malware_dataset_classifier_1.py

# import the necessary packages
from sklearn.datasets.base import Bunch
from imutils import paths
from scipy import io
import numpy as np
import random
import cv2

def load_ucsb_malware_images(datasetPath, min_images = 20, image_size = (128, 100), equal_samples = True,
	test_size = 0.33, seed = 42, flatten = False):
	# grab the image paths associated with the malware images
	imagePaths = sorted(list(paths.list_images(datasetPath)))

	# set the random seed, then initialize the data matrix and labels
	random.seed(seed)
	data = []
	labels = []

	# loop over the image paths
	for (i, imagePath) in enumerate(imagePaths):
		# load the image and convert it to grayscale
		image = cv2.imread(imagePath)
		gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

		face = cv2.resize(gray, image_size)

		# check to see if the face should be flattened into a single row
		if flatten:
			face = face.flatten()

		# update the data matrix and associated labels
		data.append(face)
		labels.append(imagePath.split("\\")[-2])

	# convert the data matrix and labels list to a NumPy array
	data = np.array(data)
	labels = np.array(labels)

	# # check to see if equal samples for each face should be used
	if equal_samples:
		# initialize the list of sampled indexes
		sampledIdxs = []

		# loop over the unique labels
		for label in np.unique(labels):
			# grab the indexes into the labels array where labels equals the current
			# label
			labelIdxs = np.where(labels == label)[0]

			# only proceed if the required number of minimum faces can be met
			if len(labelIdxs) >= min_faces:
				# randomly sample the indexes for the current label, keeping only minumum
				# supplied amount, then update the list of sampled idnexes
				labelIdxs = random.sample(list(labelIdxs), min_faces)
				sampledIdxs.extend(labelIdxs)

		# use the sampled indexes to select the appropriate data points and labels
		random.shuffle(sampledIdxs)
		data = data[sampledIdxs]
		labels = labels[sampledIdxs]

	# compute the training and testing split index
	idxs = range(0, len(data))
	random.shuffle(list(idxs))
	split = int(len(idxs) * (1.0 - test_size))

	# split the data into training and testing segments
	(trainData, testData) = (data[:split], data[split:])
	(trainLabels, testLabels) = (labels[:split], labels[split:])

	# create the training and testing bunches
	training = Bunch(name="training", data=trainData, target=trainLabels)
	testing = Bunch(name="testing", data=testData, target=testLabels)

	# return a tuple of the training, testing bunches, and original labels
	return (training, testing, labels)