一、环境配置

运行一下命令完成环境配置

pip install opencv-python numpy

二、源代码

import os
import cv2
import numpy as np


def compute_mean_std(Img_folders):
    all_file = []
    for images_path in Img_folders:
        all_file.append([os.path.join(images_path, filename) for filename in os.listdir(images_path)])
    print('------------------')

    # 统计所有图片的数量
    num_images = len(all_file[0])
    print(num_images)

    # 逐张图计算均值和方差
    mean_r = mean_g = mean_b = 0
    std_r = std_g = std_b = 0
    for i in range(len(all_file)):
        for j in range(len(all_file[i])):
            # print(all_file[i][j])
            img = cv2.imread(all_file[i][j])

            img = np.asarray(img)
            # img = img.astype(np.float32) / 255.
            img = img.astype(np.float32)

            # cv2
            im_mean, im_stds = cv2.meanStdDev(img)
            mean_r += im_mean[0]
            mean_g += im_mean[1]
            mean_b += im_mean[2]

            std_r += im_stds[0]
            std_g += im_stds[1]
            std_b += im_stds[2]
            print(f'means:[{im_mean[0][0]},{im_mean[1][0]},{im_mean[2][0]}]')
            print(f'std:[{im_stds[0][0]},{im_stds[1][0]},{im_stds[2][0]}]\n')

    # 计算平均值
    aa_r = mean_r/num_images
    aa_g = mean_g/num_images
    aa_b = mean_b/num_images

    bb_r = std_r/num_images
    bb_g = std_g/num_images
    bb_b = std_b/num_images

    print(' last mean: ', aa_r, aa_g, aa_b)
    print(' last stds: ', bb_r, bb_g, bb_b) 


if __name__ == '__main__':
    Img_folders = [r'E:\trashdatasets\combination\train\images']
    compute_mean_std(Img_folders)

• 若您的数据集分布在不同的文件夹，只需修改Img_folders即可，将每个文件夹的路径写到该列表中。

三、心灵的救赎