程序是参考了两部分的别人的代码，一个是找出照片中的人头，另一个是对两个人头照片做对比。
# -*- coding: utf-8 -*- # feimengjuan # 利用python实现多种方法来实现图像识别 import cv2 import numpy as np from matplotlib import pyplot as plt, image # 最简单的以灰度直方图作为相似比较的实现 def classify_gray_hist(image1, image2, size=(256, 256)): # 先计算直方图 # 几个参数必须用方括号括起来 # 这里直接用灰度图计算直方图，所以是使用第一个通道， # 也可以进行通道分离后，得到多个通道的直方图 # bins 取为16 image1 = cv2.resize(image1, size) image2 = cv2.resize(image2, size) hist1 = cv2.calcHist([image1], [0], None, [256], [0.0, 255.0]) hist2 = cv2.calcHist([image2], [0], None, [256], [0.0, 255.0]) # 可以比较下直方图 plt.plot(range(256), hist1, ‘r‘) plt.plot(range(256), hist2, ‘b‘) plt.show() # 计算直方图的重合度 degree = 0 for i in range(len(hist1)): if hist1[i] != hist2[i]: degree = degree + (1 - abs(hist1[i] - hist2[i]) / max(hist1[i], hist2[i])) else: degree = degree + 1 degree = degree / len(hist1) return degree # 计算单通道的直方图的相似值 def calculate(image1, image2): hist1 = cv2.calcHist([image1], [0], None, [256], [0.0, 255.0]) hist2 = cv2.calcHist([image2], [0], None, [256], [0.0, 255.0]) # 计算直方图的重合度 degree = 0 for i in range(len(hist1)): if hist1[i] != hist2[i]: degree = degree + (1 - abs(hist1[i] - hist2[i]) / max(hist1[i], hist2[i])) else: degree = degree + 1 degree = degree / len(hist1) return degree # 通过得到每个通道的直方图来计算相似度 def classify_hist_with_split(image1, image2, size=(256, 256)): # 将图像resize后，分离为三个通道，再计算每个通道的相似值 image1 = cv2.resize(image1, size) image2 = cv2.resize(image2, size) sub_image1 = cv2.split(image1) sub_image2 = cv2.split(image2) sub_data = 0 for im1, im2 in zip(sub_image1, sub_image2): sub_data += calculate(im1, im2) sub_data = sub_data / 3 return sub_data # 平均哈希算法计算 def classify_aHash(image1, image2): image1 = cv2.resize(image1, (8, 8)) image2 = cv2.resize(image2, (8, 8)) gray1 = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY) gray2 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY) hash1 = getHash(gray1) hash2 = getHash(gray2) return Hamming_distance(hash1, hash2) def classify_pHash(image1, image2): image1 = cv2.resize(image1, (32, 32)) image2 = cv2.resize(image2, (32, 32)) gray1 = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY) gray2 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY) # 将灰度图转为浮点型，再进行dct变换 dct1 = cv2.dct(np.float32(gray1)) dct2 = cv2.dct(np.float32(gray2)) # 取左上角的8*8，这些代表图片的最低频率 # 这个操作等价于c++中利用opencv实现的掩码操作 # 在python中进行掩码操作，可以直接这样取出图像矩阵的某一部分 dct1_roi = dct1[0:8, 0:8] dct2_roi = dct2[0:8, 0:8] hash1 = getHash(dct1_roi) hash2 = getHash(dct2_roi) return Hamming_distance(hash1, hash2) # 输入灰度图，返回hash def getHash(image): avreage = np.mean(image) hash = [] for i in range(image.shape[0]): for j in range(image.shape[1]): if image[i, j] > avreage: hash.append(1) else: hash.append(0) return hash # 计算汉明距离 def Hamming_distance(hash1, hash2): num = 0 for index in range(len(hash1)): if hash1[index] != hash2[index]: num += 1 return num if __name__ == ‘__main__‘: face_cascade = cv2.CascadeClassifier(‘haarcascade_frontalface_alt2.xml‘) face_cascade.load(‘F:\pycharm\py2_7\Lib\site-packages\cv2\data\haarcascade_frontalface_default.xml‘) scaling_factor = 0.5 img1 = cv2.imread(‘d://68.jpg‘) #img1 = cv2.resize(img1,(300,300)) img1 = cv2.resize(img1, None, fx=scaling_factor * 3, fy=scaling_factor * 3, interpolation=cv2.INTER_AREA) gray = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY) face_rects = face_cascade.detectMultiScale(gray, 1.1, 5) for (x, y, w, h) in face_rects: img1 = img1[y:y + h,x:x + w] #cv2.rectangle(img1, (x, y), (x + w, y + h), (0, 255, 0), 3) img2 = cv2.imread(‘d://69.jpg‘) #img2 = cv2.resize(img2,(300,300)) img2 = cv2.resize(img2, None, fx=scaling_factor * 3, fy=scaling_factor * 3, interpolation=cv2.INTER_AREA) gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY) face_rects2 = face_cascade.detectMultiScale(gray2, 1.1, 5) for (x, y, w, h) in face_rects2: img2 = img2[y:y + h,x:x + w] #cv2.rectangle(img2, (x, y), (x + w, y + h), (0, 255, 0), 3) #degree = classify_gray_hist(img1, img2) degree = classify_hist_with_split(img1,img2) #degree = classify_aHash(img1,img2) #degree = classify_pHash(img1,img2) out = format(‘相似度：%f‘ % (float)(degree*100)) print out img1 = cv2.resize(img1, (300, 300)) cv2.imshow(‘img1‘, img1) img2 = cv2.resize(img2, (300, 300)) cv2.imshow(‘img2‘,img2) #degree = classify_hist_with_split(img1,img2) # degree = classify_aHash(img1,img2) # degree = classify_pHash(img1,img2) #print degree cv2.waitKey(0)