How to use openCV's connected components with stats in python?

问题:

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.

回答1:

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 8 for connectivity type connectivity = 4   # Perform the operation output = cv2.connectedComponentsWithStats(thresh, connectivity, cv2.CV_32S) # Get the results # The first cell is the number of labels num_labels = output[0] # The second cell is the label matrix labels = output[1] # The third cell is the stat matrix stats = output[2] # The fourth cell is the centroid matrix centroids = output[3] 

Labels is a matrix the size of the input image where each element has a value equal to its label.

Stats is a matrix of the stats that the function calculates. It has a length equal to the number of labels and a width equal to the number of stats. It can be used with the OpenCV documentation for it:

Statistics output for each label, including the background label, see below for available statistics. Statistics are accessed via stats[label, COLUMN] where available columns are defined below.

• cv2.CC_STAT_LEFT The leftmost (x) coordinate which is the inclusive start of the bounding box in the horizontal direction.
• cv2.CC_STAT_TOP The topmost (y) coordinate which is the inclusive start of the bounding box in the vertical direction.
• cv2.CC_STAT_WIDTH The horizontal size of the bounding box
• cv2.CC_STAT_HEIGHT The vertical size of the bounding box
• cv2.CC_STAT_AREA The total area (in pixels) of the connected component

Centroids is a matrix with the x and y locations of each centroid. The row in this matrix corresponds to the label number.

回答2:

Adding to Zack Knopp answer, If you are using a grayscale image you can simply use:

import cv2 import numpy as np  src = cv2.imread("path\\to\\image.png", 0) binary_map = (src > 0).astype(np.uint8) output = cv2.connectedComponentsWithStats(binary_map, connectivity, cv2.CV_32S) 

When I tried using Zack Knopp answer on a grayscale image it didn't work and this was my solution.

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