
RNG rng(12345);
/// Detect edges using Threshold
threshold( src_gray, threshold_output, thresh, 255, THRESH_BINARY );
- /// Find contours
+ /// Find contours
findContours( threshold_output, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0) );
/// Find the convex hull object for each contour
vector<vector<Point> >hull( contours.size() );
for( int i = 0; i < contours.size(); i++ )
- { convexHull( Mat(contours[i]), hull[i], false ); }
+ { convexHull( Mat(contours[i]), hull[i], false ); }
/// Draw contours + hull results
Mat drawing = Mat::zeros( threshold_output.size(), CV_8UC3 );
for( int i = 0; i< contours.size(); i++ )
- {
+ {
Scalar color = Scalar( rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255) );
- drawContours( drawing, contours, i, color, 1, 8, vector<Vec4i>(), 0, Point() );
- drawContours( drawing, hull, i, color, 1, 8, vector<Vec4i>(), 0, Point() );
+ drawContours( drawing, contours, i, color, 1, 8, vector<Vec4i>(), 0, Point() );
+ drawContours( drawing, hull, i, color, 1, 8, vector<Vec4i>(), 0, Point() );
}
/// Show in a window
#. Here it is:
- ========== ==========
- |Hull_0| |Hull_1|
- ========== ==========
+ ========== ==========
+ |Hull_0| |Hull_1|
+ ========== ==========
.. |Hull_0| image:: images/Hull_Original_Image.jpg
:align: middle
.. |Hull_1| image:: images/Hull_Result.jpg
- :align: middle
+ :align: middle
.. container:: enumeratevisibleitemswithsquare
- * Use the OpenCV function :moments:`moments <>`
+ * Use the OpenCV function :moments:`moments <>`
* Use the OpenCV function :contour_area:`contourArea <>`
- * Use the OpenCV function :arc_length:`arcLength <>`
+ * Use the OpenCV function :arc_length:`arcLength <>`
Theory
======
Code
====
-This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/ShapeDescriptors/moments_demo.cpp>`_
+This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/ShapeDescriptors/moments_demo.cpp>`_
-.. code-block:: cpp
+.. code-block:: cpp
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
/// Detect edges using canny
Canny( src_gray, canny_output, thresh, thresh*2, 3 );
- /// Find contours
+ /// Find contours
findContours( canny_output, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0) );
/// Get the moments
for( int i = 0; i < contours.size(); i++ )
{ mu[i] = moments( contours[i], false ); }
- /// Get the mass centers:
+ /// Get the mass centers:
vector<Point2f> mc( contours.size() );
for( int i = 0; i < contours.size(); i++ )
{ mc[i] = Point2f( mu[i].m10/mu[i].m00 , mu[i].m01/mu[i].m00 ); }
/// Draw contours
Mat drawing = Mat::zeros( canny_output.size(), CV_8UC3 );
for( int i = 0; i< contours.size(); i++ )
- {
+ {
Scalar color = Scalar( rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255) );
- drawContours( drawing, contours, i, color, 2, 8, hierarchy, 0, Point() );
+ drawContours( drawing, contours, i, color, 2, 8, hierarchy, 0, Point() );
circle( drawing, mc[i], 4, color, -1, 8, 0 );
}
printf("\t Info: Area and Contour Length \n");
for( int i = 0; i< contours.size(); i++ )
{
- printf(" * Contour[%d] - Area (M_00) = %.2f - Area OpenCV: %.2f - Length: %.2f \n", i, mu[i].m00, contourArea(contours[i]), arcLength( contours[i], true ) );
+ printf(" * Contour[%d] - Area (M_00) = %.2f - Area OpenCV: %.2f - Length: %.2f \n", i, mu[i].m00, contourArea(contours[i]), arcLength( contours[i], true ) );
Scalar color = Scalar( rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255) );
- drawContours( drawing, contours, i, color, 2, 8, hierarchy, 0, Point() );
+ drawContours( drawing, contours, i, color, 2, 8, hierarchy, 0, Point() );
circle( drawing, mc[i], 4, color, -1, 8, 0 );
}
}
#. Here it is:
- ========== ========== ==========
- |MU_0| |MU_1| |MU_2|
- ========== ========== ==========
+ ========== ========== ==========
+ |MU_0| |MU_1| |MU_2|
+ ========== ========== ==========
.. |MU_0| image:: images/Moments_Source_Image.jpg
:width: 250pt
.. |MU_1| image:: images/Moments_Result1.jpg
:width: 250pt
- :align: middle
+ :align: middle
.. |MU_2| image:: images/Moments_Result2.jpg
:width: 250pt
- :align: middle
+ :align: middle
.. container:: enumeratevisibleitemswithsquare
- * Use the OpenCV function :point_polygon_test:`pointPolygonTest <>`
-
+ * Use the OpenCV function :point_polygon_test:`pointPolygonTest <>`
+
Theory
======
Code
====
-This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/ShapeDescriptors/pointPolygonTest_demo.cpp>`_
+This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/ShapeDescriptors/pointPolygonTest_demo.cpp>`_
-.. code-block:: cpp
+.. code-block:: cpp
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
/// Draw it in src
for( int j = 0; j < 6; j++ )
- { line( src, vert[j], vert[(j+1)%6], Scalar( 255 ), 3, 8 ); }
+ { line( src, vert[j], vert[(j+1)%6], Scalar( 255 ), 3, 8 ); }
/// Get the contours
vector<vector<Point> > contours; vector<Vec4i> hierarchy;
Mat src_copy = src.clone();
- findContours( src_copy, contours, hierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE);
+ findContours( src_copy, contours, hierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE);
/// Calculate the distances to the contour
Mat raw_dist( src.size(), CV_32FC1 );
double minVal; double maxVal;
minMaxLoc( raw_dist, &minVal, &maxVal, 0, 0, Mat() );
minVal = abs(minVal); maxVal = abs(maxVal);
-
+
/// Depicting the distances graphically
Mat drawing = Mat::zeros( src.size(), CV_8UC3 );
for( int j = 0; j < src.rows; j++ )
{ for( int i = 0; i < src.cols; i++ )
- {
+ {
if( raw_dist.at<float>(j,i) < 0 )
{ drawing.at<Vec3b>(j,i)[0] = 255 - (int) abs(raw_dist.at<float>(j,i))*255/minVal; }
else if( raw_dist.at<float>(j,i) > 0 )
- { drawing.at<Vec3b>(j,i)[2] = 255 - (int) raw_dist.at<float>(j,i)*255/maxVal; }
+ { drawing.at<Vec3b>(j,i)[2] = 255 - (int) raw_dist.at<float>(j,i)*255/maxVal; }
else
- { drawing.at<Vec3b>(j,i)[0] = 255; drawing.at<Vec3b>(j,i)[1] = 255; drawing.at<Vec3b>(j,i)[2] = 255; }
+ { drawing.at<Vec3b>(j,i)[0] = 255; drawing.at<Vec3b>(j,i)[1] = 255; drawing.at<Vec3b>(j,i)[2] = 255; }
}
}
#. Here it is:
- ========== ==========
- |PPT_0| |PPT_1|
- ========== ==========
+ ========== ==========
+ |PPT_0| |PPT_1|
+ ========== ==========
.. |PPT_0| image:: images/Point_Polygon_Test_Source_Image.png
:align: middle
.. |PPT_1| image:: images/Point_Polygon_Test_Result.jpg
- :align: middle
+ :align: middle
* Application example: Separate out regions of an image corresponding to objects which we want to analyze. This separation is based on the variation of intensity between the object pixels and the background pixels.
-* To differentiate the pixels we are interested in from the rest (which will eventually be rejected), we perform a comparison of each pixel intensity value with respect to a *threshold* (determined according to the problem to solve).
+* To differentiate the pixels we are interested in from the rest (which will eventually be rejected), we perform a comparison of each pixel intensity value with respect to a *threshold* (determined according to the problem to solve).
* Once we have separated properly the important pixels, we can set them with a determined value to identify them (i.e. we can assign them a value of :math:`0` (black), :math:`255` (white) or any value that suits your needs).
.. image:: images/Threshold_Tutorial_Theory_Example.jpg
:alt: Threshold simple example
- :align: center
+ :align: center
Types of Thresholding
-----------------------
-* OpenCV offers the function :threshold:`threshold <>` to perform thresholding operations.
+* OpenCV offers the function :threshold:`threshold <>` to perform thresholding operations.
* We can effectuate :math:`5` types of Thresholding operations with this function. We will explain them in the following subsections.
.. image:: images/Threshold_Tutorial_Theory_Base_Figure.png
:alt: Threshold Binary
- :align: center
+ :align: center
Threshold Binary
^^^^^^^^^^^^^^^^^
* This thresholding operation can be expressed as:
.. math::
-
- \texttt{dst} (x,y) = \fork{\texttt{maxVal}}{if $\texttt{src}(x,y) > \texttt{thresh}$}{0}{otherwise}
-
+
+ \texttt{dst} (x,y) = \fork{\texttt{maxVal}}{if $\texttt{src}(x,y) > \texttt{thresh}$}{0}{otherwise}
+
* So, if the intensity of the pixel :math:`src(x,y)` is higher than :math:`thresh`, then the new pixel intensity is set to a :math:`MaxVal`. Otherwise, the pixels are set to :math:`0`.
.. image:: images/Threshold_Tutorial_Theory_Binary.png
:alt: Threshold Binary
- :align: center
+ :align: center
Threshold Binary, Inverted
^^^^^^^^^^^^^^^^^^^^^^^^^^^
* This thresholding operation can be expressed as:
-
+
.. math::
-
- \texttt{dst} (x,y) = \fork{0}{if $\texttt{src}(x,y) > \texttt{thresh}$}{\texttt{maxVal}}{otherwise}
+
+ \texttt{dst} (x,y) = \fork{0}{if $\texttt{src}(x,y) > \texttt{thresh}$}{\texttt{maxVal}}{otherwise}
* If the intensity of the pixel :math:`src(x,y)` is higher than :math:`thresh`, then the new pixel intensity is set to a :math:`0`. Otherwise, it is set to :math:`MaxVal`.
-
+
.. image:: images/Threshold_Tutorial_Theory_Binary_Inverted.png
:alt: Threshold Binary Inverted
- :align: center
+ :align: center
Truncate
^^^^^^^^^
-
+
* This thresholding operation can be expressed as:
-
+
.. math::
-
- \texttt{dst} (x,y) = \fork{\texttt{threshold}}{if $\texttt{src}(x,y) > \texttt{thresh}$}{\texttt{src}(x,y)}{otherwise}
-
+
+ \texttt{dst} (x,y) = \fork{\texttt{threshold}}{if $\texttt{src}(x,y) > \texttt{thresh}$}{\texttt{src}(x,y)}{otherwise}
+
* The maximum intensity value for the pixels is :math:`thresh`, if :math:`src(x,y)` is greater, then its value is *truncated*. See figure below:
-
+
.. image:: images/Threshold_Tutorial_Theory_Truncate.png
:alt: Threshold Truncate
- :align: center
-
+ :align: center
+
Threshold to Zero
^^^^^^^^^^^^^^^^^^
* This operation can be expressed as:
-
+
.. math::
-
- \texttt{dst} (x,y) = \fork{\texttt{src}(x,y)}{if $\texttt{src}(x,y) > \texttt{thresh}$}{0}{otherwise}
+
+ \texttt{dst} (x,y) = \fork{\texttt{src}(x,y)}{if $\texttt{src}(x,y) > \texttt{thresh}$}{0}{otherwise}
* If :math:`src(x,y)` is lower than :math:`thresh`, the new pixel value will be set to :math:`0`.
.. image:: images/Threshold_Tutorial_Theory_Zero.png
:alt: Threshold Zero
- :align: center
+ :align: center
Threshold to Zero, Inverted
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
* This operation can be expressed as:
-
+
.. math::
-
- \texttt{dst} (x,y) = \fork{0}{if $\texttt{src}(x,y) > \texttt{thresh}$}{\texttt{src}(x,y)}{otherwise}
+
+ \texttt{dst} (x,y) = \fork{0}{if $\texttt{src}(x,y) > \texttt{thresh}$}{\texttt{src}(x,y)}{otherwise}
* If :math:`src(x,y)` is greater than :math:`thresh`, the new pixel value will be set to :math:`0`.
.. image:: images/Threshold_Tutorial_Theory_Zero_Inverted.png
:alt: Threshold Zero Inverted
- :align: center
+ :align: center
Code
======
-The tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/ImgProc/Threshold.cpp>`_
+The tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/ImgProc/Threshold.cpp>`_
-.. code-block:: cpp
+.. code-block:: cpp
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"
namedWindow( window_name, CV_WINDOW_AUTOSIZE );
/// Create Trackbar to choose type of Threshold
- createTrackbar( trackbar_type,
- window_name, &threshold_type,
+ createTrackbar( trackbar_type,
+ window_name, &threshold_type,
max_type, Threshold_Demo );
createTrackbar( trackbar_value,
.. code-block:: cpp
- createTrackbar( trackbar_type,
- window_name, &threshold_type,
+ createTrackbar( trackbar_type,
+ window_name, &threshold_type,
max_type, Threshold_Demo );
createTrackbar( trackbar_value,
.. image:: images/Threshold_Tutorial_Original_Image.jpg
:alt: Threshold Original Image
- :align: center
+ :align: center
#. First, we try to threshold our image with a *binary threhold inverted*. We expect that the pixels brighter than the :math:`thresh` will turn dark, which is what actually happens, as we can see in the snapshot below (notice from the original image, that the doggie's tongue and eyes are particularly bright in comparison with the image, this is reflected in the output image).
.. image:: images/Threshold_Tutorial_Result_Binary_Inverted.jpg
:alt: Threshold Result Binary Inverted
- :align: center
+ :align: center
#. Now we try with the *threshold to zero*. With this, we expect that the darkest pixels (below the threshold) will become completely black, whereas the pixels with value greater than the threshold will keep its original value. This is verified by the following snapshot of the output image:
.. image:: images/Threshold_Tutorial_Result_Zero.jpg
:alt: Threshold Result Zero
- :align: center
+ :align: center
In this tutorial you will learn how to:
.. container:: enumeratevisibleitemswithsquare
-
+
* Load an image (using :imread:`imread <>`)
* Create a named OpenCV window (using :named_window:`namedWindow <>`)
* Display an image in an OpenCV window (using :imshow:`imshow <>`)
Source Code
===========
-Download the source code from `here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/introduction/display_image/display_image.cpp>`_.
