Python face_recognition.compare_faces() Examples

def encoding_images(path):
    """
    对path路径下的子文件夹中的图片进行编码,
    TODO:
        对人脸数据进行历史库中的人脸向量进行欧式距离的比较,当距离小于某个阈值的时候提醒:
        如果相似的是本人,则跳过该条记录,并提醒已经存在,否则警告人脸过度相似问题,
    :param path:
    :return:
    """
    with open(name_and_encoding, 'w') as f:
        subdirs = [os.path.join(path, x) for x in os.listdir(path) if os.path.isdir(os.path.join(path, x))]
        for subdir in subdirs:
            print('process image name :', subdir)
            person_image_encoding = []
            for y in os.listdir(subdir):
                print("image name is ", y)
                _image = face_recognition.load_image_file(os.path.join(subdir, y))
                face_encodings = face_recognition.face_encodings(_image)
                name = os.path.split(subdir)[-1]
                if face_encodings and len(face_encodings) == 1:
                    if len(person_image_encoding) == 0:
                        person_image_encoding.append(face_encodings[0])
                        known_face_names.append(name)
                        continue
                    for i in range(len(person_image_encoding)):
                        distances = face_recognition.compare_faces(person_image_encoding, face_encodings[0], tolerance=image_thread)
                        if False in distances:
                            person_image_encoding.append(face_encodings[0])
                            known_face_names.append(name)
                            print(name, " new feature")
                            f.write(name + ":" + str(face_encodings[0]) + "\n")
                            break
                    # face_encoding = face_recognition.face_encodings(_image)[0]
                    # face_recognition.compare_faces()
            known_face_encodings.extend(person_image_encoding)
            bb = np.array(known_face_encodings)
            print("--------")
    np.save(KNOWN_FACE_ENCODINGS, known_face_encodings)
    np.save(KNOWN_FACE_NANE, known_face_names) 

def constructIndexes(self, label):
        valid_links = []
        console.section('Analyzing')
        file_name = ''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(6))
        file_name += '.jpg'
        tmp_path = os.path.join(tempfile.gettempdir(), file_name)
        console.task("Storing Image in {0}".format(tmp_path))
        for num, i in enumerate(self.profile_img):
            console.task('Analyzing {0}...'.format(i.strip()[:90]))
            urlretrieve(i, tmp_path)
            frame = cv2.imread(tmp_path)
            big_frame = cv2.resize(frame, (0, 0), fx=2.0, fy=2.0)
            rgb_small_frame = big_frame[:, :, ::-1]
            face_locations = face_recognition.face_locations(rgb_small_frame)
            face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations, num_jitters=self.num_jitters)
            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(self.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 = self.known_face_names[first_match_index]
                face_names.append(name)

            for _, name in zip(face_locations, face_names):
                if name == label:
                    valid_links.append(num)
        if os.path.isfile(tmp_path):
            console.task("Removing {0}".format(tmp_path))
            os.remove(tmp_path)
        return valid_links 

def detect_faces_in_image(file_stream):
    # Load the uploaded image file
    img = face_recognition.load_image_file(file_stream)

    # Get face encodings for any faces in the uploaded image
    uploaded_faces = face_recognition.face_encodings(img)

    # Defaults for the result object
    faces_found = len(uploaded_faces)
    faces = []

    if faces_found:
        face_encodings = list(faces_dict.values())
        for uploaded_face in uploaded_faces:
            match_results = face_recognition.compare_faces(
                face_encodings, uploaded_face)
            for idx, match in enumerate(match_results):
                if match:
                    match = list(faces_dict.keys())[idx]
                    match_encoding = face_encodings[idx]
                    dist = face_recognition.face_distance([match_encoding],
                            uploaded_face)[0]
                    faces.append({
                        "id": match,
                        "dist": dist
                    })

    return {
        "count": faces_found,
        "faces": faces
    }

# <Picture functions> #

# <Controller> 

def compare(self, img_list, img2=None):
        temp = []
        for img in img_list:
            h, w, c = img.shape
            # img = cv2.resize(img, (64, 64))
            code = face_recognition.face_encodings(img, [(0, w, h, 0)])
            if len(code) > 0:
                temp.append(code[0])
        res_list = []
        for temp_ in temp:
            res = face_recognition.compare_faces(self.img_encode_code, temp_, tolerance=self.conf_threshold)  # 越小越好
            # res 一个bool 的向量
            res_list.append(res)
        return np.sum(res_list) > 0, res_list 

