ap = argparse.ArgumentParser()
ap.add_argument('-d', '--dataset', required=True,
help='Path to input dataset')
ap.add_argument('-n', '--neighbors', required=False, type=int, default=1,
help='# of nearest neighbors for classification')
ap.add_argument('-j', '--jobs', required=False, type=int, default=-1,
help='# of jobs for k-NN distance (-1 uses all available cores)')
args = vars(ap.parse_args())
# Get list of image paths
image_paths = list(paths.list_images(args['dataset']))
# Initialize SimplePreprocessor and SimpleDatasetLoader and load data and labels
print('[INFO]: Images loading....')
sp = SimplePreprocessor(32, 32)
sdl = SimpleDatasetLoader(preprocessors=[sp])
(data, labels) = sdl.load(image_paths, verbose=500)
# Reshape from (3000, 32, 32, 3) to (3000, 32*32*3=3072)
data = data.reshape((data.shape[0], 3072))
# Print information about memory consumption
print('[INFO]: Features Matrix: {:.1f}MB'.format(
float(data.nbytes / (1024*1000.0))))
# Encode labels as integers
le = LabelEncoder()
labels = le.fit_transform(labels)
# Split data into training (75%) and testing (25%) data
horizontal_flip=True,
fill_mode="nearest")
# grab the list of images that we'll be describing, then extract
# the class label names from the image paths
print("[INFO]: Loading images....")
imagePaths = list(paths.list_images(args["dataset"]))
classNames = [pt.split(os.path.sep)[-2] for pt in imagePaths]
classNames = [str(x) for x in np.unique(classNames)]
# Initialize the image preprocessors
aap = AspectAwarePreprocessor(img_size, img_size)
itap = ImageToArrayPreprocessor()
# Load the dataset and scale the raw pixel intensities to the range [0, 1]
sdl = SimpleDatasetLoader(preprocessors=[aap, itap])
(data, labels) = sdl.load(imagePaths, verbose=500)
#data = data.astype("float") / 255.0
data = data.astype("float")
mean = np.mean(data, axis=0)
data -= mean
# Split the data into training data (75%) and testing data (25%)
(trainX, testX, trainY, testY) = train_test_split(data,
labels,
test_size=0.25,
random_state=42)
# Convert the labels from integers to vectors
lb = LabelBinarizer()
trainY = lb.fit_transform(trainY)