facetest.zip_opencv人证_人脸_人脸识别 opencv_人证对比_人证识别

# -*- coding: utf-8 -*- """ Created on Tue Jan 23 13:40:37 2018 @author: sczjwjh """ from PIL import Image import face_recognition import sys import numpy as np import cv2 import threading #import time global_encoding_dict = {} encodings= [] face2 = [] num_jitters = 1 rect = [] def faceencoding1(img): haar = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_default.xml') global global_encoding_dict boxes = [] # img = cv2.imread(imgfile) img[:,:,0] = cv2.equalizeHist(img[:,:,0]) img[:,:,1] = cv2.equalizeHist(img[:,:,1]) img[:,:,2] = cv2.equalizeHist(img[:,:,2]) image_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # image_gray = cv2.equalizeHist(image_gray) faces = haar.detectMultiScale(img, 1.3, 3) if len(faces)==0 : return False f_x,f_y,f_w,f_h= faces[0] face_image = img[f_y:f_y+f_h, f_x:f_x+f_w] boxes.append([0,f_w,f_h,0]) faces = face_recognition.face_locations(img) face_encoding = face_recognition.face_encodings(face_image,faces, num_jitters) if len(face_encoding)==0 : return False global_encoding_dict[0] = face_encoding[0] def face_enconde(img,boxes): global encodings global face2 global num_jitters face_encoding = face_recognition.face_encodings(img,boxes,num_jitters) if len(face_encoding) > 0: encodings.append(face_encoding[0]) face2.append(img) def faceencoding(img): haar = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_default.xml') global global_encoding_dict global encodings threads= [] boxes = [] global rect # encodings=[] # img = cv2.imread(imgfile) # image_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # faces = haar.detectMultiScale(img, 1.3, 5) img[:,:,0] = cv2.equalizeHist(img[:,:,0]) img[:,:,1] = cv2.equalizeHist(img[:,:,1]) img[:,:,2] = cv2.equalizeHist(img[:,:,2]) faces = face_recognition.face_locations(img) if len(faces)==0 : print('second img no face detected') return False for f_x, f_y, f_w, f_h in faces: face_image = img[f_y:f_y+f_h, f_x:f_x+f_w] rect.append([f_x, f_y, f_w, f_h]) boxes.append([0,f_w,f_h,0]) t = threading.Thread(target=face_enconde, args=(face_image,boxes,)) t.start() threads.append(t) # print('Num of Thread = {}'.format(len(threads))) for t in threads: t.join() # encodings.append(face_recognition.face_encodings(face_image)[0]) global_encoding_dict[1] = encodings def facecognition(img1,img2): #处理第一张图片 # face1_encoding = face_recognition.face_encodings(face_image1)[0] #处理第二张图片 #haar = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_default.xml') image1 = cv2.imread(img1) image2 = cv2.imread(img2) global rect t1 = threading.Thread(target=faceencoding1, args=(image1,)) t2 = threading.Thread(target=faceencoding, args=(image2,)) t1.start() t2.start() t1.join() t2.join() # face2_encoding = face_recognition.face_encodings(face_image2) if len(global_encoding_dict) < 2: # print('no face detected',len(global_encoding_dict)) return False face_distances = face_recognition.face_distance(global_encoding_dict[1], global_encoding_dict[0]) for index, distance in enumerate(face_distances): print(distance) if distance < 0.55: f_x, f_y, f_w, f_h = rect[index] # cv2.rectangle(image2, (f_x - 5, f_y - 5), (f_x + f_w + 5, f_y + f_h+5), (0, 255, 0), 10) # cv2.imwrite(img2,image2) # print(distance) return True return False if __name__ == '__main__': # start_time = time.clock() img1 = sys.argv[1] img2 = sys.argv[2] try: print(facecognition(img1,img2)) except: print(False) # print("run time:{}".format(time.clock() - start_time))