init - 初始化项目

doc/tutorials/imgproc/imgtrans/distance_transformation/distance_transform.markdown

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+Image Segmentation with Distance Transform and Watershed Algorithm {#tutorial_distance_transform}
+=============
+
+@tableofcontents
+
+@prev_tutorial{tutorial_point_polygon_test}
+@next_tutorial{tutorial_out_of_focus_deblur_filter}
+
+|    |    |
+| -: | :- |
+| Original author | Theodore Tsesmelis |
+| Compatibility | OpenCV >= 3.0 |
+
+Goal
+----
+
+In this tutorial you will learn how to:
+
+-   Use the OpenCV function @ref cv::filter2D in order to perform some laplacian filtering for image sharpening
+-   Use the OpenCV function @ref cv::distanceTransform in order to obtain the derived representation of a binary image, where the value of each pixel is replaced by its distance to the nearest background pixel
+-   Use the OpenCV function @ref cv::watershed in order to isolate objects in the image from the background
+
+Theory
+------
+
+Code
+----
+
+@add_toggle_cpp
+This tutorial code's is shown lines below. You can also download it from
+[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp).
+@include samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp
+@end_toggle
+
+@add_toggle_java
+This tutorial code's is shown lines below. You can also download it from
+[here](https://github.com/opencv/opencv/tree/master/samples/java/tutorial_code/ImgTrans/distance_transformation/ImageSegmentationDemo.java)
+@include samples/java/tutorial_code/ImgTrans/distance_transformation/ImageSegmentationDemo.java
+@end_toggle
+
+@add_toggle_python
+This tutorial code's is shown lines below. You can also download it from
+[here](https://github.com/opencv/opencv/tree/master/samples/python/tutorial_code/ImgTrans/distance_transformation/imageSegmentation.py)
+@include samples/python/tutorial_code/ImgTrans/distance_transformation/imageSegmentation.py
+@end_toggle
+
+Explanation / Result
+--------------------
+
+-   Load the source image and check if it is loaded without any problem, then show it:
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp load_image
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/ImgTrans/distance_transformation/ImageSegmentationDemo.java load_image
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/ImgTrans/distance_transformation/imageSegmentation.py load_image
+@end_toggle
+
+![](images/source.jpeg)
+
+-   Then if we have an image with a white background, it is good to transform it to black. This will help us to discriminate the foreground objects easier when we will apply the Distance Transform:
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp black_bg
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/ImgTrans/distance_transformation/ImageSegmentationDemo.java black_bg
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/ImgTrans/distance_transformation/imageSegmentation.py black_bg
+@end_toggle
+
+![](images/black_bg.jpeg)
+
+-   Afterwards we will sharpen our image in order to acute the edges of the foreground objects. We will apply a laplacian filter with a quite strong filter (an approximation of second derivative):
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp sharp
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/ImgTrans/distance_transformation/ImageSegmentationDemo.java sharp
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/ImgTrans/distance_transformation/imageSegmentation.py sharp
+@end_toggle
+
+![](images/laplace.jpeg)
+![](images/sharp.jpeg)
+
+-   Now we transform our new sharpened source image to a grayscale and a binary one, respectively:
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp bin
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/ImgTrans/distance_transformation/ImageSegmentationDemo.java bin
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/ImgTrans/distance_transformation/imageSegmentation.py bin
+@end_toggle
+
+![](images/bin.jpeg)
+
+-   We are ready now to apply the Distance Transform on the binary image. Moreover, we normalize the output image in order to be able visualize and threshold the result:
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp dist
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/ImgTrans/distance_transformation/ImageSegmentationDemo.java dist
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/ImgTrans/distance_transformation/imageSegmentation.py dist
+@end_toggle
+
+![](images/dist_transf.jpeg)
+
+-   We threshold the *dist* image and then perform some morphology operation (i.e. dilation) in order to extract the peaks from the above image:
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp peaks
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/ImgTrans/distance_transformation/ImageSegmentationDemo.java peaks
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/ImgTrans/distance_transformation/imageSegmentation.py peaks
+@end_toggle
+
+![](images/peaks.jpeg)
+
+-   From each blob then we create a seed/marker for the watershed algorithm with the help of the @ref cv::findContours function:
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp seeds
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/ImgTrans/distance_transformation/ImageSegmentationDemo.java seeds
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/ImgTrans/distance_transformation/imageSegmentation.py seeds
+@end_toggle
+
+![](images/markers.jpeg)
+
+-   Finally, we can apply the watershed algorithm, and visualize the result:
+
+@add_toggle_cpp
+@snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp watershed
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/ImgTrans/distance_transformation/ImageSegmentationDemo.java watershed
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/ImgTrans/distance_transformation/imageSegmentation.py watershed
+@end_toggle
+
+![](images/final.jpeg)