Code
----
+@add_toggle_cpp
This tutorial code's is shown lines below. You can also download it from
- [here](https://github.com/opencv/opencv/tree/3.4/samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp).
+[here](https://github.com/opencv/opencv/tree/3.4/samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp).
@include samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp
+@end_toggle
+
+@add_toggle_java
+This tutorial code's is shown lines below. You can also download it from
+[here](https://github.com/opencv/opencv/tree/3.4/samples/java/tutorial_code/ImgTrans/distance_transformation/ImageSegmentationDemo.java)
+@include samples/java/tutorial_code/ImgTrans/distance_transformation/ImageSegmentationDemo.java
+@end_toggle
+
+@add_toggle_python
+This tutorial code's is shown lines below. You can also download it from
+[here](https://github.com/opencv/opencv/tree/3.4/samples/python/tutorial_code/ImgTrans/distance_transformation/imageSegmentation.py)
+@include samples/python/tutorial_code/ImgTrans/distance_transformation/imageSegmentation.py
+@end_toggle
Explanation / Result
--------------------
--# Load the source image and check if it is loaded without any problem, then show it:
- @snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp load_image
- 
+- Load the source image and check if it is loaded without any problem, then show it:
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp load_image
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/ImgTrans/distance_transformation/ImageSegmentationDemo.java load_image
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/ImgTrans/distance_transformation/imageSegmentation.py load_image
+@end_toggle
+
+
+
+- Then if we have an image with a white background, it is good to transform it to black. This will help us to discriminate the foreground objects easier when we will apply the Distance Transform:
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp black_bg
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/ImgTrans/distance_transformation/ImageSegmentationDemo.java black_bg
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/ImgTrans/distance_transformation/imageSegmentation.py black_bg
+@end_toggle
+
+
+
+- Afterwards we will sharpen our image in order to acute the edges of the foreground objects. We will apply a laplacian filter with a quite strong filter (an approximation of second derivative):
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp sharp
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/ImgTrans/distance_transformation/ImageSegmentationDemo.java sharp
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/ImgTrans/distance_transformation/imageSegmentation.py sharp
+@end_toggle
+
+
+
+
+- Now we transform our new sharpened source image to a grayscale and a binary one, respectively:
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp bin
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/ImgTrans/distance_transformation/ImageSegmentationDemo.java bin
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/ImgTrans/distance_transformation/imageSegmentation.py bin
+@end_toggle
+
+
+
+- We are ready now to apply the Distance Transform on the binary image. Moreover, we normalize the output image in order to be able visualize and threshold the result:
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp dist
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/ImgTrans/distance_transformation/ImageSegmentationDemo.java dist
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/ImgTrans/distance_transformation/imageSegmentation.py dist
