Distance Transform

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. @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 @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 @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

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

@add_toggle_python @snippet samples/python/tutorial_code/ImgTrans/distance_transformation/imageSegmentation.py seeds @end_toggle

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