From a96c5b5d9049672f8e9504e1d2ec339520416d74 Mon Sep 17 00:00:00 2001 From: Ryan Fox Date: Tue, 10 Oct 2017 21:37:26 -0500 Subject: [PATCH] fix some grammatical errors --- doc/py_tutorials/py_calib3d/py_depthmap/py_depthmap.markdown | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/doc/py_tutorials/py_calib3d/py_depthmap/py_depthmap.markdown b/doc/py_tutorials/py_calib3d/py_depthmap/py_depthmap.markdown index 7d9a125..8ecbd2c 100644 --- a/doc/py_tutorials/py_calib3d/py_depthmap/py_depthmap.markdown +++ b/doc/py_tutorials/py_calib3d/py_depthmap/py_depthmap.markdown @@ -5,14 +5,14 @@ Goal ---- In this session, - - We will learn to create depth map from stereo images. + - We will learn to create a depth map from stereo images. Basics ------ -In last session, we saw basic concepts like epipolar constraints and other related terms. We also +In the last session, we saw basic concepts like epipolar constraints and other related terms. We also saw that if we have two images of same scene, we can get depth information from that in an intuitive -way. Below is an image and some simple mathematical formulas which proves that intuition. (Image +way. Below is an image and some simple mathematical formulas which prove that intuition. (Image Courtesy : ![image](images/stereo_depth.jpg) @@ -24,7 +24,7 @@ following result: \f$x\f$ and \f$x'\f$ are the distance between points in image plane corresponding to the scene point 3D and their camera center. \f$B\f$ is the distance between two cameras (which we know) and \f$f\f$ is the focal -length of camera (already known). So in short, above equation says that the depth of a point in a +length of camera (already known). So in short, the above equation says that the depth of a point in a scene is inversely proportional to the difference in distance of corresponding image points and their camera centers. So with this information, we can derive the depth of all pixels in an image. @@ -35,7 +35,7 @@ how we can do it with OpenCV. Code ---- -Below code snippet shows a simple procedure to create disparity map. +Below code snippet shows a simple procedure to create a disparity map. @code{.py} import numpy as np import cv2 @@ -49,7 +49,7 @@ disparity = stereo.compute(imgL,imgR) plt.imshow(disparity,'gray') plt.show() @endcode -Below image contains the original image (left) and its disparity map (right). As you can see, result +Below image contains the original image (left) and its disparity map (right). As you can see, the result is contaminated with high degree of noise. By adjusting the values of numDisparities and blockSize, you can get a better result. -- 2.7.4