def find(self, image):
        import face_recognition
        rgb_frame = image[:, :, ::-1]
        # Find all the faces and face encodings in the current frame of video
        face_locations = face_recognition.face_locations(rgb_frame, model='hog')
        face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)
        for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
            # for each face found try to match against known faces
            matches = face_recognition.compare_faces(self.__known_face_encodings, face_encoding)
            if True not in matches:
                return None
            first_match_index = matches.index(True)
            top, right, bottom, left = face_locations[first_match_index]
            return (left, top, right, bottom)
        return None 

def find_same_person(person_image_path):
    # 获取该人中的所有图片
    image_paths = os.listdir(person_image_path)
    known_face_encodings = []
    for image_path in image_paths:
        img_path = os.path.join(person_image_path, image_path)
        try:
            image = face_recognition.load_image_file(img_path)
            encodings = face_recognition.face_encodings(image, num_jitters=10)[0]
            known_face_encodings.append(encodings)
        except Exception as e:
            try:
                os.remove(img_path)
            except Exception as e:
                print(e)

    for image_path in image_paths:
        try:
            print(image_path)
            img_path = os.path.join(person_image_path, image_path)
            image = face_recognition.load_image_file(img_path)
            a_single_unknown_face_encoding = face_recognition.face_encodings(image, num_jitters=10)[0]
            results = face_recognition.compare_faces(known_face_encodings, a_single_unknown_face_encoding,
                                                     tolerance=0.5)
            results = numpy.array(results).astype(numpy.int64)
            if numpy.sum(results) > 5:
                main_path = os.path.join(person_image_path, '0.jpg')
                if os.path.exists(main_path):
                    os.remove(main_path)
                shutil.copyfile(img_path, main_path)
                break
        except:
            pass 

def encoding_images_mult_thread(path,threads=8):
    """
    对path路径下的子文件夹中的图片进行编码,
    TODO:
        对人脸数据进行历史库中的人脸向量进行欧式距离的比较,当距离小于某个阈值的时候提醒:
        如果相似的是本人,则跳过该条记录,并提醒已经存在,否则警告人脸过度相似问题,

    :param path:
    :return:
    """
    # with open("./dataset/encoded_face_names.txt", 'w') as f:
    #     lines = f.readlines()
    #     print(lines)

    with open(name_and_encoding, 'w') as f:
        subdirs = [os.path.join(path, x) for x in os.listdir(path) if os.path.isdir(os.path.join(path, x))]
        # if
        for subdir in subdirs:
            print('---name :', subdir)
            person_image_encoding = []
            for y in os.listdir(subdir):
                print("image name is ", y)
                _image = face_recognition.load_image_file(os.path.join(subdir, y))
                face_encodings = face_recognition.face_encodings(_image)
                name = os.path.split(subdir)[-1]
                if face_encodings and len(face_encodings) == 1:
                    if len(person_image_encoding) == 0:
                        person_image_encoding.append(face_encodings[0])
                        known_face_names.append(name)
                        continue

                    for i in range(len(person_image_encoding)):
                        distances = face_recognition.compare_faces(person_image_encoding, face_encodings[0], tolerance=image_thread)
                        if False in distances:
                            person_image_encoding.append(face_encodings[0])
                            known_face_names.append(name)
                            print(name, " new feature")
                            f.write(name + ":" + str(face_encodings[0]) + "\n")
                            break

                    # face_encoding = face_recognition.face_encodings(_image)[0]
                    # face_recognition.compare_faces()
            known_face_encodings.extend(person_image_encoding)
            bb = np.array(known_face_encodings)
            print("--------")

    np.save(KNOWN_FACE_ENCODINGS, known_face_encodings)
    np.save(KNOWN_FACE_NANE, known_face_names) 

def encoding_ones_images(name):
    """
    对path路径下的子文件夹中的图片进行编码,
    TODO:
        对人脸数据进行历史库中的人脸向量进行欧式距离的比较,当距离小于某个阈值的时候提醒:
        如果相似的是本人,则跳过该条记录,并提醒已经存在,否则警告人脸过度相似问题,

