Eigenfaces training image pixel size error

Question

Hello to all senior programmer! I have an error on eigenfaces image training part.

The error is : OpenCV Error: Unsupported format or combination of formats (In the Eigenfaces method all input samples (training images) must be of equal size! Expected 27889 pixels, but was 27556 pixels.) in cv::face::Eigenfaces::train, file C:\\projects\\opencv-python\\opencv_contrib\\modules\\face\\src\\eigen_faces.cpp, line 68

Which mean my pictures don't be in equal size. I try cv2.rezise() when I capture picture from camera but it still doesn't work.

here is my capture code :

import cv2
cam = cv2.VideoCapture(0)
detector = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

Id = input('enter your id: ')
sampleNum = 0

while(True):
    ret, img = cam.read()
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = detector.detectMultiScale(gray, 1.3, 5)

    for (x,y,w,h) in faces:
        cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)

        sampleNum = sampleNum+1

        cv2.imwrite("dataSet/user."+Id+'.'+str(sampleNum)+".jpg",cv2.resize
        (gray[y:y+h,x:x+w],(70,70)))

        cv2.imshow('frame',img)

    if cv2.waitKey(100) & 0xFF == ord('q'):#waitKey is for delay in video capture
        break
    elif sampleNum >= 50:#how many picture capture?
        break

cam.release()
cv2.destroyAllWindows()

and here is training part:

import cv2,os
import numpy as np


recognizer = cv2.face.EigenFaceRecognizer_create()
detector= cv2.CascadeClassifier("haarcascade_frontalface_default.xml")


def getImagesAndLabels(path):

    imagePaths=[os.path.join(path,f) for f in os.listdir(path)]
    faceSamples=[]
    Ids=[]

    for imagePath in imagePaths:

        pilImage = Image.open(imagePath).convert('L')

        imageNp = np.array(pilImage,'uint8')

        Id = int(os.path.split(imagePath)[-1].split(".")[1])

        faces = detector.detectMultiScale(imageNp)

        for (x,y,w,h) in faces:
            faceSamples.append(imageNp[y:y+h,x:x+w])
            Ids.append(Id)

     return faceSamples,Ids

faces,Ids = getImagesAndLabels('dataSet')
recognizer.train(faces, np.array(Ids))
recognizer.write('trainner/trainnerEi.yml')

PS. I adapt this code from LBPHFaceRecognizer Thank you!*3

Answer 1

• Okay, so EigenFaces only works if the dimension of all the images is same in pixel space
• Which means if one image used in training is of size 28x28 then every other image in the training as well as in testing has to be of size 28x28
• If the image size is not same then opencv will throw you that error
• The error simply says that one of the image was of 27889 dimensions in pixel space and other was 27556 dimensions pixel space.
• I would recommend you to use cv2.resize() function to make all images of the same size

• Use the below code as a reference for you training part:

   import cv2,os import numpy as np recognizer = cv2.face.EigenFaceRecognizer_create() detector= cv2.CascadeClassifier("haarcascade_frontalface_default.xml") def getImagesAndLabels(path): width_d, height_d = 280, 280 # Declare your own width and height imagePaths=[os.path.join(path,f) for f in os.listdir(path)] faceSamples=[] Ids=[] for imagePath in imagePaths: pilImage = Image.open(imagePath).convert('L') imageNp = np.array(pilImage,'uint8') Id = int(os.path.split(imagePath)[-1].split(".")[1]) faces = detector.detectMultiScale(imageNp) for (x,y,w,h) in faces: ######################################## # The line to be changed by cv2.resize() ######################################## faceSamples.append(cv2.resize(imageNp[y:y+h,x:x+w], (width_d, height_d)) Ids.append(Id) return faceSamples,Ids faces,Ids = getImagesAndLabels('dataSet') recognizer.train(faces, np.array(Ids)) recognizer.write('trainner/trainnerEi.yml')

• Keep in mind even the test images has to be of same size