Question

我希望将这两个代码组合起来只运行一次，当我将 save_detected_face.py 代码保存到jpg文件中时，检测到OpenCV CascadeClassifier，保存到jpg文件后，只要检测过程完成， train_save_face.py 就会自动运行。

这是我的示例代码： save_detected_face.py

import os
import urllib.request
import cv2
import numpy as np
from PIL import Image


path='dataset'
recognizer=cv2.face.LBPHFaceRecognizer_create();

def main():
    #cap = cv2.VideoCapture("../index.htm?clientIpAddr=192.168.1.12&IsRemote=0")
    a=0
    cap = "http://192.168.1.43:8080/shot.jpg"
    id = input('enter user id: ')
    faceDetect=cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
    eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')
    sampleNum=0

    while True:
        a = a+1
        imgResp=urllib.request.urlopen(cap)
        imgNp=np.array(bytearray(imgResp.read()),dtype=np.uint8)
        img=cv2.imdecode(imgNp,-1)

        gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
        faces=faceDetect.detectMultiScale(gray,1.3,5);
        for(x,y,w,h) in faces:
            sampleNum = sampleNum+1
            cv2.rectangle(img, (x,y),(x+w,y+h),(255,0,0),2)
    #            roi_gray= gray[y:y+h, x:x+w]
            roi_color= img[y:y+h, x:x+w]
            eyes = eye_cascade.detectMultiScale(roi_color)
            for (ex,ey,ew,eh) in eyes:
                cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),2)
                cv2.imwrite("dataSet/User."+str(id)+"."+str(sampleNum)+".jpg",gray[y:y+h,x:x+w])
        cv2.imshow('frame', img)

#  Training code

        def getImagesWithID(path):
            imagePaths=[os.path.join(path,f) for f in os.listdir(path)]
            faces=[]
            IDs=[]
            for imagePath in imagePaths:
                faceImg=Image.open(imagePath).convert('L');
                faceNp=np.array(faceImg,'uint8')
                ID=int(os.path.split(imagePath)[-1].split('.')[1])
                faces.append(faceNp)
                IDs.append(ID)
                cv2.imshow('training',faceNp)
                cv2.waitKey(10)
            return np.array(IDs), faces
            Ids, faces=getImagesWithID(path)
            recognizer.train(faces,Ids)
            recognizer.save('recognizer/trainingData.yml')

        if cv2.waitKey(100) & 0xff == ord('q'):
            break
        elif(sampleNum>20):
            break
        exit(0)
#


if __name__=='__main__':
    main()

代码： train_save_face.py

import os
import cv2
import numpy as np
from PIL import Image

recognizer=cv2.face.LBPHFaceRecognizer_create();

path='dataset'
def getImagesWithID(path):
    imagePaths=[os.path.join(path,f) for f in os.listdir(path)]
    faces=[]
    IDs=[]
    for imagePath in imagePaths:
        faceImg=Image.open(imagePath).convert('L');
        faceNp=np.array(faceImg,'uint8')
        ID=int(os.path.split(imagePath)[-1].split('.')[1])
        faces.append(faceNp)
        IDs.append(ID)
        cv2.imshow('training',faceNp)
        cv2.waitKey(10)
    return np.array(IDs), faces
Ids, faces=getImagesWithID(path)
recognizer.train(faces,Ids)
recognizer.save('recognizer/trainingData.yml')
exit(0)
print('done training')

save_detected_face.py工作正常，但是当我想训练面部时，它将无法正常工作。

Answer 1

虽然我不确定这是否正确但现在正在运行。

# -*- coding: cp1252 -*-

import os
import urllib.request
import cv2
import numpy as np
from PIL import Image



recognizer=cv2.face.LBPHFaceRecognizer_create();
path='dataset'

def main():
    #cap = cv2.VideoCapture("../index.htm?clientIpAddr=192.168.1.12&IsRemote=0")
    a=0
    cap = "http://192.168.1.43:8080/shot.jpg"
    id = input('enter user id: ')
    faceDetect=cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
    eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')
    sampleNum=0

    while True:
        a = a+1
        imgResp=urllib.request.urlopen(cap)
        imgNp=np.array(bytearray(imgResp.read()),dtype=np.uint8)
        img=cv2.imdecode(imgNp,-1)

        gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
        faces=faceDetect.detectMultiScale(gray,1.3,5);
        for(x,y,w,h) in faces:
            sampleNum = sampleNum+1
            cv2.rectangle(img, (x,y),(x+w,y+h),(255,0,0),2)
    #            roi_gray= gray[y:y+h, x:x+w]
            roi_color= img[y:y+h, x:x+w]
            eyes = eye_cascade.detectMultiScale(roi_color)
            for (ex,ey,ew,eh) in eyes:
                cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),2)
                cv2.imwrite("dataSet/User."+str(id)+"."+str(sampleNum)+".jpg",gray[y:y+h,x:x+w])
        cv2.imshow('frame', img)

        if cv2.waitKey(100) & 0xff == ord('q'):
            break
        elif(sampleNum>20):
            break
        exit(0)

def getImagesWithID(path):
    imagePaths=[os.path.join(path,f) for f in os.listdir(path)]
    faces=[]
    IDs=[]
    for imagePath in imagePaths:
        faceImg=Image.open(imagePath).convert('L');
        faceNp=np.array(faceImg,'uint8')
        ID=int(os.path.split(imagePath)[-1].split('.')[1])
        faces.append(faceNp)
        IDs.append(ID)
        cv2.imshow('training',faceNp)
        cv2.waitKey(10)
    return np.array(IDs), faces
Ids, faces=getImagesWithID(path)
recognizer.train(faces,Ids)
recognizer.save('recognizer/trainingData.yml')
exit(0)
print('done training')
#

if __name__=='__main__':
    main()
    getImagesWithID(path)

结合面部检测和训练面部opencv python

1 个答案: