251 lines
12 KiB
Python
251 lines
12 KiB
Python
# Copyright (C) 2018-2021 coneypo
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# SPDX-License-Identifier: MIT
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# Author: coneypo
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# Blog: http://www.cnblogs.com/AdaminXie
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# GitHub: https://github.com/coneypo/Dlib_face_recognition_from_camera
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# Mail: coneypo@foxmail.com
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# 摄像头实时人脸识别 / Real-time face detection and recognition
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import dlib
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import numpy as np
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import cv2
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import pandas as pd
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import os
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import time
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import logging
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from PIL import Image, ImageDraw, ImageFont
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from read_data import LoadImages, LoadStreams
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import torch.backends.cudnn as cudnn
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# Dlib 人脸 landmark 特征点检测器 / Get face landmarks
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predictor = dlib.shape_predictor('algorithm/face_recognition/data/data_dlib/shape_predictor_68_face_landmarks.dat')
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# Dlib Resnet 人脸识别模型，提取 128D 的特征矢量 / Use Dlib resnet50 model to get 128D face descriptor
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face_reco_model = dlib.face_recognition_model_v1("algorithm/face_recognition/data/data_dlib/dlib_face_recognition_resnet_model_v1.dat")
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class Face_Recognizer():
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def __init__(self, video_path = None, model=None):
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self.face_feature_known_list = [] # 用来存放所有录入人脸特征的数组 / Save the features of faces in database
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self.face_name_known_list = [] # 存储录入人脸名字 / Save the name of faces in database
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self.current_frame_face_cnt = 0 # 存储当前摄像头中捕获到的人脸数 / Counter for faces in current frame
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self.current_frame_face_feature_list = [] # 存储当前摄像头中捕获到的人脸特征 / Features of faces in current frame
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self.current_frame_face_name_list = [] # 存储当前摄像头中捕获到的所有人脸的名字 / Names of faces in current frame
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self.current_frame_face_name_position_list = [] # 存储当前摄像头中捕获到的所有人脸的名字坐标 / Positions of faces in current frame
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# Update FPS
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self.fps = 0 # FPS of current frame
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self.fps_show = 0 # FPS per second
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self.frame_start_time = 0
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self.frame_cnt = 0
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self.start_time = time.time()
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self.font = cv2.FONT_ITALIC
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self.font_chinese = ImageFont.truetype("simsun.ttc", 30)
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if video_path is not None:
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self.video_name = video_path
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else:
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self.video_name = 'vid2.mp4' # A default video file
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self.dataset = LoadImages(self.video_name)
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self.face_detector = model
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self.get_face_database()
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def use_webcam(self, source):
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source = source
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cudnn.benchmark = True
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self.dataset = LoadStreams(source)
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# 从 "features_all.csv" 读取录入人脸特征 / Read known faces from "features_all.csv"
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def get_face_database(self):
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if os.path.exists("algorithm/face_recognition/data/features_all.csv"):
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path_features_known_csv = "algorithm/face_recognition/data/features_all.csv"
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csv_rd = pd.read_csv(path_features_known_csv, header=None)
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for i in range(csv_rd.shape[0]):
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features_someone_arr = []
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self.face_name_known_list.append(csv_rd.iloc[i][0])
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for j in range(1, 129):
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if csv_rd.iloc[i][j] == '':
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features_someone_arr.append('0')
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else:
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features_someone_arr.append(csv_rd.iloc[i][j])
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self.face_feature_known_list.append(features_someone_arr)
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logging.info("Faces in Database：%d", len(self.face_feature_known_list))
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return 1
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else:
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logging.warning("'features_all.csv' not found!")
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logging.warning("Please run 'get_faces_from_camera.py' "
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"and 'features_extraction_to_csv.py' before 'face_reco_from_camera.py'")
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return 0
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# 计算两个128D向量间的欧式距离 / Compute the e-distance between two 128D features
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@staticmethod
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def return_euclidean_distance(feature_1, feature_2):
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feature_1 = np.array(feature_1)
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feature_2 = np.array(feature_2)
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dist = np.sqrt(np.sum(np.square(feature_1 - feature_2)))
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return dist
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def update_fps(self):
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now = time.time()
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# 每秒刷新 fps / Refresh fps per second
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if str(self.start_time).split(".")[0] != str(now).split(".")[0]:
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self.fps_show = self.fps
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self.start_time = now
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self.frame_time = now - self.frame_start_time
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self.fps = 1.0 / self.frame_time
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self.frame_start_time = now
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# 生成的 cv2 window 上面添加说明文字 / PutText on cv2 window
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def draw_note(self, img_rd):
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cv2.putText(img_rd, "Face Recognizer", (20, 40), self.font, 1, (255, 255, 255), 1, cv2.LINE_AA)
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cv2.putText(img_rd, "Frame: " + str(self.frame_cnt), (20, 100), self.font, 0.8, (0, 255, 0), 1,
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cv2.LINE_AA)
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cv2.putText(img_rd, "FPS: " + str(self.fps_show.__round__(2)), (20, 130), self.font, 0.8, (0, 255, 0), 1,
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cv2.LINE_AA)
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cv2.putText(img_rd, "Faces: " + str(self.current_frame_face_cnt), (20, 160), self.font, 0.8, (0, 255, 0), 1,
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cv2.LINE_AA)
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def draw_name(self, img_rd):
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# 在人脸框下面写人脸名字 / Write names under rectangle
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img = Image.fromarray(cv2.cvtColor(img_rd, cv2.COLOR_BGR2RGB))
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draw = ImageDraw.Draw(img)
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for i in range(self.current_frame_face_cnt):
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# cv2.putText(img_rd, self.current_frame_face_name_list[i], self.current_frame_face_name_position_list[i], self.font, 0.8, (0, 255, 255), 1, cv2.LINE_AA)
