156 lines
6.0 KiB
Python
156 lines
6.0 KiB
Python
# 摄像头实时人脸识别
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# Real-time face recognition
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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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# Created at 2018-05-11
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# Updated at 2019-03-23
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import dlib # 人脸处理的库 Dlib
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import numpy as np # 数据处理的库 numpy
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import cv2 # 图像处理的库 OpenCv
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import pandas as pd # 数据处理的库 Pandas
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# 人脸识别模型，提取128D的特征矢量
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# face recognition model, the object maps human faces into 128D vectors
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# Refer this tutorial: http://dlib.net/python/index.html#dlib.face_recognition_model_v1
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facerec = dlib.face_recognition_model_v1("data/data_dlib/dlib_face_recognition_resnet_model_v1.dat")
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# 计算两个128D向量间的欧式距离
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# compute the e-distance between two 128D features
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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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print("e_distance: ", dist)
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if dist > 0.4:
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return "diff"
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else:
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return "same"
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# 处理存放所有人脸特征的 csv
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path_features_known_csv = "data/features_all.csv"
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csv_rd = pd.read_csv(path_features_known_csv, header=None)
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# 用来存放所有录入人脸特征的数组
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# the array to save the features of faces in the database
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features_known_arr = []
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# 读取已知人脸数据
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# print known faces
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for i in range(csv_rd.shape[0]):
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features_someone_arr = []
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for j in range(0, len(csv_rd.ix[i, :])):
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features_someone_arr.append(csv_rd.ix[i, :][j])
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features_known_arr.append(features_someone_arr)
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print("Faces in Database：", len(features_known_arr))
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# Dlib 检测器和预测器
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# The detector and predictor will be used
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detector = dlib.get_frontal_face_detector()
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predictor = dlib.shape_predictor('data/data_dlib/shape_predictor_68_face_landmarks.dat')
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# 创建 cv2 摄像头对象
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# cv2.VideoCapture(0) to use the default camera of PC,
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# and you can use local video name by use cv2.VideoCapture(filename)
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cap = cv2.VideoCapture(0)
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# cap.set(propId, value)
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# 设置视频参数，propId 设置的视频参数，value 设置的参数值
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cap.set(3, 480)
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# cap.isOpened() 返回 true/false 检查初始化是否成功
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# when the camera is open
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while cap.isOpened():
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flag, img_rd = cap.read()
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kk = cv2.waitKey(1)
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# 取灰度
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img_gray = cv2.cvtColor(img_rd, cv2.COLOR_RGB2GRAY)
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# 人脸数 faces
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faces = detector(img_gray, 0)
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# 待会要写的字体 font to write later
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font = cv2.FONT_HERSHEY_COMPLEX
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# 存储当前摄像头中捕获到的所有人脸的坐标/名字
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# the list to save the positions and names of current faces captured
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pos_namelist = []
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name_namelist = []
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# 按下 q 键退出
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# press 'q' to exit
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if kk == ord('q'):
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break
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else:
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# 检测到人脸 when face detected
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if len(faces) != 0:
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# 获取当前捕获到的图像的所有人脸的特征，存储到 features_cap_arr
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# get the features captured and save into features_cap_arr
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features_cap_arr = []
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for i in range(len(faces)):
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shape = predictor(img_rd, faces[i])
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features_cap_arr.append(facerec.compute_face_descriptor(img_rd, shape))
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# 遍历捕获到的图像中所有的人脸
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# traversal all the faces in the database
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for k in range(len(faces)):
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# 让人名跟随在矩形框的下方
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# 确定人名的位置坐标
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# 先默认所有人不认识，是 unknown
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# set the default names of faces with "unknown"
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name_namelist.append("unknown")
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# 每个捕获人脸的名字坐标 the positions of faces captured
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pos_namelist.append(tuple([faces[k].left(), int(faces[k].bottom() + (faces[k].bottom() - faces[k].top())/4)]))
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# 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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for i in range(len(features_known_arr)):
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print("with person_", str(i+1), "the ", end='')
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# 将某张人脸与存储的所有人脸数据进行比对
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compare = return_euclidean_distance(features_cap_arr[k], features_known_arr[i])
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if compare == "same": # 找到了相似脸
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# 在这里修改 person_1, person_2 ... 的名字
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# 这里只写了前三个
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# 可以在这里改称 Jack, Tom and others
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# Here you can modify the names shown on the camera
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if i == 0:
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name_namelist[k] = "Person 1"
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elif i == 1:
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name_namelist[k] = "Person 2"
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elif i == 2:
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name_namelist[k] = "Person 3"
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# 矩形框
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# 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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cv2.rectangle(img_rd, tuple([d.left(), d.top()]), tuple([d.right(), d.bottom()]), (0, 255, 255), 2)
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# 在人脸框下面写人脸名字
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# write names under rectangle
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for i in range(len(faces)):
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cv2.putText(img_rd, name_namelist[i], pos_namelist[i], font, 0.8, (0, 255, 255), 1, cv2.LINE_AA)
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print("Faces in camera now:", name_namelist, "\n")
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cv2.putText(img_rd, "Press 'q': Quit", (20, 450), font, 0.8, (84, 255, 159), 1, cv2.LINE_AA)
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cv2.putText(img_rd, "Face Recognition", (20, 40), font, 1, (0, 0, 0), 1, cv2.LINE_AA)
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cv2.putText(img_rd, "Faces: " + str(len(faces)), (20, 100), font, 1, (0, 0, 255), 1, cv2.LINE_AA)
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# 窗口显示 show with opencv
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cv2.imshow("camera", img_rd)
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# 释放摄像头 release camera
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cap.release()
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# 删除建立的窗口 delete all the windows
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cv2.destroyAllWindows() |