python人脸识别demo

import face_recognition as fr import cv2 import numpy as np import os import time from playsound import playsound path = "./train/" known_names = [] known_name_encodings = [] print("train:") images = os.listdir(path) for file_name in images: print(file_name) image = fr.load_image_file(path + file_name) image_path = path + file_name encoding = fr.face_encodings(image)[0] #print(encoding) known_name_encodings.append(encoding) known_names.append(os.path.splitext(os.path.basename(image_path))[0].capitalize()) print(known_names) #调用摄像头 cam = cv2.VideoCapture(0) minW = 0.1*cam.get(3) minH = 0.1*cam.get(4) while True: print("cam.read") ret,img = cam.read() #展示捕获图片 print("imshow") cv2.imshow('camera',img) k = cv2.waitKey(20) print("input key is ",k) if k == 27: # esc break #test_image = "./test/test.jpg" #image = cv2.imread(test_image) image = img # image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) print("face_locations:") face_locations = fr.face_locations(image) print("face_encodings:") face_encodings = fr.face_encodings(image, face_locations) for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings): print("compare_faces:") matches = fr.compare_faces(known_name_encodings, face_encoding, tolerance=0.4) name = "" print(matches) print("face_distance:") face_distances = fr.face_distance(known_name_encodings, face_encoding) print(face_distances) best_match = np.argmin(face_distances) print(best_match) if matches[best_match]: name = known_names[best_match] print(name) if(name.lower()=="lintax"): playsound('./audio/hello_lintax.mp3') cv2.rectangle(image, (left, top), (right, bottom), (0, 0, 255), 2) cv2.rectangle(image, (left, bottom - 15), (right, bottom), (0, 0, 255), cv2.FILLED) font = cv2.FONT_HERSHEY_DUPLEX cv2.putText(image, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1) #cv2.imwrite("./output.jpg", image) time.sleep(5) #释放资源 cam.release() cv2.destroyAllWindows()