+Download the source code from `here <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/introduction/display_image/display_image.cpp>`_.
.. literalinclude:: ../../../../samples/cpp/tutorial_code/introduction/display_image/display_image.cpp
:language: cpp
In OpenCV 2 we have multiple modules. Each one takes care of a different area or approach towards image processing. You could already observe this in the structure of the user guide of these tutorials itself. Before you use any of them you first need to include the header files where the content of each individual module is declared.
-You'll almost always end up using the:
+You'll almost always end up using the:
.. container:: enumeratevisibleitemswithsquare
:tab-width: 4
:lines: 17
-.. note::
+.. note::
- OpenCV offers support for the image formats Windows bitmap (bmp), portable image formats (pbm, pgm, ppm) and Sun raster (sr, ras). With help of plugins (you need to specify to use them if you build yourself the library, nevertheless in the packages we ship present by default) you may also load image formats like JPEG (jpeg, jpg, jpe), JPEG 2000 (jp2 - codenamed in the CMake as Jasper), TIFF files (tiff, tif) and portable network graphics (png). Furthermore, OpenEXR is also a possibility.
+ OpenCV offers support for the image formats Windows bitmap (bmp), portable image formats (pbm, pgm, ppm) and Sun raster (sr, ras). With help of plugins (you need to specify to use them if you build yourself the library, nevertheless in the packages we ship present by default) you may also load image formats like JPEG (jpeg, jpg, jpe), JPEG 2000 (jp2 - codenamed in the CMake as Jasper), TIFF files (tiff, tif) and portable network graphics (png). Furthermore, OpenEXR is also a possibility.
-After checking that the image data was loaded correctly, we want to display our image, so we create an OpenCV window using the :named_window:`namedWindow <>` function. These are automatically managed by OpenCV once you create them. For this you need to specify its name and how it should handle the change of the image it contains from a size point of view. It may be:
+After checking that the image data was loaded correctly, we want to display our image, so we create an OpenCV window using the :named_window:`namedWindow <>` function. These are automatically managed by OpenCV once you create them. For this you need to specify its name and how it should handle the change of the image it contains from a size point of view. It may be:
.. container:: enumeratevisibleitemswithsquare
- + *CV_WINDOW_AUTOSIZE* is the only supported one if you do not use the Qt backend. In this case the window size will take up the size of the image it shows. No resize permitted!
- + *CV_WINDOW_NORMAL* on Qt you may use this to allow window resize. The image will resize itself according to the current window size. By using the | operator you also need to specify if you would like the image to keep its aspect ratio (*CV_WINDOW_KEEPRATIO*) or not (*CV_WINDOW_FREERATIO*).
+ + *CV_WINDOW_AUTOSIZE* is the only supported one if you do not use the Qt backend. In this case the window size will take up the size of the image it shows. No resize permitted!
+ + *CV_WINDOW_NORMAL* on Qt you may use this to allow window resize. The image will resize itself according to the current window size. By using the | operator you also need to specify if you would like the image to keep its aspect ratio (*CV_WINDOW_KEEPRATIO*) or not (*CV_WINDOW_FREERATIO*).
.. literalinclude:: ../../../../samples/cpp/tutorial_code/introduction/display_image/display_image.cpp
:language: cpp
:lines: 25
:tab-width: 4
-Finally, to update the content of the OpenCV window with a new image use the :imshow:`imshow <>` function. Specify the OpenCV window name to update and the image to use during this operation:
+Finally, to update the content of the OpenCV window with a new image use the :imshow:`imshow <>` function. Specify the OpenCV window name to update and the image to use during this operation:
.. literalinclude:: ../../../../samples/cpp/tutorial_code/introduction/display_image/display_image.cpp
:language: cpp
.. container:: enumeratevisibleitemswithsquare
- * Compile your code and then run the executable giving an image path as argument. If you're on Windows the executable will of course contain an *exe* extension too. Of course assure the image file is near your program file.
+ * Compile your code and then run the executable giving an image path as argument. If you're on Windows the executable will of course contain an *exe* extension too. Of course assure the image file is near your program file.
.. code-block:: bash
.. image:: images/Display_Image_Tutorial_Result.jpg
:alt: Display Image Tutorial - Final Result
- :align: center
+ :align: center
.. raw:: html
-.. _howToWriteTutorial:\r\rHow to write a tutorial for OpenCV?\r***********************************\r\rOkay, so assume you have just finished a project of yours implementing something based on OpenCV and you want to present/share it with the community. Luckily, OpenCV is an *open source project*. This means that in theory anyone has access to the full source code and may extend it. While making a robust and practical library (like OpenCV) is great, the success of a library also depends on how user friendly it is. To improve on this aspect, the OpenCV team has already been listening to user feedback from its :opencv_group:`Yahoo user group <>` and by making samples you can find in the source directories sample folder. The addition of the tutorials (in both online and PDF format) is an extension of these efforts. \r\rGoal\r====\r\r.. _reST: http://docutils.sourceforge.net/rst.html\r.. |reST| replace:: reStructuredText\r.. |Sphinx| replace:: Sphinx\r.. _Sphinx: http://sphinx.pocoo.org/\r\rThe tutorials are just as an important part of the library as the implementation of those crafty data structures and algorithms you can find in OpenCV. Therefore, the source codes for the tutorials are part of the library. And yes, I meant source codes. The reason for this formulation is that the tutorials are written by using the |Sphinx|_ documentation generation system. This is based on the popular python documentation system called |reST|_ (reST). ReStructuredText is a really neat language that by using a few simple conventions (indentation, directives) and emulating old school e-mail writing techniques (text only) tries to offer a simple way to create and edit documents. Sphinx extends this with some new features and creates the resulting document in both HTML (for web) and PDF (for offline usage) format.\r\rUsually, an OpenCV tutorial has the following parts:\r\r1. A source code demonstration of an OpenCV feature: \r \r a. One or more CPP, Python, Java or other type of files depending for what OpenCV offers support and for what language you make the tutorial. \r #. Occasionaly, input resource files required for running your tutorials application.\r\r\r#. A table of content entry (so people may easily find the tutorial): \r \r a. Adding your stuff to the tutorials table of content (**reST** file). \r #. Add an image file near the TOC entry. \r\r\r#. The content of the tutorial itself: \r \r a. The **reST** text of the tutorial\r #. Images following the idea that "*A picture is worth a thousand words*". \r #. For more complex demonstrations you may create a video.\r\rAs you can see you will need at least some basic knowledge of the *reST* system in order to complete the task at hand with success. However, don't worry *reST* (and *Sphinx*) was made with simplicity in mind. It is easy to grasp its basics. I found that the `OpenAlea documentations introduction on this subject <http://openalea.gforge.inria.fr/doc/openalea/doc/_build/html/source/tutorial/rest_syntax.html>`_ (or the `Thomas Cokelaer one <http://thomas-cokelaer.info/tutorials/sphinx/rest_syntax.html>`_ ) should enough for this. If for some directive or feature you need a more in-depth description look it up in the official |reST|_ help files or at the |Sphinx|_ documentation.\r\rIn our world achieving some tasks is possible in multiple ways. However, some of the roads to take may have obvious or hidden advantages over others. Then again, in some other cases it may come down to just simple user preference. Here, I'll present how I decided to write the tutorials, based on my personal experience. If for some of them you know a better solution and you can back it up feel free to use that. I've nothing against it, as long as it gets the job done in an elegant fashion. \r\rNow the best would be if you could make the integration yourself. For this you need first to have the source code. I recommend following the guides for your operating system on acquiring OpenCV sources. For Linux users look :ref:`here <Linux-Installation>` and for :ref:`Windows here <Windows_Installation>`. You must also install python and sphinx with its dependencies in order to be able to build the documentation. \r\rOnce you have downloaded the repository to your hard drive you can take a look in the OpenCV directory to make sure you have both the samples and doc folder present. Anyone may download the trunk source files from :file:`/svn/opencv/trunk/` . Nevertheless, not everyone has upload (commit/submit) rights. This is to protect the integrity of the library. If you plan doing more than one tutorial, and would like to have an account with commit user rights you should first register an account at http://code.opencv.org/ and then contact dr. Gary Bradski at -delete-bradski@-delete-willowgarage.com. Otherwise, you can just send the resulting files to us via the :opencv_group:`Yahoo user group <>` or to me at -delete-bernat@-delete-primeranks.net and I'll add it. If you have questions, suggestions or constructive critics I will gladly listen to them. If you send it to the OpenCV group please tag its subject with a **[Tutorial]** entry. \r\rFormat the Source Code\r======================\r\rBefore I start this let it be clear: the main goal is to have a working sample code. However, for your tutorial to be of a top notch quality you should follow a few guide lines I am going to present here. \r\rIn case you have an application by using the older interface (with *IplImage*, *CVMat*, *cvLoadImage* and such) consider migrating it to the new C++ interface. The tutorials are intended to be an up to date help for our users. And as of OpenCV 2 the OpenCV emphasis on using the less error prone and clearer C++ interface. Therefore, if possible please convert your code to the C++ interface. For this it may help to read the :ref:`InteroperabilityWithOpenCV1` tutorial. However, once you have an OpenCV 2 working code, then you should make your source code snippet as easy to read as possible. Here're a couple of advices for this: \r\r.. container:: enumeratevisibleitemswithsquare\r\r + Add a standard output with the description of what your program does. Keep it short and yet, descriptive. This output is at the start of the program. In my example files this usually takes the form of a *help* function containing the output. This way both the source file viewer and application runner can see what all is about in your sample. Here's an instance of this: \r\r .. code-block:: cpp\r\r void help()\r {\r cout\r << "--------------------------------------------------------------------------" << endl\r << "This program shows how to write video files. You can extract the R or G or B color channel "\r << " of the input video. You can choose to use the source codec (Y) or select a custom one. (N)"<< endl\r << "Usage:" << endl\r << "./video-write inputvideoName [ R | G | B] [Y | N]" << endl\r << "--------------------------------------------------------------------------" << endl\r << endl;\r }\r // ...\r int main(int argc, char *argv[], char *window_name)\r {\r help();\r // here comes the actual source code\r }\r\r Additionally, finalize the description with a short usage guide. This way the user will know how to call your programs, what leads us to the next point. \r\r + Prefer command line argument controlling instead of hard coded one. If your program has some variables that may be changed use command line arguments for this. The tutorials, can be a simple try-out ground for the user. If you offer command line controlling for the input image (for example), then you offer the possibility for the user to try it out with his/her own images, without the need to mess in the source code. In the upper example you can see that the input image, channel and codec selection may all be changed from the command line. Just compile the program and run it with your own input arguments. \r\r + Be as verbose as possible. There is no shame in filling the source code with comments. This way the more advanced user may figure out what's happening right from the sample code. This advice goes for the output console too. Specify to the user what's happening. Never leave the user hanging there and thinking on: "Is this program now crashing or just doing some computationally intensive task?." So, if you do a training task that may take some time, make sure you print out a message about this before starting and after finishing it. \r\r + Throw out unnecessary stuff from your source code. This is a warning to not take the previous point too seriously. Balance is the key. If it's something that can be done in a fewer lines or simpler than that's the way you should do it. Nevertheless, if for some reason you have such sections notify the user why you have chosen to do so. Keep the amount of information as low as possible, while still getting the job done in an elegant way. \r\r + Put your sample file into the :file:`opencv/samples/cpp/tutorial_code/sectionName` folder. If you write a tutorial for other languages than cpp, then change that part of the path. Before completing this you need to decide that to what section (module) does your tutorial goes. Think about on what module relies most heavily your code and that is the one to use. If the answer to this question is more than one modules then the *general* section is the one to use. For finding the *opencv* directory open up your file system and navigate where you downloaded our repository.\r\r + If the input resources are hard to acquire for the end user consider adding a few of them to the :file:`opencv/samples/cpp/tutorial_code/images`. Make sure that who reads your code can try it out!\r\rAdd the TOC entry\r=================\r\rFor this you will need to know some |reST|_. There is no going around this. |reST|_ files have **rst** extensions. However, these are simple text files. Use any text editor you like. Finding a text editor that offers syntax highlighting for |reST|_ was quite a challenge at the time of writing this tutorial. In my experience, `Intype <http://intype.info/>`_ is a solid option on Windows, although there is still place for improvement. \r\rAdding your source code to a table of content is important for multiple reasons. First and foremost this will allow for the user base to find your tutorial from our websites tutorial table of content. Secondly, if you omit this *Sphinx* will throw a warning that your tutorial file isn't part of any TOC tree entry. And there is nothing more than the developer team hates than an ever increasing warning/error list for their builds. *Sphinx* also uses this to build up the previous-back-up buttons on the website. Finally, omitting this step will lead to that your tutorial will **not** be added to the PDF version of the tutorials. \r\rNavigate to the :file:`opencv/doc/tutorials/section/table_of_content_section` folder (where the section is the module to which you're adding the tutorial). Open the *table_of_content_section* file. Now this may have two forms. If no prior tutorials are present in this section that there is a template message about this and has the following form:\r\r.. code-block:: rst\r\r .. _Table-Of-Content-Section:\r\r Section title\r -----------------------------------------------------------\r\r Description about the section.\r\r .. include:: ../../definitions/noContent.rst\r\r .. raw:: latex\r\r \pagebreak\r\rThe first line is a reference to the section title in the reST system. The section title will be a link and you may refer to it via the ``:ref:`` directive. The *include* directive imports the template text from the definitions directories *noContent.rst* file. *Sphinx* does not creates the PDF from scratch. It does this by first creating a latex file. Then creates the PDF from the latex file. With the *raw* directive you can directly add to this output commands. Its unique argument is for what kind of output to add the content of the directive. For the PDFs it may happen that multiple sections will overlap on a single page. To avoid this at the end of the TOC we add a *pagebreak* latex command, that hints to the LATEX system that the next line should be on a new page. \r\rIf you have one of this, try to transform it to the following form: \r\r.. include:: ../../definitions/tocDefinitions.rst \r\r.. code-block:: rst\r\r .. _Table-Of-Content-Section:\r\r Section title\r -----------------------------------------------------------\r\r .. include:: ../../definitions/tocDefinitions.rst\r\r +\r .. tabularcolumns:: m{100pt} m{300pt}\r .. cssclass:: toctableopencv\r\r =============== ======================================================\r |MatBasicIma| **Title:** :ref:`matTheBasicImageContainer`\r\r *Compatibility:* > OpenCV 2.0\r\r *Author:* |Author_BernatG|\r\r You will learn how to store images in the memory and how to print out their content to the console.