Python版OpenCV中cv2.compare()函数及相关代码段含义解析咨询

First, let's restate your code for context:

def volume_analysis(self, threshold_image):
    gamma = 0.05
    flag = False
    no_of_pixels = cv2.compare(threshold_image, 0, cv2.CMP_GT).sum() / 255
    if self.prevNoOfPixels is not None:
        if abs(self.prevNoOfPixels - no_of_pixels) / self.prevNoOfPixels > gamma:
            flag = True
    self.prevNoOfPixels = no_of_pixels
    return flag

Your Initial Guess: Mostly Correct! #

You're on the right track with your reasoning, but there's a key detail to clarify:

• cv2.compare(threshold_image, 0, cv2.CMP_GT) does compare each pixel in your threshold image to 0. For every pixel that’s greater than 0, it outputs 255 (full white); for pixels equal to or less than 0, it outputs 0 (black). This gives you a binary matrix matching the size of your input image.
• .sum() calculates the total of all values in this binary matrix. Since all matching pixels are 255, dividing by 255 converts that sum into the actual count of non-zero pixels in the original threshold image—this part you nailed perfectly!

Breaking Down Each Component #

1. cv2.compare(src1, src2, cmpop) #

This function performs element-wise comparison between two inputs (either two images, or one image and a scalar value) and returns a new image of the same size as the input:

• src1: The first input (your threshold_image here)
• src2: The second input (a scalar 0 in your code; OpenCV automatically expands this to a matrix matching threshold_image's dimensions)
• cmpop: The type of comparison to run. cv2.CMP_GT is just one option—other common ones include:
  • cv2.CMP_EQ: Equal to
  • cv2.CMP_LT: Less than
  • cv2.CMP_GE: Greater than or equal to
  • cv2.CMP_LE: Less than or equal to
  • cv2.CMP_NE: Not equal to

2. cv2.CMP_GT #

This is an enum value that tells cv2.compare to execute a "greater than" check. For each pixel position, if the value in src1 is strictly larger than the value in src2, the output pixel is set to 255 (the maximum possible value for an 8-bit image); otherwise, it’s set to 0.

3. .sum() on the Result Matrix #

In OpenCV’s Python bindings, the output of cv2.compare is a numpy.ndarray (since OpenCV relies heavily on NumPy for array operations). The .sum() method here is NumPy’s array summation function, which adds up every element in the matrix.

Since our comparison result uses 255 for matching pixels and 0 for non-matching, dividing the total sum by 255 gives us the exact count of pixels in the original threshold image that were greater than 0—exactly what your code needs for no_of_pixels.

Note on OpenCV Python Documentation #

It’s true that OpenCV’s Python docs are often bundled with C++ docs, but the behavior of the Python bindings mirrors the C++ implementation almost exactly. You can reference OpenCV’s core module docs (even if examples are in C++) for parameter definitions and behavior explanations. For array operations like .sum(), you can directly refer to NumPy’s official documentation, since all OpenCV images in Python are NumPy arrays under the hood.

内容的提问来源于stack exchange，提问作者A7sus4