videofast.py

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()