Resize HOG feature for Scikit-Learn classifier

Question

I'm trying to execute this code that processes 70 images and extracts Histogram of Oriented Gradients (HOG) features. These are passed to a classifier (Scikit-Learn).

However, an error is raised:

hog_image = hog_image_rescaled.resize((200, 200), Image.ANTIALIAS)
TypeError: an integer is required

I do not understand why, because with attempting with a single image works correctly.

#Hog Feature

from skimage.feature import hog
from skimage import data, color, exposure
import cv2
import matplotlib.pyplot as plt
from PIL import Image
import os
import glob
import numpy as np
from numpy import array

listagrigie = []

path = 'img/'
for infile in glob.glob( os.path.join(path, '*.jpg') ):
    print("current file is: " + infile )
    colorato = Image.open(infile)
    greyscale = colorato.convert('1')

    #hog feature
    fd, hog_image = hog(greyscale, orientations=8, pixels_per_cell=(16, 16),
                    cells_per_block=(1, 1), visualise=True)

    plt.figure(figsize=(8, 4))
    print(type(fd))
    plt.subplot(121).set_axis_off()
    plt.imshow(grigiscala, cmap=plt.cm.gray)
    plt.title('Input image')

    # Rescale histogram for better display
    hog_image_rescaled = exposure.rescale_intensity(hog_image, in_range=(0, 0.02))
    print("hog 1 immagine shape")
    print(hog_image_rescaled.shape)

    hog_image = hog_image_rescaled.resize((200, 200), Image.ANTIALIAS)    
    listagrigie.append(hog_image)
    target.append(i)

print("ARRAY of gray matrices")

print(len(listagrigie))
grigiume = np.dstack(listagrigie)
print(grigiume.shape)
grigiume = np.rollaxis(grigiume, -1)
print(grigiume.shape)

from sklearn import svm, metrics

n_samples = len(listagrigie)
data = grigiume.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], target[:n_samples / 2])

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

print("predicted")

Answer 1

You should rescale the source image (named colorato in your example) to (200, 200), then extract the HOG features and then pass the list of fd vectors to your machine learning models. The hog_image are just meant to visualize the feature descriptors in a user friendly manner. The actual features are returned in the fd variable.