    :param path:
    :return:
    """
    # with open("./dataset/encoded_face_names.txt", 'w') as f:
    #     lines = f.readlines()
    #     print(lines)

    with open(name_and_encoding, 'w') as f:
        image_dirs = os.path.join(data_path, name)
        files = [os.path.join(image_dirs, x) for x in os.listdir(image_dirs) if os.path.isfile(os.path.join(image_dirs, x))]
        print('---name :', files)
        person_image_encoding = []
        for image_path in files:
            print("image name is ", image_path)
            _image = face_recognition.load_image_file(image_path )
            face_encodings = face_recognition.face_encodings(_image)
            # name = os.path.split(image_path)[1]
            if face_encodings and len(face_encodings) == 1:
                if len(person_image_encoding) == 0:
                    person_image_encoding.append(face_encodings[0])
                    known_face_names.append(name)
                    continue

                for i in range(len(person_image_encoding)):
                    distances = face_recognition.compare_faces(person_image_encoding, face_encodings[0], tolerance=image_thread)
                    if False in distances:
                        person_image_encoding.append(face_encodings[0])
                        known_face_names.append(name)
                        print(name, " new feature")
                        f.write(name + ":" + str(face_encodings[0]) + "\n")
                        break

                # face_encoding = face_recognition.face_encodings(_image)[0]
                # face_recognition.compare_faces()
        known_face_encodings.extend(person_image_encoding)
        bb = np.array(known_face_encodings)
        print("--------")

    KNOWN_FACE_ENCODINGS = "./dataset/known_face_encodings_{}.npy"  # 已知人脸向量
    KNOWN_FACE_NANE = "./dataset/known_face_name_{}.npy"  # 已知人脸名称
    np.save(KNOWN_FACE_ENCODINGS.format(int(time.time())), known_face_encodings)
    np.save(KNOWN_FACE_NANE.format(int(time.time())), known_face_names) 

def face_compare(frame,process_this_frame):
    print ("compare")
    # Resize frame of video to 1/4 size for faster face recognition processing
    small_frame = cv2.resize(frame, (0, 0), fx=0.50, fy=0.50)

    # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
    rgb_small_frame = small_frame[:, :, ::-1]

    # Only process every other frame of video to save time
    if process_this_frame:
        # Find all the faces and face encodings in the current frame of video
        face_locations = face_recognition.face_locations(rgb_small_frame)
        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]

            face_names.append(name)

    process_this_frame = not process_this_frame

    return face_names
    # 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 *= 2
        right *= 2
        bottom *= 2
        left *= 2
        #cv2.rectangle(frame, (left, bottom+36), (right, bottom), (0, 0, 0), cv2.FILLED)
        font = cv2.FONT_HERSHEY_DUPLEX
        cv2.putText(frame, name, (left + 6, bottom+20), font, 0.3, (255, 255, 255), 1)
        print ("text print")

