code: def extract_hog_features(img):
      img = cv2.resize(img, target_img_size)
      win_size = (100, 100)
      cell_size = (4, 4)
      block_size_in_cells = (2, 2)
  
      block_size = (
          block_size_in_cells[1] * cell_size[1],
          block_size_in_cells[0] * cell_size[0],
      )
      block_stride = (cell_size[1], cell_size[0])
      nbins = 9  # Number of orientation bins
      hog = cv2.HOGDescriptor(
          win_size, block_size, block_stride, cell_size, nbins
      )
      h = hog.compute(img)
      h = h.flatten()
      return h.flatten()
  
file_path: Mozart/src/train.py
imports: import cv2
  import random
  import imutils
  from glob import glob
  from sklearn.model_selection import train_test_split
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.neural_network import MLPClassifier
  from sklearn import svm
  import numpy
  import matplotlib.pyplot
  import pickle
label_desc: Use image node_id 3 for calling a global function with example usage: extract_hog_features(img) and returns: h