Python 3 利用 Dlib 19.7 实现摄像头人脸检测特征点标定，,0.引言　　　利用p

Python 3 利用 Dlib 19.7 实现摄像头人脸检测特征点标定，,0.引言　　　利用p

0.引言

　　　利用python开发，借助Dlib库捕获摄像头中的人脸，进行实时特征点标定；

　　　　　　图1 工程效果示例（gif）

　　　　　　图2 工程效果示例（静态图片）

　　　（实现比较简单，代码量也比较少，适合入门或者兴趣学习。）

1.开发环境

　　python:　　3.6.3

　　dlib:　　　 19.7

　　OpenCv, numpy

1 import dlib         # 人脸识别的库dlib2 import numpy as np  # 数据处理的库numpy3 import cv2          # 图像处理的库OpenCv

2.源码介绍

　　其实实现很简单，主要分为两个部分：摄像头调用+人脸特征点标定

2.1 摄像头调用

　　介绍下opencv中摄像头的调用方法；

　　利用 cap = cv2.VideoCapture(0) 创建一个对象；

　　（具体可以参考官方文档：https://docs.opencv.org/2.4/modules/highgui/doc/reading_and_writing_images_and_video.html）

 1 # 2018-2-26 2 # By TimeStamp 3 # cnblogs: http://www.cnblogs.com/AdaminXie 4  5 """ 6 cv2.VideoCapture(), 创建cv2摄像头对象/ open the default camera 7  8     Python: cv2.VideoCapture() → <VideoCapture object> 9 10     Python: cv2.VideoCapture(filename) → <VideoCapture object>    11     filename – name of the opened video file (eg. video.avi) or image sequence (eg. img_%02d.jpg, which will read samples like img_00.jpg, img_01.jpg, img_02.jpg, ...)12 13     Python: cv2.VideoCapture(device) → <VideoCapture object>14     device – id of the opened video capturing device (i.e. a camera index). If there is a single camera connected, just pass 0.15 16 """17 cap = cv2.VideoCapture(0)18 19 20 """21 cv2.VideoCapture.set(propId, value)，设置视频参数;22 23     propId:24     CV_CAP_PROP_POS_MSEC Current position of the video file in milliseconds.25     CV_CAP_PROP_POS_FRAMES 0-based index of the frame to be decoded/captured next.26     CV_CAP_PROP_POS_AVI_RATIO Relative position of the video file: 0 - start of the film, 1 - end of the film.27     CV_CAP_PROP_FRAME_WIDTH Width of the frames in the video stream.28     CV_CAP_PROP_FRAME_HEIGHT Height of the frames in the video stream.29     CV_CAP_PROP_FPS Frame rate.30     CV_CAP_PROP_FOURCC 4-character code of codec.31     CV_CAP_PROP_FRAME_COUNT Number of frames in the video file.32     CV_CAP_PROP_FORMAT Format of the Mat objects returned by retrieve() .33     CV_CAP_PROP_MODE Backend-specific value indicating the current capture mode.34     CV_CAP_PROP_BRIGHTNESS Brightness of the image (only for cameras).35     CV_CAP_PROP_CONTRAST Contrast of the image (only for cameras).36     CV_CAP_PROP_SATURATION Saturation of the image (only for cameras).37     CV_CAP_PROP_HUE Hue of the image (only for cameras).38     CV_CAP_PROP_GAIN Gain of the image (only for cameras).39     CV_CAP_PROP_EXPOSURE Exposure (only for cameras).40     CV_CAP_PROP_CONVERT_RGB Boolean flags indicating whether images should be converted to RGB.41     CV_CAP_PROP_WHITE_BALANCE_U The U value of the whitebalance setting (note: only supported by DC1394 v 2.x backend currently)42     CV_CAP_PROP_WHITE_BALANCE_V The V value of the whitebalance setting (note: only supported by DC1394 v 2.x backend currently)43     CV_CAP_PROP_RECTIFICATION Rectification flag for stereo cameras (note: only supported by DC1394 v 2.x backend currently)44     CV_CAP_PROP_ISO_SPEED The ISO speed of the camera (note: only supported by DC1394 v 2.x backend currently)45     CV_CAP_PROP_BUFFERSIZE Amount of frames stored in internal buffer memory (note: only supported by DC1394 v 2.x backend currently)46     47     value: 设置的参数值/ Value of the property48 """49 cap.set(3, 480)50 51 """52 cv2.VideoCapture.isOpened(), 检查摄像头初始化是否成功 / check if we succeeded53 返回true或false54 """55 cap.isOpened()56 57 """ 58 cv2.VideoCapture.read([imgage]) -> retval,image, 读取视频 / Grabs, decodes and returns the next video frame59 返回两个值：60     一个是布尔值true/false，用来判断读取视频是否成功/是否到视频末尾61     图像对象，图像的三维矩阵62 """63 flag, im_rd = cap.read()

