#coding:utf-8 
 
import cv2 
import sys 
from PIL import Image 
 
 
def CatchUsbVideo(window_name, camera_idx): 
  cv2.namedWindow(window_name) 
 
  # 視頻來源，可以來自一段已存好的視頻，也可以直接來自USB攝像頭 
  cap = cv2.VideoCapture(camera_idx) 
 
  # 告訴OpenCV使用人臉識(shí)別分類器 
  classfier = cv2.CascadeClassifier("C:\\opencv\\build\\etc\\haarcascades\\haarcascade_frontalface_alt2.xml") 
 
  # 識(shí)別出人臉后要畫的邊框的顏色，RGB格式 
  color = (0, 255, 0) 
 
  while cap.isOpened(): 
    ok, frame = cap.read() # 讀取一幀數(shù)據(jù) 
    if not ok: 
      break 
 
      # 將當(dāng)前幀轉(zhuǎn)換成灰度圖像 
    grey = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) 
 
    # 人臉檢測，1.2和2分別為圖片縮放比例和需要檢測的有效點(diǎn)數(shù) 
    faceRects = classfier.detectMultiScale(grey, scaleFactor=1.2, minNeighbors=3, minSize=(32, 32)) 
    if len(faceRects) > 0: # 大于0則檢測到人臉 
      for faceRect in faceRects: # 單獨(dú)框出每一張人臉 
        x, y, w, h = faceRect 
        cv2.rectangle(frame, (x - 10, y - 10), (x + w + 10, y + h + 10), color, 3) #5控制綠色框的粗細(xì) 
 
    # 顯示圖像 
    cv2.imshow(window_name, frame) 
    c = cv2.waitKey(10) 
    if c & 0xFF == ord('q'): 
      break 
 
      # 釋放攝像頭并銷毀所有窗口 
  cap.release() 
  cv2.destroyAllWindows() 
 
 
if __name__ == '__main__': 
  CatchUsbVideo("FaceRect", 0) 
  # if len(sys.argv) != 2: 
  #   print("Usage:%s camera_id\r\n" % (sys.argv[0])) 
  # else: 
  #   CatchUsbVideo("識(shí)別人臉區(qū)域", int(sys.argv[1]))