# starting video streaming 

def detect_faces_in_image(file_stream):
    # Pre-calculated face encoding of Obama generated with face_recognition.face_encodings(img)
    known_face_encoding = [-0.09634063,  0.12095481, -0.00436332, -0.07643753,  0.0080383,
                            0.01902981, -0.07184699, -0.09383309,  0.18518871, -0.09588896,
                            0.23951106,  0.0986533 , -0.22114635, -0.1363683 ,  0.04405268,
                            0.11574756, -0.19899382, -0.09597053, -0.11969153, -0.12277931,
                            0.03416885, -0.00267565,  0.09203379,  0.04713435, -0.12731361,
                           -0.35371891, -0.0503444 , -0.17841317, -0.00310897, -0.09844551,
                           -0.06910533, -0.00503746, -0.18466514, -0.09851682,  0.02903969,
                           -0.02174894,  0.02261871,  0.0032102 ,  0.20312519,  0.02999607,
                           -0.11646006,  0.09432904,  0.02774341,  0.22102901,  0.26725179,
                            0.06896867, -0.00490024, -0.09441824,  0.11115381, -0.22592428,
                            0.06230862,  0.16559327,  0.06232892,  0.03458837,  0.09459756,
                           -0.18777156,  0.00654241,  0.08582542, -0.13578284,  0.0150229 ,
                            0.00670836, -0.08195844, -0.04346499,  0.03347827,  0.20310158,
                            0.09987706, -0.12370517, -0.06683611,  0.12704916, -0.02160804,
                            0.00984683,  0.00766284, -0.18980607, -0.19641446, -0.22800779,
                            0.09010898,  0.39178532,  0.18818057, -0.20875394,  0.03097027,
                           -0.21300618,  0.02532415,  0.07938635,  0.01000703, -0.07719778,
                           -0.12651891, -0.04318593,  0.06219772,  0.09163868,  0.05039065,
                           -0.04922386,  0.21839413, -0.02394437,  0.06173781,  0.0292527 ,
                            0.06160797, -0.15553983, -0.02440624, -0.17509389, -0.0630486 ,
                            0.01428208, -0.03637431,  0.03971229,  0.13983178, -0.23006812,
                            0.04999552,  0.0108454 , -0.03970895,  0.02501768,  0.08157793,
                           -0.03224047, -0.04502571,  0.0556995 , -0.24374914,  0.25514284,
                            0.24795187,  0.04060191,  0.17597422,  0.07966681,  0.01920104,
                           -0.01194376, -0.02300822, -0.17204897, -0.0596558 ,  0.05307484,
                            0.07417042,  0.07126575,  0.00209804]

    # Load the uploaded image file
    img = face_recognition.load_image_file(file_stream)
    # Get face encodings for any faces in the uploaded image
    unknown_face_encodings = face_recognition.face_encodings(img)

    face_found = False
    is_obama = False

    if len(unknown_face_encodings) > 0:
        face_found = True
        # See if the first face in the uploaded image matches the known face of Obama
        match_results = face_recognition.compare_faces([known_face_encoding], unknown_face_encodings[0])
        if match_results[0]:
            is_obama = True

    # Return the result as json
    result = {
        "face_found_in_image": face_found,
        "is_picture_of_obama": is_obama
    }
    return jsonify(result) 

def process(worker_id, read_frame_list, write_frame_list, Global, worker_num):
    known_face_encodings = Global.known_face_encodings
    known_face_names = Global.known_face_names
    while not Global.is_exit:

        # Wait to read
        while Global.read_num != worker_id or Global.read_num != prev_id(Global.buff_num, worker_num):
            # If the user has requested to end the app, then stop waiting for webcam frames
            if Global.is_exit:
                break

            time.sleep(0.01)

        # Delay to make the video look smoother
        time.sleep(Global.frame_delay)

        # Read a single frame from frame list
        frame_process = read_frame_list[worker_id]

        # Expect next worker to read frame
        Global.read_num = next_id(Global.read_num, worker_num)

        # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
        rgb_frame = frame_process[:, :, ::-1]

        # Find all the faces and face encodings in the frame of video, cost most time
        face_locations = face_recognition.face_locations(rgb_frame)
        face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)

        # Loop through each face in this frame of video
        for (top, right, bottom, left), face_encoding in zip(face_locations, 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]

            # Draw a box around the face
            cv2.rectangle(frame_process, (left, top), (right, bottom), (0, 0, 255), 2)

            # Draw a label with a name below the face
            cv2.rectangle(frame_process, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
            font = cv2.FONT_HERSHEY_DUPLEX
            cv2.putText(frame_process, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)

