视频人脸检测

  1. import numpy as np
  2. import cv2
  4. face_cascade = cv2.CascadeClassifier('./data/haarcascades/haarcascade_frontalface_default.xml')
  5. eye_cascade = cv2.CascadeClassifier('./data/haarcascades/haarcascade_eye.xml')
  9. # 创建一个 VideoCapture 对象，参数是设备的索引即摄像机的编号或者 Video 的文件名
  10. # 这里的 0 是指第一台摄像机，以此类推
  11. cap = cv2.VideoCapture(0)
  12. ret, frame = cap.read()
  13. while (ret):
  14.     # while cap.isOpened():
  15.     # 一帧一帧的捕获
  16.     ret, frame = cap.read()
  17.     #gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
  18.     faces = face_cascade.detectMultiScale(frame, 1.3, 5)
  19.     for (x, y, w, h) in faces:
  20.         img = cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
  21.         #roi_gray = gray[y:y+h, x:x+w]
  22.         roi_color = img[y:y+h, x:x+w]
  23.         eyes = eye_cascade.detectMultiScale(roi_color)
  24.         for (ex, ey, ew, eh) in eyes:
  25.             cv2.rectangle(roi_color, (ex, ey), (ex+ew, ey+eh), (0, 255, 0), 2)
  27.     cv2.imshow("frame", frame)
  28.     if cv2.waitKey(1) & 0xFF == 27:  # ord('q'):
  29.         break
  30. # cap.release()
  31. cv2.destroyAllWindows()
  1. import cv2 as cv
  3. capture = cv.VideoCapture(0)
  4. face_detector = cv.CascadeClassifier("C:/Anaconda3/Library/etc/haarcascades/haarcascade_frontalface_default.xml")
  5. smile_detector = cv.CascadeClassifier("C:/Anaconda3/Library/etc/haarcascades/haarcascade_smile.xml")
  7. while True:
  8.     ret, image = capture.read()
  9.     if ret is True:
  10.         cv.imshow("frame", image)
  11.         faces = face_detector.detectMultiScale(image, scaleFactor=1.05, minNeighbors=3,
  12.                                           minSize=(30, 30), maxSize=(300, 300))
  13.         for x, y, width, height in faces:
  14.             cv.rectangle(image, (x, y), (x+width, y+height), (0, 0, 255), 2, cv.LINE_8, 0)
  15.         roi = image[y:y+height,x:x+width]
  16.         smiles = smile_detector.detectMultiScale(roi, scaleFactor=1.7, minNeighbors=3,
  17.                                                minSize=(15, 15), maxSize=(100, 100))
  18.         for sx, sy, sw, sh in smiles:
  19.             cv.rectangle(roi, (sx, sy), (sx + sw, sy + sh), (0, 255, 0), 1)
  21.         cv.imshow("faces", image)
  22.         c = cv.waitKey(50)
  23.         if c == 27:
  24.             break
  25.     else:
  26.         break
  28. cv.destroyAllWindows()

图片人脸检测

  1. import numpy as np
  2. import cv2
  4. face_cascade = cv2.CascadeClassifier('./data/haarcascades/haarcascade_frontalface_default.xml')
  5. eye_cascade = cv2.CascadeClassifier('./data/haarcascades/haarcascade_eye.xml')
  9. img = cv2.imread("F:/jupyter/OpenCV-Python-Tutorial/Tutorial/sample_img/lena.jpg")
  11. faces = face_cascade.detectMultiScale(img, 1.3, 5)
  12. for (x,y,w,h) in faces:
  13.     img = cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
  14.     roi_gray = img[y:y+h, x:x+w]
  15.     roi_color = img[y:y+h, x:x+w]
  16.     eyes = eye_cascade.detectMultiScale(roi_gray)
  17.     for (ex,ey,ew,eh) in eyes:
  18.         cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),2)
  20. cv2.imshow('img',img)
  21. cv2.waitKey(0)
  22. cv2.destroyAllWindows()

