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
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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
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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）
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# 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()
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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
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# 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()
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原文链接：https://www.cnblogs.com/AdaminXie/p/8472743.html