init - 初始化项目

doc/tutorials/imgproc/histograms/histogram_equalization/histogram_equalization.markdown

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+Histogram Equalization {#tutorial_histogram_equalization}
+======================
+
+@tableofcontents
+
+@prev_tutorial{tutorial_warp_affine}
+@next_tutorial{tutorial_histogram_calculation}
+
+|    |    |
+| -: | :- |
+| Original author | Ana Huamán |
+| Compatibility | OpenCV >= 3.0 |
+
+Goal
+----
+
+In this tutorial you will learn:
+
+-   What an image histogram is and why it is useful
+-   To equalize histograms of images by using the OpenCV function @ref cv::equalizeHist
+
+Theory
+------
+
+### What is an Image Histogram?
+
+-   It is a graphical representation of the intensity distribution of an image.
+-   It quantifies the number of pixels for each intensity value considered.
+
+![](images/Histogram_Equalization_Theory_0.jpg)
+
+### What is Histogram Equalization?
+
+-   It is a method that improves the contrast in an image, in order to stretch out the intensity
+    range (see also the corresponding <a href="https://en.wikipedia.org/wiki/Histogram_equalization">Wikipedia entry</a>).
+-   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.
+
+![](images/Histogram_Equalization_Theory_1.jpg)
+
+### How does it work?
+
+-   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 spread
+    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 \f$H(i)\f$, its
+    *cumulative distribution* \f$H^{'}(i)\f$ is:
+
+    \f[H^{'}(i) = \sum_{0 \le j < i} H(j)\f]
+
+    To use this as a remapping function, we have to normalize \f$H^{'}(i)\f$ 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:
+
+    ![](images/Histogram_Equalization_Theory_2.jpg)
+
+-   Finally, we use a simple remapping procedure to obtain the intensity values of the equalized
+    image:
+
+    \f[equalized( x, y ) = H^{'}( src(x,y) )\f]
+
+Code
+----
+
+-   **What does this program do?**
+    -   Loads an image
+    -   Convert the original image to grayscale
+    -   Equalize the Histogram by using the OpenCV function @ref cv::equalizeHist
+    -   Display the source and equalized images in a window.
+
+@add_toggle_cpp
+-   **Downloadable code**: Click
+    [here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/Histograms_Matching/EqualizeHist_Demo.cpp)
+
+-   **Code at glance:**
+    @include samples/cpp/tutorial_code/Histograms_Matching/EqualizeHist_Demo.cpp
+@end_toggle
+
+@add_toggle_java
+-   **Downloadable code**: Click
+    [here](https://github.com/opencv/opencv/tree/master/samples/java/tutorial_code/Histograms_Matching/histogram_equalization/EqualizeHistDemo.java)
+
+-   **Code at glance:**
+    @include samples/java/tutorial_code/Histograms_Matching/histogram_equalization/EqualizeHistDemo.java
+@end_toggle
+
+@add_toggle_python
+-   **Downloadable code**: Click
+    [here](https://github.com/opencv/opencv/tree/master/samples/python/tutorial_code/Histograms_Matching/histogram_equalization/EqualizeHist_Demo.py)
+
+-   **Code at glance:**
+    @include samples/python/tutorial_code/Histograms_Matching/histogram_equalization/EqualizeHist_Demo.py
+@end_toggle
+
+Explanation
+-----------
+
+-   Load the source image:
+
+    @add_toggle_cpp
+    @snippet samples/cpp/tutorial_code/Histograms_Matching/EqualizeHist_Demo.cpp Load image
+    @end_toggle
+
+    @add_toggle_java
+    @snippet samples/java/tutorial_code/Histograms_Matching/histogram_equalization/EqualizeHistDemo.java Load image
+    @end_toggle
+
+    @add_toggle_python
+    @snippet samples/python/tutorial_code/Histograms_Matching/histogram_equalization/EqualizeHist_Demo.py Load image
+    @end_toggle
+
+-   Convert it to grayscale:
+
+    @add_toggle_cpp
+    @snippet samples/cpp/tutorial_code/Histograms_Matching/EqualizeHist_Demo.cpp Convert to grayscale
+    @end_toggle
+
+    @add_toggle_java
+    @snippet samples/java/tutorial_code/Histograms_Matching/histogram_equalization/EqualizeHistDemo.java Convert to grayscale
+    @end_toggle
+
+    @add_toggle_python
+    @snippet samples/python/tutorial_code/Histograms_Matching/histogram_equalization/EqualizeHist_Demo.py Convert to grayscale
+    @end_toggle
+
+-   Apply histogram equalization with the function @ref cv::equalizeHist :
+
+    @add_toggle_cpp
+    @snippet samples/cpp/tutorial_code/Histograms_Matching/EqualizeHist_Demo.cpp Apply Histogram Equalization
+    @end_toggle
+
+    @add_toggle_java
+    @snippet samples/java/tutorial_code/Histograms_Matching/histogram_equalization/EqualizeHistDemo.java Apply Histogram Equalization
+    @end_toggle
+
+    @add_toggle_python
+    @snippet samples/python/tutorial_code/Histograms_Matching/histogram_equalization/EqualizeHist_Demo.py Apply Histogram Equalization
+    @end_toggle
+    As it can be easily seen, the only arguments are the original image and the output (equalized)
+    image.
+
+-   Display both images (original and equalized):
+
+    @add_toggle_cpp
+    @snippet samples/cpp/tutorial_code/Histograms_Matching/EqualizeHist_Demo.cpp Display results
+    @end_toggle
+
+    @add_toggle_java
+    @snippet samples/java/tutorial_code/Histograms_Matching/histogram_equalization/EqualizeHistDemo.java Display results
+    @end_toggle
+
+    @add_toggle_python
+    @snippet samples/python/tutorial_code/Histograms_Matching/histogram_equalization/EqualizeHist_Demo.py Display results
+    @end_toggle
+
+-   Wait until user exists the program
+
+    @add_toggle_cpp
+    @snippet samples/cpp/tutorial_code/Histograms_Matching/EqualizeHist_Demo.cpp Wait until user exits the program
+    @end_toggle
+
+    @add_toggle_java
+    @snippet samples/java/tutorial_code/Histograms_Matching/histogram_equalization/EqualizeHistDemo.java Wait until user exits the program
+    @end_toggle
+
+    @add_toggle_python
+    @snippet samples/python/tutorial_code/Histograms_Matching/histogram_equalization/EqualizeHist_Demo.py Wait until user exits the program
+    @end_toggle
+
+Results
+-------
+
+-#  To appreciate better the results of equalization, let's introduce an image with not much
+    contrast, such as:
+
+    ![](images/Histogram_Equalization_Original_Image.jpg)
+
+    which, by the way, has this histogram:
+
+    ![](images/Histogram_Equalization_Original_Histogram.jpg)
+
+    notice that the pixels are clustered around the center of the histogram.
+
+-#  After applying the equalization with our program, we get this result:
+
+    ![](images/Histogram_Equalization_Equalized_Image.jpg)
+
+    this image has certainly more contrast. Check out its new histogram like this:
+
+    ![](images/Histogram_Equalization_Equalized_Histogram.jpg)
+
+    Notice how the number of pixels is more distributed through the intensity range.
+
+@note
+Are you wondering how did we draw the Histogram figures shown above? Check out the following
+tutorial!