import face_recognition import cv2 import numpy as np import os import pickle # This is a demo of running face recognition on live video from your webcam. It's a little more complicated than the # other example, but it includes some basic performance tweaks to make things run a lot faster: # 1. Process each video frame at 1/4 resolution (though still display it at full resolution) # 2. Only detect faces in every other frame of video. # PLEASE NOTE: This example requires OpenCV (the `cv2` library) to be installed only to read from your webcam. # OpenCV is *not* required to use the face_recognition library. It's only required if you want to run this # specific demo. If you have trouble installing it, try any of the other demos that don't require it instead. # Get a reference to webcam #0 (the default one) def get_emb(file_name): if os.path.exists(file_name): file_ = face_recognition.load_image_file(file_name) emb = face_recognition.face_encodings(file_)[0] np.save(file_name.replace(".jpg",'.npy'), emb) else: emb = np.load(file_name) return emb def input_an_image(image_file, person_name, ori_img_dir='images/ori_images',img_emb_dir='images/img_emb'): image_file_dir=os.path.join(ori_img_dir,person_name) emb_file_dir=os.path.join(img_emb_dir,person_name) if not os.path.exists(image_file_dir): os.mkdir(image_file_dir) os.mkdir(emb_file_dir) file_ind=0 else: file_ind=len(os.listdir(image_file_dir)) file_ = face_recognition.load_image_file(image_file) emb = face_recognition.face_encodings(file_)[0] emb_file=image_file.split('.')[0]+f'_{file_ind}.npy' emb_file_out_path=os.path.join(emb_file_dir,emb_file) np.save(emb_file_out_path, emb) return emb def init_load_embs(img_emb_dir='images/img_emb'): persons=os.listdir(img_emb_dir) i=0 ind2person=dict() for oneperson in persons: oneperson_dir=os.path.join(img_emb_dir,oneperson) oneperson_list=os.listdir(oneperson_dir) for oneperson_j in oneperson_list: emb_id=i i+=1 emb=np.load(os.path.join(oneperson_dir,oneperson_j)) ind2person[emb_id]=dict(person=oneperson,emb=emb) return ind2person if __name__=="__main__": ind2person=init_load_embs() video_capture = cv2.VideoCapture(0) emb=input_an_image('youpeng.jpg', "youpeng") ind2person[len(list(ind2person.values()))]=dict(person="youpeng",emb=emb) # img_emb_dir='images/img_emb' # ori_img_dir='images/ori_images' # if not os.path.exists(img_emb_dir): # os.mkdir(img_emb_dir) # if not os.path.exists(ori_img_dir): # os.mkdir(ori_img_dir) # # os.listdir() # Load a sample picture and learn how to recognize it. # file_list=["obama.jpg","biden.jpg","mengqi.jpg","xinyi.jpg","sixian.jpg","wang.jpg","chenmengqi.jpg",'yilin.jpg','youpeng.jpg','wangyibo.jpg'] # Create arrays of known face encodings and their names # known_face_encodings = [ # obama_face_encoding, # biden_face_encoding, # me_face_encoding, # wang_face_encoding # ] # known_face_names = [ # "Barack Obama", # "Joe Biden", # "me", # "wang" # ] known_face_encodings=[v['emb'] for k,v in ind2person.items()] # known_face_encodings=[get_emb(f) for f in file_list] # known_face_names=[st.replace('.jpg','')for st in file_list] # Initialize some variables face_locations = [] face_encodings = [] face_names = [] process_this_frame = True while True: # Grab a single frame of video ret, frame = video_capture.read() # Only process every other frame of video to save time if process_this_frame: # Resize frame of video to 1/4 size for faster face recognition processing small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25) # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses) rgb_small_frame = small_frame[:, :, ::-1] # Find all the faces and face encodings in the current frame of video face_locations = face_recognition.face_locations(rgb_small_frame, number_of_times_to_upsample=1)#, model="cnn") face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations) face_names = [] for face_encoding in face_encodings: # See if the face is a match for the known face(s) matches = face_recognition.compare_faces(known_face_encodings, face_encoding) name = "Unknown" # # If a match was found in known_face_encodings, just use the first one. # if True in matches: # first_match_index = matches.index(True) # name = known_face_names[first_match_index] # Or instead, use the known face with the smallest distance to the new face face_distances = face_recognition.face_distance(known_face_encodings, face_encoding) best_match_index = np.argmin(face_distances) if matches[best_match_index]: # name = known_face_names[best_match_index] name = ind2person[best_match_index]['person'] face_names.append(name) process_this_frame = not process_this_frame # Display the results for (top, right, bottom, left), name in zip(face_locations, face_names): # Scale back up face locations since the frame we detected in was scaled to 1/4 size top *= 4 right *= 4 bottom *= 4 left *= 4 # Draw a box around the face cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2) # Draw a label with a name below the face 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) # Display the resulting image cv2.imshow('Video', frame) # Hit 'q' on the keyboard to quit! if cv2.waitKey(1) & 0xFF == ord('q'): break # Release handle to the webcam video_capture.release() cv2.destroyAllWindows()