OpenCV  4.10.0-dev
Open Source Computer Vision
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Image Segmentation with Distance Transform and Watershed Algorithm

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Original author Theodore Tsesmelis
Compatibility OpenCV >= 3.0


In this tutorial you will learn how to:

  • Use the OpenCV function cv::filter2D in order to perform some laplacian filtering for image sharpening
  • Use the OpenCV function 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 cv::watershed in order to isolate objects in the image from the background



Explanation / Result

  • Load the source image and check if it is loaded without any problem, then show it:
  • 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:
  • 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):

  • Now we transform our new sharpened source image to a grayscale and a binary one, respectively:
  • 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:
  • We threshold the dist image and then perform some morphology operation (i.e. dilation) in order to extract the peaks from the above image:
  • From each blob then we create a seed/marker for the watershed algorithm with the help of the cv::findContours function:
  • Finally, we can apply the watershed algorithm, and visualize the result: