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Merge pull request #9827 from ryanfox:patch-2

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Alexander Alekhin 2017-10-11 10:58:04 +00:00
commit 1ea1ff197d
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doc/py_tutorials/py_calib3d/py_depthmap/py_depthmap.markdown
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@ -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.