1.对于RGB三通道图片,直接用两层for循环的话,效率比较低
2.可以先将RGB图片转为灰度图片,再利用numpy.where的广播机制统计像素个数。这里有一个前提是提前知道与灰度图片的像素值相对应RGB颜色。
代码如下:
from PIL import Image import numpy as np import cv2 img_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) #这里得到灰度像素值0对应(0,0,0),62对应(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 像素个数:{} 占比:%{}".format(color_0_0_0,color_0_0_0/pixel_sum*100)) print("19_69_139 像素个数:{} 占比:%{}".format(color_19_69_139,color_19_69_139/pixel_sum*100))
补充:OpenCV---如何统计图像的像素分布值个数(6)
代码如下:
import cv2 as cv import matplotlib.pyplot as plt import numpy as np def 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()
运行效果:
像素分布统计图
代码解释:
import cv2 as cv import matplotlib.pyplot as plt import numpy as np def statistics(): src = cv.imread("D:/matplotlib/0.jpg") cv.imshow("q",src) h,w,ch = np.shape(src) #读取图像属性 gray = cv.cvtColor(src,cv.COLOR_BGR2GRAY) #将图像转换成灰度图, cv.imshow("gray",gray) hest = np.zeros([256],dtype = np.int32) #建立空白数组 for row in range(h): for col in range(w): pv = gray[row,col] hest[pv] +=1 #统计不同像素值出现的频率 plt.plot(hest,color = "r") plt.xlim([0,256]) plt.show() #画出统计图 cv.waitKey(0) cv.destroyAllWindows() statistics()
以上为个人经验,希望能给大家一个参考,也希望大家多多支持服务器之家。如有错误或未考虑完全的地方,望不吝赐教。
原文链接:https://blog.csdn.net/qq_33768643/article/details/105724834