init - 初始化项目
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Lee Nony
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Histogram Comparison {#tutorial_histogram_comparison}
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====================
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@tableofcontents
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@prev_tutorial{tutorial_histogram_calculation}
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@next_tutorial{tutorial_back_projection}
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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 how to:
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- Use the function @ref cv::compareHist to get a numerical parameter that express how well two
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histograms match with each other.
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- Use different metrics to compare histograms
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Theory
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------
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- To compare two histograms ( \f$H_{1}\f$ and \f$H_{2}\f$ ), first we have to choose a *metric*
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(\f$d(H_{1}, H_{2})\f$) to express how well both histograms match.
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- OpenCV implements the function @ref cv::compareHist to perform a comparison. It also offers 4
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different metrics to compute the matching:
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-# **Correlation ( CV_COMP_CORREL )**
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\f[d(H_1,H_2) = \frac{\sum_I (H_1(I) - \bar{H_1}) (H_2(I) - \bar{H_2})}{\sqrt{\sum_I(H_1(I) - \bar{H_1})^2 \sum_I(H_2(I) - \bar{H_2})^2}}\f]
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where
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\f[\bar{H_k} = \frac{1}{N} \sum _J H_k(J)\f]
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and \f$N\f$ is the total number of histogram bins.
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-# **Chi-Square ( CV_COMP_CHISQR )**
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\f[d(H_1,H_2) = \sum _I \frac{\left(H_1(I)-H_2(I)\right)^2}{H_1(I)}\f]
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-# **Intersection ( method=CV_COMP_INTERSECT )**
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\f[d(H_1,H_2) = \sum _I \min (H_1(I), H_2(I))\f]
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-# **Bhattacharyya distance ( CV_COMP_BHATTACHARYYA )**
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\f[d(H_1,H_2) = \sqrt{1 - \frac{1}{\sqrt{\bar{H_1} \bar{H_2} N^2}} \sum_I \sqrt{H_1(I) \cdot H_2(I)}}\f]
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Code
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----
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- **What does this program do?**
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- Loads a *base image* and 2 *test images* to be compared with it.
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- Generate 1 image that is the lower half of the *base image*
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- Convert the images to HSV format
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- Calculate the H-S histogram for all the images and normalize them in order to compare them.
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- Compare the histogram of the *base image* with respect to the 2 test histograms, the
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histogram of the lower half base image and with the same base image histogram.
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- Display the numerical matching parameters obtained.
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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/compareHist_Demo.cpp)
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- **Code at glance:**
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@include samples/cpp/tutorial_code/Histograms_Matching/compareHist_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_comparison/CompareHistDemo.java)
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- **Code at glance:**
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@include samples/java/tutorial_code/Histograms_Matching/histogram_comparison/CompareHistDemo.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_comparison/compareHist_Demo.py)
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- **Code at glance:**
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@include samples/python/tutorial_code/Histograms_Matching/histogram_comparison/compareHist_Demo.py
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@end_toggle
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Explanation
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-----------
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- Load the base image (src_base) and the other two test images:
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/Histograms_Matching/compareHist_Demo.cpp Load three images with different environment settings
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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_comparison/CompareHistDemo.java Load three images with different environment settings
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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_comparison/compareHist_Demo.py Load three images with different environment settings
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@end_toggle
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- Convert them to HSV format:
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/Histograms_Matching/compareHist_Demo.cpp Convert to HSV
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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_comparison/CompareHistDemo.java Convert to HSV
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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_comparison/compareHist_Demo.py Convert to HSV
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@end_toggle
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- Also, create an image of half the base image (in HSV format):
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/Histograms_Matching/compareHist_Demo.cpp Convert to HSV half
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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_comparison/CompareHistDemo.java Convert to HSV half
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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_comparison/compareHist_Demo.py Convert to HSV half
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@end_toggle
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- Initialize the arguments to calculate the histograms (bins, ranges and channels H and S ).
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/Histograms_Matching/compareHist_Demo.cpp Using 50 bins for hue and 60 for saturation
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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_comparison/CompareHistDemo.java Using 50 bins for hue and 60 for saturation
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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_comparison/compareHist_Demo.py Using 50 bins for hue and 60 for saturation
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@end_toggle
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- Calculate the Histograms for the base image, the 2 test images and the half-down base image:
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/Histograms_Matching/compareHist_Demo.cpp Calculate the histograms for the HSV images
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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_comparison/CompareHistDemo.java Calculate the histograms for the HSV images
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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_comparison/compareHist_Demo.py Calculate the histograms for the HSV images
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@end_toggle
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- Apply sequentially the 4 comparison methods between the histogram of the base image (hist_base)
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and the other histograms:
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/Histograms_Matching/compareHist_Demo.cpp Apply the histogram comparison methods
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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_comparison/CompareHistDemo.java Apply the histogram comparison methods
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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_comparison/compareHist_Demo.py Apply the histogram comparison methods
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@end_toggle
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Results
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-------
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-# We use as input the following images:
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where the first one is the base (to be compared to the others), the other 2 are the test images.
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We will also compare the first image with respect to itself and with respect of half the base
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image.
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-# We should expect a perfect match when we compare the base image histogram with itself. Also,
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compared with the histogram of half the base image, it should present a high match since both
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are from the same source. For the other two test images, we can observe that they have very
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different lighting conditions, so the matching should not be very good:
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-# Here the numeric results we got with OpenCV 3.4.1:
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*Method* | Base - Base | Base - Half | Base - Test 1 | Base - Test 2
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----------------- | ------------ | ------------ | -------------- | ---------------
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*Correlation* | 1.000000 | 0.880438 | 0.20457 | 0.0664547
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*Chi-square* | 0.000000 | 4.6834 | 2697.98 | 4763.8
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*Intersection* | 18.8947 | 13.022 | 5.44085 | 2.58173
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*Bhattacharyya* | 0.000000 | 0.237887 | 0.679826 | 0.874173
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For the *Correlation* and *Intersection* methods, the higher the metric, the more accurate the
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match. As we can see, the match *base-base* is the highest of all as expected. Also we can observe
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that the match *base-half* is the second best match (as we predicted). For the other two metrics,
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the less the result, the better the match. We can observe that the matches between the test 1 and
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test 2 with respect to the base are worse, which again, was expected.
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