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Image Segmentation with Distance Transform and Watershed Algorithm {#tutorial_distance_transform}
=============
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
----
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).
@include samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp
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
![](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:
@snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp black_bg
![](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):
@snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp sharp
![](images/laplace.jpeg)
![](images/sharp.jpeg)
-# 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
![](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:
@snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp dist
![](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:
@snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp peaks
![](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:
@snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp seeds
![](images/markers.jpeg)
-# Finally, we can apply the watershed algorithm, and visualize the result:
@snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp watershed
![](images/final.jpeg)
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