+@end_toggle
+
+
+
+- We threshold the *dist* image and then perform some morphology operation (i.e. dilation) in order to extract the peaks from the above image:
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp peaks
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/ImgTrans/distance_transformation/ImageSegmentationDemo.java peaks
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/ImgTrans/distance_transformation/imageSegmentation.py peaks
+@end_toggle
+
+
+
+- From each blob then we create a seed/marker for the watershed algorithm with the help of the @ref cv::findContours function:
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp seeds
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/ImgTrans/distance_transformation/ImageSegmentationDemo.java seeds
+@end_toggle
--# Then if we have an image with a white background, it is good to transform it to black. This will help us to discriminate the foreground objects easier when we will apply the Distance Transform:
- @snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp black_bg
- 
+@add_toggle_python
+@snippet samples/python/tutorial_code/ImgTrans/distance_transformation/imageSegmentation.py seeds
+@end_toggle
--# Afterwards we will sharpen our image in order to acute the edges of the foreground objects. We will apply a laplacian filter with a quite strong filter (an approximation of second derivative):
- @snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp sharp
- 
- 
+
--# Now we transform our new sharpened source image to a grayscale and a binary one, respectively:
- @snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp bin
- 
+- Finally, we can apply the watershed algorithm, and visualize the result:
--# We are ready now to apply the Distance Transform on the binary image. Moreover, we normalize the output image in order to be able visualize and threshold the result:
- @snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp dist
- 
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp watershed
+@end_toggle
--# We threshold the *dist* image and then perform some morphology operation (i.e. dilation) in order to extract the peaks from the above image:
- @snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp peaks
- 
+@add_toggle_java
+@snippet samples/java/tutorial_code/ImgTrans/distance_transformation/ImageSegmentationDemo.java watershed
+@end_toggle
--# From each blob then we create a seed/marker for the watershed algorithm with the help of the @ref cv::findContours function:
- @snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp seeds
- 
+@add_toggle_python
+@snippet samples/python/tutorial_code/ImgTrans/distance_transformation/imageSegmentation.py watershed
+@end_toggle
--# Finally, we can apply the watershed algorithm, and visualize the result:
- @snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp watershed
- 
\ No newline at end of file
+
- @subpage tutorial_distance_transform
+ *Languages:* C++, Java, Python
+
*Compatibility:* \> OpenCV 2.0
*Author:* Theodore Tsesmelis
/**
- * @function Watershed_and_Distance_Transform.cpp
* @brief Sample code showing how to segment overlapping objects using Laplacian filtering, in addition to Watershed and Distance Transformation
* @author OpenCV Team
*/
using namespace std;
using namespace cv;
-int main()
+int main(int argc, char *argv[])
{
-//! [load_image]
+ //! [load_image]
// Load the image
- Mat src = imread("../data/cards.png");
-
- // Check if everything was fine
- if (!src.data)
+ CommandLineParser parser( argc, argv, "{@input | ../data/cards.png | input image}" );
+ Mat src = imread( parser.get<String>( "@input" ) );
+ if( src.empty() )
+ {
+ cout << "Could not open or find the image!\n" << endl;
+ cout << "Usage: " << argv[0] << " <Input image>" << endl;
return -1;
+ }
// Show source image
imshow("Source Image", src);
-//! [load_image]
+ //! [load_image]
-//! [black_bg]
+ //! [black_bg]
// Change the background from white to black, since that will help later to extract
// better results during the use of Distance Transform
- for( int x = 0; x < src.rows; x++ ) {
- for( int y = 0; y < src.cols; y++ ) {
- if ( src.at<Vec3b>(x, y) == Vec3b(255,255,255) ) {
- src.at<Vec3b>(x, y)[0] = 0;
- src.at<Vec3b>(x, y)[1] = 0;
- src.at<Vec3b>(x, y)[2] = 0;
- }
+ for ( int i = 0; i < src.rows; i++ ) {
+ for ( int j = 0; j < src.cols; j++ ) {
+ if ( src.at<Vec3b>(i, j) == Vec3b(255,255,255) )
+ {
+ src.at<Vec3b>(i, j)[0] = 0;
+ src.at<Vec3b>(i, j)[1] = 0;
+ src.at<Vec3b>(i, j)[2] = 0;
+ }
}
}
// Show output image
imshow("Black Background Image", src);
-//! [black_bg]
+ //! [black_bg]
-//! [sharp]
- // Create a kernel that we will use for accuting/sharpening our image
+ //! [sharp]
+ // Create a kernel that we will use to sharpen our image
Mat kernel = (Mat_<float>(3,3) <<
- 1, 1, 1,
- 1, -8, 1,
- 1, 1, 1); // an approximation of second derivative, a quite strong kernel
+ 1, 1, 1,
+ 1, -8, 1,
+ 1, 1, 1); // an approximation of second derivative, a quite strong kernel
// do the laplacian filtering as it is
// well, we need to convert everything in something more deeper then CV_8U
// BUT a 8bits unsigned int (the one we are working with) can contain values from 0 to 255
// so the possible negative number will be truncated
Mat imgLaplacian;
- Mat sharp = src; // copy source image to another temporary one
- filter2D(sharp, imgLaplacian, CV_32F, kernel);
+ filter2D(src, imgLaplacian, CV_32F, kernel);
+ Mat sharp;
src.convertTo(sharp, CV_32F);
Mat imgResult = sharp - imgLaplacian;
// imshow( "Laplace Filtered Image", imgLaplacian );
imshow( "New Sharped Image", imgResult );
-//! [sharp]
+ //! [sharp]
- src = imgResult; // copy back
-
-//! [bin]
+ //! [bin]
// Create binary image from source image
Mat bw;
- cvtColor(src, bw, COLOR_BGR2GRAY);
+ cvtColor(imgResult, bw, COLOR_BGR2GRAY);
threshold(bw, bw, 40, 255, THRESH_BINARY | THRESH_OTSU);
imshow("Binary Image", bw);
-//! [bin]
+ //! [bin]
-//! [dist]
+ //! [dist]
// Perform the distance transform algorithm
Mat dist;
distanceTransform(bw, dist, DIST_L2, 3);
// Normalize the distance image for range = {0.0, 1.0}
// so we can visualize and threshold it
- normalize(dist, dist, 0, 1., NORM_MINMAX);
+ normalize(dist, dist, 0, 1.0, NORM_MINMAX);
imshow("Distance Transform Image", dist);
-//! [dist]
+ //! [dist]
-//! [peaks]
+ //! [peaks]
// Threshold to obtain the peaks
// This will be the markers for the foreground objects
- threshold(dist, dist, .4, 1., THRESH_BINARY);
+ threshold(dist, dist, 0.4, 1.0, THRESH_BINARY);
// Dilate a bit the dist image
- Mat kernel1 = Mat::ones(3, 3, CV_8UC1);
+ Mat kernel1 = Mat::ones(3, 3, CV_8U);
dilate(dist, dist, kernel1);
imshow("Peaks", dist);
-//! [peaks]
+ //! [peaks]
-//! [seeds]
+ //! [seeds]
// Create the CV_8U version of the distance image
// It is needed for findContours()
Mat dist_8u;
findContours(dist_8u, contours, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);
// Create the marker image for the watershed algorithm
- Mat markers = Mat::zeros(dist.size(), CV_32SC1);
+ Mat markers = Mat::zeros(dist.size(), CV_32S);
// Draw the foreground markers