\r\r =============== =====================================================\r\r .. |MatBasicIma| image:: images/matTheBasicImageStructure.jpg\r :height: 90pt\r :width: 90pt\r\r .. raw:: latex\r\r \pagebreak\r\r .. toctree::\r :hidden:\r\r ../mat - the basic image container/mat - the basic image container\r\rIf this is already present just add a new section of the content between the include and the raw directives (excluding those lines). Here you'll see a new include directive. This should be present only once in a TOC tree and the reST file contains the definitions of all the authors contributing to the OpenCV tutorials. We are a multicultural community and some of our name may contain some funky characters. However, reST **only supports** ANSI characters. Luckily we can specify Unicode characters with the *unicode* directive. Doing this for all of your tutorials is a troublesome procedure. Therefore, the tocDefinitions file contains the definition of your author name. Add it here once and afterwards just use the replace construction. For example here's the definition for my name: \r\r.. code-block:: rst\r\r .. |Author_BernatG| unicode:: Bern U+00E1 t U+0020 G U+00E1 bor\r\rThe ``|Author_BernatG|`` is the text definitions alias. I can use later this to add the definition, like I've done in the TOCs *Author* part. After the ``::`` and a space you start the definition. If you want to add an UNICODE character (non-ASCI) leave an empty space and specify it in the format U+(UNICODE code). To find the UNICODE code of a character I recommend using the `FileFormat <http://www.fileformat.info>`_ websites service. Spaces are trimmed from the definition, therefore we add a space by its UNICODE character (U+0020). \r\rUntil the *raw* directive what you can see is a TOC tree entry. Here's how a TOC entry will look like: \r\r+\r .. tabularcolumns:: m{100pt} m{300pt}\r .. cssclass:: toctableopencv\r\r =============== ======================================================\r |MatBasicIma| **Title:** :ref:`matTheBasicImageContainer`\r\r *Compatibility:* > OpenCV 2.0\r\r *Author:* |Author_BernatG|\r\r You will learn how to store images in the memory and how to print out their content to the console.\r\r =============== ======================================================\r\r .. |MatBasicIma| image:: images/matTheBasicImageStructure.jpg\r :height: 90pt\r :width: 90pt\r\rAs you can see we have an image to the left and a description box to the right. To create two boxes we use a table with two columns and a single row. In the left column is the image and in the right one the description. However, the image directive is way too long to fit in a column. Therefore, we need to use the substitution definition system. We add this definition after the TOC tree. All images for the TOC tree are to be put in the images folder near its |reST|_ file. We use the point measurement system because we are also creating PDFs. PDFs are printable documents, where there is no such thing that pixels (px), just points (pt). And while generally space is no problem for web pages (we have monitors with **huge** resolutions) the size of the paper (A4 or letter) is constant and will be for a long time in the future. Therefore, size constrains come in play more like for the PDF, than the generated HTML code. \r\rNow your images should be as small as possible, while still offering the intended information for the user. Remember that the tutorial will become part of the OpenCV source code. If you add large images (that manifest in form of large image size) it will just increase the size of the repository pointlessly. If someone wants to download it later, its download time will be that much longer. Not to mention the larger PDF size for the tutorials and the longer load time for the web pages. In terms of pixels a TOC image should not be larger than 120 X 120 pixels. Resize your images if they are larger! \r\r.. note::\r\r If you add a larger image and specify a smaller image size, *Sphinx* will not resize that. At build time will add the full size image and the resize will be done by your browser after the image is loaded. A 120 X 120 image is somewhere below 10KB. If you add a 110KB image, you have just pointlessly added a 100KB extra data to transfer over the internet for every user!\r\rGenerally speaking you shouldn't need to specify your images size (excluding the TOC entries). If no such is found *Sphinx* will use the size of the image itself (so no resize occurs). Then again if for some reason you decide to specify a size that should be the **width** of the image rather than its height. The reason for this again goes back to the PDFs. On a PDF page the height is larger than the width. In the PDF the images will not be resized. If you specify a size that does not fit in the page, then what does not fits in **will be cut off**. When creating your images for your tutorial you should try to keep the image widths below 500 pixels, and calculate with around 400 point page width when specifying image widths. \r\rThe image format depends on the content of the image. If you have some complex scene (many random like colors) then use *jpg*. Otherwise, prefer using *png*. They are even some tools out there that optimize the size of *PNG* images, such as `PNGGauntlet <http://pnggauntlet.com/>`_. Use them to make your images as small as possible in size. \r\rNow on the right side column of the table we add the information about the tutorial: \r\r.. container:: enumeratevisibleitemswithsquare\r\r + In the first line it is the title of the tutorial. However, there is no need to specify it explicitly. We use the reference system. We'll start up our tutorial with a reference specification, just like in case of this TOC entry with its `` .. _Table-Of-Content-Section:`` . If after this you have a title (pointed out by the following line of -), then Sphinx will replace the ``:ref:`Table-Of-Content-Section``` directive with the tile of the section in reference form (creates a link in web page). Here's how the definition looks in my case: \r\r .. code-block:: rst\r\r .. _matTheBasicImageContainer:\r\r Mat - The Basic Image Container\r *******************************\r\r Note, that according to the |reST|_ rules the * should be as long as your title. \r\r + Compatibility. What version of OpenCV is required to run your sample code. \r\r + Author. Use the substitution markup of |reST|_. \r\r + A short sentence describing the essence of your tutorial. \r\rNow before each TOC entry you need to add the three lines of: \r\r.. code-block:: cpp\r\r + \r .. tabularcolumns:: m{100pt} m{300pt}\r .. cssclass:: toctableopencv\r\rThe plus sign (+) is to enumerate tutorials by using bullet points. So for every TOC entry we have a corresponding bullet point represented by the +. Sphinx is highly indenting sensitive. Indentation is used to express from which point until to which point does a construction last. Un-indentation means end of that construction. So to keep all the bullet points to the same group the following TOC entries (until the next +) should be indented by two spaces. \r\rHere, I should also mention that **always** prefer using spaces instead of tabs. Working with only spaces makes possible that if we both use monotype fonts we will see the same thing. Tab size is text editor dependent and as should be avoided. *Sphinx* translates all tabs into 8 spaces before interpreting it. \r\rIt turns out that the automatic formatting of both the HTML and PDF(LATEX) system messes up our tables. Therefore, we need to help them out a little. For the PDF generation we add the ``.. tabularcolumns:: m{100pt} m{300pt}`` directive. This means that the first column should be 100 points wide and middle aligned. For the HTML look we simply name the following table of a *toctableopencv* class type. Then, we can modify the look of the table by modifying the CSS of our web page. The CSS definitions go into the :file:`opencv/doc/_themes/blue/static/default.css_t` file. \r\r.. code-block:: css\r\r .toctableopencv\r {\r width: 100% ; \r table-layout: fixed;\r }\r\r\r .toctableopencv colgroup col:first-child\r {\r width: 100pt !important;\r max-width: 100pt !important;\r min-width: 100pt !important;\r }\r\r .toctableopencv colgroup col:nth-child(2) \r {\r width: 100% !important;\r }\r\rHowever, you should not need to modify this. Just add these three lines (plus keep the two space indentation) for all TOC entries you add. At the end of the TOC file you'll find: \r\r.. code-block:: rst\r\r .. raw:: latex\r\r \pagebreak\r\r .. toctree::\r :hidden:\r\r ../mat - the basic image container/mat - the basic image container\r\rThe page break entry comes for separating sections and should be only one in a TOC tree |reST|_ file. Finally, at the end of the TOC tree we need to add our tutorial to the *Sphinx* TOC tree system. *Sphinx* will generate from this the previous-next-up information for the HTML file and add items to the PDF according to the order here. By default this TOC tree directive generates a simple table of contents. However, we already created a fancy looking one so we no longer need this basic one. Therefore, we add the *hidden* option to do not show it. \r\rThe path is of a relative type. We step back in the file system and then go into the :file:`mat - the basic image container` directory for the :file:`mat - the basic image container.rst` file. Putting out the *rst* extension for the file is optional. \r\rWrite the tutorial\r==================\r\rCreate a folder with the name of your tutorial. Preferably, use small letters only. Then create a text file in this folder with *rst* extension and the same name. If you have images for the tutorial create an :file:`images` folder and add your images there. When creating your images follow the guidelines described in the previous part!\r\rNow here's our recommendation for the structure of the tutorial (although, remember that this is not carved in the stone; if you have a better idea, use it!): \r\r\r.. container:: enumeratevisibleitemswithsquare\r\r + Create the reference point and the title. \r\r .. code-block:: rst\r\r .. _matTheBasicImageContainer:\r\r Mat - The Basic Image Container\r *******************************\r\r You start the tutorial by specifying a reference point by the ``.. _matTheBasicImageContainer:`` and then its title. The name of the reference point should be a unique one over the whole documentation. Therefore, do not use general names like *tutorial1*. Use the * character to underline the title for its full width. The subtitles of the tutorial should be underlined with = charachter.\r\r + Goals. You start your tutorial by specifying what you will present. You can also enumerate the sub jobs to be done. For this you can use a bullet point construction. There is a single configuration file for both the reference manual and the tutorial documentation. In the reference manuals at the argument enumeration we do not want any kind of bullet point style enumeration. Therefore, by default all the bullet points at this level are set to do not show the dot before the entries in the HTML. You can override this by putting the bullet point in a container. I've defined a square type bullet point view under the name *enumeratevisibleitemswithsquare*. The CSS style definition for this is again in the :file:`opencv\doc\_themes\blue\static\default.css_t` file. Here's a quick example of using it: \r\r .. code-block:: rst\r\r .. container:: enumeratevisibleitemswithsquare\r\r + Create the reference point and the title. \r + Second entry\r + Third entry\r\r Note that you need the keep the indentation of the container directive. Directive indentations are always three (3) spaces. Here you may even give usage tips for your sample code. \r\r + Source code. Present your samples code to the user. It's a good idea to offer a quick download link for the HTML page by using the *download* directive and pointing out where the user may find your source code in the file system by using the *file* directive: \r\r .. code-block:: rst\r\r Text :file:`samples/cpp/tutorial_code/highgui/video-write/` folder of the OpenCV source library\r or :download:`text to appear in the webpage \r <../../../../samples/cpp/tutorial_code/HighGUI/video-write/video-write.cpp>`.\r\r For the download link the path is a relative one, hence the multiple back stepping operations (..). Then you can add the source code either by using the *code block* directive or the *literal include* one. In case of the code block you will need to actually add all the source code text into your |reST|_ text and also apply the required indentation: \r\r .. code-block:: rst\r\r .. code-block:: cpp \r\r int i = 0; \r l = ++j; \r\r The only argument of the directive is the language used (here CPP). Then you add the source code into its content (meaning one empty line after the directive) by keeping the indentation of the directive (3 spaces). With the *literal include* directive you do not need to add the source code of the sample. You just specify the sample and *Sphinx* will load it for you, during build time. Here's an example usage: \r\r .. code-block:: rst\r\r .. literalinclude:: ../../../../samples/cpp/tutorial_code/HighGUI/video-write/video-write.cpp\r :language: cpp\r :linenos:\r :tab-width: 4\r :lines: 1-8, 21-22, 24-\r\r After the directive you specify a relative path to the file from what to import. It has four options: the language to use, if you add the ``:linenos:`` the line numbers will be shown, you can specify the tab size with the ``:tab-width:`` and you do not need to load the whole file, you can show just the important lines. Use the *lines* option to do not show redundant information (such as the *help* function). Here basically you specify ranges, if the second range line number is missing than that means that until the end of the file. The ranges specified here do no need to be in an ascending order, you may even reorganize the structure of how you want to show your sample inside the tutorial.\r\r + The tutorial. Well here goes the explanation for why and what have you used. Try to be short, clear, concise and yet a thorough one. There's no magic formula. Look into a few already made tutorials and start out from there. Try to mix sample OpenCV code with your explanations. If with words is hard to describe something do not hesitate to add in a reasonable size image, to overcome this issue.\r\r When you present OpenCV functionality it's a good idea to give a link to the used OpenCV data structure or function. Because the OpenCV tutorials and reference manual are in separate PDF files it is not possible to make this link work for the PDF format. Therefore, we use here only web page links to the **opencv.itseez.com** website. The OpenCV functions and data structures may be used for multiple tasks. Nevertheless, we want to avoid that every users creates its own reference to a commonly used function. So for this we use the global link collection of *Sphinx*. This is defined in the file:`opencv/doc/conf.py` configuration file. Open it and go all the way down to the last entry: \r\r .. code-block:: py\r\r # ---- External links for tutorials -----------------\r extlinks = {\r 'huivideo' : ('http://opencv.itseez.com/modules/highgui/doc/reading_and_writing_images_and_video.html#%s', None)\r }\r\r In short here we defined a new **huivideo** directive that refers to an external webpage link. Its usage is: \r\r .. code-block:: rst\r\r A sample function of the highgui modules image write and read page is the :huivideo:`imread() function <imread>`. \r\r Which turns to: A sample function of the highgui modules image write and read page is the :huivideo:`imread() function <imread>`. The argument you give between the <> will be put in place of the ``%s`` in the upper definition, and as the link will anchor to the correct function. To find out the anchor of a given function just open up a web page, search for the function and click on it. In the address bar it should appear like: ``http://opencv.itseez.com/modules/highgui/doc/reading_and_writing_images_and_video.html#imread`` . Look here for the name of the directives for each page of the OpenCV reference manual. If none present for one of them feel free to add one for it. \r\r For formulas you can add LATEX code that will translate in the web pages into images. You do this by using the *math* directive. A usage tip: \r\r .. code-block:: latex\r\r .. math::\r\r MSE = \frac{1}{c*i*j} \sum{(I_1-I_2)^2}\r\r That after build turns into: \r\r .. math::\r\r MSE = \frac{1}{c*i*j} \sum{(I_1-I_2)^2}\r\r You can even use it inline as ``:math:` MSE = \frac{1}{c*i*j} \sum{(I_1-I_2)^2}``` that turns into :math:`MSE = \frac{1}{c*i*j} \sum{(I_1-I_2)^2}`. \r\r If you use some crazy LATEX library extension you need to add those to the ones to use at build time. Look into the file:`opencv/doc/conf.py` configuration file for more information on this.\r\r + Results. Well, here depending on your program show one of more of the following: \r\r - Console outputs by using the code block directive. \r - Output images. \r - Runtime videos, visualization. For this use your favorite screens capture software. `Camtasia Studio <http://www.techsmith.com/camtasia/>`_ certainly is one of the better choices, however their prices are out of this world. `CamStudio <http://camstudio.org/>`_ is a free alternative, but less powerful. If you do a video you can upload it to YouTube and then use the raw directive with HTML option to embed it into the generated web page: \r\r .. code-block:: rst \r\r You may observe a runtime instance of this on the `YouTube here <https://www.youtube.com/watch?v=jpBwHxsl1_0>`_. \r\r .. raw:: html\r\r <div align="center">\r <iframe title="Creating a video with OpenCV" width="560" height="349" src="http://www.youtube.com/embed/jpBwHxsl1_0?rel=0&loop=1" frameborder="0" allowfullscreen align="middle"></iframe>\r </div>\r\r This results in the text and video: You may observe a runtime instance of this on the `YouTube here <https://www.youtube.com/watch?v=jpBwHxsl1_0>`_. \r\r .. raw:: html\r\r <div align="center">\r <iframe title="Creating a video with OpenCV" width="560" height="349" src="http://www.youtube.com/embed/jpBwHxsl1_0?rel=0&loop=1" frameborder="0" allowfullscreen align="middle"></iframe>\r </div>\r\r When these aren't self-explanatory make sure to throw in a few guiding lines about what and why we can see.\r\r + Build the documentation and check for errors or warnings. In the CMake make sure you check or pass the option for building documentation. Then simply build the **docs** project for the PDF file and the **docs_html** project for the web page. Read the output of the build and check for errors/warnings for what you have added. This is also the time to observe and correct any kind of *not so good looking* parts. Remember to keep clean our build logs. \r\r + Read again your tutorial and check for both programming and spelling errors. If found any, please correct them.\r\r\rTake home the pride and joy of a job well done!\r===============================================\r\rOnce you are done contact me or dr. Gary Bradski with the tutorial. We may submit the tutorial ourselves to the trunk branch of our repository or ask you to do so. \r\rNow, to see your work **live** you may need to wait some time. The PDFs are updated usually at the launch of a new OpenCV version. The web pages are a little more diverse. They are automatically rebuilt in each evening. However, the **opencv.itseez.com** website contains only the most recent **stable branch** of OpenCV. Currently this is 2.3. When we add something new (like a tutorial) that first goes to the **trunk branch** of our repository. A build of this you may find on the **opencv.itseez.com/trunk** website. Although, we try to make a build every night occasionally we might freeze any of the branches to fix upcoming issues. During this it may take a little longer to see your work *live*, however if you submited it, be sure that eventually it will show up. \r\rIf you have any questions or advices relating to this tutorial you can contact me at -delete-bernat@-delete-primeranks.net. Of course, delete the -delete- parts of that e-mail address.\r
