python 3利用Dlib 19.7实现摄像头人脸检测特征点标定

Python  /  管理员 发布于 7年前   181

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

import dlib     # 人脸识别的库dlibimport numpy as np # 数据处理的库numpyimport 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）

# 2018-2-26# By TimeStamp# cnblogs: http://www.cnblogs.com/AdaminXie"""cv2.VideoCapture(), 创建cv2摄像头对象/ open the default camera  Python: cv2.VideoCapture() → <VideoCapture object>  Python: cv2.VideoCapture(filename) → <VideoCapture object>    filename C 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, ...)  Python: cv2.VideoCapture(device) → <VideoCapture object>  device C id of the opened video capturing device (i.e. a camera index). If there is a single camera connected, just pass 0."""cap = cv2.VideoCapture(0)"""cv2.VideoCapture.set(propId, value)，设置视频参数;  propId:  CV_CAP_PROP_POS_MSEC Current position of the video file in milliseconds.  CV_CAP_PROP_POS_FRAMES 0-based index of the frame to be decoded/captured next.  CV_CAP_PROP_POS_AVI_RATIO Relative position of the video file: 0 - start of the film, 1 - end of the film.  CV_CAP_PROP_FRAME_WIDTH Width of the frames in the video stream.  CV_CAP_PROP_FRAME_HEIGHT Height of the frames in the video stream.  CV_CAP_PROP_FPS Frame rate.  CV_CAP_PROP_FOURCC 4-character code of codec.  CV_CAP_PROP_FRAME_COUNT Number of frames in the video file.  CV_CAP_PROP_FORMAT Format of the Mat objects returned by retrieve() .  CV_CAP_PROP_MODE Backend-specific value indicating the current capture mode.  CV_CAP_PROP_BRIGHTNESS Brightness of the image (only for cameras).  CV_CAP_PROP_CONTRAST Contrast of the image (only for cameras).  CV_CAP_PROP_SATURATION Saturation of the image (only for cameras).  CV_CAP_PROP_HUE Hue of the image (only for cameras).  CV_CAP_PROP_GAIN Gain of the image (only for cameras).  CV_CAP_PROP_EXPOSURE Exposure (only for cameras).  CV_CAP_PROP_CONVERT_RGB Boolean flags indicating whether images should be converted to RGB.  CV_CAP_PROP_WHITE_BALANCE_U The U value of the whitebalance setting (note: only supported by DC1394 v 2.x backend currently)  CV_CAP_PROP_WHITE_BALANCE_V The V value of the whitebalance setting (note: only supported by DC1394 v 2.x backend currently)  CV_CAP_PROP_RECTIFICATION Rectification flag for stereo cameras (note: only supported by DC1394 v 2.x backend currently)  CV_CAP_PROP_ISO_SPEED The ISO speed of the camera (note: only supported by DC1394 v 2.x backend currently)  CV_CAP_PROP_BUFFERSIZE Amount of frames stored in internal buffer memory (note: only supported by DC1394 v 2.x backend currently)    value: 设置的参数值/ Value of the property"""cap.set(3, 480)"""cv2.VideoCapture.isOpened(), 检查摄像头初始化是否成功 / check if we succeeded返回true或false"""cap.isOpened()""" cv2.VideoCapture.read([imgage]) -> retval,image, 读取视频 / Grabs, decodes and returns the next video frame返回两个值：  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  图像对象，图像的三维矩阵"""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（python3利用Dlib19.7实现人脸68个特征点标定）； 

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；

# 2018-2-26

# By TimeStamp# cnblogs: http://www.cnblogs.com/AdaminXie# github: https://github.com/coneypo/Dlib_face_detection_from_cameraimport dlib           #人脸识别的库dlibimport numpy as np       #数据处理的库numpyimport cv2           #图像处理的库OpenCv# dlib预测器detector = dlib.get_frontal_face_detector()predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')# 创建cv2摄像头对象cap = cv2.VideoCapture(0)# cap.set(propId, value)# 设置视频参数，propId设置的视频参数，value设置的参数值cap.set(3, 480)# 截图screenshoot的计数器cnt = 0# cap.isOpened（） 返回true/false 检查初始化是否成功while(cap.isOpened()):  # cap.read()  # 返回两个值：  #  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  #  图像对象，图像的三维矩阵  flag, im_rd = cap.read()  # 每帧数据延时1ms，延时为0读取的是静态帧  k = cv2.waitKey(1)  # 取灰度  img_gray = cv2.cvtColor(im_rd, cv2.COLOR_RGB2GRAY)  # 人脸数rects  rects = detector(img_gray, 0)  #print(len(rects))  # 待会要写的字体  font = cv2.FONT_HERSHEY_SIMPLEX  # 标68个点  if(len(rects)!=0):    # 检测到人脸    for i in range(len(rects)):      landmarks = np.matrix([[p.x, p.y] for p in predictor(im_rd, rects[i]).parts()])      for idx, point in enumerate(landmarks):        # 68点的坐标        pos = (point[0, 0], point[0, 1])        # 利用cv2.circle给每个特征点画一个圈，共68个        cv2.circle(im_rd, pos, 2, color=(0, 255, 0))        # 利用cv2.putText输出1-68        cv2.putText(im_rd, str(idx + 1), pos, font, 0.2, (0, 0, 255), 1, cv2.LINE_AA)    cv2.putText(im_rd, "faces: "+str(len(rects)), (20,50), font, 1, (0, 0, 255), 1, cv2.LINE_AA)  else:    # 没有检测到人脸    cv2.putText(im_rd, "no face", (20, 50), font, 1, (0, 0, 255), 1, cv2.LINE_AA)  # 添加说明  im_rd = cv2.putText(im_rd, "s: screenshot", (20, 400), font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)  im_rd = cv2.putText(im_rd, "q: quit", (20, 450), font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)  # 按下s键保存  if (k == ord('s')):    cnt+=1    cv2.imwrite("screenshoot"+str(cnt)+".jpg", im_rd)  # 按下q键退出  if(k==ord('q')):    break  # 窗口显示  cv2.imshow("camera", im_rd)# 释放摄像头cap.release()# 删除建立的窗口cv2.destroyAllWindows()

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