"""
從視屏中識(shí)別人臉，并實(shí)時(shí)標(biāo)出面部特征點(diǎn)
"""
import sys
import dlib # 人臉識(shí)別的庫(kù)dlib
import numpy as np # 數(shù)據(jù)處理的庫(kù)numpy
import cv2 # 圖像處理的庫(kù)OpenCv
 
class face_emotion():
 def __init__(self):
  # 使用特征提取器get_frontal_face_detector
  self.detector = dlib.get_frontal_face_detector()
  # dlib的68點(diǎn)模型，使用作者訓(xùn)練好的特征預(yù)測(cè)器
  self.predictor = dlib.shape_predictor("F:/face.dat")
 
  # 建cv2攝像頭對(duì)象，這里使用電腦自帶攝像頭，如果接了外部攝像頭，則自動(dòng)切換到外部攝像頭
  self.cap = cv2.VideoCapture(0)
  # 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
  self.cap.set(3, 480)
  # 截圖screenshoot的計(jì)數(shù)器
  self.cnt = 0
 
 def learning_face(self):
 
  # 眉毛直線擬合數(shù)據(jù)緩沖
  line_brow_x = []
  line_brow_y = []
 
  # cap.isOpened（） 返回true/false 檢查初始化是否成功
  while (self.cap.isOpened()):
 
   # cap.read()
   # 返回兩個(gè)值：
   # 一個(gè)布爾值true/false，用來(lái)判斷讀取視頻是否成功/是否到視頻末尾
   # 圖像對(duì)象，圖像的三維矩陣
   flag, im_rd = self.cap.read()
 
   # 每幀數(shù)據(jù)延時(shí)1ms，延時(shí)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)
 
   # 使用人臉檢測(cè)器檢測(cè)每一幀圖像中的人臉。并返回人臉數(shù)rects
   faces = self.detector(img_gray, 0)
 
   # 待會(huì)要顯示在屏幕上的字體
   font = cv2.FONT_HERSHEY_SIMPLEX
 
   # 如果檢測(cè)到人臉
   if (len(faces) != 0):
 
    # 對(duì)每個(gè)人臉都標(biāo)出68個(gè)特征點(diǎn)
    for i in range(len(faces)):
     # enumerate方法同時(shí)返回?cái)?shù)據(jù)對(duì)象的索引和數(shù)據(jù)，k為索引，d為faces中的對(duì)象
     for k, d in enumerate(faces):
      # 用紅色矩形框出人臉
      cv2.rectangle(im_rd, (d.left(), d.top()), (d.right(), d.bottom()), (0, 0, 255))
      # 計(jì)算人臉熱別框邊長(zhǎng)
      self.face_width = d.right() - d.left()
 
      # 使用預(yù)測(cè)器得到68點(diǎn)數(shù)據(jù)的坐標(biāo)
      shape = self.predictor(im_rd, d)
      # 圓圈顯示每個(gè)特征點(diǎn)
      for i in range(68):
       cv2.circle(im_rd, (shape.part(i).x, shape.part(i).y), 2, (0, 255, 0), -1, 8)
       # cv2.putText(im_rd, str(i), (shape.part(i).x, shape.part(i).y), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
       #   (255, 255, 255))
 
      # 分析任意n點(diǎn)的位置關(guān)系來(lái)作為表情識(shí)別的依據(jù)
      mouth_width = (shape.part(54).x - shape.part(48).x) / self.face_width # 嘴巴咧開(kāi)程度
      mouth_higth = (shape.part(66).y - shape.part(62).y) / self.face_width # 嘴巴張開(kāi)程度
      # print("嘴巴寬度與識(shí)別框?qū)挾戎龋?,mouth_width_arv)
      # print("嘴巴高度與識(shí)別框高度之比：",mouth_higth_arv)
 