\ No newline at end of file
+.. _howToWriteTutorial:\r\rHow to write a tutorial for OpenCV?\r***********************************\r\rOkay, so assume you have just finished a project of yours implementing something based on OpenCV and you want to present/share it with the community. Luckily, OpenCV is an *open source project*. This means that in theory anyone has access to the full source code and may extend it. While making a robust and practical library (like OpenCV) is great, the success of a library also depends on how user friendly it is. To improve on this aspect, the OpenCV team has already been listening to user feedback from its :opencv_group:`Yahoo user group <>` and by making samples you can find in the source directories sample folder. The addition of the tutorials (in both online and PDF format) is an extension of these efforts.\r\rGoal\r====\r\r.. _reST: http://docutils.sourceforge.net/rst.html\r.. |reST| replace:: reStructuredText\r.. |Sphinx| replace:: Sphinx\r.. _Sphinx: http://sphinx.pocoo.org/\r\rThe tutorials are just as an important part of the library as the implementation of those crafty data structures and algorithms you can find in OpenCV. Therefore, the source codes for the tutorials are part of the library. And yes, I meant source codes. The reason for this formulation is that the tutorials are written by using the |Sphinx|_ documentation generation system. This is based on the popular python documentation system called |reST|_ (reST). ReStructuredText is a really neat language that by using a few simple conventions (indentation, directives) and emulating old school e-mail writing techniques (text only) tries to offer a simple way to create and edit documents. Sphinx extends this with some new features and creates the resulting document in both HTML (for web) and PDF (for offline usage) format.\r\rUsually, an OpenCV tutorial has the following parts:\r\r1. A source code demonstration of an OpenCV feature:\r\r a. One or more CPP, Python, Java or other type of files depending for what OpenCV offers support and for what language you make the tutorial.\r #. Occasionaly, input resource files required for running your tutorials application.\r\r\r#. A table of content entry (so people may easily find the tutorial):\r\r a. Adding your stuff to the tutorials table of content (**reST** file).\r #. Add an image file near the TOC entry.\r\r\r#. The content of the tutorial itself:\r\r a. The **reST** text of the tutorial\r #. Images following the idea that "*A picture is worth a thousand words*".\r #. For more complex demonstrations you may create a video.\r\rAs you can see you will need at least some basic knowledge of the *reST* system in order to complete the task at hand with success. However, don't worry *reST* (and *Sphinx*) was made with simplicity in mind. It is easy to grasp its basics. I found that the `OpenAlea documentations introduction on this subject <http://openalea.gforge.inria.fr/doc/openalea/doc/_build/html/source/tutorial/rest_syntax.html>`_ (or the `Thomas Cokelaer one <http://thomas-cokelaer.info/tutorials/sphinx/rest_syntax.html>`_ ) should enough for this. If for some directive or feature you need a more in-depth description look it up in the official |reST|_ help files or at the |Sphinx|_ documentation.\r\rIn our world achieving some tasks is possible in multiple ways. However, some of the roads to take may have obvious or hidden advantages over others. Then again, in some other cases it may come down to just simple user preference. Here, I'll present how I decided to write the tutorials, based on my personal experience. If for some of them you know a better solution and you can back it up feel free to use that. I've nothing against it, as long as it gets the job done in an elegant fashion.\r\rNow the best would be if you could make the integration yourself. For this you need first to have the source code. I recommend following the guides for your operating system on acquiring OpenCV sources. For Linux users look :ref:`here <Linux-Installation>` and for :ref:`Windows here <Windows_Installation>`. You must also install python and sphinx with its dependencies in order to be able to build the documentation.\r\rOnce you have downloaded the repository to your hard drive you can take a look in the OpenCV directory to make sure you have both the samples and doc folder present. Anyone may download the trunk source files from :file:`git://code.opencv.org/opencv.git` . Nevertheless, not everyone has upload (commit/submit) rights. This is to protect the integrity of the library. If you plan doing more than one tutorial, and would like to have an account with commit user rights you should first register an account at http://code.opencv.org/ and then contact dr. Gary Bradski at -delete-bradski@-delete-willowgarage.com. Otherwise, you can just send the resulting files to us via the :opencv_group:`Yahoo user group <>` or to me at -delete-bernat@-delete-primeranks.net and I'll add it. If you have questions, suggestions or constructive critics I will gladly listen to them. If you send it to the OpenCV group please tag its subject with a **[Tutorial]** entry.\r\rFormat the Source Code\r======================\r\rBefore I start this let it be clear: the main goal is to have a working sample code. However, for your tutorial to be of a top notch quality you should follow a few guide lines I am going to present here.\r\rIn case you have an application by using the older interface (with *IplImage*, *CVMat*, *cvLoadImage* and such) consider migrating it to the new C++ interface. The tutorials are intended to be an up to date help for our users. And as of OpenCV 2 the OpenCV emphasis on using the less error prone and clearer C++ interface. Therefore, if possible please convert your code to the C++ interface. For this it may help to read the :ref:`InteroperabilityWithOpenCV1` tutorial. However, once you have an OpenCV 2 working code, then you should make your source code snippet as easy to read as possible. Here're a couple of advices for this:\r\r.. container:: enumeratevisibleitemswithsquare\r\r + Add a standard output with the description of what your program does. Keep it short and yet, descriptive. This output is at the start of the program. In my example files this usually takes the form of a *help* function containing the output. This way both the source file viewer and application runner can see what all is about in your sample. Here's an instance of this:\r\r .. code-block:: cpp\r\r void help()\r {\r cout\r << "--------------------------------------------------------------------------" << endl\r << "This program shows how to write video files. You can extract the R or G or B color channel "\r << " of the input video. You can choose to use the source codec (Y) or select a custom one. (N)"<< endl\r << "Usage:" << endl\r << "./video-write inputvideoName [ R | G | B] [Y | N]" << endl\r << "--------------------------------------------------------------------------" << endl\r << endl;\r }\r // ...\r int main(int argc, char *argv[], char *window_name)\r {\r help();\r // here comes the actual source code\r }\r\r Additionally, finalize the description with a short usage guide. This way the user will know how to call your programs, what leads us to the next point.\r\r + Prefer command line argument controlling instead of hard coded one. If your program has some variables that may be changed use command line arguments for this. The tutorials, can be a simple try-out ground for the user. If you offer command line controlling for the input image (for example), then you offer the possibility for the user to try it out with his/her own images, without the need to mess in the source code. In the upper example you can see that the input image, channel and codec selection may all be changed from the command line. Just compile the program and run it with your own input arguments.\r\r + Be as verbose as possible. There is no shame in filling the source code with comments. This way the more advanced user may figure out what's happening right from the sample code. This advice goes for the output console too. Specify to the user what's happening. Never leave the user hanging there and thinking on: "Is this program now crashing or just doing some computationally intensive task?." So, if you do a training task that may take some time, make sure you print out a message about this before starting and after finishing it.\r\r + Throw out unnecessary stuff from your source code. This is a warning to not take the previous point too seriously. Balance is the key. If it's something that can be done in a fewer lines or simpler than that's the way you should do it. Nevertheless, if for some reason you have such sections notify the user why you have chosen to do so. Keep the amount of information as low as possible, while still getting the job done in an elegant way.\r\r + Put your sample file into the :file:`opencv/samples/cpp/tutorial_code/sectionName` folder. If you write a tutorial for other languages than cpp, then change that part of the path. Before completing this you need to decide that to what section (module) does your tutorial goes. Think about on what module relies most heavily your code and that is the one to use. If the answer to this question is more than one modules then the *general* section is the one to use. For finding the *opencv* directory open up your file system and navigate where you downloaded our repository.\r\r + If the input resources are hard to acquire for the end user consider adding a few of them to the :file:`opencv/samples/cpp/tutorial_code/images`. Make sure that who reads your code can try it out!\r\rAdd the TOC entry\r=================\r\rFor this you will need to know some |reST|_. There is no going around this. |reST|_ files have **rst** extensions. However, these are simple text files. Use any text editor you like. Finding a text editor that offers syntax highlighting for |reST|_ was quite a challenge at the time of writing this tutorial. In my experience, `Intype <http://intype.info/>`_ is a solid option on Windows, although there is still place for improvement.\r\rAdding your source code to a table of content is important for multiple reasons. First and foremost this will allow for the user base to find your tutorial from our websites tutorial table of content. Secondly, if you omit this *Sphinx* will throw a warning that your tutorial file isn't part of any TOC tree entry. And there is nothing more than the developer team hates than an ever increasing warning/error list for their builds. *Sphinx* also uses this to build up the previous-back-up buttons on the website. Finally, omitting this step will lead to that your tutorial will **not** be added to the PDF version of the tutorials.\r\rNavigate to the :file:`opencv/doc/tutorials/section/table_of_content_section` folder (where the section is the module to which you're adding the tutorial). Open the *table_of_content_section* file. Now this may have two forms. If no prior tutorials are present in this section that there is a template message about this and has the following form:\r\r.. code-block:: rst\r\r .. _Table-Of-Content-Section:\r\r Section title\r -----------------------------------------------------------\r\r Description about the section.\r\r .. include:: ../../definitions/noContent.rst\r\r .. raw:: latex\r\r \pagebreak\r\rThe first line is a reference to the section title in the reST system. The section title will be a link and you may refer to it via the ``:ref:`` directive. The *include* directive imports the template text from the definitions directories *noContent.rst* file. *Sphinx* does not creates the PDF from scratch. It does this by first creating a latex file. Then creates the PDF from the latex file. With the *raw* directive you can directly add to this output commands. Its unique argument is for what kind of output to add the content of the directive. For the PDFs it may happen that multiple sections will overlap on a single page. To avoid this at the end of the TOC we add a *pagebreak* latex command, that hints to the LATEX system that the next line should be on a new page.\r\rIf you have one of this, try to transform it to the following form:\r\r.. include:: ../../definitions/tocDefinitions.rst\r\r.. code-block:: rst\r\r .. _Table-Of-Content-Section:\r\r Section title\r -----------------------------------------------------------\r\r .. include:: ../../definitions/tocDefinitions.rst\r\r +\r .. tabularcolumns:: m{100pt} m{300pt}\r .. cssclass:: toctableopencv\r\r =============== ======================================================\r |MatBasicIma| **Title:** :ref:`matTheBasicImageContainer`\r\r *Compatibility:* > OpenCV 2.0\r\r *Author:* |Author_BernatG|\r\r You will learn how to store images in the memory and how to print out their content to the console.\r\r =============== =====================================================\r\r .. |MatBasicIma| image:: images/matTheBasicImageStructure.jpg\r :height: 90pt\r :width: 90pt\r\r .. raw:: latex\r\r \pagebreak\r\r .. toctree::\r :hidden:\r\r ../mat - the basic image container/mat - the basic image container\r\rIf this is already present just add a new section of the content between the include and the raw directives (excluding those lines). Here you'll see a new include directive. This should be present only once in a TOC tree and the reST file contains the definitions of all the authors contributing to the OpenCV tutorials. We are a multicultural community and some of our name may contain some funky characters. However, reST **only supports** ANSI characters. Luckily we can specify Unicode characters with the *unicode* directive. Doing this for all of your tutorials is a troublesome procedure. Therefore, the tocDefinitions file contains the definition of your author name. Add it here once and afterwards just use the replace construction. For example here's the definition for my name:\r\r.. code-block:: rst\r\r .. |Author_BernatG| unicode:: Bern U+00E1 t U+0020 G U+00E1 bor\r\rThe ``|Author_BernatG|`` is the text definitions alias. I can use later this to add the definition, like I've done in the TOCs *Author* part. After the ``::`` and a space you start the definition. If you want to add an UNICODE character (non-ASCI) leave an empty space and specify it in the format U+(UNICODE code). To find the UNICODE code of a character I recommend using the `FileFormat <http://www.fileformat.info>`_ websites service. Spaces are trimmed from the definition, therefore we add a space by its UNICODE character (U+0020).\r\rUntil the *raw* directive what you can see is a TOC tree entry. Here's how a TOC entry will look like:\r\r+\r .. tabularcolumns:: m{100pt} m{300pt}\r .. cssclass:: toctableopencv\r\r =============== ======================================================\r |MatBasicIma| **Title:** :ref:`matTheBasicImageContainer`\r\r *Compatibility:* > OpenCV 2.0\r\r *Author:* |Author_BernatG|\r\r You will learn how to store images in the memory and how to print out their content to the console.