Question

I watch out this example: http://scikit-learn.org/stable/auto_examples/plot_digits_classification.html#example-plot-digits-classification-py
on handwritten digits in scikit-learn python library.

i would like to prepare a 3d array (N * a* b) where N is my images number (75) and a* b is the matrix of an image (like in the example a 8×8 shape).
My problem is: i have signs in a different shapes for every image: (202, 230), (250, 322).. and give me
this error: ValueError: array dimensions must agree except for d_0 in this code:

#here there is the error:
grigiume = np.dstack(listagrigie)
print(grigiume.shape)
grigiume=np.rollaxis(grigiume,-1)
print(grigiume.shape)

There is a manner to resize all images in a standard size (i.e. 200×200) or a manner to have a 3d array with matrix(a,b) where a != from b and do not give me an error in this code:

data = digits.images.reshape((n_samples, -1))
classifier.fit(data[:n_samples / 2], digits.target[:n_samples / 2])

My code:

import os
import glob
import numpy as np
from numpy import array
listagrigie = []

path = 'resize2/'
for infile in glob.glob( os.path.join(path, '*.jpg') ):
    print("current file is: " + infile )
    colorato = cv2.imread(infile)
    grigiscala = cv2.cvtColor(colorato,cv2.COLOR_BGR2GRAY)
    listagrigie.append(grigiscala)

print(len(listagrigie))

#here there is the error:
grigiume = np.dstack(listagrigie)
print(grigiume.shape)
grigiume=np.rollaxis(grigiume,-1)
print(grigiume.shape)


#last step
n_samples = len(digits.images)
data = digits.images.reshape((n_samples, -1))

# Create a classifier: a support vector classifier
classifier = svm.SVC(gamma=0.001)

# We learn the digits on the first half of the digits
classifier.fit(data[:n_samples / 2], digits.target[:n_samples / 2])

# Now predict the value of the digit on the second half:
expected = digits.target[n_samples / 2:]
predicted = classifier.predict(data[n_samples / 2:])

print "Classification report for classifier %s:\n%s\n" % (
classifier, metrics.classification_report(expected, predicted))
print "Confusion matrix:\n%s" % metrics.confusion_matrix(expected, predicted)

for index, (image, prediction) in enumerate(
    zip(digits.images[n_samples / 2:], predicted)[:4]):
    pl.subplot(2, 4, index + 5)
    pl.axis('off')
    pl.imshow(image, cmap=pl.cm.gray_r, interpolation='nearest')
    pl.title('Prediction: %i' % prediction)

pl.show()

Answer 1

You have to resize all your images to a fixed size. For instance using the Image class of PIL or Pillow:

from PIL import Image
image = Image.open("/path/to/input_image.jpeg")
image.thumbnail((200, 200), Image.ANTIALIAS)
image.save("/path/to/output_image.jpeg")

Edit: the above won’t work, try instead resize:

from PIL import Image
image = Image.open("/path/to/input_image.jpeg")
image = image.resize((200, 200), Image.ANTIALIAS)
image.save("/path/to/output_image.jpeg")

Edit 2: there might be a way to preserve the aspect ratio and pad the rest with black pixels but I don’t know how to do in a few PIL calls. You could use PIL.Image.thumbnail and use numpy to do the padding though.