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diff --git a/doc/tutorials/imgproc/imgtrans/distance_transformation/distance_transform.markdown b/doc/tutorials/imgproc/imgtrans/distance_transformation/distance_transform.markdown index 12ef87fc7d..ca1ec47258 100644 --- a/doc/tutorials/imgproc/imgtrans/distance_transformation/distance_transform.markdown +++ b/doc/tutorials/imgproc/imgtrans/distance_transformation/distance_transform.markdown @@ -16,42 +16,152 @@ 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/3.4/samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp). +[here](https://github.com/opencv/opencv/tree/3.4/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/3.4/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/3.4/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: - @snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp load_image -  +- 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 + + + +- 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 + + + +- 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 + + + + +- 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 + + + +- 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 + + + +- 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 + + + +- 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 --# 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: - @snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp black_bg -  +@add_toggle_python +@snippet samples/python/tutorial_code/ImgTrans/distance_transformation/imageSegmentation.py seeds +@end_toggle --# 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): - @snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp sharp -  -  + --# Now we transform our new sharpened source image to a grayscale and a binary one, respectively: - @snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp bin -  +- Finally, we can apply the watershed algorithm, and visualize the result: --# 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: - @snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp dist -  +@add_toggle_cpp +@snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp watershed +@end_toggle --# We threshold the *dist* image and then perform some morphology operation (i.e. dilation) in order to extract the peaks from the above image: - @snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp peaks -  +@add_toggle_java +@snippet samples/java/tutorial_code/ImgTrans/distance_transformation/ImageSegmentationDemo.java watershed +@end_toggle --# From each blob then we create a seed/marker for the watershed algorithm with the help of the @ref cv::findContours function: - @snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp seeds -  +@add_toggle_python +@snippet samples/python/tutorial_code/ImgTrans/distance_transformation/imageSegmentation.py watershed +@end_toggle --# Finally, we can apply the watershed algorithm, and visualize the result: - @snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp watershed - 
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