init - 初始化项目

doc/tutorials/imgproc/histograms/back_projection/back_projection.markdown

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+Back Projection {#tutorial_back_projection}
+===============
+
+@tableofcontents
+
+@prev_tutorial{tutorial_histogram_comparison}
+@next_tutorial{tutorial_template_matching}
+
+|    |    |
+| -: | :- |
+| Original author | Ana Huamán |
+| Compatibility | OpenCV >= 3.0 |
+
+Goal
+----
+
+In this tutorial you will learn:
+
+-   What is Back Projection and why it is useful
+-   How to use the OpenCV function @ref cv::calcBackProject to calculate Back Projection
+-   How to mix different channels of an image by using the OpenCV function @ref cv::mixChannels
+
+Theory
+------
+
+### What is Back Projection?
+
+-   Back Projection is a way of recording how well the pixels of a given image fit the distribution
+    of pixels in a histogram model.
+-   To make it simpler: For Back Projection, you calculate the histogram model of a feature and then
+    use it to find this feature in an image.
+-   Application example: If you have a histogram of flesh color (say, a Hue-Saturation histogram ),
+    then you can use it to find flesh color areas in an image:
+
+### How does it work?
+
+-   We explain this by using the skin example:
+-   Let's say you have gotten a skin histogram (Hue-Saturation) based on the image below. The
+    histogram besides is going to be our *model histogram* (which we know represents a sample of
+    skin tonality). You applied some mask to capture only the histogram of the skin area:
+    ![T0](images/Back_Projection_Theory0.jpg)
+    ![T1](images/Back_Projection_Theory1.jpg)
+
+-   Now, let's imagine that you get another hand image (Test Image) like the one below: (with its
+    respective histogram):
+    ![T2](images/Back_Projection_Theory2.jpg)
+    ![T3](images/Back_Projection_Theory3.jpg)
+
+
+-   What we want to do is to use our *model histogram* (that we know represents a skin tonality) to
+    detect skin areas in our Test Image. Here are the steps
+    -#  In each pixel of our Test Image (i.e. \f$p(i,j)\f$ ), collect the data and find the
+        correspondent bin location for that pixel (i.e. \f$( h_{i,j}, s_{i,j} )\f$ ).
+    -#  Lookup the *model histogram* in the correspondent bin - \f$( h_{i,j}, s_{i,j} )\f$ - and read
+        the bin value.
+    -#  Store this bin value in a new image (*BackProjection*). Also, you may consider to normalize
+        the *model histogram* first, so the output for the Test Image can be visible for you.
+    -#  Applying the steps above, we get the following BackProjection image for our Test Image:
+
+        ![](images/Back_Projection_Theory4.jpg)
+
+    -#  In terms of statistics, the values stored in *BackProjection* represent the *probability*
+        that a pixel in *Test Image* belongs to a skin area, based on the *model histogram* that we
+        use. For instance in our Test image, the brighter areas are more probable to be skin area
+        (as they actually are), whereas the darker areas have less probability (notice that these
+        "dark" areas belong to surfaces that have some shadow on it, which in turns affects the
+        detection).
+
+Code
+----
+
+-   **What does this program do?**
+    -   Loads an image
+    -   Convert the original to HSV format and separate only *Hue* channel to be used for the
+        Histogram (using the OpenCV function @ref cv::mixChannels )
+    -   Let the user to enter the number of bins to be used in the calculation of the histogram.
+    -   Calculate the histogram (and update it if the bins change) and the backprojection of the
+        same image.
+    -   Display the backprojection and the histogram in windows.
+
+@add_toggle_cpp
+-   **Downloadable code**:
+    -   Click
+        [here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/Histograms_Matching/calcBackProject_Demo1.cpp)
+        for the basic version (explained in this tutorial).
+    -   For stuff slightly fancier (using H-S histograms and floodFill to define a mask for the
+        skin area) you can check the [improved
+        demo](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/Histograms_Matching/calcBackProject_Demo2.cpp)
+    -   ...or you can always check out the classical
+        [camshiftdemo](https://github.com/opencv/opencv/tree/master/samples/cpp/camshiftdemo.cpp)
+        in samples.
+
+-   **Code at glance:**
+@include samples/cpp/tutorial_code/Histograms_Matching/calcBackProject_Demo1.cpp
+@end_toggle
+
+@add_toggle_java
+-   **Downloadable code**:
+    -   Click
+        [here](https://github.com/opencv/opencv/tree/master/samples/java/tutorial_code/Histograms_Matching/back_projection/CalcBackProjectDemo1.java)
+        for the basic version (explained in this tutorial).