      # 通過(guò)兩個(gè)眉毛上的10個(gè)特征點(diǎn)，分析挑眉程度和皺眉程度
      brow_sum = 0 # 高度之和
      frown_sum = 0 # 兩邊眉毛距離之和
      for j in range(17, 21):
       brow_sum += (shape.part(j).y - d.top()) + (shape.part(j + 5).y - d.top())
       frown_sum += shape.part(j + 5).x - shape.part(j).x
       line_brow_x.append(shape.part(j).x)
       line_brow_y.append(shape.part(j).y)
 
      # self.brow_k, self.brow_d = self.fit_slr(line_brow_x, line_brow_y) # 計(jì)算眉毛的傾斜程度
      tempx = np.array(line_brow_x)
      tempy = np.array(line_brow_y)
      z1 = np.polyfit(tempx, tempy, 1) # 擬合成一次直線
      self.brow_k = -round(z1[0], 3) # 擬合出曲線的斜率和實(shí)際眉毛的傾斜方向是相反的
 
      brow_hight = (brow_sum / 10) / self.face_width # 眉毛高度占比
      brow_width = (frown_sum / 5) / self.face_width # 眉毛距離占比
      # print("眉毛高度與識(shí)別框高度之比：",round(brow_arv/self.face_width,3))
      # print("眉毛間距與識(shí)別框高度之比：",round(frown_arv/self.face_width,3))
 
      # 眼睛睜開(kāi)程度
      eye_sum = (shape.part(41).y - shape.part(37).y + shape.part(40).y - shape.part(38).y +
         shape.part(47).y - shape.part(43).y + shape.part(46).y - shape.part(44).y)
      eye_hight = (eye_sum / 4) / self.face_width
      # print("眼睛睜開(kāi)距離與識(shí)別框高度之比：",round(eye_open/self.face_width,3))
 
      # 分情況討論
      # 張嘴，可能是開(kāi)心或者驚訝
      if round(mouth_higth >= 0.03):
       if eye_hight >= 0.056:
        cv2.putText(im_rd, "amazing", (d.left(), d.bottom() + 20), cv2.FONT_HERSHEY_SIMPLEX,
           0.8,
           (0, 0, 255), 2, 4)
       else:
        cv2.putText(im_rd, "happy", (d.left(), d.bottom() + 20), cv2.FONT_HERSHEY_SIMPLEX, 0.8,
           (0, 0, 255), 2, 4)
 
      # 沒(méi)有張嘴，可能是正常和生氣
      else:
       if self.brow_k <= -0.3:
        cv2.putText(im_rd, "angry", (d.left(), d.bottom() + 20), cv2.FONT_HERSHEY_SIMPLEX, 0.8,
           (0, 0, 255), 2, 4)
       else:
        cv2.putText(im_rd, "nature", (d.left(), d.bottom() + 20), cv2.FONT_HERSHEY_SIMPLEX, 0.8,
           (0, 0, 255), 2, 4)
 
    # 標(biāo)出人臉數(shù)
    cv2.putText(im_rd, "Faces: " + str(len(faces)), (20, 50), font, 1, (0, 0, 255), 1, cv2.LINE_AA)
   else:
    # 沒(méi)有檢測(cè)到人臉
    cv2.putText(im_rd, "No Face", (20, 50), font, 1, (0, 0, 255), 1, cv2.LINE_AA)
 
   # 添加說(shuō)明
   im_rd = cv2.putText(im_rd, "S: screenshot", (20, 400), font, 0.8, (0, 0, 255), 1, cv2.LINE_AA)
   im_rd = cv2.putText(im_rd, "Q: quit", (20, 450), font, 0.8, (0, 0, 255), 1, cv2.LINE_AA)
 
   # 按下s鍵截圖保存
   if (k == ord('s')):
    self.cnt += 1
    cv2.imwrite("screenshoot" + str(self.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
  self.cap.release()
 
  # 刪除建立的窗口
  cv2.destroyAllWindows()
 
if __name__ == "__main__":
 my_face = face_emotion()
 my_face.learning_face()