def test__process_sample__without_transformations(self):
nb_frames = 3
frame_size = (20, 20)
img_paths = GeneratorUtils.pick_at_intervals(
GeneratorUtils.get_sample_images(self.fake_sample), nb_frames)
expected = [
GeneratorUtils.process_img(img_path, frame_size)
for img_path in img_paths
]
actual = process_sample(self.fake_sample, nb_frames, frame_size)
npt.assert_equal(desired=expected, actual=actual)
def test__process_sample__with_transformations(self):
nb_frames = 3
frame_size = (20, 20)
transformations = [A.HorizontalFlip(p=1)]
img_paths = GeneratorUtils.pick_at_intervals(
GeneratorUtils.get_sample_images(self.fake_sample), nb_frames)
img_arrays = [
GeneratorUtils.process_img(img_path, frame_size)
for img_path in img_paths
]
expected = GeneratorUtils.augment(img_arrays, transformations)
actual = process_sample(self.fake_sample,
nb_frames,
frame_size,
transformations=transformations)
npt.assert_equal(desired=expected, actual=actual)
def test__pandas_prediction_generator__yields_correct_output(self):
batch_size = self.nb_samples
nb_frames = 3
frame_size = (50, 50)
gen = PandasPredictionGenerator(self.source,
self.data_path,
batch_size=batch_size,
nb_frames=3,
frame_size=frame_size)
expected = []
for i in range(1, batch_size + 1):
sample = pandasgeneratorutils.process_sample(self.data_path /
f'{i}',
nb_frames=nb_frames,
frame_size=frame_size)
expected.append(sample)
expected = np.stack(expected)
actual = gen.__getitem__(0)
npt.assert_equal(actual, expected)
def process_sample(self, sample: Path):
return pandasgeneratorutils.process_sample(sample, self.nb_frames,
self.frame_size, self.dtype,
self.transformations)
def process_sample(self, sample):
return pandasgeneratorutils.process_sample(sample, self.nb_frames,
self.frame_size)