for (size_t i = 0; i < contours.size(); i++)
- drawContours(markers, contours, static_cast<int>(i), Scalar::all(static_cast<int>(i)+1), -1);
+ {
+ drawContours(markers, contours, static_cast<int>(i), Scalar(static_cast<int>(i)+1), -1);
+ }
// Draw the background marker
- circle(markers, Point(5,5), 3, CV_RGB(255,255,255), -1);
+ circle(markers, Point(5,5), 3, Scalar(255), -1);
imshow("Markers", markers*10000);
-//! [seeds]
+ //! [seeds]
-//! [watershed]
+ //! [watershed]
// Perform the watershed algorithm
- watershed(src, markers);
+ watershed(imgResult, markers);
- Mat mark = Mat::zeros(markers.size(), CV_8UC1);
- markers.convertTo(mark, CV_8UC1);
+ Mat mark;
+ markers.convertTo(mark, CV_8U);
bitwise_not(mark, mark);
-// imshow("Markers_v2", mark); // uncomment this if you want to see how the mark
- // image looks like at that point
+ // imshow("Markers_v2", mark); // uncomment this if you want to see how the mark
+ // image looks like at that point
// Generate random colors
vector<Vec3b> colors;
for (size_t i = 0; i < contours.size(); i++)
{
- int b = theRNG().uniform(0, 255);
- int g = theRNG().uniform(0, 255);
- int r = theRNG().uniform(0, 255);
+ int b = theRNG().uniform(0, 256);
+ int g = theRNG().uniform(0, 256);
+ int r = theRNG().uniform(0, 256);
colors.push_back(Vec3b((uchar)b, (uchar)g, (uchar)r));
}
{
int index = markers.at<int>(i,j);
if (index > 0 && index <= static_cast<int>(contours.size()))
+ {
dst.at<Vec3b>(i,j) = colors[index-1];
- else
- dst.at<Vec3b>(i,j) = Vec3b(0,0,0);
+ }
}
}
// Visualize the final image
imshow("Final Result", dst);
-//! [watershed]
+ //! [watershed]
- waitKey(0);
+ waitKey();
return 0;
}
--- /dev/null
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Random;
+
+import org.opencv.core.Core;
+import org.opencv.core.CvType;
+import org.opencv.core.Mat;
+import org.opencv.core.MatOfPoint;
+import org.opencv.core.Point;
+import org.opencv.core.Scalar;
+import org.opencv.highgui.HighGui;
+import org.opencv.imgcodecs.Imgcodecs;
+import org.opencv.imgproc.Imgproc;
+
+/**
+ *
+ * @brief Sample code showing how to segment overlapping objects using Laplacian filtering, in addition to Watershed
+ * and Distance Transformation
+ *
+ */
+class ImageSegmentation {
+ public void run(String[] args) {
+ //! [load_image]
+ // Load the image
+ String filename = args.length > 0 ? args[0] : "../data/cards.png";
+ Mat srcOriginal = Imgcodecs.imread(filename);
+ if (srcOriginal.empty()) {
+ System.err.println("Cannot read image: " + filename);
+ System.exit(0);
+ }
+
+ // Show source image
+ HighGui.imshow("Source Image", srcOriginal);
+ //! [load_image]
+
+ //! [black_bg]
+ // Change the background from white to black, since that will help later to
+ // extract
+ // better results during the use of Distance Transform
+ Mat src = srcOriginal.clone();
+ byte[] srcData = new byte[(int) (src.total() * src.channels())];
+ src.get(0, 0, srcData);
+ for (int i = 0; i < src.rows(); i++) {
+ for (int j = 0; j < src.cols(); j++) {
+ if (srcData[(i * src.cols() + j) * 3] == (byte) 255 && srcData[(i * src.cols() + j) * 3 + 1] == (byte) 255
+ && srcData[(i * src.cols() + j) * 3 + 2] == (byte) 255) {
+ srcData[(i * src.cols() + j) * 3] = 0;
+ srcData[(i * src.cols() + j) * 3 + 1] = 0;
+ srcData[(i * src.cols() + j) * 3 + 2] = 0;
+ }
+ }
+ }
+ src.put(0, 0, srcData);
+
+ // Show output image
+ HighGui.imshow("Black Background Image", src);
+ //! [black_bg]
+
+ //! [sharp]
+ // Create a kernel that we will use to sharpen our image
+ Mat kernel = new Mat(3, 3, CvType.CV_32F);
+ // an approximation of second derivative, a quite strong kernel