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draw.text(xy=self.current_frame_face_name_position_list[i], text=str(self.current_frame_face_name_list[i]), font=self.font_chinese,
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fill=(255, 255, 0))
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img_rd = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
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return img_rd
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# 修改显示人名 / Show names in chinese
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def show_chinese_name(self):
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# Default known name: person_1, person_2, person_3
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if self.current_frame_face_cnt >= 1:
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# 修改录入的人脸姓名 / Modify names in face_name_known_list to chinese name
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self.face_name_known_list[0] = '张三'.encode('utf-8').decode()
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# self.face_name_known_list[1] = '张四'.encode('utf-8').decode()
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def detect_faces(self, img):
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results = self.face_detector(img)
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face_results = dlib.rectangles()
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for obj in results.xyxy[0]:
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if obj[-1] == 0: # 0 is the class ID for 'person'
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# Draw bounding boxes around people
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xmin, ymin, xmax, ymax = map(int, obj[:4])
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xmin, ymin, xmax, ymax = convert_to_square(xmin, ymin, xmax, ymax)
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if xmax <= xmin or ymax <= ymin:
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continue
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rectangle = dlib.rectangle(xmin, ymin, xmax, ymax)
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face_results.append(rectangle)
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return face_results
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# 处理获取的视频流，进行人脸识别 / Face detection and recognition from input video stream
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def get_frame(self):
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# 1. 读取存放所有人脸特征的 csv / Read known faces from "features.all.csv"
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if self.get_face_database():
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self.frame_cnt += 1
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logging.debug("Frame %d starts", self.frame_cnt)
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for im0s in self.dataset:
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if self.dataset.mode == 'stream':
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img_rd = im0s[0].copy()
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else:
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img_rd = im0s.copy()
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faces = self.detect_faces(img_rd)
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self.draw_note(img_rd)
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self.current_frame_face_feature_list = []
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self.current_frame_face_cnt = 0
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self.current_frame_face_name_position_list = []
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self.current_frame_face_name_list = []
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# 2. 检测到人脸 / Face detected in current frame
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if len(faces) != 0:
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# 3. 获取当前捕获到的图像的所有人脸的特征 / Compute the face descriptors for faces in current frame
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for i in range(len(faces)):
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shape = predictor(img_rd, faces[i])
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print(face_reco_model.compute_face_descriptor(img_rd, shape))
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self.current_frame_face_feature_list.append(face_reco_model.compute_face_descriptor(img_rd, shape))
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# 4. 遍历捕获到的图像中所有的人脸 / Traversal all the faces in the database
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for k in range(len(faces)):
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logging.info("For face %d in camera:", k+1)
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# 先默认所有人不认识，是 unknown / Set the default names of faces with "unknown"
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self.current_frame_face_name_list.append("unknown")
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# 每个捕获人脸的名字坐标 / Positions of faces captured
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self.current_frame_face_name_position_list.append(tuple(
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[faces[k].left(), int(faces[k].bottom() + (faces[k].bottom() - faces[k].top()) / 4)]))
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# 5. 对于某张人脸，遍历所有存储的人脸特征
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# For every faces detected, compare the faces in the database
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current_frame_e_distance_list = []
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for i in range(len(self.face_feature_known_list)):
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# 如果 person_X 数据不为空
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if str(self.face_feature_known_list[i][0]) != '0.0':
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e_distance_tmp = self.return_euclidean_distance(self.current_frame_face_feature_list[k],
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self.face_feature_known_list[i])
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logging.info(" With person %s, the e-distance is %f", str(i + 1), e_distance_tmp)
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current_frame_e_distance_list.append(e_distance_tmp)
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else:
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# 空数据 person_X
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current_frame_e_distance_list.append(999999999)
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# 6. 寻找出最小的欧式距离匹配 / Find the one with minimum e-distance
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similar_person_num = current_frame_e_distance_list.index(min(current_frame_e_distance_list))
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logging.info("Minimum e-distance with %s: %f", self.face_name_known_list[similar_person_num], min(current_frame_e_distance_list))
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if min(current_frame_e_distance_list) < 0.4:
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self.current_frame_face_name_list[k] = self.face_name_known_list[similar_person_num]
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logging.info("Face recognition result: %s", self.face_name_known_list[similar_person_num])
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else:
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logging.info("Face recognition result: Unknown person")
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logging.info("\n")
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# 矩形框 / Draw rectangle
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for kk, d in enumerate(faces):
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# 绘制矩形框
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# print(kk,d)
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cv2.rectangle(img_rd, tuple([d.left(), d.top()]), tuple([d.right(), d.bottom()]),
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(0, 255, 0), 2)
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self.update_fps()
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self.current_frame_face_cnt = len(faces)
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img_rd = self.draw_name(img_rd)
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else:
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img_with_name = img_rd
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ret, jpeg = cv2.imencode(".jpg", img_rd)
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return jpeg.tobytes()
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def convert_to_square(xmin, ymin, xmax, ymax):
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# convert to square location
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center_x = (xmin + xmax) // 2
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center_y = (ymin + ymax) // 2
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square_length = ((xmax - xmin) + (ymax - ymin)) // 2 // 2
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square_length *= 1.1
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xmin = int(center_x - square_length)
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ymin = int(center_y - square_length)
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xmax = int(center_x + square_length)
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ymax = int(center_y + square_length)
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return xmin, ymin, xmax, ymax
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