python人脸识别与专注度检测源码

import dlib import numpy as np import cv2 import os import pandas as pd import time def run(): detector = dlib.get_frontal_face_detector() predictor = dlib.shape_predictor('data/data_dlib/shape_predictor_68_face_landmarks.dat') face_reco_model = dlib.face_recognition_model_v1("data/data_dlib/dlib_face_recognition_resnet_model_v1.dat") class Face_Recognizer: def __init__(self): self.font = cv2.FONT_ITALIC # For FPS self.frame_time = 0 self.frame_start_time = 0 self.fps = 0 # cnt for frame self.frame_cnt = 0 self.features_known_list = [] self.name_known_list = [] self.last_frame_centroid_list = [] self.current_frame_centroid_list = [] self.last_frame_names_list = [] self.current_frame_face_name_list = [] self.last_frame_face_cnt = 0 self.current_frame_face_cnt = 0 self.current_frame_face_X_e_distance_list = [] self.current_frame_face_position_list = [] self.current_frame_face_features_list = [] self.last_current_frame_centroid_e_distance = 0 def get_face_database(self): if os.path.exists("data/features_all.csv"): path_features_known_csv = "data/features_all.csv" csv_rd = pd.read_csv(path_features_known_csv, header=None) for i in range(csv_rd.shape[0]): features_someone_arr = [] for j in range(0, 128): if csv_rd.iloc[i][j] == '': features_someone_arr.append('0') else: features_someone_arr.append(csv_rd.iloc[i][j]) self.features_known_list.append(features_someone_arr) self.name_known_list.append("Person_" + str(i + 1)) print("Faces in Database：", len(self.features_known_list)) return 1 else: print('##### Warning #####', '\n') print("'features_all.csv' not found!") print( "Please run 'get_faces_from_camera.py' and 'features_extraction_to_csv.py' before 'face_reco_from_camera.py'", '\n') print('##### End Warning #####') return 0 # Get the fps of video stream def update_fps(self): now = time.time() self.frame_time = now - self.frame_start_time self.fps = 1.0 / self.frame_time self.frame_start_time = now # Compute the e-distance between two 128D features @staticmethod def return_euclidean_distance(feature_1, feature_2): feature_1 = np.array(feature_1) feature_2 = np.array(feature_2) dist = np.sqrt(np.sum(np.square(feature_1 - feature_2))) return dist # / Use centroid tracker to link face_x in current frame with person_x in last frame def centroid_tracker(self): for i in range(len(self.current_frame_centroid_list)): e_distance_current_frame_person_x_list = [] # For object 1 in current_frame, compute e-distance with object 1/2/3/4/... in last frame for j in range(len(self.last_frame_centroid_list)): self.last_current_frame_centroid_e_distance = self.return_euclidean_distance( self.current_frame_centroid_list[i], self.last_frame_centroid_list[j]) e_distance_current_frame_person_x_list.append( self.last_current_frame_centroid_e_distance) last_frame_num = e_distance_current_frame_person_x_list.index( min(e_distance_current_frame_person_x_list)) self.current_frame_face_name_list[i] = self.last_frame_face_name_list[last_frame_num] # putText on cv2 window def draw_note(self, img_rd): # Add some statements cv2.putText(img_rd, "Face recognizer with OT", (20, 40), self.font, 1, (255, 255, 255), 1, cv2.LINE_AA) cv2.putText(img_rd, "FPS: " + str(self.fps.__round__(2)), (20, 100), self.font, 0.8, (0, 255, 0), 1, cv2.LINE_AA) cv2.putText(img_rd, "Faces: " + str(self.current_frame_face_cnt), (20, 130), self.font, 0.8, (0, 255, 0), 1, cv2.LINE_AA) cv2.putText(img_rd, "Q: Quit", (20, 450), self.font, 0.8, (255, 255, 255), 1, cv2.LINE_AA) for i in range(len(self.current_frame_face_name_list)): cv2.putText(img_rd, "Face " + str(i + 1), tuple( [int(self.current_frame_centroid_list[i][0]), int(self.current_frame_centroid_list[i][1])]), self.font, 0.8, (255, 190, 0), 1, cv2.LINE_AA) # Face detection and recognition wit OT from input video stream def process(self, stream): if self.get_face_database(): while stream.isOpened(): self.frame_cnt += 1 print(">>> Frame " + str(self.frame_cnt) + " starts") flag, img_rd = stream.read() kk = cv2.waitKey(1) faces = detector(img_rd, 0) if self.current_frame_face_name_list == ['Person_2', 'Person_2']: break # Update cnt for faces in frames self.last_frame_face_cnt = self.current_frame_face_cnt self.current_frame_face_cnt = len(faces) # Update the face name list in last frame self.last_frame_face_name_list = self.current_frame_face_name_list[:] # update frame centroid list self.last_frame_centroid_list = self.current_frame_centroid_list self.current_frame_centroid_list = [] print(" >>> current_frame_face_cnt: ", self.current_frame_face_cnt) # 2.1. if cnt not changes if self.current_frame_face_cnt == self.last_frame_face_cnt: print(" >>> scene 1: 当前帧和上一帧相比没有发生人脸数变化 / no faces cnt changes in this frame!!!") self.current_frame_face_position_list = [] if self.current_frame_face_cnt != 0: # 2.1.1 Get ROI positions for k, d in enumerate(faces): self.current_frame_face_position_list.append(tuple( [faces[k].left(), int(faces[k].bottom() + (faces[k].bottom() - faces[k].top()) / 4)])) self.current_frame_centroid_list.append( [int(faces[k].left() + faces[k].right()) / 2, int(faces[k].top() + faces[k].bottom()) / 2]) height = (d.bottom() - d.top()) width = (d.right() - d.left()) hh = int(height / 2) ww = int(width / 2) cv2.rectangle(img_rd, tuple([d.left() - ww, d.top() - hh]), tuple([d.right() + ww, d.bottom() + hh]), (255, 255, 255), 2) # multi-faces in current frames, use centroid tracker to track if self.current_frame_face_cnt != 1: self.centroid_tracker() for i in range(self.current_frame_face_cnt): # 6.2 w