图像均值化仅生成竖线的原因排查与代码问题分析

我尝试开发一个计算图像像素均值的程序，但运行后仅生成了竖线。以下是当前代码：

from typing import List

import cv2 as cv
import numpy as np


def main():
    # Importing two of the same imagae for debugigng purposes
    a = cv.imread("Datasets/OTIS/OTIS_PNG_Gray/Fixed Patterns/Pattern1/Pattern1_001.png", cv.IMREAD_GRAYSCALE)
    b = cv.imread("Datasets/OTIS/OTIS_PNG_Gray/Fixed Patterns/Pattern1/Pattern1_001.png", cv.IMREAD_GRAYSCALE)

    s = [a, b]
    avg = [[0] * len(a[0])] * len(a)
    print(f"rows: {len(a)} cols: {len(a[0])}")
    print(f"rows: {len(avg)} cols: {len(avg[0])}")

    for i in range(len(a)):
        for j in range(len(a[0])):
            # print(f"({i}, {j}): {temp_mean(s, i, j)}")
            avg[i][j] = temp_mean(s, i, j) / 255

    avim = np.array(avg)
    print(f"rows: {len(avim)} cols: {len(avim[0])}")

    cv.imshow("title", avim)

    cv.waitKey(0)


def temp_mean(seq: List[List[List[any]]], i: int, j: int):
    out = 0

    for im in seq:
        out += im[i][j]

    return out / len(seq)


if __name__ == '__main__':
    main()

原图：

生成图：

问题根源 #

1. 列表初始化错误：[[0] * len(a[0])] * len(a) 会创建多个指向同一行列表的引用，修改任意一行的元素时，所有行的对应位置都会被同步修改，最终导致图像呈现竖线。
2. 类型注解不匹配：temp_mean函数的seq参数标注为List[List[List[any]]]，但实际传入的是numpy数组列表，类型定义错误。
3. 图像数据类型问题：计算后的avim为float类型，直接传入cv.imshow可能存在显示异常，需确保数据类型符合要求。

修复后的代码 #

from typing import List
import cv2 as cv
import numpy as np


def main():
    # 导入两张相同图片用于调试
    a = cv.imread("Datasets/OTIS/OTIS_PNG_Gray/Fixed Patterns/Pattern1/Pattern1_001.png", cv.IMREAD_GRAYSCALE)
    b = cv.imread("Datasets/OTIS/OTIS_PNG_Gray/Fixed Patterns/Pattern1/Pattern1_001.png", cv.IMREAD_GRAYSCALE)

    s = [a, b]
    # 正确初始化二维列表，避免引用重复
    avg = [[0 for _ in range(len(a[0]))] for _ in range(len(a))]
    print(f"rows: {len(a)} cols: {len(a[0])}")
    print(f"rows: {len(avg)} cols: {len(avg[0])}")

    for i in range(len(a)):
        for j in range(len(a[0])):
            avg[i][j] = temp_mean(s, i, j) / 255

    # 转换为float32类型，适配cv.imshow的显示要求
    avim = np.array(avg, dtype=np.float32)
    print(f"rows: {len(avim)} cols: {len(avim[0])}")

    cv.imshow("title", avim)
    cv.waitKey(0)
    cv.destroyAllWindows()  # 释放窗口资源


def temp_mean(seq: List[np.ndarray], i: int, j: int) -> float:
    out = 0.0
    for im in seq:
        out += im[i][j]
    return out / len(seq)


if __name__ == '__main__':
    main()

更高效的实现方式 #

利用numpy的广播特性可以省去手动遍历像素的步骤，大幅提升效率：

import cv2 as cv
import numpy as np

def main():
    a = cv.imread("Datasets/OTIS/OTIS_PNG_Gray/Fixed Patterns/Pattern1/Pattern1_001.png", cv.IMREAD_GRAYSCALE)
    b = cv.imread("Datasets/OTIS/OTIS_PNG_Gray/Fixed Patterns/Pattern1/Pattern1_001.png", cv.IMREAD_GRAYSCALE)
    
    # 直接计算多张图片的均值
    avg = np.mean([a, b], axis=0) / 255
    # 转换为float32类型确保显示正常
    avg = avg.astype(np.float32)
    
    cv.imshow("title", avg)
    cv.waitKey(0)
    cv.destroyAllWindows()

if __name__ == '__main__':
    main()

内容的提问来源于stack exchange，提问作者A Stick