\r\r =============== ======================================================\r\r .. |MatBasicIma| image:: images/matTheBasicImageStructure.jpg\r :height: 90pt\r :width: 90pt\r\rAs you can see we have an image to the left and a description box to the right. To create two boxes we use a table with two columns and a single row. In the left column is the image and in the right one the description. However, the image directive is way too long to fit in a column. Therefore, we need to use the substitution definition system. We add this definition after the TOC tree. All images for the TOC tree are to be put in the images folder near its |reST|_ file. We use the point measurement system because we are also creating PDFs. PDFs are printable documents, where there is no such thing that pixels (px), just points (pt). And while generally space is no problem for web pages (we have monitors with **huge** resolutions) the size of the paper (A4 or letter) is constant and will be for a long time in the future. Therefore, size constrains come in play more like for the PDF, than the generated HTML code.\r\rNow your images should be as small as possible, while still offering the intended information for the user. Remember that the tutorial will become part of the OpenCV source code. If you add large images (that manifest in form of large image size) it will just increase the size of the repository pointlessly. If someone wants to download it later, its download time will be that much longer. Not to mention the larger PDF size for the tutorials and the longer load time for the web pages. In terms of pixels a TOC image should not be larger than 120 X 120 pixels. Resize your images if they are larger!\r\r.. note::\r\r If you add a larger image and specify a smaller image size, *Sphinx* will not resize that. At build time will add the full size image and the resize will be done by your browser after the image is loaded. A 120 X 120 image is somewhere below 10KB. If you add a 110KB image, you have just pointlessly added a 100KB extra data to transfer over the internet for every user!\r\rGenerally speaking you shouldn't need to specify your images size (excluding the TOC entries). If no such is found *Sphinx* will use the size of the image itself (so no resize occurs). Then again if for some reason you decide to specify a size that should be the **width** of the image rather than its height. The reason for this again goes back to the PDFs. On a PDF page the height is larger than the width. In the PDF the images will not be resized. If you specify a size that does not fit in the page, then what does not fits in **will be cut off**. When creating your images for your tutorial you should try to keep the image widths below 500 pixels, and calculate with around 400 point page width when specifying image widths.\r\rThe image format depends on the content of the image. If you have some complex scene (many random like colors) then use *jpg*. Otherwise, prefer using *png*. They are even some tools out there that optimize the size of *PNG* images, such as `PNGGauntlet <http://pnggauntlet.com/>`_. Use them to make your images as small as possible in size.\r\rNow on the right side column of the table we add the information about the tutorial:\r\r.. container:: enumeratevisibleitemswithsquare\r\r + In the first line it is the title of the tutorial. However, there is no need to specify it explicitly. We use the reference system. We'll start up our tutorial with a reference specification, just like in case of this TOC entry with its `` .. _Table-Of-Content-Section:`` . If after this you have a title (pointed out by the following line of -), then Sphinx will replace the ``:ref:`Table-Of-Content-Section``` directive with the tile of the section in reference form (creates a link in web page). Here's how the definition looks in my case:\r\r .. code-block:: rst\r\r .. _matTheBasicImageContainer:\r\r Mat - The Basic Image Container\r *******************************\r\r Note, that according to the |reST|_ rules the * should be as long as your title.\r\r + Compatibility. What version of OpenCV is required to run your sample code.\r\r + Author. Use the substitution markup of |reST|_.\r\r + A short sentence describing the essence of your tutorial.\r\rNow before each TOC entry you need to add the three lines of:\r\r.. code-block:: cpp\r\r +\r .. tabularcolumns:: m{100pt} m{300pt}\r .. cssclass:: toctableopencv\r\rThe plus sign (+) is to enumerate tutorials by using bullet points. So for every TOC entry we have a corresponding bullet point represented by the +. Sphinx is highly indenting sensitive. Indentation is used to express from which point until to which point does a construction last. Un-indentation means end of that construction. So to keep all the bullet points to the same group the following TOC entries (until the next +) should be indented by two spaces.\r\rHere, I should also mention that **always** prefer using spaces instead of tabs. Working with only spaces makes possible that if we both use monotype fonts we will see the same thing. Tab size is text editor dependent and as should be avoided. *Sphinx* translates all tabs into 8 spaces before interpreting it.\r\rIt turns out that the automatic formatting of both the HTML and PDF(LATEX) system messes up our tables. Therefore, we need to help them out a little. For the PDF generation we add the ``.. tabularcolumns:: m{100pt} m{300pt}`` directive. This means that the first column should be 100 points wide and middle aligned. For the HTML look we simply name the following table of a *toctableopencv* class type. Then, we can modify the look of the table by modifying the CSS of our web page. The CSS definitions go into the :file:`opencv/doc/_themes/blue/static/default.css_t` file.\r\r.. code-block:: css\r\r .toctableopencv\r {\r width: 100% ;\r table-layout: fixed;\r }\r\r\r .toctableopencv colgroup col:first-child\r {\r width: 100pt !important;\r max-width: 100pt !important;\r min-width: 100pt !important;\r }\r\r .toctableopencv colgroup col:nth-child(2)\r {\r width: 100% !important;\r }\r\rHowever, you should not need to modify this. Just add these three lines (plus keep the two space indentation) for all TOC entries you add. At the end of the TOC file you'll find:\r\r.. code-block:: rst\r\r .. raw:: latex\r\r \pagebreak\r\r .. toctree::\r :hidden:\r\r ../mat - the basic image container/mat - the basic image container\r\rThe page break entry comes for separating sections and should be only one in a TOC tree |reST|_ file. Finally, at the end of the TOC tree we need to add our tutorial to the *Sphinx* TOC tree system. *Sphinx* will generate from this the previous-next-up information for the HTML file and add items to the PDF according to the order here. By default this TOC tree directive generates a simple table of contents. However, we already created a fancy looking one so we no longer need this basic one. Therefore, we add the *hidden* option to do not show it.\r\rThe path is of a relative type. We step back in the file system and then go into the :file:`mat - the basic image container` directory for the :file:`mat - the basic image container.rst` file. Putting out the *rst* extension for the file is optional.\r\rWrite the tutorial\r==================\r\rCreate a folder with the name of your tutorial. Preferably, use small letters only. Then create a text file in this folder with *rst* extension and the same name. If you have images for the tutorial create an :file:`images` folder and add your images there. When creating your images follow the guidelines described in the previous part!\r\rNow here's our recommendation for the structure of the tutorial (although, remember that this is not carved in the stone; if you have a better idea, use it!):\r\r\r.. container:: enumeratevisibleitemswithsquare\r\r + Create the reference point and the title.\r\r .. code-block:: rst\r\r .. _matTheBasicImageContainer:\r\r Mat - The Basic Image Container\r *******************************\r\r You start the tutorial by specifying a reference point by the ``.. _matTheBasicImageContainer:`` and then its title. The name of the reference point should be a unique one over the whole documentation. Therefore, do not use general names like *tutorial1*. Use the * character to underline the title for its full width. The subtitles of the tutorial should be underlined with = charachter.\r\r + Goals. You start your tutorial by specifying what you will present. You can also enumerate the sub jobs to be done. For this you can use a bullet point construction. There is a single configuration file for both the reference manual and the tutorial documentation. In the reference manuals at the argument enumeration we do not want any kind of bullet point style enumeration. Therefore, by default all the bullet points at this level are set to do not show the dot before the entries in the HTML. You can override this by putting the bullet point in a container. I've defined a square type bullet point view under the name *enumeratevisibleitemswithsquare*. The CSS style definition for this is again in the :file:`opencv\doc\_themes\blue\static\default.css_t` file. Here's a quick example of using it:\r\r .. code-block:: rst\r\r .. container:: enumeratevisibleitemswithsquare\r\r + Create the reference point and the title.\r + Second entry\r + Third entry\r\r Note that you need the keep the indentation of the container directive. Directive indentations are always three (3) spaces. Here you may even give usage tips for your sample code.\r\r + Source code. Present your samples code to the user. It's a good idea to offer a quick download link for the HTML page by using the *download* directive and pointing out where the user may find your source code in the file system by using the *file* directive:\r\r .. code-block:: rst\r\r Text :file:`samples/cpp/tutorial_code/highgui/video-write/` folder of the OpenCV source library\r or :download:`text to appear in the webpage\r <../../../../samples/cpp/tutorial_code/HighGUI/video-write/video-write.cpp>`.\r\r For the download link the path is a relative one, hence the multiple back stepping operations (..). Then you can add the source code either by using the *code block* directive or the *literal include* one. In case of the code block you will need to actually add all the source code text into your |reST|_ text and also apply the required indentation:\r\r .. code-block:: rst\r\r .. code-block:: cpp\r\r int i = 0;\r l = ++j;\r\r The only argument of the directive is the language used (here CPP). Then you add the source code into its content (meaning one empty line after the directive) by keeping the indentation of the directive (3 spaces). With the *literal include* directive you do not need to add the source code of the sample. You just specify the sample and *Sphinx* will load it for you, during build time. Here's an example usage:\r\r .. code-block:: rst\r\r .. literalinclude:: ../../../../samples/cpp/tutorial_code/HighGUI/video-write/video-write.cpp\r :language: cpp\r :linenos:\r :tab-width: 4\r :lines: 1-8, 21-22, 24-\r\r After the directive you specify a relative path to the file from what to import. It has four options: the language to use, if you add the ``:linenos:`` the line numbers will be shown, you can specify the tab size with the ``:tab-width:`` and you do not need to load the whole file, you can show just the important lines. Use the *lines* option to do not show redundant information (such as the *help* function). Here basically you specify ranges, if the second range line number is missing than that means that until the end of the file. The ranges specified here do no need to be in an ascending order, you may even reorganize the structure of how you want to show your sample inside the tutorial.\r\r + The tutorial. Well here goes the explanation for why and what have you used. Try to be short, clear, concise and yet a thorough one. There's no magic formula. Look into a few already made tutorials and start out from there. Try to mix sample OpenCV code with your explanations. If with words is hard to describe something do not hesitate to add in a reasonable size image, to overcome this issue.\r\r When you present OpenCV functionality it's a good idea to give a link to the used OpenCV data structure or function. Because the OpenCV tutorials and reference manual are in separate PDF files it is not possible to make this link work for the PDF format. Therefore, we use here only web page links to the **opencv.itseez.com** website. The OpenCV functions and data structures may be used for multiple tasks. Nevertheless, we want to avoid that every users creates its own reference to a commonly used function. So for this we use the global link collection of *Sphinx*. This is defined in the file:`opencv/doc/conf.py` configuration file. Open it and go all the way down to the last entry:\r\r .. code-block:: py\r\r # ---- External links for tutorials -----------------\r extlinks = {\r 'huivideo' : ('http://opencv.itseez.com/modules/highgui/doc/reading_and_writing_images_and_video.html#%s', None)\r }\r\r In short here we defined a new **huivideo** directive that refers to an external webpage link. Its usage is:\r\r .. code-block:: rst\r\r A sample function of the highgui modules image write and read page is the :huivideo:`imread() function <imread>`.\r\r Which turns to: A sample function of the highgui modules image write and read page is the :huivideo:`imread() function <imread>`. The argument you give between the <> will be put in place of the ``%s`` in the upper definition, and as the link will anchor to the correct function. To find out the anchor of a given function just open up a web page, search for the function and click on it. In the address bar it should appear like: ``http://opencv.itseez.com/modules/highgui/doc/reading_and_writing_images_and_video.html#imread`` . Look here for the name of the directives for each page of the OpenCV reference manual. If none present for one of them feel free to add one for it.\r\r For formulas you can add LATEX code that will translate in the web pages into images. You do this by using the *math* directive. A usage tip:\r\r .. code-block:: latex\r\r .. math::\r\r MSE = \frac{1}{c*i*j} \sum{(I_1-I_2)^2}\r\r That after build turns into:\r\r .. math::\r\r MSE = \frac{1}{c*i*j} \sum{(I_1-I_2)^2}\r\r You can even use it inline as ``:math:` MSE = \frac{1}{c*i*j} \sum{(I_1-I_2)^2}``` that turns into :math:`MSE = \frac{1}{c*i*j} \sum{(I_1-I_2)^2}`.\r\r If you use some crazy LATEX library extension you need to add those to the ones to use at build time. Look into the file:`opencv/doc/conf.py` configuration file for more information on this.\r\r + Results. Well, here depending on your program show one of more of the following:\r\r - Console outputs by using the code block directive.\r - Output images.\r - Runtime videos, visualization. For this use your favorite screens capture software. `Camtasia Studio <http://www.techsmith.com/camtasia/>`_ certainly is one of the better choices, however their prices are out of this world. `CamStudio <http://camstudio.org/>`_ is a free alternative, but less powerful. If you do a video you can upload it to YouTube and then use the raw directive with HTML option to embed it into the generated web page:\r\r .. code-block:: rst\r\r You may observe a runtime instance of this on the `YouTube here <https://www.youtube.com/watch?v=jpBwHxsl1_0>`_.\r\r .. raw:: html\r\r <div align="center">\r <iframe title="Creating a video with OpenCV" width="560" height="349" src="http://www.youtube.com/embed/jpBwHxsl1_0?rel=0&loop=1" frameborder="0" allowfullscreen align="middle"></iframe>\r </div>\r\r This results in the text and video: You may observe a runtime instance of this on the `YouTube here <https://www.youtube.com/watch?v=jpBwHxsl1_0>`_.\r\r .. raw:: html\r\r <div align="center">\r <iframe title="Creating a video with OpenCV" width="560" height="349" src="http://www.youtube.com/embed/jpBwHxsl1_0?rel=0&loop=1" frameborder="0" allowfullscreen align="middle"></iframe>\r </div>\r\r When these aren't self-explanatory make sure to throw in a few guiding lines about what and why we can see.\r\r + Build the documentation and check for errors or warnings. In the CMake make sure you check or pass the option for building documentation. Then simply build the **docs** project for the PDF file and the **docs_html** project for the web page. Read the output of the build and check for errors/warnings for what you have added. This is also the time to observe and correct any kind of *not so good looking* parts. Remember to keep clean our build logs.\r\r + Read again your tutorial and check for both programming and spelling errors. If found any, please correct them.\r\r\rTake home the pride and joy of a job well done!\r===============================================\r\rOnce you are done contact me or dr. Gary Bradski with the tutorial. We may submit the tutorial ourselves to the trunk branch of our repository or ask you to do so.\r\rNow, to see your work **live** you may need to wait some time. The PDFs are updated usually at the launch of a new OpenCV version. The web pages are a little more diverse. They are automatically rebuilt in each evening. However, the **opencv.itseez.com** website contains only the most recent **stable branch** of OpenCV. Currently this is 2.3. When we add something new (like a tutorial) that first goes to the **trunk branch** of our repository. A build of this you may find on the **opencv.itseez.com/trunk** website. Although, we try to make a build every night occasionally we might freeze any of the branches to fix upcoming issues. During this it may take a little longer to see your work *live*, however if you submited it, be sure that eventually it will show up.\r\rIf you have any questions or advices relating to this tutorial you can contact me at -delete-bernat@-delete-primeranks.net. Of course, delete the -delete- parts of that e-mail address.\r
\ No newline at end of file
* :cascade_classifier_load:`load <>` to load a .xml classifier file. It can be either a Haar or a LBP classifer
* :cascade_classifier_detect_multiscale:`detectMultiScale <>` to perform the detection.
