python编写自适应积分二值化代码

以下是Python编写自适应积分二值化代码的示例：

import cv2

def adaptive_integral_thresholding(image, block_size=51, constant=2):
    # Convert the image to grayscale
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    
    # Calculate the integral image
    integral = cv2.integral(gray)
    
    # Initialize the output image
    output = gray.copy()
    
    # Loop through each pixel in the image
    for i in range(gray.shape[0]):
        for j in range(gray.shape[1]):
            # Calculate the coordinates of the block around the pixel
            y1 = max(0, i - block_size // 2)
            y2 = min(gray.shape[0] - 1, i + block_size // 2)
            x1 = max(0, j - block_size // 2)
            x2 = min(gray.shape[1] - 1, j + block_size // 2)
            
            # Calculate the sum and count of pixels in the block
            count = (y2 - y1 + 1) * (x2 - x1 + 1)
            if y1 == 0 and x1 == 0:
                # Special case for the top left corner of the image
                s = integral[y2, x2]
            elif y1 == 0:
                # Special case for the top row of the image
                s = integral[y2, x2] - integral[y2, x1 - 1]
            elif x1 == 0:
                # Special case for the left column of the image
                s = integral[y2, x2] - integral[y1 - 1, x2]
            else:
                # General case
                s = integral[y2, x2] - integral[y2, x1 - 1] - integral[y1 - 1, x2] + integral[y1 - 1, x1 - 1]
            
            # Calculate the threshold for the block
            threshold = (s / count) + constant
            
            # Set the pixel value in the output image
            if gray[i, j] > threshold:
                output[i, j] = 255
            else:
                output[i, j] = 0
    
    return output

该函数接受一个彩色图像并返回一个二值化图像，使用自适应积分阈值技术。它计算图像的积分图像，然后对每个像素计算其周围的块的平均值，并使用该平均值加上一个常数作为阈值。如果像素的值大于阈值，则将其设置为255（白色），否则将其设置为0（黑色）。该函数使用默认的块大小为51和常数为2，但可以使用函数的参数进行更改。