我已经修改了您的脚本，以便您可以在测试图像中找到（大约）绿色百分比。我添加了一些 cmets 来解释代码：
# Imports
import cv2
import numpy as np
# Read image
imagePath = "D://opencvImages//"
img = cv2.imread(imagePath+"leaves.jpg")
# Here, you define your target color as
# a tuple of three values: RGB
green = [130, 158, 0]
# You define an interval that covers the values
# in the tuple and are below and above them by 20
diff = 20
# Be aware that opencv loads image in BGR format,
# that's why the color values have been adjusted here:
boundaries = [([green[2], green[1]-diff, green[0]-diff],
[green[2]+diff, green[1]+diff, green[0]+diff])]
# Scale your BIG image into a small one:
scalePercent = 0.3
# Calculate the new dimensions
width = int(img.shape[1] * scalePercent)
height = int(img.shape[0] * scalePercent)
newSize = (width, height)
# Resize the image:
img = cv2.resize(img, newSize, None, None, None, cv2.INTER_AREA)
# check out the image resized:
cv2.imshow("img resized", img)
cv2.waitKey(0)
# for each range in your boundary list:
for (lower, upper) in boundaries:
# You get the lower and upper part of the interval:
lower = np.array(lower, dtype=np.uint8)
upper = np.array(upper, dtype=np.uint8)
# cv2.inRange is used to binarize (i.e., render in white/black) an image
# All the pixels that fall inside your interval [lower, uipper] will be white
# All the pixels that do not fall inside this interval will
# be rendered in black, for all three channels:
mask = cv2.inRange(img, lower, upper)
# Check out the binary mask:
cv2.imshow("binary mask", mask)
cv2.waitKey(0)
# Now, you AND the mask and the input image
# All the pixels that are white in the mask will
# survive the AND operation, all the black pixels
# will remain black
output = cv2.bitwise_and(img, img, mask=mask)
# Check out the ANDed mask:
cv2.imshow("ANDed mask", output)
cv2.waitKey(0)
# You can use the mask to count the number of white pixels.
# Remember that the white pixels in the mask are those that
# fall in your defined range, that is, every white pixel corresponds
# to a green pixel. Divide by the image size and you got the
# percentage of green pixels in the original image:
ratio_green = cv2.countNonZero(mask)/(img.size/3)
# This is the color percent calculation, considering the resize I did earlier.
colorPercent = (ratio_green * 100) / scalePercent
# Print the color percent, use 2 figures past the decimal point
print('green pixel percentage:', np.round(colorPercent, 2))
# numpy's hstack is used to stack two images horizontally,
# so you see the various images generated in one figure:
cv2.imshow("images", np.hstack([img, output]))
cv2.waitKey(0)
输出：
green pixel percentage: 89.89
我已经制作了一些图像，这是绿色的二进制掩码：
这是ANDed 出掩码和输入图像：
关于这个sn-p的一些补充说明：
-
加载带有OpenCV 的图像时要小心，因为它们已加载到
BGR 格式而不是通常的 RGB。在这里，sn-p 有这个
通过反转边界列表中的元素来覆盖，但保持
睁大眼睛看看这个常见的陷阱。
-
您的输入图像太大而无法正确显示
cv2.imshow。我调整了它的大小并进行了处理。在末尾，
你看我在最后的百分比中考虑了这个调整大小的比例
计算。
-
取决于您定义的目标颜色和您的差异
使用，您可能会产生负值。在这种情况下，对于
例如，对于 R = 0 值，在减去 diff 之后，您将
获取-20。当您对颜色进行编码时，这没有意义
无符号 8 位的强度。这些值必须在[0, 255] 范围内。
使用此方法注意负值。
现在，您可能会发现该方法不是很健壮。根据您的操作，您可以切换到HSV color space 以获得更好、更准确的二进制掩码。
你可以试试HSV-based这个掩码：
# The HSV mask values, defined for the green color:
lowerValues = np.array([29, 89, 70])
upperValues = np.array([179, 255, 255])
# Convert the image to HSV:
hsvImage = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
# Create the HSV mask
hsvMask = cv2.inRange(hsvImage, lowerValues, upperValues)
# AND mask & input image:
hsvOutput = cv2.bitwise_and(img, img, mask=hsvMask)
这会给你这个漂亮的蒙版图像：