python 3利用Dlib 19.7实现摄像头人脸检测特征点标定
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dlib import numpy as np # 数据处理的库numpy 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)
# 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 – 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 – 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_camera import dlib #人脸识别的库dlib import numpy as np #数据处理的库numpy import 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()
如果对您有帮助,欢迎在GitHub上star本项目。
以上就是本文的全部内容,希望对大家的学习有所帮助,也希望大家多多支持脚本之家。
相关文章
Python使用metaclass实现Singleton模式的方法
这篇文章主要介绍了Python使用metaclass实现Singleton模式的方法,实例分析了Python基于metaclass实现单例模式的相关技巧,具有一定参考借鉴价值,需要的朋友可以参考下2015-05-05
最新评论