faceRecognition.zip_DEMO_FaceRecognition_opencv_人脸识别_人脸识别demo

#coding=utf-8 #人脸识别类 - 使用face_recognition模块 import cv2 import face_recognition import os path = os.path.abspath(os.path.dirname(__file__)) # 模型数据图片目录 path = os.path.join(path, 'img') cap = cv2.VideoCapture(0) total_image_name = [] total_face_encoding = [] for fn in os.listdir(path): #fn 表示的是文件名q total_face_encoding.append( face_recognition.face_encodings( face_recognition.load_image_file(path + "/" + fn))[0]) fn = fn[:(len(fn) - 4)] #截取图片名（这里应该把images文件中的图片名命名为为人物名） total_image_name.append(fn) #图片名字列表 while (1): ret, frame = cap.read() # 发现在视频帧所有的脸和face_enqcodings face_locations = face_recognition.face_locations(frame) face_encodings = face_recognition.face_encodings(frame, face_locations) # 在这个视频帧中循环遍历每个人脸 for (top, right, bottom, left), face_encoding in zip( face_locations, face_encodings): # 看看面部是否与已知人脸相匹配。 name = "Unknown" for i, v in enumerate(total_face_encoding): match = face_recognition.compare_faces( [v], face_encoding, tolerance=0.5) if match[0]: name = total_image_name[i] break # 画出一个框，框住脸 cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2) # 画出一个带名字的标签，放在框下 cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED) font = cv2.FONT_HERSHEY_DUPLEX cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1) # 显示结果图像 cv2.imshow('Video', frame) if cv2.waitKey(1) & 0xFF == ord('q'): break cap.release() cv2.destroyAllWindows()