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.

解決方案

The OpenCV connectedComponents() docs don't mention Python but it actually is implemented. See for e.g. this SO question.

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 difference is that 4-way connectivity just checks the top, bottom, left, and right pixels and sees if they connect; 8-way checks if any of the eight neighboring pixels connect. If you have diagonal connections (like you do here) you should specify connectivity=8. Note that it just numbers each component and gives them increasing integer labels starting at 0. So all the zeros are connected, all the ones are connected, etc. If you want to visualize them, you can map those numbers to specific colors. I like to map them to different hues, combine them into an HSV image, and then convert to BGR to display. Here's an example with your image:

import cv2

import numpy as np

img = cv2.imread('eGaIy.jpg', 0)

img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)[1] # ensure binary

ret, labels = cv2.connectedComponents(img)

# Map component labels to hue val

label_hue = np.uint8(179*labels/np.max(labels))

blank_ch = 255*np.ones_like(label_hue)

labeled_img = cv2.merge([label_hue, blank_ch, blank_ch])

# cvt to BGR for display

labeled_img = cv2.cvtColor(labeled_img, cv2.COLOR_HSV2BGR)

# set bg label to black

labeled_img[label_hue==0] = 0

cv2.imshow('labeled.png', labeled_img)

cv2.waitKey()

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