I am new to Opencv and was trying out some face recognition tutorials online.
The problem is the faces detected in the images are of different sizes.So I am passing numpy arrays(of the faces detected) of same size to the EigenFaceRecognizer.But it gives the aforementioned error.Can you guys suggest how to fix it?
#!/usr/bin/python
# Import the required modules
import cv2, os
import numpy as np
from PIL import Image
# For face detection we will use the Haar Cascade provided by OpenCV.
cascadePath = "/home/gaurav/opencv/data/haarcascades/haarcascade_frontalface_default.xml"
faceCascade = cv2.CascadeClassifier(cascadePath)
recognizer = cv2.face.createEigenFaceRecognizer()
#sizetuple=(320,243)
def get_images_and_labels(path):
image_paths = [os.path.join(path, f) for f in os.listdir(path) if not f.endswith('.sad')]
# images will contains face images
images = []
# labels will contains the label that is assigned to the image
labels = []
for image_path in image_paths:
# Read the image and convert to grayscale
image_pil = Image.open(image_path).convert('L')
# Convert the image format into numpy array
image = np.array(image_pil, 'uint8')
# Get the label of the image
nbr = int(os.path.split(image_path)[1].split(".")[0].replace("subject", ""))
# Detect the face in the image
faces = faceCascade.detectMultiScale(image,scaleFactor=1.1)
# If face is detected, append the face to images and the label to labels
for (x, y, w, h) in faces:
images.append(image[y: y + 130, x: x + 130])
labels.append(nbr)
cv2.imshow("Adding faces to traning set...", image[y: y + 130, x: x + 130])
cv2.waitKey(50)
# return the images list and labels list
return images, labels
# Path to the Yale Dataset
path = './yalefaces'
# Call the get_images_and_labels function and get the face images and the
# corresponding labels
images, labels = get_images_and_labels(path)
cv2.destroyAllWindows()
for one in images:
print "size:",one.size
#Perform the tranining
recognizer.train(images, np.array(labels))
# Append the images with the extension .sad into image_paths
image_paths = [os.path.join(path, f) for f in os.listdir(path) if f.endswith('.sad')]
for image_path in image_paths:
predict_image_pil = Image.open(image_path).convert('L')
predict_image = np.array(predict_image_pil, 'uint8')
faces = faceCascade.detectMultiScale(predict_image)
for (x, y, w, h) in faces:
nbr_predicted, conf = recognizer.predict(predict_image[y: y + 130, x: x + 130])
nbr_actual = int(os.path.split(image_path)[1].split(".")[0].replace("subject", ""))
if nbr_actual == nbr_predicted:
print "{} is Correctly Recognized with confidence {}".format(nbr_actual, conf)
else:
print "{} is Incorrect Recognized as {}".format(nbr_actual, nbr_predicted)
cv2.imshow("Recognizing Face", predict_image[y: y + 130, x: x + 130])
cv2.waitKey(1300)
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