init - 初始化项目
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Histogram Equalization {#tutorial_histogram_equalization}
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======================
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@tableofcontents
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@prev_tutorial{tutorial_warp_affine}
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@next_tutorial{tutorial_histogram_calculation}
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| | |
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| -: | :- |
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| Original author | Ana Huamán |
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| Compatibility | OpenCV >= 3.0 |
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Goal
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----
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In this tutorial you will learn:
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- What an image histogram is and why it is useful
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- To equalize histograms of images by using the OpenCV function @ref cv::equalizeHist
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Theory
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------
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### What is an Image Histogram?
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- It is a graphical representation of the intensity distribution of an image.
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- It quantifies the number of pixels for each intensity value considered.
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### What is Histogram Equalization?
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- It is a method that improves the contrast in an image, in order to stretch out the intensity
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range (see also the corresponding <a href="https://en.wikipedia.org/wiki/Histogram_equalization">Wikipedia entry</a>).
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- To make it clearer, from the image above, you can see that the pixels seem clustered around the
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middle of the available range of intensities. What Histogram Equalization does is to *stretch
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out* this range. Take a look at the figure below: The green circles indicate the
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*underpopulated* intensities. After applying the equalization, we get an histogram like the
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figure in the center. The resulting image is shown in the picture at right.
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### How does it work?
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- Equalization implies *mapping* one distribution (the given histogram) to another distribution (a
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wider and more uniform distribution of intensity values) so the intensity values are spread
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over the whole range.
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- To accomplish the equalization effect, the remapping should be the *cumulative distribution
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function (cdf)* (more details, refer to *Learning OpenCV*). For the histogram \f$H(i)\f$, its
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*cumulative distribution* \f$H^{'}(i)\f$ is:
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\f[H^{'}(i) = \sum_{0 \le j < i} H(j)\f]
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To use this as a remapping function, we have to normalize \f$H^{'}(i)\f$ such that the maximum value
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is 255 ( or the maximum value for the intensity of the image ). From the example above, the
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cumulative function is:
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- Finally, we use a simple remapping procedure to obtain the intensity values of the equalized
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image:
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\f[equalized( x, y ) = H^{'}( src(x,y) )\f]
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Code
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----
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- **What does this program do?**
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- Loads an image
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- Convert the original image to grayscale
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- Equalize the Histogram by using the OpenCV function @ref cv::equalizeHist
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- Display the source and equalized images in a window.
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@add_toggle_cpp
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- **Downloadable code**: Click
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[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/Histograms_Matching/EqualizeHist_Demo.cpp)
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- **Code at glance:**
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@include samples/cpp/tutorial_code/Histograms_Matching/EqualizeHist_Demo.cpp
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@end_toggle
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@add_toggle_java
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- **Downloadable code**: Click
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[here](https://github.com/opencv/opencv/tree/master/samples/java/tutorial_code/Histograms_Matching/histogram_equalization/EqualizeHistDemo.java)
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- **Code at glance:**
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@include samples/java/tutorial_code/Histograms_Matching/histogram_equalization/EqualizeHistDemo.java
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@end_toggle
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@add_toggle_python
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- **Downloadable code**: Click
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[here](https://github.com/opencv/opencv/tree/master/samples/python/tutorial_code/Histograms_Matching/histogram_equalization/EqualizeHist_Demo.py)
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- **Code at glance:**
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@include samples/python/tutorial_code/Histograms_Matching/histogram_equalization/EqualizeHist_Demo.py
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@end_toggle
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Explanation
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-----------
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- Load the source image:
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/Histograms_Matching/EqualizeHist_Demo.cpp Load image
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@end_toggle
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@add_toggle_java
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@snippet samples/java/tutorial_code/Histograms_Matching/histogram_equalization/EqualizeHistDemo.java Load image
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@end_toggle
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@add_toggle_python
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@snippet samples/python/tutorial_code/Histograms_Matching/histogram_equalization/EqualizeHist_Demo.py Load image
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@end_toggle
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- Convert it to grayscale:
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/Histograms_Matching/EqualizeHist_Demo.cpp Convert to grayscale
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@end_toggle
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@add_toggle_java
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@snippet samples/java/tutorial_code/Histograms_Matching/histogram_equalization/EqualizeHistDemo.java Convert to grayscale
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@end_toggle
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@add_toggle_python
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@snippet samples/python/tutorial_code/Histograms_Matching/histogram_equalization/EqualizeHist_Demo.py Convert to grayscale
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@end_toggle
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- Apply histogram equalization with the function @ref cv::equalizeHist :
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/Histograms_Matching/EqualizeHist_Demo.cpp Apply Histogram Equalization
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@end_toggle
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@add_toggle_java
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@snippet samples/java/tutorial_code/Histograms_Matching/histogram_equalization/EqualizeHistDemo.java Apply Histogram Equalization
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@end_toggle
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@add_toggle_python
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@snippet samples/python/tutorial_code/Histograms_Matching/histogram_equalization/EqualizeHist_Demo.py Apply Histogram Equalization
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@end_toggle
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As it can be easily seen, the only arguments are the original image and the output (equalized)
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image.
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- Display both images (original and equalized):
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/Histograms_Matching/EqualizeHist_Demo.cpp Display results
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@end_toggle
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@add_toggle_java
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@snippet samples/java/tutorial_code/Histograms_Matching/histogram_equalization/EqualizeHistDemo.java Display results
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@end_toggle
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@add_toggle_python
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@snippet samples/python/tutorial_code/Histograms_Matching/histogram_equalization/EqualizeHist_Demo.py Display results
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@end_toggle
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- Wait until user exists the program
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/Histograms_Matching/EqualizeHist_Demo.cpp Wait until user exits the program
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@end_toggle
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@add_toggle_java
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@snippet samples/java/tutorial_code/Histograms_Matching/histogram_equalization/EqualizeHistDemo.java Wait until user exits the program
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@end_toggle
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@add_toggle_python
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@snippet samples/python/tutorial_code/Histograms_Matching/histogram_equalization/EqualizeHist_Demo.py Wait until user exits the program
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@end_toggle
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Results
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-------
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-# To appreciate better the results of equalization, let's introduce an image with not much
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contrast, such as:
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which, by the way, has this histogram:
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notice that the pixels are clustered around the center of the histogram.
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-# After applying the equalization with our program, we get this result:
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this image has certainly more contrast. Check out its new histogram like this:
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Notice how the number of pixels is more distributed through the intensity range.
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@note
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Are you wondering how did we draw the Histogram figures shown above? Check out the following
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tutorial!
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