+ float[] kernelData = new float[(int) (kernel.total() * kernel.channels())];
+ kernelData[0] = 1; kernelData[1] = 1; kernelData[2] = 1;
+ kernelData[3] = 1; kernelData[4] = -8; kernelData[5] = 1;
+ kernelData[6] = 1; kernelData[7] = 1; kernelData[8] = 1;
+ kernel.put(0, 0, kernelData);
+
+ // do the laplacian filtering as it is
+ // well, we need to convert everything in something more deeper then CV_8U
+ // because the kernel has some negative values,
+ // and we can expect in general to have a Laplacian image with negative values
+ // BUT a 8bits unsigned int (the one we are working with) can contain values
+ // from 0 to 255
+ // so the possible negative number will be truncated
+ Mat imgLaplacian = new Mat();
+ Imgproc.filter2D(src, imgLaplacian, CvType.CV_32F, kernel);
+ Mat sharp = new Mat();
+ src.convertTo(sharp, CvType.CV_32F);
+ Mat imgResult = new Mat();
+ Core.subtract(sharp, imgLaplacian, imgResult);
+
+ // convert back to 8bits gray scale
+ imgResult.convertTo(imgResult, CvType.CV_8UC3);
+ imgLaplacian.convertTo(imgLaplacian, CvType.CV_8UC3);
+
+ // imshow( "Laplace Filtered Image", imgLaplacian );
+ HighGui.imshow("New Sharped Image", imgResult);
+ //! [sharp]
+
+ //! [bin]
+ // Create binary image from source image
+ Mat bw = new Mat();
+ Imgproc.cvtColor(imgResult, bw, Imgproc.COLOR_BGR2GRAY);
+ Imgproc.threshold(bw, bw, 40, 255, Imgproc.THRESH_BINARY | Imgproc.THRESH_OTSU);
+ HighGui.imshow("Binary Image", bw);
+ //! [bin]
+
+ //! [dist]
+ // Perform the distance transform algorithm
+ Mat dist = new Mat();
+ Imgproc.distanceTransform(bw, dist, Imgproc.DIST_L2, 3);
+
+ // Normalize the distance image for range = {0.0, 1.0}
+ // so we can visualize and threshold it
+ Core.normalize(dist, dist, 0, 1., Core.NORM_MINMAX);
+ Mat distDisplayScaled = dist.mul(dist, 255);
+ Mat distDisplay = new Mat();
+ distDisplayScaled.convertTo(distDisplay, CvType.CV_8U);
+ HighGui.imshow("Distance Transform Image", distDisplay);
+ //! [dist]
+
+ //! [peaks]
+ // Threshold to obtain the peaks
+ // This will be the markers for the foreground objects
+ Imgproc.threshold(dist, dist, .4, 1., Imgproc.THRESH_BINARY);
+
+ // Dilate a bit the dist image
+ Mat kernel1 = Mat.ones(3, 3, CvType.CV_8U);
+ Imgproc.dilate(dist, dist, kernel1);
+ Mat distDisplay2 = new Mat();
+ dist.convertTo(distDisplay2, CvType.CV_8U);
+ distDisplay2 = distDisplay2.mul(distDisplay2, 255);
+ HighGui.imshow("Peaks", distDisplay2);
+ //! [peaks]
+
+ //! [seeds]
+ // Create the CV_8U version of the distance image
+ // It is needed for findContours()
+ Mat dist_8u = new Mat();
+ dist.convertTo(dist_8u, CvType.CV_8U);
+
+ // Find total markers
+ List<MatOfPoint> contours = new ArrayList<>();
+ Mat hierarchy = new Mat();
+ Imgproc.findContours(dist_8u, contours, hierarchy, Imgproc.RETR_EXTERNAL, Imgproc.CHAIN_APPROX_SIMPLE);
+
+ // Create the marker image for the watershed algorithm
+ Mat markers = Mat.zeros(dist.size(), CvType.CV_32S);
+
+ // Draw the foreground markers
+ for (int i = 0; i < contours.size(); i++) {
+ Imgproc.drawContours(markers, contours, i, new Scalar(i + 1), -1);
+ }
+
+ // Draw the background marker
+ Imgproc.circle(markers, new Point(5, 5), 3, new Scalar(255, 255, 255), -1);
+ Mat markersScaled = markers.mul(markers, 10000);
+ Mat markersDisplay = new Mat();
+ markersScaled.convertTo(markersDisplay, CvType.CV_8U);
+ HighGui.imshow("Markers", markersDisplay);
+ //! [seeds]
+
+ //! [watershed]
+ // Perform the watershed algorithm
+ Imgproc.watershed(imgResult, markers);
+
+ Mat mark = Mat.zeros(markers.size(), CvType.CV_8U);