2.2 人脸特征点标定

　　调用预测器“shape_predictor_68_face_landmarks.dat”进行68点标定，这是dlib训练好的模型，可以直接调用进行人脸68个人脸特征点的标定；

　　具体可以参考我的另一篇博客（http://www.cnblogs.com/AdaminXie/p/8137580.html）；

2.3 源码

　　实现的方法比较简单：

　　　　利用cv2.VideoCapture() 创建摄像头对象，然后利用 flag, im_rd = cv2.VideoCapture.read() 读取摄像头视频，im_rd就是视频中的一帧帧图像；

　　　　然后就类似于单张图像进行人脸检测，对这一帧帧的图像im_rd利用dlib进行特征点标定，然后绘制特征点；

　　　　你可以按下s键来获取当前截图，或者按下q键来退出摄像头；

 1 # 2018-2-26 2 # By TimeStamp 3 # cnblogs: http://www.cnblogs.com/AdaminXie 4 # github: https://github.com/coneypo/Dlib_face_detection_from_camera 5  6 import dlib                     #人脸识别的库dlib 7 import numpy as np              #数据处理的库numpy 8 import cv2                      #图像处理的库OpenCv 9 10 # dlib预测器11 detector = dlib.get_frontal_face_detector()12 predictor = dlib.shape_predictor(‘shape_predictor_68_face_landmarks.dat‘)13 14 # 创建cv2摄像头对象15 cap = cv2.VideoCapture(0)16 17 # cap.set(propId, value)18 # 设置视频参数，propId设置的视频参数，value设置的参数值19 cap.set(3, 480)20 21 # 截图screenshoot的计数器22 cnt = 023 24 # cap.isOpened（） 返回true/false 检查初始化是否成功25 while(cap.isOpened()):26 27     # cap.read()28     # 返回两个值：29     #    一个布尔值true/false，用来判断读取视频是否成功/是否到视频末尾30     #    图像对象，图像的三维矩阵31     flag, im_rd = cap.read()32 33     # 每帧数据延时1ms，延时为0读取的是静态帧34     k = cv2.waitKey(1)35 36     # 取灰度37     img_gray = cv2.cvtColor(im_rd, cv2.COLOR_RGB2GRAY)38 39     # 人脸数rects40     rects = detector(img_gray, 0)41 42     #print(len(rects))43 44     # 待会要写的字体45     font = cv2.FONT_HERSHEY_SIMPLEX46 47     # 标68个点48     if(len(rects)!=0):49         # 检测到人脸50         for i in range(len(rects)):51             landmarks = np.matrix([[p.x, p.y] for p in predictor(im_rd, rects[i]).parts()])52 53             for idx, point in enumerate(landmarks):54                 # 68点的坐标55                 pos = (point[0, 0], point[0, 1])56 57                 # 利用cv2.circle给每个特征点画一个圈，共68个58                 cv2.circle(im_rd, pos, 2, color=(0, 255, 0))59 60                 # 利用cv2.putText输出1-6861                 cv2.putText(im_rd, str(idx + 1), pos, font, 0.2, (0, 0, 255), 1, cv2.LINE_AA)62         cv2.putText(im_rd, "faces: "+str(len(rects)), (20,50), font, 1, (0, 0, 255), 1, cv2.LINE_AA)63     else:64         # 没有检测到人脸65         cv2.putText(im_rd, "no face", (20, 50), font, 1, (0, 0, 255), 1, cv2.LINE_AA)66 67     # 添加说明68     im_rd = cv2.putText(im_rd, "s: screenshot", (20, 400), font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)69     im_rd = cv2.putText(im_rd, "q: quit", (20, 450), font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)70 71     # 按下s键保存72     if (k == ord(‘s‘)):73         cnt+=174         cv2.imwrite("screenshoot"+str(cnt)+".jpg", im_rd)75 76     # 按下q键退出77     if(k==ord(‘q‘)):78         break79 80     # 窗口显示81     cv2.imshow("camera", im_rd)82 83 # 释放摄像头84 cap.release()85 86 # 删除建立的窗口87 cv2.destroyAllWindows()

# 请尊重他人劳动成果，转载或者使用源码请注明出处：http://www.cnblogs.com/AdaminXie

# 如果对您有帮助，欢迎在GitHub上star本项目:https://github.com/coneypo/Dlib_face_detection_from_camera

Python 3 利用 Dlib 19.7 实现摄像头人脸检测特征点标定