Python3利用Dlib实现摄像头实时人脸检测和平铺显示示例

# OpenCv 调用摄像头
# 默认调用笔记本摄像头

# Author:  coneypo
# Blog:   http://www.cnblogs.com/AdaminXie
# GitHub:  https://github.com/coneypo/Dlib_face_cut
# Mail:   coneypo@foxmail.com

import cv2

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)

# cap.isOpened() 返回 true/false, 检查摄像头初始化是否成功
print(cap.isOpened())

# cap.read()
""" 
返回两个值
  先返回一个布尔值, 如果视频读取正确, 则为 True, 如果错误, 则为 False; 
  也可用来判断是否到视频末尾;
  
  再返回一个值, 为每一帧的图像, 该值是一个三维矩阵;
  
  通用接收方法为: 
    ret,frame = cap.read();
    ret: 布尔值;
    frame: 图像的三维矩阵;
    这样 ret 存储布尔值, frame 存储图像;
    
    若使用一个变量来接收两个值, 如:
      frame = cap.read()
    则 frame 为一个元组, 原来使用 frame 处需更改为 frame[1]
"""

while cap.isOpened():
  ret_flag, img_camera = cap.read()
  cv2.imshow("camera", img_camera)

  # 每帧数据延时 1ms, 延时为0, 读取的是静态帧
  k = cv2.waitKey(1)

  # 按下 's' 保存截图
  if k == ord('s'):
    cv2.imwrite("test.jpg", img_camera)

  # 按下 'q' 退出
  if k == ord('q'):
    break

# 释放所有摄像头
cap.release()

# 删除建立的所有窗口
cv2.destroyAllWindows()

# created at 2017-11-27
# updated at 2018-09-06

# Author:  coneypo
# Dlib:   http://dlib.net/
# Blog:   http://www.cnblogs.com/AdaminXie/
# Github:  https://github.com/coneypo/Dlib_examples

# create object of OpenCv
# use OpenCv to read and show images

import dlib
import cv2

# 使用 Dlib 的正面人脸检测器 frontal_face_detector
detector = dlib.get_frontal_face_detector()

# 图片所在路径
# read image
img = cv2.imread("imgs/faces_2.jpeg")

# 使用 detector 检测器来检测图像中的人脸
# use detector of Dlib to detector faces
faces = detector(img, 1)
print("人脸数 / Faces in all: ", len(faces))

# Traversal every face
for i, d in enumerate(faces):
  print("第", i+1, "个人脸的矩形框坐标：",
     "left:", d.left(), "right:", d.right(), "top:", d.top(), "bottom:", d.bottom())
  cv2.rectangle(img, tuple([d.left(), d.top()]), tuple([d.right(), d.bottom()]), (0, 255, 255), 2)

cv2.namedWindow("img", 2)
cv2.imshow("img", img)
cv2.waitKey(0)

# 检测到人脸
if len(faces) != 0:
  # 记录每次开始写入人脸像素的宽度位置
  faces_start_width = 0

  for face in faces:
    # 绘制矩形框
    cv2.rectangle(img_rd, tuple([face.left(), face.top()]), tuple([face.right(), face.bottom()]),
           (0, 255, 255), 2)

    height = face.bottom() - face.top()
    width = face.right() - face.left()

    ### 进行人脸裁减 ###
    # 如果没有超出摄像头边界
    if (face.bottom() < 480) and (face.right() < 640) and \
        ((face.top() + height) < 480) and ((face.left() + width) < 640):
      # 填充
      for i in range(height):
        for j in range(width):
          img_rd[i][faces_start_width + j] = \
            img_rd[face.top() + i][face.left() + j]

    # 更新 faces_start_width 的坐标
    faces_start_width += width

# 调用摄像头实时单个/多个人脸检测，并依次在摄像头窗口，实时平铺显示检测到的人脸；

# Author:  coneypo
# Blog:   http://www.cnblogs.com/AdaminXie
# GitHub:  https://github.com/coneypo/Dlib_face_cut

import dlib
import cv2
import time

# 储存截图的目录
path_screenshots = "data/images/screenshots/"

detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('data/dlib/shape_predictor_68_face_landmarks.dat')

# 创建 cv2 摄像头对象
cap = cv2.VideoCapture(0)

# 设置视频参数，propId 设置的视频参数，value 设置的参数值
cap.set(3, 960)

# 截图 screenshots 的计数器
ss_cnt = 0

while cap.isOpened():
  flag, img_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(img_rd, cv2.COLOR_RGB2GRAY)

  # 人脸数
  faces = detector(img_gray, 0)

  # 待会要写的字体
  font = cv2.FONT_HERSHEY_SIMPLEX

  # 按下 'q' 键退出
  if k == ord('q'):
    break
  else:
    # 检测到人脸
    if len(faces) != 0:
      # 记录每次开始写入人脸像素的宽度位置
      faces_start_width = 0

      for face in faces:
        # 绘制矩形框
        cv2.rectangle(img_rd, tuple([face.left(), face.top()]), tuple([face.right(), face.bottom()]),
               (0, 255, 255), 2)

        height = face.bottom() - face.top()
        width = face.right() - face.left()

        ### 进行人脸裁减 ###
        # 如果没有超出摄像头边界
        if (face.bottom() < 480) and (face.right() < 640) and \
            ((face.top() + height) < 480) and ((face.left() + width) < 640):
          # 填充
          for i in range(height):
            for j in range(width):
              img_rd[i][faces_start_width + j] = \
                img_rd[face.top() + i][face.left() + j]

        # 更新 faces_start_width 的坐标
        faces_start_width += width

      cv2.putText(img_rd, "Faces in all: " + str(len(faces)), (20, 350), font, 0.8, (0, 0, 0), 1, cv2.LINE_AA)

    else:
      # 没有检测到人脸
      cv2.putText(img_rd, "no face", (20, 350), font, 0.8, (0, 0, 0), 1, cv2.LINE_AA)

    # 添加说明
    img_rd = cv2.putText(img_rd, "Press 'S': Screen shot", (20, 400), font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
    img_rd = cv2.putText(img_rd, "Press 'Q': Quit", (20, 450), font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)

  # 按下 's' 键保存
  if k == ord('s'):
    ss_cnt += 1
    print(path_screenshots + "screenshot" + "_" + str(ss_cnt) + "_" + time.strftime("%Y-%m-%d-%H-%M-%S",
                                            time.localtime()) + ".jpg")
    cv2.imwrite(path_screenshots + "screenshot" + "_" + str(ss_cnt) + "_" + time.strftime("%Y-%m-%d-%H-%M-%S",
                                               time.localtime()) + ".jpg",
          img_rd)

  cv2.namedWindow("camera", 1)
  cv2.imshow("camera", img_rd)

# 释放摄像头
cap.release()

# 删除建立的窗口
cv2.destroyAllWindows()