+ markers.convertTo(mark, CvType.CV_8UC1);
+ Core.bitwise_not(mark, mark);
+ // imshow("Markers_v2", mark); // uncomment this if you want to see how the mark
+ // image looks like at that point
+
+ // Generate random colors
+ Random rng = new Random(12345);
+ List<Scalar> colors = new ArrayList<>(contours.size());
+ for (int i = 0; i < contours.size(); i++) {
+ int b = rng.nextInt(256);
+ int g = rng.nextInt(256);
+ int r = rng.nextInt(256);
+
+ colors.add(new Scalar(b, g, r));
+ }
+
+ // Create the result image
+ Mat dst = Mat.zeros(markers.size(), CvType.CV_8UC3);
+ byte[] dstData = new byte[(int) (dst.total() * dst.channels())];
+ dst.get(0, 0, dstData);
+
+ // Fill labeled objects with random colors
+ int[] markersData = new int[(int) (markers.total() * markers.channels())];
+ markers.get(0, 0, markersData);
+ for (int i = 0; i < markers.rows(); i++) {
+ for (int j = 0; j < markers.cols(); j++) {
+ int index = markersData[i * markers.cols() + j];
+ if (index > 0 && index <= contours.size()) {
+ dstData[(i * dst.cols() + j) * 3 + 0] = (byte) colors.get(index - 1).val[0];
+ dstData[(i * dst.cols() + j) * 3 + 1] = (byte) colors.get(index - 1).val[1];
+ dstData[(i * dst.cols() + j) * 3 + 2] = (byte) colors.get(index - 1).val[2];
+ } else {
+ dstData[(i * dst.cols() + j) * 3 + 0] = 0;
+ dstData[(i * dst.cols() + j) * 3 + 1] = 0;
+ dstData[(i * dst.cols() + j) * 3 + 2] = 0;
+ }
+ }
+ }
+ dst.put(0, 0, dstData);
+
+ // Visualize the final image
+ HighGui.imshow("Final Result", dst);
+ //! [watershed]
+
+ HighGui.waitKey();
+ System.exit(0);
+ }
+}
+
+public class ImageSegmentationDemo {
+ public static void main(String[] args) {
+ // Load the native OpenCV library
+ System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
+
+ new ImageSegmentation().run(args);
+ }
+}
--- /dev/null
+from __future__ import print_function
+import cv2 as cv
+import numpy as np
+import argparse
+import random as rng
+
+rng.seed(12345)
+
+## [load_image]
+# Load the image
+parser = argparse.ArgumentParser(description='Code for Image Segmentation with Distance Transform and Watershed Algorithm.\
+ Sample code showing how to segment overlapping objects using Laplacian filtering, \
+ in addition to Watershed and Distance Transformation')
+parser.add_argument('--input', help='Path to input image.', default='../data/cards.png')
+args = parser.parse_args()
+
+src = cv.imread(args.input)
+if src is None:
+ print('Could not open or find the image:', args.input)
+ exit(0)
+
+# Show source image
+cv.imshow('Source Image', src)
+## [load_image]
+
+## [black_bg]
+# Change the background from white to black, since that will help later to extract
+# better results during the use of Distance Transform
+src[np.all(src == 255, axis=2)] = 0
+
+# Show output image
+cv.imshow('Black Background Image', src)
+## [black_bg]
+
+## [sharp]
+# Create a kernel that we will use to sharpen our image
+# an approximation of second derivative, a quite strong kernel
+kernel = np.array([[1, 1, 1], [1, -8, 1], [1, 1, 1]], dtype=np.float32)
+
+# do the laplacian filtering as it is
+# well, we need to convert everything in something more deeper then CV_8U
+# because the kernel has some negative values,
+# and we can expect in general to have a Laplacian image with negative values
+# BUT a 8bits unsigned int (the one we are working with) can contain values from 0 to 255
+# so the possible negative number will be truncated
+imgLaplacian = cv.filter2D(src, cv.CV_32F, kernel)
+sharp = np.float32(src)
+imgResult = sharp - imgLaplacian
+
+# convert back to 8bits gray scale
