一种可能的解决方案是将图像转换为
CMYK
颜色空间并提取 K
(关键 - 黑色)通道,对其进行阈值处理并应用一些形态学来清理二值图像。
OpenCV没有实现从
BGR
到CMYK
的转换,所以我们必须手动计算K
通道。代码如下所示:
# Imports
import cv2
import numpy as np
# Read image
imagePath = "D://opencvImages//"
inputImage = cv2.imread(imagePath + "A6RXi.png")
# Conversion to CMYK (just the K channel):
# Convert to float and divide by 255:
imgFloat = inputImage.astype(np.float) / 255.
# Calculate channel K:
kChannel = 1 - np.max(imgFloat, axis=2)
# Convert back to uint 8:
kChannel = (255 * kChannel).astype(np.uint8)
这是K(黑色)通道:
现在,使用固定值对图像进行阈值处理。在这种情况下,我将阈值设置为
190
:
# Threshold image:
binaryThresh = 190
_, binaryImage = cv2.threshold(kChannel, binaryThresh, 255, cv2.THRESH_BINARY)
这是二值图像:
它有点吵,但如果我们实现区域过滤器,我们可以去除较小的斑点。该函数在本文末尾定义。让我们应用 minimum 值为
100
的过滤器。所有小于此的斑点都将被删除:
# Filter small blobs:
minArea = 100
binaryImage = areaFilter(minArea, binaryImage)
这是过滤后的图像:
酷。让我们用封闭过滤器改善斑点的形态:
# Use a little bit of morphology to clean the mask:
# Set kernel (structuring element) size:
kernelSize = 3
# Set morph operation iterations:
opIterations = 2
# Get the structuring element:
morphKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (kernelSize, kernelSize))
# Perform closing:
binaryImage = cv2.morphologyEx(binaryImage, cv2.MORPH_CLOSE, morphKernel, None, None, opIterations, cv2.BORDER_REFLECT101)
cv2.imshow("binaryImage [closed]", binaryImage)
cv2.waitKey(0)
这是最终结果:
这就是
areaFilter
功能。它接收一个最小区域和一个二值图像,它返回没有小斑点的图像:
def areaFilter(minArea, inputImage):
# Perform an area filter on the binary blobs:
componentsNumber, labeledImage, componentStats, componentCentroids = \
cv2.connectedComponentsWithStats(inputImage, connectivity=4)
# Get the indices/labels of the remaining components based on the area stat
# (skip the background component at index 0)
remainingComponentLabels = [i for i in range(1, componentsNumber) if componentStats[i][4] >= minArea]
# Filter the labeled pixels based on the remaining labels,
# assign pixel intensity to 255 (uint8) for the remaining pixels
filteredImage = np.where(np.isin(labeledImage, remainingComponentLabels) == True, 255, 0).astype('uint8')
return filteredImage