test_face.py

import face_recognition
import cv2
import numpy as np
import os
from face_api import detect_frame
image_templates_dir = "./img_templates"


def load_image_templates(image_templates_dir):
    """
    从指定的模板目录获取到人脸模板照片，返回两个list
    第一个list为人脸图片数据encoding的列表，第二个list为相应的人名的列表
    """
    if os.path.isdir(image_templates_dir):
        images = os.listdir(image_templates_dir)
        face_encodings = []
        face_tags = []
        for image in images:
            path = os.path.join(image_templates_dir, image)  # 拼接每个图片的实际路径
            face_image = face_recognition.load_image_file(path)
            face_encoding = face_recognition.face_encodings(
                face_image)[0]  # 获取每个人脸图片的encoding
            face_tag = os.path.splitext(image)[0]  # 获取每个图片对应的用户的名字
            face_encodings.append(face_encoding)
            face_tags.append(face_tag)
        return face_encodings, face_tags
    else:
        print("请输入一个有效的模板目录！")
        exit(-1)


def main():
    # 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)
    video_capture = cv2.VideoCapture(0)

    # Load a sample picture and learn how to recognize it.

    known_face_encodings, known_face_names = load_image_templates(
        image_templates_dir)

    # 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()

        # 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]

        # 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]

        #         # 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]

        #         face_names.append(name)
        if process_this_frame:
            face_locations, face_names = detect_frame(
                rgb_small_frame, known_face_encodings, known_face_names)
        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()


if __name__ == "__main__":
    main()