def test_images_are_resized(self): loader = ImageLoader() loader.load_from(imageLocation, 1) images = loader.get_images() for image in images: self.assertEqual('(30, 30, 3)', str(image.shape))
def xtest_TrainModel(self): imageLoader = ImageLoader() imageLoader.load_from('./test-images', 1) model = RoadSignModel(imageLoader) self.assertEqual(False, model.is_trained()) model.train() self.assertEqual(True, model.is_trained())
def test_SaveModel(self): imageLoader = ImageLoader() imageLoader.load_from('./test-images', 1) model = RoadSignModel(imageLoader) os.remove('model.h5') model.train() model.save() self.assertEqual(True, os.path.exists('model.h5'))
def test_load_images_from_two_categories(self): numberOfImages = 60 loader = ImageLoader() loader.load_from(imageLocation, 2) images = loader.get_images() labels = loader.get_labels() self.assertEqual(numberOfImages, len(images)) self.assertIs(np.ndarray, type(images)) self.assertIs(np.ndarray, type(images[0])) np.testing.assert_array_equal(np.zeros(30), labels[:30]) np.testing.assert_array_equal(np.ones(30), labels[30:numberOfImages]) self.assertIs(np.ndarray, type(labels))
def test_load_images_from_one_category(self): numberOfImages = 30 loader = ImageLoader() loader.load_from(imageLocation, 1) images = loader.get_images() labels = loader.get_labels() self.assertEqual(numberOfImages, len(images)) self.assertIs(np.ndarray, type(images)) self.assertIs(np.ndarray, type(images[0])) np.testing.assert_array_equal(np.zeros(numberOfImages), labels) self.assertIs(np.ndarray, type(labels))
from ImageLoader import ImageLoader from RoadSignModel import RoadSignModel imageLoader = ImageLoader() imageLoader.load_from('C:\\Users\\RTNDZ\\archive\\Train', 43) model = RoadSignModel(imageLoader) model.train() model.save()