+imgResult = np.clip(imgResult, 0, 255)
+imgResult = imgResult.astype('uint8')
+imgLaplacian = np.clip(imgLaplacian, 0, 255)
+imgLaplacian = np.uint8(imgLaplacian)
+
+#cv.imshow('Laplace Filtered Image', imgLaplacian)
+cv.imshow('New Sharped Image', imgResult)
+## [sharp]
+
+## [bin]
+# Create binary image from source image
+bw = cv.cvtColor(imgResult, cv.COLOR_BGR2GRAY)
+_, bw = cv.threshold(bw, 40, 255, cv.THRESH_BINARY | cv.THRESH_OTSU)
+cv.imshow('Binary Image', bw)
+## [bin]
+
+## [dist]
+# Perform the distance transform algorithm
+dist = cv.distanceTransform(bw, cv.DIST_L2, 3)
+
+# Normalize the distance image for range = {0.0, 1.0}
+# so we can visualize and threshold it
+cv.normalize(dist, dist, 0, 1.0, cv.NORM_MINMAX)
+cv.imshow('Distance Transform Image', dist)
+## [dist]
+
+## [peaks]
+# Threshold to obtain the peaks
+# This will be the markers for the foreground objects
+_, dist = cv.threshold(dist, 0.4, 1.0, cv.THRESH_BINARY)
+
+# Dilate a bit the dist image
+kernel1 = np.ones((3,3), dtype=np.uint8)
+dist = cv.dilate(dist, kernel1)
+cv.imshow('Peaks', dist)
+## [peaks]
+
+## [seeds]
+# Create the CV_8U version of the distance image
+# It is needed for findContours()
+dist_8u = dist.astype('uint8')
+
+# Find total markers
+_, contours, _ = cv.findContours(dist_8u, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
+
+# Create the marker image for the watershed algorithm
+markers = np.zeros(dist.shape, dtype=np.int32)
+
+# Draw the foreground markers
+for i in range(len(contours)):
+ cv.drawContours(markers, contours, i, (i+1), -1)
+
+# Draw the background marker
+cv.circle(markers, (5,5), 3, (255,255,255), -1)
+cv.imshow('Markers', markers*10000)
+## [seeds]
+
+## [watershed]
+# Perform the watershed algorithm
+cv.watershed(imgResult, markers)
+
+#mark = np.zeros(markers.shape, dtype=np.uint8)
+mark = markers.astype('uint8')
+mark = cv.bitwise_not(mark)
+# uncomment this if you want to see how the mark
+# image looks like at that point
+#cv.imshow('Markers_v2', mark)
+
+# Generate random colors
+colors = []
+for contour in contours:
+ colors.append((rng.randint(0,256), rng.randint(0,256), rng.randint(0,256)))
+
+# Create the result image
+dst = np.zeros((markers.shape[0], markers.shape[1], 3), dtype=np.uint8)
+
+# Fill labeled objects with random colors
+for i in range(markers.shape[0]):
+ for j in range(markers.shape[1]):
+ index = markers[i,j]
+ if index > 0 and index <= len(contours):
+ dst[i,j,:] = colors[index-1]
+
+# Visualize the final image
+cv.imshow('Final Result', dst)
+## [watershed]
+
+cv.waitKey()
#-- Filter matches using the Lowe's ratio test
ratio_thresh = 0.7
good_matches = []
-for matches in knn_matches:
- if len(matches) > 1:
- if matches[0].distance / matches[1].distance <= ratio_thresh:
- good_matches.append(matches[0])
+for m,n in knn_matches:
+ if m.distance / n.distance <= ratio_thresh:
+ good_matches.append(m)
#-- Draw matches
img_matches = np.empty((max(img1.shape[0], img2.shape[0]), img1.shape[1]+img2.shape[1], 3), dtype=np.uint8)
#-- Filter matches using the Lowe's ratio test
ratio_thresh = 0.75
good_matches = []
-for matches in knn_matches:
- if len(matches) > 1:
- if matches[0].distance / matches[1].distance <= ratio_thresh:
- good_matches.append(matches[0])
+for m,n in knn_matches:
+ if m.distance / n.distance <= ratio_thresh:
+ good_matches.append(m)
#-- Draw matches
img_matches = np.empty((max(img_object.shape[0], img_scene.shape[0]), img_object.shape[1]+img_scene.shape[1], 3), dtype=np.uint8)
projects / platform / upstream / opencv.git / commitdiff