connected-components

How to implement connected component labeling in python with open cv? This is an image example: I need connected component labeling to separate objects on a black and white image. The OpenCV 3.0 docs for connectedComponents() don't mention Python but it actually is implemented. See for e.g. this SO question . On OpenCV 3.4.0 and above, the docs do include the Python signatures, as can be seen on the current master docs . The function call is simple: retval, labels = cv2.connectedComponents(img) and you can specify a parameter connectivity to check for 4- or 8-way (default) connectivity. The

I would like to find all the connected components of a graph where the components have more than one element. using the clusters gives the membership to different clusters and using cliques does not give connected components. This is a follow up from multiple intersection of lists in R My main goal was to find all the groups of lists which have elements in common with each other. Thanks in advance! You can use the results from clusters to subset your nodes according to the cluster size. library(igraph) # example graph set.seed(1) g <- erdos.renyi.game(20, 1/20) V(g)$name <- letters[1:20] par

I am looking for an example of how to use OpenCV's ConnectedComponentsWithStats() function in python, note this is only available with OpenCV 3 or newer. The official documentation only shows the API for C++, even though the function exists when compiled for python. I could not find it anywhere online. The function works as follows: # Import the cv2 library import cv2 # Read the image you want connected components of src = cv2.imread('/directorypath/image.bmp') # Threshold it so it becomes binary ret, thresh = cv2.threshold(src,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU) # You need to choose 4 or

I have been trying to find all connected components using 8 neighbors in a binary image, without using the function "bwlabel". For example, my input matrix is: a = 1 1 0 0 0 0 0 1 1 0 0 1 1 0 1 1 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 I would to have something like this: a = 1 1 0 0 0 0 0 1 1 0 0 2 2 0 1 1 0 0 0 2 0 1 1 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 There are 3 connected objects in this image. This is a common problem in image processing. There are many variations, such as flood filling a region in an image, or finding what pixels belong to the same region. One common