Python opencv计算批量图片的BGR各自的均值

#coding:utf-8#第一种方式 很慢很慢 自己写的如何计算均值
'''
import cv2
import os
def access_pixels(frame):
    print(frame.shape)  #shape内包含三个元素：按顺序为高、宽、通道数
    height = frame.shape[0]
    weight = frame.shape[1]
    channels = frame.shape[2]
    value_b = 0
    value_g = 0
    value_r = 0
    print("weight : %s, height : %s, channel : %s" %(weight, height, channels))
    for row in range(height):            #遍历高
        for col in range(weight):         #遍历宽
            value_b = value_b + frame[row, col, 0]
            value_g = value_g + frame[row, col, 1]
            value_r = value_r + frame[row, col, 2]
    singlePicValue_b = value_b / (height * weight)
    singlePicValue_g = value_g / (height * weight)
    singlePicValue_r = value_r / (height * weight)
    return singlePicValue_b,singlePicValue_g,singlePicValue_rdef computeMeanValue(path):
    imgList1 = os.listdir(path)
    mean_b = 0
    mean_g = 0
    mean_r = 0
    for i in range(len(imgList1)):
        srcframe = cv2.imread(os.path.join(path,imgList1[i]))
        singlePicValue_b,singlePicValue_g,singlePicValue_r = access_pixels(srcframe)
        mean_b = mean_b + singlePicValue_b
        mean_g = mean_g + singlePicValue_g
        mean_r = mean_r + singlePicValue_r
    all_mean_b = mean_b / len(imgList1)
    all_mean_g = mean_g / len(imgList1)
    all_mean_r = mean_r / len(imgList1)
    print (i)
    return all_mean_b,all_mean_g,all_mean_rall_mean_b,all_mean_g,all_mean_r = computeMeanValue('./spinningData/src')
print (all_mean_b,all_mean_g,all_mean_r)
'''#第二种方式  借助numpy  很快 几秒
import os
import cv2
import numpy as np
path = './spinningData/src'
def compute(path):
    file_names = os.listdir(path)
    per_image_Rmean = []
    per_image_Gmean = []
    per_image_Bmean = []
    for file_name in file_names:
        img = cv2.imread(os.path.join(path, file_name), 1)
        per_image_Bmean.append(np.mean(img[:,:,0]))
        per_image_Gmean.append(np.mean(img[:,:,1]))
        per_image_Rmean.append(np.mean(img[:,:,2]))
    R_mean = np.mean(per_image_Rmean)
    G_mean = np.mean(per_image_Gmean)
    B_mean = np.mean(per_image_Bmean)
    return R_mean, G_mean, B_meanif __name__ == '__main__':
    R, G, B= compute(path)
    print(R, G ,B)