视频人脸表情检测

  1. #!usr/bin/env python
  2. # -*- coding:utf-8 _*-
  3. """
  4. @author:asus_pc
  5. @file: face_detector.py
  6. @time: 2019/08/17
  7. """
  9. # 开发环境
  10. # Python3.6
  12. # Python 2/3 compatibility
  14. import cv2  # 4.0.0
  15. import dlib  # 19.8.1 到 https://pypi.org/simple/dlib/ 下载 whl 文件 pip install *.whl 安装
  16. import numpy as np  # 1.16.2
  17. from pathlib import Path
  18. import sys
  23. # 配置 Dlib 关键点检测路径
  24. # 文件可以从 http://dlib.net/files/ 下载
  25. PREDICTOR_PATH = "C:/BLOG/Computer_Vision_Project/Computer_Vision_Project/Facial_Expression_Recognition/6_server_demo/static/face_detector_trained/shape_predictor_68_face_landmarks.dat"
  26. predictor = dlib.shape_predictor(PREDICTOR_PATH)  # 关键点检测
  27. # 配置人脸检测器路径
  28. cascade_path = "C:/Anaconda3/Lib/site-packages/cv2/data/haarcascade_frontalface_default.xml"  # 在 opencv github 可以找到
  29. # 初始化分类器
  30. cascade = cv2.CascadeClassifier(cascade_path)
  33. # 调用 cascade.detectMultiScale 人脸检测器和 Dlib 的关键点检测算法 predictor 获得关键点结果
  34. def get_landmarks(im):
  35.     try:
  36.         rects = cascade.detectMultiScale(im, 1.3, 5)  # 进行多尺度检测
  37.         if len(rects) == 1:
  38.             x, y, w, h = rects[0]
  39.             rect = dlib.rectangle(int(x), int(
  40.                 y), int(x + w), int(y + h))  # 获得检测框
  41.             # 调用 dlib 关键点检测
  42.             return np.matrix([[p.x, p.y] for p in predictor(im, rect).parts()])
  43.     except:
  44.         return None
  47. #  打印关键点信息方便调试
  48. def annotat_landmarks(im, landmarks):
  49.     im = im.copy()
  50.     for idx, point in enumerate(landmarks):
  51.         pos = (point[0, 0], point[0, 1])
  52.         cv2.putText(im, str(idx),
  53.                     pos,
  54.                     fontFace=cv2.FONT_HERSHEY_SCRIPT_SIMPLEX,
  55.                     fontScale=0.4,
  56.                     color=(0, 0, 255))
  57.         cv2.circle(im, pos, 5, color=(0, 255, 255))
  58.     return im
  61. def get_mouth(im):
  62.     # 得到 68 个关键点
  63.     landmarks = get_landmarks(im)
  64.     if landmarks is not None:
  65.         # print(landmarks)
  66.         xmin = 10000
  67.         xmax = 0
  68.         ymin = 10000
  69.         ymax = 0
  70.         # 根据最外围的关键点获取包围嘴唇的最小矩形框
  71.         # 68 个关键点是从
  72.         # 左耳朵0 -下巴-右耳朵16-左眉毛（17-21）-右眉毛（22-26）-左眼睛（36-41）
  73.         # 右眼睛（42-47）-鼻子从上到下（27-30）-鼻孔（31-35）
  74.         # 嘴巴外轮廓（48-59）嘴巴内轮廓（60-67）
  75.         for i in range(48, 67):
  76.             x = landmarks[i, 0]
  77.             y = landmarks[i, 1]
  78.             if x < xmin:
  79.                 xmin = x
  80.             if x > xmax:
  81.                 xmax = x
  82.             if y < ymin:
  83.                 ymin = y
  84.             if y > ymax:
  85.                 ymax = y
  86.         # print("xmin", xmin)
  87.         # print("xmax", xmax)
  88.         # print("ymin", ymin)
  89.         # print("ymax", ymax)
  90.         roiwidth = xmax - xmin  # 矩形框的宽和高
  91.         roiheight = ymax - ymin
  92.         roi = im[ymin:ymax, xmin:xmax, :]
  93.         # cv2.imshow("roi_0", roi)
  94.         # 将最小矩形扩大 1.5 倍，获得最终矩形框
  95.         if roiwidth > roiheight:  # 宽和高哪个大哪个就 ×1.5 倍
  96.             dstlen = 1.5 * roiwidth
  97.         else:
  98.             dstlen = 1.5 * roiheight
  100.         diff_xlen = dstlen - roiwidth
  101.         diff_ylen = dstlen - roiheight
  102.         newx = xmin
  103.         newy = ymin
  104.         imagerows, imagecols, ch = im.shape
  105.         # print("imagerows, imagecols", imagerows, imagecols)
  106.         if newx >= diff_xlen / 2 and newx + roiwidth + diff_xlen / 2 < imagecols:
  107.             newx = newx - diff_xlen / 2
  108.         elif newx < diff_xlen / 2:
  109.             newx = 0
  110.         else:
  111.             newx = imagecols - dstlen
  113.         if newy >= diff_ylen / 2 and newy + roiheight + diff_ylen / 2 < imagerows:
  114.             newy = newy - diff_ylen / 2
  115.         elif newy < diff_ylen / 2:
  116.             newy = 0
  117.         else:
  118.             newy = imagecols - dstlen
  120.         roi = im[int(newy):int(newy + dstlen), int(newx):int(newx + dstlen), :]
  121.         return roi  # 得到人脸关键点就返回嘴巴区域
  123.     return None # 否则返回空
  124. #         cv2.imshow("roi", roi)
  125. #         cv2.imwrite(dst+im_path.name, roi)
  128. if __name__ == '__main__':
  129.     # 创建一个 VideoCapture 对象，参数是设备的索引即摄像机的编号或者 Video 的文件名
  130.     # 这里的 0 是指第一台摄像机，以此类推
  132.     cap = cv2.VideoCapture(0)
  133.     ret, frame = cap.read()
  134.     while (ret):
  135.         # while cap.isOpened():
  136.         # 一帧一帧的捕获
  137.         ret, frame = cap.read()
  138.         landmarks = get_landmarks(frame)
  139.         if landmarks is not None:
  140.             frame = annotat_landmarks(frame, landmarks)
  141.         cv2.imshow("frame", frame)
  142.         if cv2.waitKey(1) & 0xFF == 27:  # ord('q'):
  143.             break
  146. cv2.waitKey(0)
  147. cv2.destroyAllWindows()