def test_should_return_batches_equivalent_to_number_of_frames_2(self):
video_loader = OpenCVReader(file_url='dummy.avi', batch_size=-1)
batches = list(video_loader.read())
expected = list(self.create_dummy_frames())
self.assertEqual(len(batches), NUM_FRAMES)
actual = [batch.frames.to_dict('records')[0] for batch in batches]
self.assertTrue(custom_list_of_dicts_equal(actual, expected))
def test_should_return_batches_equivalent_to_number_of_frames(self):
video_info = DataFrameMetadata("dataset_1", 'dummy.avi')
video_loader = VideoLoader(video_info)
batches = list(video_loader.load())
dummy_frames = list(self.create_dummy_frames())
self.assertEqual(len(batches), NUM_FRAMES)
expected = [batch.frames.to_dict('records')[0] for batch in batches]
self.assertTrue(custom_list_of_dicts_equal(dummy_frames, expected))
def test_should_start_frame_number_from_two(self):
video_loader = OpenCVReader(
file_url='dummy.avi', batch_size=NUM_FRAMES, start_frame_id=2)
batches = list(video_loader.read())
expected = list(self.create_dummy_frames(
filters=[i for i in range(0, NUM_FRAMES)], start_id=2))
self.assertEqual(1, len(batches))
actual = [batch.frames.to_dict('records')[0] for batch in batches]
self.assertTrue(custom_list_of_dicts_equal(actual, expected))
def test_should_return_only_few_frames_when_limit_is_specified(self):
video_info = DataFrameMetadata("dataset_1", 'dummy.avi')
limit = 4
video_loader = VideoLoader(video_info, limit=limit)
dummy_frames = list(
self.create_dummy_frames(filters=[i for i in range(limit)]))
batches = list(video_loader.load())
self.assertEqual(limit, len(batches))
expected = [batch.frames.to_dict('records')[0] for batch in batches]
self.assertTrue(custom_list_of_dicts_equal(dummy_frames, expected))
def test_should_skip_first_two_frames_with_offset_two(self):
video_info = DataFrameMetadata("dataset_1", 'dummy.avi')
video_loader = VideoLoader(video_info, offset=2)
dummy_frames = list(
self.create_dummy_frames(
filters=[i for i in range(2, NUM_FRAMES)]))
batches = list(video_loader.load())
self.assertEqual(NUM_FRAMES - 2, len(batches))
expected = [batch.frames.to_dict('records')[0] for batch in batches]
self.assertTrue(custom_list_of_dicts_equal(dummy_frames, expected))
def test_should_return_half_then_number_of_batches_with_skip_of_two(self):
video_info = DataFrameMetadata("dataset_1", 'dummy.avi')
video_loader = VideoLoader(video_info, skip_frames=2)
batches = list(video_loader.load())
dummy_frames = list(
self.create_dummy_frames(
filters=[i * 2 for i in range(NUM_FRAMES // 2)]))
self.assertEqual(len(batches), NUM_FRAMES / 2)
expected = [batch.frames.to_dict('records')[0] for batch in batches]
self.assertTrue(custom_list_of_dicts_equal(dummy_frames, expected))
def test_should_return_single_batch_if_batch_size_equal_to_no_of_frames(
self):
video_info = DataFrameMetadata("dataset_1", 'dummy.avi')
video_loader = VideoLoader(video_info, batch_size=NUM_FRAMES)
dummy_frames = list(
self.create_dummy_frames(filters=[i for i in range(NUM_FRAMES)]))
batches = list(video_loader.load())
self.assertEqual(1, len(batches))
expected = batches[0].frames.to_dict('records')
self.assertTrue(custom_list_of_dicts_equal(dummy_frames, expected))
def test_should_skip_first_two_frames_and_batch_size_equal_to_no_of_frames(
self):
video_loader = OpenCVReader(
file_url='dummy.avi', batch_size=NUM_FRAMES, offset=2)
batches = list(video_loader.read())
expected = list(self.create_dummy_frames(
filters=[i for i in range(2, NUM_FRAMES)]))
self.assertEqual(1, len(batches))
actual = [batch.frames.to_dict('records')[0] for batch in batches]
self.assertTrue(custom_list_of_dicts_equal(actual, expected))
def test_load_frame_load_frames_using_petastorm(self, mock):
dummy_values = map(lambda i: self.DummyRow(i,
np.ones((2, 2, 3)) * i),
range(3))
mock.return_value = self.DummyReader(dummy_values)
video_info = DataFrameMetadata("dataset_1", 'dummy.avi')
video_loader = PetastormLoader(video_info,
curr_shard=3,
total_shards=3)
actual = list(video_loader._load_frames())
expected = [value._asdict() for value in dummy_values]
self.assertTrue(custom_list_of_dicts_equal(expected, actual))