+    -   For stuff slightly fancier (using H-S histograms and floodFill to define a mask for the
+        skin area) you can check the [improved
+        demo](https://github.com/opencv/opencv/tree/master/samples/java/tutorial_code/Histograms_Matching/back_projection/CalcBackProjectDemo2.java)
+    -   ...or you can always check out the classical
+        [camshiftdemo](https://github.com/opencv/opencv/tree/master/samples/cpp/camshiftdemo.cpp)
+        in samples.
+
+-   **Code at glance:**
+@include samples/java/tutorial_code/Histograms_Matching/back_projection/CalcBackProjectDemo1.java
+@end_toggle
+
+@add_toggle_python
+-   **Downloadable code**:
+    -   Click
+        [here](https://github.com/opencv/opencv/tree/master/samples/python/tutorial_code/Histograms_Matching/back_projection/calcBackProject_Demo1.py)
+        for the basic version (explained in this tutorial).
+    -   For stuff slightly fancier (using H-S histograms and floodFill to define a mask for the
+        skin area) you can check the [improved
+        demo](https://github.com/opencv/opencv/tree/master/samples/python/tutorial_code/Histograms_Matching/back_projection/calcBackProject_Demo2.py)
+    -   ...or you can always check out the classical
+        [camshiftdemo](https://github.com/opencv/opencv/tree/master/samples/cpp/camshiftdemo.cpp)
+        in samples.
+
+-   **Code at glance:**
+@include samples/python/tutorial_code/Histograms_Matching/back_projection/calcBackProject_Demo1.py
+@end_toggle
+
+Explanation
+-----------
+
+-   Read the input image:
+
+    @add_toggle_cpp
+    @snippet samples/cpp/tutorial_code/Histograms_Matching/calcBackProject_Demo1.cpp Read the image
+    @end_toggle
+
+    @add_toggle_java
+    @snippet samples/java/tutorial_code/Histograms_Matching/back_projection/CalcBackProjectDemo1.java Read the image
+    @end_toggle
+
+    @add_toggle_python
+    @snippet samples/python/tutorial_code/Histograms_Matching/back_projection/calcBackProject_Demo1.py Read the image
+    @end_toggle
+
+-   Transform it to HSV format:
+
+    @add_toggle_cpp
+    @snippet samples/cpp/tutorial_code/Histograms_Matching/calcBackProject_Demo1.cpp Transform it to HSV
+    @end_toggle
+
+    @add_toggle_java
+    @snippet samples/java/tutorial_code/Histograms_Matching/back_projection/CalcBackProjectDemo1.java Transform it to HSV
+    @end_toggle
+
+    @add_toggle_python
+    @snippet samples/python/tutorial_code/Histograms_Matching/back_projection/calcBackProject_Demo1.py Transform it to HSV
+    @end_toggle
+
+-   For this tutorial, we will use only the Hue value for our 1-D histogram (check out the fancier
+    code in the links above if you want to use the more standard H-S histogram, which yields better
+    results):
+
+    @add_toggle_cpp
+    @snippet samples/cpp/tutorial_code/Histograms_Matching/calcBackProject_Demo1.cpp Use only the Hue value
+    @end_toggle
+
+    @add_toggle_java
+    @snippet samples/java/tutorial_code/Histograms_Matching/back_projection/CalcBackProjectDemo1.java Use only the Hue value
+    @end_toggle
+
+    @add_toggle_python
+    @snippet samples/python/tutorial_code/Histograms_Matching/back_projection/calcBackProject_Demo1.py Use only the Hue value
+    @end_toggle
+
+-   as you see, we use the function @ref cv::mixChannels to get only the channel 0 (Hue) from
+    the hsv image. It gets the following parameters:
+    -   **&hsv:** The source array from which the channels will be copied
+    -   **1:** The number of source arrays
+    -   **&hue:** The destination array of the copied channels
+    -   **1:** The number of destination arrays
+    -   **ch[] = {0,0}:** The array of index pairs indicating how the channels are copied. In this
+        case, the Hue(0) channel of &hsv is being copied to the 0 channel of &hue (1-channel)
+    -   **1:** Number of index pairs
+
+-   Create a Trackbar for the user to enter the bin values. Any change on the Trackbar means a call
+    to the **Hist_and_Backproj** callback function.