-
+
Theory
======
Code
====
-This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/objectDetection/objectDetection.cpp>`_ . The second version (using LBP for face detection) can be `found here <http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/objectDetection/objectDetection2.cpp>`_
+This tutorial code's is shown lines below. You can also download it from `here <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/objectDetection/objectDetection.cpp>`_ . The second version (using LBP for face detection) can be `found here <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/objectDetection/objectDetection2.cpp>`_
-.. code-block:: cpp
+.. code-block:: cpp
#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/highgui/highgui.hpp"
//-- 1. Load the cascades
if( !face_cascade.load( face_cascade_name ) ){ printf("--(!)Error loading\n"); return -1; };
if( !eyes_cascade.load( eyes_cascade_name ) ){ printf("--(!)Error loading\n"); return -1; };
-
+
//-- 2. Read the video stream
capture = cvCaptureFromCAM( -1 );
if( capture )
while( true )
{
frame = cvQueryFrame( capture );
-
+
//-- 3. Apply the classifier to the frame
if( !frame.empty() )
{ detectAndDisplay( frame ); }
else
{ printf(" --(!) No captured frame -- Break!"); break; }
-
+
int c = waitKey(10);
- if( (char)c == 'c' ) { break; }
+ if( (char)c == 'c' ) { break; }
}
}
return 0;
for( int j = 0; j < eyes.size(); j++ )
{
- Point center( faces[i].x + eyes[j].x + eyes[j].width*0.5, faces[i].y + eyes[j].y + eyes[j].height*0.5 );
+ Point center( faces[i].x + eyes[j].x + eyes[j].width*0.5, faces[i].y + eyes[j].y + eyes[j].height*0.5 );
int radius = cvRound( (eyes[j].width + eyes[j].height)*0.25 );
circle( frame, center, radius, Scalar( 255, 0, 0 ), 4, 8, 0 );
}
- }
+ }
//-- Show what you got
imshow( window_name, frame );
}
:align: center
:height: 300pt
- Remember to copy the files *haarcascade_frontalface_alt.xml* and *haarcascade_eye_tree_eyeglasses.xml* in your current directory. They are located in *opencv/data/haarcascades*
+ Remember to copy the files *haarcascade_frontalface_alt.xml* and *haarcascade_eye_tree_eyeglasses.xml* in your current directory. They are located in *opencv/data/haarcascades*
-#. This is the result of using the file *lbpcascade_frontalface.xml* (LBP trained) for the face detection. For the eyes we keep using the file used in the tutorial.
+#. This is the result of using the file *lbpcascade_frontalface.xml* (LBP trained) for the face detection. For the eyes we keep using the file used in the tutorial.
.. image:: images/Cascade_Classifier_Tutorial_Result_LBP.jpg
:align: center
- :height: 300pt
+ :height: 300pt
OpenCV Tutorials
################
-The following links describe a set of basic OpenCV tutorials. All the source code mentioned here is provide as part of the OpenCV regular releases, so check before you start copy & pasting the code. The list of tutorials below is automatically generated from reST files located in our SVN repository.
+The following links describe a set of basic OpenCV tutorials. All the source code mentioned here is provide as part of the OpenCV regular releases, so check before you start copy & pasting the code. The list of tutorials below is automatically generated from reST files located in our GIT repository.
As always, we would be happy to hear your comments and receive your contributions on any tutorial.
.. tabularcolumns:: m{100pt} m{300pt}
.. cssclass:: toctableopencv
-
+
=========== =======================================================
|Introduct| You will learn how to setup OpenCV on your computer!
-
+
=========== =======================================================
-
+
.. |Introduct| image:: images/introduction.jpg
:height: 80pt
:width: 80pt
.. tabularcolumns:: m{100pt} m{300pt}
.. cssclass:: toctableopencv
-
+
=========== =======================================================
|Core| Here you will learn the about the basic building blocks of the library. A must read and know for understanding how to manipulate the images on a pixel level.
-
+
=========== =======================================================
-
+
.. |Core| image:: images/core.jpg
:height: 80pt
:width: 80pt
.. tabularcolumns:: m{100pt} m{300pt}
.. cssclass:: toctableopencv
-
+
=========== =======================================================
|ImgProc| In this section you will learn about the image processing (manipulation) functions inside OpenCV.
-
+
=========== =======================================================
-
+
.. |ImgProc| image:: images/imgproc.jpg
:height: 80pt
:width: 80pt
.. tabularcolumns:: m{100pt} m{300pt}
.. cssclass:: toctableopencv
-
+
=========== =======================================================
- |HighGui| This section contains valuable tutorials about how to read/save your image/video files and how to use the built-in graphical user interface of the library.
-
+ |HighGui| This section contains valuable tutorials about how to read/save your image/video files and how to use the built-in graphical user interface of the library.
+
=========== =======================================================
-
+
.. |HighGui| image:: images/highgui.jpg
:height: 80pt
:width: 80pt
.. tabularcolumns:: m{100pt} m{300pt}
.. cssclass:: toctableopencv
-
+
=========== =======================================================
- |Calib3D| Although we got most of our images in a 2D format they do come from a 3D world. Here you will learn how to find out from the 2D images information about the 3D world.
-
+ |Calib3D| Although we got most of our images in a 2D format they do come from a 3D world. Here you will learn how to find out from the 2D images information about the 3D world.
+
=========== =======================================================
-
+
.. |Calib3D| image:: images/calib3d.jpg
:height: 80pt
:width: 80pt
.. tabularcolumns:: m{100pt} m{300pt}
.. cssclass:: toctableopencv
-
+
=========== =======================================================
|Featur2D| Learn about how to use the feature points detectors, descriptors and matching framework found inside OpenCV.
-
+
=========== =======================================================
-
+
.. |Featur2D| image:: images/feature2D.jpg
:height: 80pt
:width: 80pt
:alt: feature2D Icon
* :ref:`Table-Of-Content-Video`
-
+
.. tabularcolumns:: m{100pt} m{300pt}
.. cssclass:: toctableopencv
-
+
=========== =======================================================
- |Video| Look here in order to find use on your video stream algoritms like: motion extraction, feature tracking and foreground extractions.
-
+ |Video| Look here in order to find use on your video stream algoritms like: motion extraction, feature tracking and foreground extractions.
+
=========== =======================================================
-
+
.. |Video| image:: images/video.jpg
:height: 80pt
:width: 80pt
.. tabularcolumns:: m{100pt} m{300pt}
.. cssclass:: toctableopencv
-
+
=========== =======================================================
|ObjDetect| Ever wondered how your digital camera detects peoples and faces? Look here to find out!
-
+
=========== =======================================================
-
+
.. |ObjDetect| image:: images/objdetect.jpg
:height: 80pt
:width: 80pt
:alt: objdetect Icon
* :ref:`Table-Of-Content-Ml`
-
+
.. tabularcolumns:: m{100pt} m{300pt}
.. cssclass:: toctableopencv
-
+
=========== =======================================================
|ml| Use the powerfull machine learning classes for statistical classification, regression and clustering of data.
-
+
=========== =======================================================
-
+
.. |ml| image:: images/ml.jpg
:height: 80pt
:width: 80pt
.. tabularcolumns:: m{100pt} m{300pt}
.. cssclass:: toctableopencv
-
+
=========== =======================================================
- |GPU| Squeeze out every little computation power from your system by using the power of your video card to run the OpenCV algorithms.
-
+ |GPU| Squeeze out every little computation power from your system by using the power of your video card to run the OpenCV algorithms.
+
=========== =======================================================
-
+
.. |GPU| image:: images/gpu.jpg
:height: 80pt
:width: 80pt
.. tabularcolumns:: m{100pt} m{300pt}
.. cssclass:: toctableopencv
-
+
=========== =======================================================
|General| These tutorials are the bottom of the iceberg as they link together multiple of the modules presented above in order to solve complex problems.
-
+
=========== =======================================================
-
+
.. |General| image:: images/general.jpg
:height: 80pt
:width: 80pt
Install OpenNI library (from here http://www.openni.org/downloadfiles) and PrimeSensor Module for OpenNI (from here https://github.com/avin2/SensorKinect). The installation should be done to default folders listed in the instructions of these products, e.g.:
.. code-block:: text
-
+
OpenNI:
Linux & MacOSX:
Libs into: /usr/lib
Bins into: c:/Program Files/Prime Sense/Sensor/Bin
If one or both products were installed to the other folders, the user should change corresponding CMake variables ``OPENNI_LIB_DIR``, ``OPENNI_INCLUDE_DIR`` or/and ``OPENNI_PRIME_SENSOR_MODULE_BIN_DIR``.
-
+
#.
Configure OpenCV with OpenNI support by setting ``WITH_OPENNI`` flag in CMake. If OpenNI is found in install folders OpenCV will be built with OpenNI library (see a status ``OpenNI`` in CMake log) whereas PrimeSensor Modules can not be found (see a status ``OpenNI PrimeSensor Modules`` in CMake log). Without PrimeSensor module OpenCV will be successfully compiled with OpenNI library, but ``VideoCapture`` object will not grab data from Kinect sensor.
VideoCapture capture( CV_CAP_OPENNI );
for(;;)
{
- Mat depthMap;
+ Mat depthMap;
capture >> depthMap;
-
+
if( waitKey( 30 ) >= 0 )
break;
}
{
Mat depthMap;
Mat rgbImage
-
+
capture.grab();
-
+
capture.retrieve( depthMap, OPENNI_DEPTH_MAP );
capture.retrieve( bgrImage, OPENNI_BGR_IMAGE );
-
+
if( waitKey( 30 ) >= 0 )
break;
}
For setting and getting some property of sensor` data generators use ``VideoCapture::set`` and ``VideoCapture::get`` methods respectively, e.g. ::
- VideoCapture capture( CV_CAP_OPENNI );
+ VideoCapture capture( CV_CAP_OPENNI );
capture.set( CV_CAP_OPENNI_IMAGE_GENERATOR_OUTPUT_MODE, CV_CAP_OPENNI_VGA_30HZ );
cout << "FPS " << capture.get( CV_CAP_OPENNI_IMAGE_GENERATOR+CV_CAP_PROP_FPS ) << endl;
Flags specifing the needed generator type must be used in combination with particular generator property. The following properties of cameras available through OpenNI interfaces are supported:
-*
+*
For image generator:
-
+
- ``CV_CAP_PROP_OPENNI_OUTPUT_MODE`` -- Three output modes are supported: ``CV_CAP_OPENNI_VGA_30HZ`` used by default (image generator returns images in VGA resolution with 30 FPS), ``CV_CAP_OPENNI_SXGA_15HZ`` (image generator returns images in SXGA resolution with 15 FPS) and ``CV_CAP_OPENNI_SXGA_30HZ`` (image generator returns images in SXGA resolution with 30 FPS, the mode is supported by XtionPRO Live); depth generator's maps are always in VGA resolution.
-
-*
+
+*
For depth generator:
- ``CV_CAP_PROP_OPENNI_REGISTRATION`` -- Flag that registers the remapping depth map to image map by changing depth generator's view point (if the flag is ``"on"``) or sets this view point to its normal one (if the flag is ``"off"``). The registration process’s resulting images are pixel-aligned,which means that every pixel in the image is aligned to a pixel in the depth image.
-
+
Next properties are available for getting only:
-
+
- ``CV_CAP_PROP_OPENNI_FRAME_MAX_DEPTH`` -- A maximum supported depth of Kinect in mm.
- - ``CV_CAP_PROP_OPENNI_BASELINE`` -- Baseline value in mm.
- - ``CV_CAP_PROP_OPENNI_FOCAL_LENGTH`` -- A focal length in pixels.
+ - ``CV_CAP_PROP_OPENNI_BASELINE`` -- Baseline value in mm.
+ - ``CV_CAP_PROP_OPENNI_FOCAL_LENGTH`` -- A focal length in pixels.
- ``CV_CAP_PROP_FRAME_WIDTH`` -- Frame width in pixels.
- ``CV_CAP_PROP_FRAME_HEIGHT`` -- Frame height in pixels.
- ``CV_CAP_PROP_FPS`` -- Frame rate in FPS.
