如何使用dlib获得眼睛和嘴巴的长度

Question

我正在开展一个哈欠检测项目，我正在使用dlib和opencv来检测视频中的脸部和地标。

我希望得到眼睛和嘴巴的长度。

这是我到目前为止所做的事情

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()

请帮助我如何获得睁眼和嘴巴的长度。

Answer 1

从这个数字

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文件

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]

嘴坐标文件

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]