import dlib     # 人臉識別的庫dlib
import numpy as np # 數(shù)據(jù)處理的庫numpy
import cv2     # 圖像處理的庫OpenCv 

# 2018-2-26
# By TimeStamp
# cnblogs: http://www.cnblogs.com/AdaminXie

"""
cv2.VideoCapture(), 創(chuàng)建cv2攝像頭對象/ 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)，設(shè)置視頻參數(shù);

  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: 設(shè)置的參數(shù)值/ Value of the property
"""
cap.set(3, 480)

"""
cv2.VideoCapture.isOpened(), 檢查攝像頭初始化是否成功 / check if we succeeded
返回true或false
"""
cap.isOpened()

""" 
cv2.VideoCapture.read([imgage]) -> retval,image, 讀取視頻 / Grabs, decodes and returns the next video frame
返回兩個值：
  一個是布爾值true/false，用來判斷讀取視頻是否成功/是否到視頻末尾
  圖像對象，圖像的三維矩陣
"""
flag, im_rd = cap.read()

# By TimeStamp
# cnblogs: http://www.cnblogs.com/AdaminXie
# github: https://github.com/coneypo/Dlib_face_detection_from_camera

import dlib           #人臉識別的庫dlib
import numpy as np       #數(shù)據(jù)處理的庫numpy
import cv2           #圖像處理的庫OpenCv

# dlib預(yù)測器
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')

# 創(chuàng)建cv2攝像頭對象
cap = cv2.VideoCapture(0)

# cap.set(propId, value)
# 設(shè)置視頻參數(shù)，propId設(shè)置的視頻參數(shù)，value設(shè)置的參數(shù)值
cap.set(3, 480)

# 截圖screenshoot的計數(shù)器
cnt = 0

# cap.isOpened（） 返回true/false 檢查初始化是否成功
while(cap.isOpened()):

  # cap.read()
  # 返回兩個值：
  #  一個布爾值true/false，用來判斷讀取視頻是否成功/是否到視頻末尾
  #  圖像對象，圖像的三維矩陣
  flag, im_rd = cap.read()

  # 每幀數(shù)據(jù)延時1ms，延時為0讀取的是靜態(tài)幀
  k = cv2.waitKey(1)

  # 取灰度
  img_gray = cv2.cvtColor(im_rd, cv2.COLOR_RGB2GRAY)

  # 人臉數(shù)rects
  rects = detector(img_gray, 0)

  #print(len(rects))

  # 待會要寫的字體
  font = cv2.FONT_HERSHEY_SIMPLEX

  # 標68個點
  if(len(rects)!=0):
    # 檢測到人臉
    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點的坐標
        pos = (point[0, 0], point[0, 1])

        # 利用cv2.circle給每個特征點畫一個圈，共68個
        cv2.circle(im_rd, pos, 2, color=(0, 255, 0))

        # 利用cv2.putText輸出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:
    # 沒有檢測到人臉
    cv2.putText(im_rd, "no face", (20, 50), font, 1, (0, 0, 255), 1, cv2.LINE_AA)

  # 添加說明
  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鍵保存
  if (k == ord('s')):
    cnt+=1
    cv2.imwrite("screenshoot"+str(cnt)+".jpg", im_rd)

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

  # 窗口顯示
  cv2.imshow("camera", im_rd)

# 釋放攝像頭
cap.release()

# 刪除建立的窗口
cv2.destroyAllWindows()