如何在python中使用带有统计信息的openCV的连接组件？
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How to use openCV's connected components with stats in python?
提问by Zack Knopp
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.
我正在寻找如何在 python 中使用 OpenCV 的 ConnectedComponentsWithStats() 函数的示例，请注意这仅适用于 OpenCV 3 或更新版本。官方文档只显示了 C++ 的 API，即使该函数在为 python 编译时存在。我在网上的任何地方都找不到。
回答by Zack Knopp
The function works as follows:
该函数的工作原理如下：
# 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]
Labelsis a matrix the size of the input image where each element has a value equal to its label.
标签是输入图像大小的矩阵，其中每个元素的值等于其标签。
Statsis 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:
Stats是函数计算的统计数据的矩阵。它的长度等于标签的数量，宽度等于统计数据的数量。它可以与 OpenCV 文档一起使用：
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_LEFTThe leftmost (x) coordinate which is the inclusive start of the bounding box in the horizontal direction.
- cv2.CC_STAT_TOPThe topmost (y) coordinate which is the inclusive start of the bounding box in the vertical direction.
- cv2.CC_STAT_WIDTHThe horizontal size of the bounding box
- cv2.CC_STAT_HEIGHTThe vertical size of the bounding box
- cv2.CC_STAT_AREAThe total area (in pixels) of the connected component
每个标签的统计输出，包括背景标签，有关可用统计信息，请参见下文。统计信息通过stats[label, COLUMN]访问 ，其中可用的列定义如下。
- cv2.CC_STAT_LEFT最左边的 (x) 坐标，它是水平方向边界框的包含起点。
- cv2.CC_STAT_TOP最顶部 (y) 坐标，它是垂直方向边界框的包含起点。
- cv2.CC_STAT_WIDTH边界框的水平尺寸
- cv2.CC_STAT_HEIGHT边界框的垂直尺寸
- cv2.CC_STAT_AREA连接组件的总面积（以像素为单位）
Centroidsis a matrix with the x and y locations of each centroid. The row in this matrix corresponds to the label number.
质心是一个矩阵，其中包含每个质心的 x 和 y 位置。该矩阵中的行对应于标签编号。
回答by Dan Erez
I have come here a few times to remember how it works and each time I have to reduce the above code to :
我来过这里几次来记住它是如何工作的，每次我都必须将上面的代码简化为：
_, thresh = cv2.threshold(src,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
connectivity = 4 # You need to choose 4 or 8 for connectivity type
num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(thresh , connectivity , cv2.CV_32S)
Hopefully, it's useful for everyone :)
希望对大家有用:)
回答by Barel Levy
Adding to Zack Knoppanswer,
If you are using a grayscale image you can simply use:
添加Zack Knopp回答，如果您使用的是灰度图像，您可以简单地使用：
import cv2
import numpy as np
src = cv2.imread("path\to\image.png", 0)
binary_map = (src > 0).astype(np.uint8)
connectivity = 4 # or whatever you prefer
output = cv2.connectedComponentsWithStats(binary_map, connectivity, cv2.CV_32S)
When I tried using Zack Knoppanswer on a grayscale image it didn't work and this was my solution.
当我尝试Zack Knopp在灰度图像上使用答案时，它不起作用，这是我的解决方案。