+
+    @add_toggle_cpp
+    @snippet samples/cpp/tutorial_code/Histograms_Matching/calcBackProject_Demo1.cpp Create Trackbar to enter the number of bins
+    @end_toggle
+
+    @add_toggle_java
+    @snippet samples/java/tutorial_code/Histograms_Matching/back_projection/CalcBackProjectDemo1.java Create Trackbar to enter the number of bins
+    @end_toggle
+
+    @add_toggle_python
+    @snippet samples/python/tutorial_code/Histograms_Matching/back_projection/calcBackProject_Demo1.py Create Trackbar to enter the number of bins
+    @end_toggle
+
+-   Show the image and wait for the user to exit the program:
+
+    @add_toggle_cpp
+    @snippet samples/cpp/tutorial_code/Histograms_Matching/calcBackProject_Demo1.cpp Show the image
+    @end_toggle
+
+    @add_toggle_java
+    @snippet samples/java/tutorial_code/Histograms_Matching/back_projection/CalcBackProjectDemo1.java Show the image
+    @end_toggle
+
+    @add_toggle_python
+    @snippet samples/python/tutorial_code/Histograms_Matching/back_projection/calcBackProject_Demo1.py Show the image
+    @end_toggle
+
+-   **Hist_and_Backproj function:** Initialize the arguments needed for @ref cv::calcHist . The
+    number of bins comes from the Trackbar:
+
+    @add_toggle_cpp
+    @snippet samples/cpp/tutorial_code/Histograms_Matching/calcBackProject_Demo1.cpp initialize
+    @end_toggle
+
+    @add_toggle_java
+    @snippet samples/java/tutorial_code/Histograms_Matching/back_projection/CalcBackProjectDemo1.java initialize
+    @end_toggle
+
+    @add_toggle_python
+    @snippet samples/python/tutorial_code/Histograms_Matching/back_projection/calcBackProject_Demo1.py initialize
+    @end_toggle
+
+-   Calculate the Histogram and normalize it to the range \f$[0,255]\f$
+
+    @add_toggle_cpp
+    @snippet samples/cpp/tutorial_code/Histograms_Matching/calcBackProject_Demo1.cpp Get the Histogram and normalize it
+    @end_toggle
+
+    @add_toggle_java
+    @snippet samples/java/tutorial_code/Histograms_Matching/back_projection/CalcBackProjectDemo1.java Get the Histogram and normalize it
+    @end_toggle
+
+    @add_toggle_python
+    @snippet samples/python/tutorial_code/Histograms_Matching/back_projection/calcBackProject_Demo1.py Get the Histogram and normalize it
+    @end_toggle
+
+-   Get the Backprojection of the same image by calling the function @ref cv::calcBackProject
+
+    @add_toggle_cpp
+    @snippet samples/cpp/tutorial_code/Histograms_Matching/calcBackProject_Demo1.cpp Get Backprojection
+    @end_toggle
+
+    @add_toggle_java
+    @snippet samples/java/tutorial_code/Histograms_Matching/back_projection/CalcBackProjectDemo1.java Get Backprojection
+    @end_toggle
+
+    @add_toggle_python
+    @snippet samples/python/tutorial_code/Histograms_Matching/back_projection/calcBackProject_Demo1.py Get Backprojection
+    @end_toggle
+
+-   all the arguments are known (the same as used to calculate the histogram), only we add the
+    backproj matrix, which will store the backprojection of the source image (&hue)
+
+-   Display backproj:
+
+    @add_toggle_cpp
+    @snippet samples/cpp/tutorial_code/Histograms_Matching/calcBackProject_Demo1.cpp Draw the backproj
+    @end_toggle
+
+    @add_toggle_java
+    @snippet samples/java/tutorial_code/Histograms_Matching/back_projection/CalcBackProjectDemo1.java Draw the backproj
+    @end_toggle
+
+    @add_toggle_python
+    @snippet samples/python/tutorial_code/Histograms_Matching/back_projection/calcBackProject_Demo1.py Draw the backproj
+    @end_toggle
+
+-   Draw the 1-D Hue histogram of the image:
+
+    @add_toggle_cpp
+    @snippet samples/cpp/tutorial_code/Histograms_Matching/calcBackProject_Demo1.cpp Draw the histogram
+    @end_toggle
+
+    @add_toggle_java
+    @snippet samples/java/tutorial_code/Histograms_Matching/back_projection/CalcBackProjectDemo1.java Draw the histogram
+    @end_toggle
+
+    @add_toggle_python
+    @snippet samples/python/tutorial_code/Histograms_Matching/back_projection/calcBackProject_Demo1.py Draw the histogram
+    @end_toggle
+
+Results
+-------
+
+Here are the output by using a sample image ( guess what? Another hand ). You can play with the
+bin values and you will observe how it affects the results:
+![R0](images/Back_Projection1_Source_Image.jpg)
+![R1](images/Back_Projection1_Histogram.jpg)
+![R2](images/Back_Projection1_BackProj.jpg)