*
Some typical flags combinations "generator type + property" are defined as single flags:
-
+
- ``CV_CAP_OPENNI_IMAGE_GENERATOR_OUTPUT_MODE = CV_CAP_OPENNI_IMAGE_GENERATOR + CV_CAP_PROP_OPENNI_OUTPUT_MODE``
- ``CV_CAP_OPENNI_DEPTH_GENERATOR_BASELINE = CV_CAP_OPENNI_DEPTH_GENERATOR + CV_CAP_PROP_OPENNI_BASELINE``
- ``CV_CAP_OPENNI_DEPTH_GENERATOR_FOCAL_LENGTH = CV_CAP_OPENNI_DEPTH_GENERATOR + CV_CAP_PROP_OPENNI_FOCAL_LENGTH``
- ``CV_CAP_OPENNI_DEPTH_GENERATOR_REGISTRATION = CV_CAP_OPENNI_DEPTH_GENERATOR + CV_CAP_PROP_OPENNI_REGISTRATION``
-
+
For more information please refer to the example of usage openni_capture.cpp_ in ``opencv/samples/cpp`` folder.
-.. _openni_capture.cpp: http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/openni_capture.cpp
+.. _openni_capture.cpp: http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/openni_capture.cpp
Usage:
./build_framework.py <outputdir>
-
-By cmake conventions (and especially if you work with OpenCV SVN repository),
+
+By cmake conventions (and especially if you work with OpenCV repository),
the output dir should not be a subdirectory of OpenCV source tree.
-
+
Script will create <outputdir>, if it's missing, and a few its subdirectories:
-
+
<outputdir>
build/
iPhoneOS/
def build_opencv(srcroot, buildroot, target):
"builds OpenCV for device or simulator"
-
+
builddir = os.path.join(buildroot, target)
if not os.path.isdir(builddir):
os.makedirs(builddir)
os.system("cmake %s ." % (cmakeargs,))
else:
os.system("cmake %s %s" % (cmakeargs, srcroot))
-
+
for wlib in [builddir + "/modules/world/UninstalledProducts/libopencv_world.a",
builddir + "/lib/Release/libopencv_world.a"]:
if os.path.isfile(wlib):
os.remove(wlib)
-
+
os.system("xcodebuild -parallelizeTargets -jobs 8 -sdk %s -configuration Release -target ALL_BUILD" % target.lower())
os.system("xcodebuild -sdk %s -configuration Release -target install install" % target.lower())
os.chdir(currdir)
-
+
def put_framework_together(srcroot, dstroot):
"constructs the framework directory after all the targets are built"
-
+
# find the list of targets (basically, ["iPhoneOS", "iPhoneSimulator"])
targetlist = glob.glob(os.path.join(dstroot, "build", "*"))
targetlist = [os.path.basename(t) for t in targetlist]
-
+
# set the current dir to the dst root
currdir = os.getcwd()
framework_dir = dstroot + "/opencv2.framework"
shutil.rmtree(framework_dir)
os.makedirs(framework_dir)
os.chdir(framework_dir)
-
+
# determine OpenCV version (without subminor part)
tdir0 = "../build/" + targetlist[0]
cfg = open(tdir0 + "/cvconfig.h", "rt")
opencv_version = l[l.find("\"")+1:l.rfind(".")]
break
cfg.close()
-
+
# form the directory tree
dstdir = "Versions/A"
os.makedirs(dstdir + "/Resources")
# copy headers
shutil.copytree(tdir0 + "/install/include/opencv2", dstdir + "/Headers")
-
+
# make universal static lib
wlist = " ".join(["../build/" + t + "/lib/Release/libopencv_world.a" for t in targetlist])
os.system("lipo -create " + wlist + " -o " + dstdir + "/opencv2")
-
+
# form Info.plist
srcfile = open(srcroot + "/ios/Info.plist.in", "rt")
dstfile = open(dstdir + "/Resources/Info.plist", "wt")
dstfile.write(l.replace("${VERSION}", opencv_version))
srcfile.close()
dstfile.close()
-
+
# copy cascades
# TODO ...
-
+
# make symbolic links
os.symlink(dstdir + "/Headers", "Headers")
os.symlink(dstdir + "/Resources", "Resources")
os.symlink(dstdir + "/opencv2", "opencv2")
os.symlink("A", "Versions/Current")
-
-
+
+
def build_framework(srcroot, dstroot):
"main function to do all the work"
-
+
for target in ["iPhoneOS", "iPhoneSimulator"]:
build_opencv(srcroot, os.path.join(dstroot, "build"), target)
-
+
put_framework_together(srcroot, dstroot)
-
+
if __name__ == "__main__":
if len(sys.argv) != 2:
print "Usage:\n\t./build_framework.py <outputdir>\n\n"
sys.exit(0)
-
+
build_framework(os.path.abspath(os.path.join(os.path.dirname(sys.argv[0]), "..")), os.path.abspath(sys.argv[1]))
* Make a class and specify ``Algorithm`` as its base class.
* The algorithm parameters should be the class members. See ``Algorithm::get()`` for the list of possible types of the parameters.
* Add public virtual method ``AlgorithmInfo* info() const;`` to your class.
- * Add constructor function, ``AlgorithmInfo`` instance and implement the ``info()`` method. The simplest way is to take http://code.opencv.org/svn/opencv/trunk/opencv/modules/ml/src/ml_init.cpp as the reference and modify it according to the list of your parameters.
+ * Add constructor function, ``AlgorithmInfo`` instance and implement the ``info()`` method. The simplest way is to take http://code.opencv.org/projects/opencv/repository/revisions/master/entry/modules/ml/src/ml_init.cpp as the reference and modify it according to the list of your parameters.
* Add some public function (e.g. ``initModule_<mymodule>()``) that calls info() of your algorithm and put it into the same source file as ``info()`` implementation. This is to force C++ linker to include this object file into the target application. See ``Algorithm::create()`` for details.
Utilizing Multiple GPUs
-----------------------
-In the current version, each of the OpenCV GPU algorithms can use only a single GPU. So, to utilize multiple GPUs, you have to manually distribute the work between GPUs.
+In the current version, each of the OpenCV GPU algorithms can use only a single GPU. So, to utilize multiple GPUs, you have to manually distribute the work between GPUs.
Switching active devie can be done using :ocv:func:`gpu::setDevice()` function. For more details please read Cuda C Programing Guide.
While developing algorithms for multiple GPUs, note a data passing overhead. For primitive functions and small images, it can be significant, which may eliminate all the advantages of having multiple GPUs. But for high-level algorithms, consider using multi-GPU acceleration. For example, the Stereo Block Matching algorithm has been successfully parallelized using the following algorithm:
With this algorithm, a dual GPU gave a 180
%
performance increase comparing to the single Fermi GPU. For a source code example, see
-http://code.opencv.org/svn/opencv/trunk/opencv/samples/gpu/.
+http://code.opencv.org/projects/opencv/repository/revisions/master/entry/samples/gpu/.
The primary use of the function is in multi-camera environments, especially when the cameras do not have hardware synchronization. That is, you call ``VideoCapture::grab()`` for each camera and after that call the slower method ``VideoCapture::retrieve()`` to decode and get frame from each camera. This way the overhead on demosaicing or motion jpeg decompression etc. is eliminated and the retrieved frames from different cameras will be closer in time.
-Also, when a connected camera is multi-head (for example, a stereo camera or a Kinect device), the correct way of retrieving data from it is to call `VideoCapture::grab` first and then call :ocv:func:`VideoCapture::retrieve` one or more times with different values of the ``channel`` parameter. See http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/kinect_maps.cpp
+Also, when a connected camera is multi-head (for example, a stereo camera or a Kinect device), the correct way of retrieving data from it is to call `VideoCapture::grab` first and then call :ocv:func:`VideoCapture::retrieve` one or more times with different values of the ``channel`` parameter. See http://code.opencv.org/projects/opencv/repository/revisions/master/entry/samples/cpp/kinect_maps.cpp
VideoCapture::retrieve
:param winname: Window name
- :param onMouse: Mouse callback. See OpenCV samples, such as http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/ffilldemo.cpp, on how to specify and use the callback.
+ :param onMouse: Mouse callback. See OpenCV samples, such as http://code.opencv.org/projects/opencv/repository/revisions/master/entry/samples/cpp/ffilldemo.cpp, on how to specify and use the callback.
:param userdata: The optional parameter passed to the callback.
The functions ``approxPolyDP`` approximate a curve or a polygon with another curve/polygon with less vertices so that the distance between them is less or equal to the specified precision. It uses the Douglas-Peucker algorithm
http://en.wikipedia.org/wiki/Ramer-Douglas-Peucker_algorithm
-See http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/contours.cpp for the function usage model.
+See http://code.opencv.org/projects/opencv/repository/revisions/master/entry/samples/cpp/contours.cpp for the function usage model.
ApproxChains
The feature used in a particular classifier is specified by its shape (1a, 2b etc.), position within the region of interest and the scale (this scale is not the same as the scale used at the detection stage, though these two scales are multiplied). For example, in the case of the third line feature (2c) the response is calculated as the difference between the sum of image pixels under the rectangle covering the whole feature (including the two white stripes and the black stripe in the middle) and the sum of the image pixels under the black stripe multiplied by 3 in order to compensate for the differences in the size of areas. The sums of pixel values over a rectangular regions are calculated rapidly using integral images (see below and the :ocv:func:`integral` description).
To see the object detector at work, have a look at the facedetect demo:
-http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/facedetect.cpp
+http://code.opencv.org/projects/opencv/repository/revisions/master/entry/samples/cpp/facedetect.cpp
The following reference is for the detection part only. There is a separate application called ``opencv_traincascade`` that can train a cascade of boosted classifiers from a set of samples.
im = cv.LoadImageM(fileName, False)
im3 = cv.LoadImageM(fileName, True)
except: # if local copy cannot be opened, try downloading it
- url = 'http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/left01.jpg'
+ url = 'http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/left01.jpg'
filedata = urllib2.urlopen(url).read()
imagefiledata = cv.CreateMatHeader(1, len(filedata), cv.CV_8UC1)
cv.SetData(imagefiledata, filedata, len(filedata))
im3 = cv.DecodeImageM(imagefiledata, cv.CV_LOAD_IMAGE_COLOR)
chessboard_dim = ( 9, 6 )
-
+
found_all, corners = cv.FindChessboardCorners( im, chessboard_dim )
print found_all, len(corners)
cv.DrawChessboardCorners( im3, chessboard_dim, corners, found_all )
-
+
cv.ShowImage("win", im3);
cv.WaitKey()
cv.DestroyAllWindows()
try:
img0 = cv.LoadImage(name, cv.CV_LOAD_IMAGE_COLOR)
except IOError:
- urlbase = 'http://code.opencv.org/svn/opencv/trunk/opencv/samples/c/'
+ urlbase = 'http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/c/'
file = name.split('/')[-1]
filedata = urllib2.urlopen(urlbase+file).read()
imagefiledata = cv.CreateMatHeader(1, len(filedata), cv.CV_8UC1)
cv.Rectangle(self.hist_image, (int(i * bin_w), self.hist_image.height),
(int((i + 1) * bin_w), self.hist_image.height - cv.Round(self.hist.bins[i])),
cv.ScalarAll(0), -1, 8, 0)
-
+
cv.ShowImage("histogram", self.hist_image)
if __name__ == "__main__":
if len(sys.argv) > 1:
src_image = cv.GetMat(cv.LoadImage(sys.argv[1], 0))
else:
- url = 'http://code.opencv.org/svn/opencv/trunk/opencv/samples/c/baboon.jpg'
+ url = 'http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/c/baboon.jpg'
filedata = urllib2.urlopen(url).read()
imagefiledata = cv.CreateMatHeader(1, len(filedata), cv.CV_8UC1)
cv.SetData(imagefiledata, filedata, len(filedata))
dst_size = cv.GetSize(dst_arr)
if dst_size != size:
- cv.Error( cv.CV_StsUnmatchedSizes, "cv.ShiftDFT", "Source and Destination arrays must have equal sizes", __FILE__, __LINE__ )
+ cv.Error( cv.CV_StsUnmatchedSizes, "cv.ShiftDFT", "Source and Destination arrays must have equal sizes", __FILE__, __LINE__ )
if(src_arr is dst_arr):
tmp = cv.CreateMat(size[1]/2, size[0]/2, cv.GetElemType(src_arr))
-
+
cx = size[0] / 2
cy = size[1] / 2 # image center
if(src_arr is not dst_arr):
if( not cv.CV_ARE_TYPES_EQ( q1, d1 )):
- cv.Error( cv.CV_StsUnmatchedFormats, "cv.ShiftDFT", "Source and Destination arrays must have the same format", __FILE__, __LINE__ )
-
+ cv.Error( cv.CV_StsUnmatchedFormats, "cv.ShiftDFT", "Source and Destination arrays must have the same format", __FILE__, __LINE__ )
+
cv.Copy(q3, d1)
cv.Copy(q4, d2)
cv.Copy(q1, d3)
cv.Copy(q2, d4)
-
+
else:
cv.Copy(q3, tmp)
cv.Copy(q1, q3)
cv.Copy(tmp, q2)
if __name__ == "__main__":
-
+
if len(sys.argv) > 1:
im = cv.LoadImage( sys.argv[1], cv.CV_LOAD_IMAGE_GRAYSCALE)
else:
- url = 'http://code.opencv.org/svn/opencv/trunk/opencv/samples/c/baboon.jpg'
+ url = 'http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/c/baboon.jpg'
filedata = urllib2.urlopen(url).read()
imagefiledata = cv.CreateMatHeader(1, len(filedata), cv.CV_8UC1)
cv.SetData(imagefiledata, filedata, len(filedata))
def on_trackbar(edge_thresh):
cv.Threshold(gray, edge, float(edge_thresh), float(edge_thresh), cv.CV_THRESH_BINARY)
- #Distance transform
+ #Distance transform
cv.DistTransform(edge, dist, cv.CV_DIST_L2, cv.CV_DIST_MASK_5)
cv.ConvertScale(dist, dist, 5000.0, 0)
cv.Pow(dist, dist, 0.5)
-
+
cv.ConvertScale(dist, dist32s, 1.0, 0.5)
cv.AndS(dist32s, cv.ScalarAll(255), dist32s, None)
cv.ConvertScale(dist32s, dist8u1, 1, 0)
if len(sys.argv) > 1:
gray = cv.LoadImage(sys.argv[1], cv.CV_LOAD_IMAGE_GRAYSCALE)
else:
- url = 'http://code.opencv.org/svn/opencv/trunk/opencv/samples/c/stuff.jpg'
+ url = 'http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/c/stuff.jpg'
filedata = urllib2.urlopen(url).read()
imagefiledata = cv.CreateMatHeader(1, len(filedata), cv.CV_8UC1)
cv.SetData(imagefiledata, filedata, len(filedata))
# Create a window
cv.NamedWindow(wndname, 1)
- # create a toolbar
+ # create a toolbar
cv.CreateTrackbar(tbarname, wndname, edge_thresh, 255, on_trackbar)
# Show the image
# copy edge points
cv.Copy(im, col_edge, edge)
-
+
# show the im
cv.ShowImage(win_name, col_edge)
if len(sys.argv) > 1:
im = cv.LoadImage( sys.argv[1], cv.CV_LOAD_IMAGE_COLOR)
else:
- url = 'http://code.opencv.org/svn/opencv/trunk/opencv/samples/c/fruits.jpg'
+ url = 'http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/c/fruits.jpg'
filedata = urllib2.urlopen(url).read()
imagefiledata = cv.CreateMatHeader(1, len(filedata), cv.CV_8UC1)
cv.SetData(imagefiledata, filedata, len(filedata))
if( is_mask ):
my_mask = mask
cv.Threshold( mask, mask, 1, 128, cv.CV_THRESH_BINARY );
-
+
if( is_color ):
-
+
color = cv.CV_RGB( r, g, b );
comp = cv.FloodFill( color_img, seed, color, cv.CV_RGB( lo, lo, lo ),
cv.CV_RGB( up, up, up ), flags, my_mask );
cv.ShowImage( "image", color_img );
-
+
else:
-
+
brightness = cv.RealScalar((r*2 + g*7 + b + 5)/10);
comp = cv.FloodFill( gray_img, seed, brightness, cv.RealScalar(lo),
cv.RealScalar(up), flags, my_mask );
cv.ShowImage( "image", gray_img );
-
+
print "%g pixels were repainted" % comp[0]
if( is_mask ):
cv.ShowImage( "mask", mask );
-
-
+
+
if __name__ == "__main__":
-
+
if len(sys.argv) > 1:
im = cv.LoadImage( sys.argv[1], cv.CV_LOAD_IMAGE_COLOR)
else:
- url = 'http://code.opencv.org/svn/opencv/trunk/opencv/samples/c/fruits.jpg'
+ url = 'http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/c/fruits.jpg'
filedata = urllib2.urlopen(url).read()
imagefiledata = cv.CreateMatHeader(1, len(filedata), cv.CV_8UC1)
cv.SetData(imagefiledata, filedata, len(filedata))
print "\tg - use gradient floodfill with floating(relative) range"
print "\t4 - use 4-connectivity mode"
print "\t8 - use 8-connectivity mode"
-
+
color_img = cv.CloneImage( im );
gray_img0 = cv.CreateImage( (color_img.width, color_img.height), 8, 1 );
cv.CvtColor( color_img, gray_img0, cv.CV_BGR2GRAY );
cv.SetMouseCallback( "image", on_mouse );
- while True:
+ while True:
if( is_color ):
cv.ShowImage( "image", color_img );
else:
sys.exit(0)
elif c == ord('c'):
if( is_color ):
-
+
print("Grayscale mode is set");
cv.CvtColor( color_img, gray_img, cv.CV_BGR2GRAY );
is_color = 0;
-
+
else:
-
+
print("Color mode is set");
cv.Copy( im, color_img, None );
cv.Zero( mask );
is_color = 1;
-
+
elif c == ord('m'):
if( is_mask ):
cv.DestroyWindow( "mask" );
is_mask = 0;
-
+
else:
cv.NamedWindow( "mask", 0 );
cv.Zero( mask );
cv.ShowImage( "mask", mask );
is_mask = 1;
-
+
elif c == ord('r'):
print("Original image is restored");
cv.Copy( im, color_img, None );
cv.CreateTrackbar("Threshold", "Result", slider_pos, 255, self.process_image)
self.process_image(slider_pos)
- def process_image(self, slider_pos):
+ def process_image(self, slider_pos):
"""
This function finds contours, draws them and their approximation by ellipses.
