dlibを使用して目と口の長さを取得する方法

Question

私は夜明け検出のプロジェクトに取り組んでいます。私はdlibとopencvを使ってビデオの顔と目印を検出しています。

目と口の長さを手に入れたいです。

これは私が開いて目や口の長さを取得する方法を私を助けてください今

import sys 
import os 
import dlib 
import glob 
from skimage import io 
import cv2 
import time 

if len(sys.argv) != 3: 
    print("") 
    exit() 

predictor_path = sys.argv[1] 
faces_folder_path = sys.argv[2] 

vidcap = cv2.VideoCapture('video.avi') 

detector = dlib.get_frontal_face_detector() 
predictor = dlib.shape_predictor(predictor_path) 
win = dlib.image_window() 

while vidcap.isOpened(): 
    success, image = vidcap.read() 
    if success: 
     win.clear_overlay() 
     win.set_image(image) 

     # Ask the detector to find the bounding boxes of each face. The 1 in the 
     # second argument indicates that we should upsample the image 1 time. This 
     # will make everything bigger and allow us to detect more faces. 
     dets = detector(image, 1) 
     print("Number of faces detected: {}".format(len(dets))) 
     for k, d in enumerate(dets): 
      print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
      k, d.left(), d.top(), d.right(), d.bottom())) 
      # Get the landmarks/parts for the face in box d. 
      shape = predictor(image, d) 
      print(shape) 
      print("Part 0: {}, Part 1: {},Part 2: {} ...".format(shape.part(0),shape.part(1),shape.part(2))) 
      # Draw the face landmarks on the screen. 
      win.add_overlay(shape) 
      win.add_overlay(dets) 
     time.sleep(0.01) 
cv2.destroyAllWindows() 
vidcap.release() 

までやっていることです。

Answer 1

この図から

import Paths 
import globals 
from globals import ClassifierFiles 
import numpy as np 
import cv2 
import time 
import dlib 
import math 
import eyeCoordinates 
import mouthCoordinates 
from globals import Threshold 
from globals import yawnFolder 
import os 
import openface 
VIDEO_PATHS = [] 


readVideo('v.avi')#test video of faces 



def readVideo(video): 
    global no,yes 
    video_capture = cv2.VideoCapture(video) 
    detector = dlib.get_frontal_face_detector() #Face detector 
    predictor = dlib.shape_predictor(ClassifierFiles.shapePredicter) #Landmark identifier 
    face_aligner = openface.AlignDlib(ClassifierFiles.shapePredicter) 

    u = 0 
    while True: 
     ret, frame = video_capture.read() 
     if frame != None: 
      gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) 
      # clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8)) 
      # clahe_image = clahe.apply(gray) 

      detections = detector(frame, 1) #Detect the faces in the image 

      for k,d in enumerate(detections): #For each detected face 
       shape = predictor(frame, d) #Get coordinates 
       vec = np.empty([68, 2], dtype = int) 
       coor = [] 
       for i in range(1,68): #There are 68 landmark points on each face 
        #cv2.circle(frame, (shape.part(i).x, shape.part(i).y), 1, (0,0,255), thickness=1) 
        coor.append([shape.part(i).x, shape.part(i).y]) 
        vec[i][0] = shape.part(i).x 
        vec[i][1] = shape.part(i).y 

       #RightEye and LeftEye coordinates 
       rightEye = eyeCoordinates.distanceRightEye(coor) 
       leftEye = eyeCoordinates.distanceLeftEye(coor) 
       eyes = (rightEye + leftEye)/2 

       #Mouth coordinates 
       mouth = mouthCoordinates.distanceBetweenMouth(coor) 

       print(eyes,mouth) 
       #prints both eyes average distance 
       #prints mouth distance 
      break 

     if cv2.waitKey(1) & 0xFF == ord('q'): 
      break 



if __name__ == '__main__': 
    VIDEO_PATHS = Paths.videosPaths() 
    init() 

eyeCoordinatesが

import distanceFormulaCalculator 

def distanceRightEye(c): 
    eR_36,eR_37,eR_38,eR_39,eR_40,eR_41 = 0,0,0,0,0,0 
    eR_36 = c[35] 
    eR_37 = c[36] 
    eR_38 = c[37] 
    eR_39 = c[38] 
    eR_40 = c[39] 
    eR_41 = c[40] 
    x1 = distanceFormulaCalculator.distanceFormula(eR_37,eR_41) 
    x2 = distanceFormulaCalculator.distanceFormula(eR_38,eR_40) 
    return ((x1+x2)/2) 

def distanceLeftEye(c): 
    eL_42,eL_43,eL_44,eL_45,eL_46,eL_47 = 0,0,0,0,0,0 
    eL_42 = c[41] 
    eL_43 = c[42] 
    eL_44 = c[43] 
    eL_45 = c[44] 
    eL_46 = c[45] 
    eL_47 = c[46] 
    x1 = distanceFormulaCalculator.distanceFormula(eL_43,eL_47) 
    x2 = distanceFormulaCalculator.distanceFormula(eL_44,eL_46) 
    return ((x1+x2)/2) 



def eyePoints(): 
    return [36,37,38,39,40,41,42,43,44,45,46,47] 

口座標ファイルshape.partの間の距離を測定していない理由を

import distanceFormulaCalculator 

def distanceBetweenMouth(c): 
    m_60,m_61,m_62,m_63,m_64,m_65,m_66,m_67 = 0,0,0,0,0,0,0,0 
    m_60 = c[59] 
    m_61 = c[60] 
    m_62 = c[61] 
    m_63 = c[62] 
    m_64 = c[63] 
    m_65 = c[64] 
    m_66 = c[65] 
    m_67 = c[66] 
    x1 = distanceFormulaCalculator.distanceFormula(m_61,m_67) 
    x2 = distanceFormulaCalculator.distanceFormula(m_62,m_66) 
    x3 = distanceFormulaCalculator.distanceFormula(m_63,m_65) 
    return ((x1+x2+x3)/3) 



def mouthPoints(): 
    return [60,61,62,63,64,65,66,67]