        # Wait to write
        while Global.write_num != worker_id:
            time.sleep(0.01)

        # Send frame to global
        write_frame_list[worker_id] = frame_process

        # Expect next worker to write frame
        Global.write_num = next_id(Global.write_num, worker_num) 

def detect_faces_in_image(file_stream):
    # 用face_recognition.face_encodings(img)接口提前把奥巴马人脸的编码录入
    known_face_encoding = [-0.09634063,  0.12095481, -0.00436332, -0.07643753,  0.0080383,
                            0.01902981, -0.07184699, -0.09383309,  0.18518871, -0.09588896,
                            0.23951106,  0.0986533 , -0.22114635, -0.1363683 ,  0.04405268,
                            0.11574756, -0.19899382, -0.09597053, -0.11969153, -0.12277931,
                            0.03416885, -0.00267565,  0.09203379,  0.04713435, -0.12731361,
                           -0.35371891, -0.0503444 , -0.17841317, -0.00310897, -0.09844551,
                           -0.06910533, -0.00503746, -0.18466514, -0.09851682,  0.02903969,
                           -0.02174894,  0.02261871,  0.0032102 ,  0.20312519,  0.02999607,
                           -0.11646006,  0.09432904,  0.02774341,  0.22102901,  0.26725179,
                            0.06896867, -0.00490024, -0.09441824,  0.11115381, -0.22592428,
                            0.06230862,  0.16559327,  0.06232892,  0.03458837,  0.09459756,
                           -0.18777156,  0.00654241,  0.08582542, -0.13578284,  0.0150229 ,
                            0.00670836, -0.08195844, -0.04346499,  0.03347827,  0.20310158,
                            0.09987706, -0.12370517, -0.06683611,  0.12704916, -0.02160804,
                            0.00984683,  0.00766284, -0.18980607, -0.19641446, -0.22800779,
                            0.09010898,  0.39178532,  0.18818057, -0.20875394,  0.03097027,
                           -0.21300618,  0.02532415,  0.07938635,  0.01000703, -0.07719778,
                           -0.12651891, -0.04318593,  0.06219772,  0.09163868,  0.05039065,
                           -0.04922386,  0.21839413, -0.02394437,  0.06173781,  0.0292527 ,
                            0.06160797, -0.15553983, -0.02440624, -0.17509389, -0.0630486 ,
                            0.01428208, -0.03637431,  0.03971229,  0.13983178, -0.23006812,
                            0.04999552,  0.0108454 , -0.03970895,  0.02501768,  0.08157793,
                           -0.03224047, -0.04502571,  0.0556995 , -0.24374914,  0.25514284,
                            0.24795187,  0.04060191,  0.17597422,  0.07966681,  0.01920104,
                           -0.01194376, -0.02300822, -0.17204897, -0.0596558 ,  0.05307484,
                            0.07417042,  0.07126575,  0.00209804]

    # 载入用户上传的图片
    img = face_recognition.load_image_file(file_stream)
    # 为用户上传的图片中的人脸编码
    unknown_face_encodings = face_recognition.face_encodings(img)

    face_found = False
    is_obama = False

    if len(unknown_face_encodings) > 0:
        face_found = True
        # 看看图片中的第一张脸是不是奥巴马
        match_results = face_recognition.compare_faces([known_face_encoding], unknown_face_encodings[0])
        if match_results[0]:
            is_obama = True

    # 讲识别结果以json键值对的数据结构输出
    result = {
        "face_found_in_image": face_found,
        "is_picture_of_obama": is_obama
    }
    return jsonify(result) 

def knn_face_classifier(encoding, compare_face_tolerance, name_threshold, name_count):
    # attempt to match each face in the input image to our known encodings
    matches = face_recognition.compare_faces(data['encodings'],
        encoding, compare_face_tolerance)

    # Assume face is unknown to start with. 
    name = 'Unknown'

    # check to see if we have found a match
    if True in matches:
        # find the indexes of all matched faces then initialize a
        # dictionary to count the total number of times each face
        # was matched
        matchedIdxs = [i for (i, b) in enumerate(matches) if b]

        # init all name counts to 0
        counts = {n: 0 for n in data['names']}
        #print('initial counts {}'.format(counts))

        # loop over the matched indexes and maintain a count for
        # each recognized face
        for i in matchedIdxs:
            name = data['names'][i]
            counts[name] = counts.get(name, 0) + 1
        #print('counts {}'.format(counts))

        # Find face name with the max count value.
        max_value = max(counts.values())
        max_name = max(counts, key=counts.get)

        # Compare each recognized face against the max face name.
        # The max face name count must be greater than a certain value for
        # it to be valid. This value is set at a percentage of the number of
        # embeddings for that face name. 
        name_thresholds = [max_value > value + name_threshold * name_count[max_name]
            for name, value in counts.items() if name != max_name]

        # If max face name passes against all other faces then declare it valid.
        if all(name_thresholds):
            name = max_name
            print('kkn says this is {}'.format(name))
        else:
            name = None
            print('kkn cannot recognize face')

    return name