I have an error on eigenfaces image recognition part

Question

The error is :

"id, confidence = recognizer.predict(gray[y:y+h,x:x+w]) cv2.error: OpenCV(4.0.0) C:\projects\opencv-python\opencv_contrib\modules\face\src\eigen_faces.cpp:121: error: (-5:Bad argument) Wrong input image size. Reason: Training and Test images must be of equal size! Expected an image with 12100 elements, but got 25281. in function 'cv::face::Eigenfaces::predict'"

I adapt this code from LBPHFaceRecognizer then change to EigenFaceRecognizer

import cv2
import numpy as np
import os 

recognizer = cv2.face.EigenFaceRecognizer_create()
recognizer.read('trainer/trainer.yml')
cascadePath = "haarcascade_frontalface_default.xml"
faceCascade = cv2.CascadeClassifier(cascadePath);

font = cv2.FONT_HERSHEY_SIMPLEX



#iniciate id counter
id = 0

# names related to ids: example ==> Marcelo: id=1,  etc
names = ['None', 'sabri', 'Naim' , 'Acap'] 

# Initialize and start realtime video capture
 cam = cv2.VideoCapture(0)
 cam.set(3, 640) # set video widht
 cam.set(4, 480) # set video height

# Define min window size to be recognized as a face
minW = 0.1*cam.get(3)
minH = 0.1*cam.get(4)

while True:

ret, img =cam.read()
img = cv2.flip(img, 1) # Flip vertically

gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

faces = faceCascade.detectMultiScale( 
    gray,
    scaleFactor = 1.2,
    minNeighbors = 5,
    minSize = (int(minW), int(minH)),
   )

for(x,y,w,h) in faces:

    cv2.rectangle(img, (x,y), (x+w,y+h), (0,255,0), 2)

    id, confidence = recognizer.predict(gray[y:y+h,x:x+w])

    # Check if confidence is less them 100 ==> "0" is perfect match 
    if (confidence < 100):
        id = names[id]
        confidence = "  {0}%".format(round(100 - confidence))
    else:
        id = "unknown"
        confidence = "  {0}%".format(round(100 - confidence))

    cv2.putText(img, str(id), (x+5,y-5), font, 1, (255,255,255), 2)
    cv2.putText(img, str(confidence), (x+5,y+h-5), font, 1, (255,255,0), 1)  

cv2.imshow('camera',img) 

k = cv2.waitKey(10) & 0xff # Press 'ESC' for exiting video
if k == 27:
    break

# Do a bit of cleanup
print("\n [INFO] Exiting Program and cleanup stuff")
cam.release()
cv2.destroyAllWindows()

Answer 1

Essentially, the size of your training image is different than your testing image. In your code, you have set the input as 480*640 and you have not resized it before you feed to the prediction model. If your training size is 480*640 your testing size should be 480*640. You can use cv2.resize() to resize your testing image or your training image or both.

Answer 2

This my code training

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

# Path for face image database
path = 'dataset'

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

# function to get the images and label data
def getImagesAndLabels(path):

    height_d, width_d = 480, 640  # Declare your own width and height

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

    for imagePath in imagePaths:

    PIL_img = Image.open(imagePath).convert('L') # convert it to grayscale
    img_numpy = np.array(PIL_img,'uint8')

    id = int(os.path.split(imagePath)[-1].split(".")[1])
    faces = detector.detectMultiScale(img_numpy)

    for (x,y,w,h) in faces:
        faceSamples.append(cv2.resize(img_numpy[y:y+h,x:x+w], (height_d, width_d )))
        ids.append(id)

return faceSamples,ids

print ("\n [INFO] Training faces. It will take a few seconds. Wait ...")
faces,ids = getImagesAndLabels(path)
recognizer.train(faces, np.array(ids))

# Save the model into trainer/trainer.yml
recognizer.write('trainer/trainer.yml') # recognizer.save() worked on Mac, but not on Pi

# Print the numer of faces trained and end program
print("\n [INFO] {0} faces trained. Exiting Program".format(len(np.unique(ids))))