"""
stor = cv.CreateMemStorage()
-
+
# Create the destination images
image02 = cv.CloneImage(self.source_image)
cv.Zero(image02)
PointArray2D32f = cv.CreateMat(1, len(c), cv.CV_32FC2)
for (i, (x, y)) in enumerate(c):
PointArray2D32f[0, i] = (x, y)
-
+
# Draw the current contour in gray
gray = cv.CV_RGB(100, 100, 100)
cv.DrawContours(image04, c, gray, gray,0,1,8,(0,0))
-
+
# Fits ellipse to current contour.
(center, size, angle) = cv.FitEllipse2(PointArray2D32f)
-
+
# Convert ellipse data from float to integer representation.
center = (cv.Round(center[0]), cv.Round(center[1]))
size = (cv.Round(size[0] * 0.5), cv.Round(size[1] * 0.5))
-
+
# Draw ellipse in random color
color = cv.CV_RGB(random.randrange(256),random.randrange(256),random.randrange(256))
cv.Ellipse(image04, center, size,
if len(sys.argv) > 1:
source_image = cv.LoadImage(sys.argv[1], cv.CV_LOAD_IMAGE_GRAYSCALE)
else:
- url = 'http://code.opencv.org/svn/opencv/trunk/opencv/samples/c/stuff.jpg'
+ url = 'http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/c/stuff.jpg'
filedata = urllib2.urlopen(url).read()
imagefiledata = cv.CreateMatHeader(1, len(filedata), cv.CV_8UC1)
cv.SetData(imagefiledata, filedata, len(filedata))
source_image = cv.DecodeImage(imagefiledata, cv.CV_LOAD_IMAGE_GRAYSCALE)
-
+
# Create windows.
cv.NamedWindow("Source", 1)
cv.NamedWindow("Result", 1)
filename = sys.argv[1]
src = cv.LoadImage(filename, cv.CV_LOAD_IMAGE_GRAYSCALE)
else:
- url = 'http://code.opencv.org/svn/opencv/trunk/opencv/doc/pics/building.jpg'
+ url = 'http://code.opencv.org/projects/opencv/repository/revisions/master/raw/doc/pics/building.jpg'
filedata = urllib2.urlopen(url).read()
imagefiledata = cv.CreateMatHeader(1, len(filedata), cv.CV_8UC1)
cv.SetData(imagefiledata, filedata, len(filedata))
for (rho, theta) in lines[:100]:
a = cos(theta)
b = sin(theta)
- x0 = a * rho
+ x0 = a * rho
y0 = b * rho
pt1 = (cv.Round(x0 + 1000*(-b)), cv.Round(y0 + 1000*(a)))
pt2 = (cv.Round(x0 - 1000*(-b)), cv.Round(y0 - 1000*(a)))
if len(sys.argv) > 1:
img0 = cv.LoadImage( sys.argv[1], cv.CV_LOAD_IMAGE_COLOR)
else:
- url = 'http://code.opencv.org/svn/opencv/trunk/opencv/samples/c/fruits.jpg'
+ url = 'http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/c/fruits.jpg'
filedata = urllib2.urlopen(url).read()
imagefiledata = cv.CreateMatHeader(1, len(filedata), cv.CV_8UC1)
cv.SetData(imagefiledata, filedata, len(filedata))
print "\tr - restore the original image"
print "\ti or ENTER - run inpainting algorithm"
print "\t\t(before running it, paint something on the image)"
-
+
cv.NamedWindow("image", 1)
cv.NamedWindow("inpainted image", 1)
cv.ShowImage("inverse log-polar", src2)
if __name__ == "__main__":
-
+
if len(sys.argv) > 1:
src = cv.LoadImage( sys.argv[1], cv.CV_LOAD_IMAGE_COLOR)
else:
- url = 'http://code.opencv.org/svn/opencv/trunk/opencv/samples/c/fruits.jpg'
+ url = 'http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/c/fruits.jpg'
filedata = urllib2.urlopen(url).read()
imagefiledata = cv.CreateMatHeader(1, len(filedata), cv.CV_8UC1)
cv.SetData(imagefiledata, filedata, len(filedata))
src = cv.DecodeImage(imagefiledata, cv.CV_LOAD_IMAGE_COLOR)
-
+
cv.NamedWindow("original", 1)
cv.NamedWindow("log-polar", 1)
cv.NamedWindow("inverse log-polar", 1)
-
-
+
+
dst = cv.CreateImage((256, 256), 8, 3)
src2 = cv.CreateImage(cv.GetSize(src), 8, 3)
-
+
cv.SetMouseCallback("original", on_mouse)
on_mouse(cv.CV_EVENT_LBUTTONDOWN, src.width/2, src.height/2, None, None)
-
+
cv.ShowImage("original", src)
cv.WaitKey()
cv.DestroyAllWindows()
if len(sys.argv) > 1:
src = cv.LoadImage(sys.argv[1], cv.CV_LOAD_IMAGE_COLOR)
else:
- url = 'http://code.opencv.org/svn/opencv/trunk/opencv/samples/c/fruits.jpg'
+ url = 'http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/c/fruits.jpg'
filedata = urllib2.urlopen(url).read()
imagefiledata = cv.CreateMatHeader(1, len(filedata), cv.CV_8UC1)
cv.SetData(imagefiledata, filedata, len(filedata))
if len(sys.argv) > 1:
img0 = cv.LoadImageM( sys.argv[1], cv.CV_LOAD_IMAGE_COLOR)
else:
- url = 'http://code.opencv.org/svn/opencv/trunk/opencv/samples/c/lena.jpg'
+ url = 'http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/c/lena.jpg'
filedata = urllib2.urlopen(url).read()
imagefiledata = cv.CreateMatHeader(1, len(filedata), cv.CV_8UC1)
cv.SetData(imagefiledata, filedata, len(filedata))
if len(sys.argv) > 1:
img0 = cv.LoadImage( sys.argv[1], cv.CV_LOAD_IMAGE_COLOR)
else:
- url = 'http://code.opencv.org/svn/opencv/trunk/opencv/samples/c/fruits.jpg'
+ url = 'http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/c/fruits.jpg'
filedata = urllib2.urlopen(url).read()
imagefiledata = cv.CreateMatHeader(1, len(filedata), cv.CV_8UC1)
cv.SetData(imagefiledata, filedata, len(filedata))
cv.AddWeighted(wshed, 0.5, img_gray, 0.5, 0, wshed)
cv.ShowImage("watershed transform", wshed)
cv.DestroyAllWindows()
-
+
'''\r
-SVN and KNearest digit recognition.\r
+SVM and KNearest digit recognition.\r
\r
Sample loads a dataset of handwritten digits from 'digits.png'.\r
-Then it trains a SVN and KNearest classifiers on it and evaluates\r
-their accuracy. \r
+Then it trains a SVM and KNearest classifiers on it and evaluates\r
+their accuracy.\r
\r
Following preprocessing is applied to the dataset:\r
- Moment-based image deskew (see deskew())\r
\r
class SVM(StatModel):\r
def __init__(self, C = 1, gamma = 0.5):\r
- self.params = dict( kernel_type = cv2.SVM_RBF, \r
+ self.params = dict( kernel_type = cv2.SVM_RBF,\r
svm_type = cv2.SVM_C_SVC,\r
C = C,\r
gamma = gamma )\r
resp = model.predict(samples)\r
err = (labels != resp).mean()\r
print 'error: %.2f %%' % (err*100)\r
- \r
+\r
confusion = np.zeros((10, 10), np.int32)\r
for i, j in zip(labels, resp):\r
confusion[i, j] += 1\r
hist = np.hstack(hists)\r
\r
# transform to Hellinger kernel\r
- eps = 1e-7 \r
+ eps = 1e-7\r
hist /= hist.sum() + eps\r
hist = np.sqrt(hist)\r
hist /= norm(hist) + eps\r
print __doc__\r
\r
digits, labels = load_digits(DIGITS_FN)\r
- \r
+\r
print 'preprocessing...'\r
# shuffle digits\r
rand = np.random.RandomState(321)\r
shuffle = rand.permutation(len(digits))\r
digits, labels = digits[shuffle], labels[shuffle]\r
- \r
+\r
digits2 = map(deskew, digits)\r
samples = preprocess_hog(digits2)\r
- \r
+\r
train_n = int(0.9*len(samples))\r
cv2.imshow('test set', mosaic(25, digits[train_n:]))\r
digits_train, digits_test = np.split(digits2, [train_n])\r
samples_train, samples_test = np.split(samples, [train_n])\r
labels_train, labels_test = np.split(labels, [train_n])\r
\r
- \r
+\r
print 'training KNearest...'\r
model = KNearest(k=4)\r
model.train(samples_train, labels_train)\r
'''\r
-Digit recognition adjustment. \r
-Grid search is used to find the best parameters for SVN and KNearest classifiers.\r
-SVM adjustment follows the guidelines given in \r
+Digit recognition adjustment.\r
+Grid search is used to find the best parameters for SVM and KNearest classifiers.\r
+SVM adjustment follows the guidelines given in\r
http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf\r
\r
-Threading or cloud computing (with http://www.picloud.com/)) may be used \r
+Threading or cloud computing (with http://www.picloud.com/)) may be used\r
to speedup the computation.\r
\r
Usage:\r
digits_adjust.py [--model {svm|knearest}] [--cloud] [--env <PiCloud environment>]\r
- \r
+\r
--model {svm|knearest} - select the classifier (SVM is the default)\r
--cloud - use PiCloud computing platform\r
--env - cloud environment name\r
\r
from digits import *\r
\r
-try: \r
+try:\r
import cloud\r
have_cloud = True\r
except ImportError:\r
have_cloud = False\r
- \r
+\r
\r
\r
def cross_validate(model_class, params, samples, labels, kfold = 3, pool = None):\r
pool = ThreadPool(processes=cv2.getNumberOfCPUs())\r
ires = pool.imap_unordered(f, jobs)\r
return ires\r
- \r
+\r
def adjust_SVM(self):\r
Cs = np.logspace(0, 10, 15, base=2)\r
gammas = np.logspace(-7, 4, 15, base=2)\r
params = dict(C = Cs[i], gamma=gammas[j])\r
score = cross_validate(SVM, params, samples, labels)\r
return i, j, score\r
- \r
+\r
ires = self.run_jobs(f, np.ndindex(*scores.shape))\r
for count, (i, j, score) in enumerate(ires):\r
scores[i, j] = score\r
if __name__ == '__main__':\r
import getopt\r
import sys\r
- \r
+\r
print __doc__\r
\r
args, _ = getopt.getopt(sys.argv[1:], '', ['model=', 'cloud', 'env='])\r
projects / profile / ivi / opencv.git / commitdiff