1.對(duì)于RGB三通道圖片，直接用兩層for循環(huán)的話，效率比較低

2.可以先將RGB圖片轉(zhuǎn)為灰度圖片，再利用numpy.where的廣播機(jī)制統(tǒng)計(jì)像素個(gè)數(shù)。這里有一個(gè)前提是提前知道與灰度圖片的像素值相對(duì)應(yīng)RGB顏色。

代碼如下：

from PIL import Imageimport numpy as npimport cv2img_L = np.array(Image.open(’test.png’).convert('L'))img_RGB = np.array(Image.open(’test.png’).convert('RGB'))# temp = {}# for i in range(img_L.shape[0]):# for j in range(img_L.shape[1]):# if not temp.get(int(img_L[i][j])):# temp[int(img_L[i][j])] = list(img_RGB[i][j])# print(temp)#這里得到灰度像素值0對(duì)應(yīng)(0,0,0),62對(duì)應(yīng)(19,69,139)color_0_0_0 = np.where(img_L == 0)[0].shape[0]color_19_69_139 = np.where(img_L == 62)[0].shape[0]pixel_sum = img_L.shape[0] * img_L.shape[1]print('0_0_0 像素個(gè)數(shù)：{} 占比：%{}'.format(color_0_0_0,color_0_0_0/pixel_sum*100))print('19_69_139 像素個(gè)數(shù)：{} 占比：%{}'.format(color_19_69_139,color_19_69_139/pixel_sum*100))

補(bǔ)充：OpenCV---如何統(tǒng)計(jì)圖像的像素分布值個(gè)數(shù)（6）

import cv2 as cvimport matplotlib.pyplot as pltimport numpy as npdef statistics(): src = cv.imread('D:/matplotlib/0.jpg') cv.imshow('q',src) h,w,ch = np.shape(src) gray = cv.cvtColor(src,cv.COLOR_BGR2GRAY) cv.imshow('gray',gray) hest = np.zeros([256],dtype = np.int32) for row in range(h): for col in range(w): pv = gray[row,col] hest[pv] +=1 plt.plot(hest,color = 'r') plt.xlim([0,256]) plt.show() cv.waitKey(0) cv.destroyAllWindows()statistics()運(yùn)行效果：

像素分布統(tǒng)計(jì)圖

import cv2 as cvimport matplotlib.pyplot as pltimport numpy as npdef statistics(): src = cv.imread('D:/matplotlib/0.jpg') cv.imshow('q',src) h,w,ch = np.shape(src) #讀取圖像屬性 gray = cv.cvtColor(src,cv.COLOR_BGR2GRAY) #將圖像轉(zhuǎn)換成灰度圖， cv.imshow('gray',gray) hest = np.zeros([256],dtype = np.int32) #建立空白數(shù)組 for row in range(h): for col in range(w): pv = gray[row,col] hest[pv] +=1 #統(tǒng)計(jì)不同像素值出現(xiàn)的頻率 plt.plot(hest,color = 'r') plt.xlim([0,256]) plt.show() #畫出統(tǒng)計(jì)圖 cv.waitKey(0) cv.destroyAllWindows()statistics()

以上為個(gè)人經(jīng)驗(yàn)，希望能給大家一個(gè)參考，也希望大家多多支持好吧啦網(wǎng)。如有錯(cuò)誤或未考慮完全的地方，望不吝賜教。