def test_read_keyframes_all(self):
with temp_video(60, 300, 300, 5, video_codec="mpeg4") as (fname, data):
video_list = [fname]
dataset = VideoKeyframeDataset(video_list)
self.assertEqual(len(dataset), 1)
data1 = dataset[0]
self.assertEqual(data1.shape, torch.Size((5, 300, 300, 3)))
self.assertEqual(data1.dtype, torch.uint8)
return
self.assertTrue(False)
def test_read_keyframes_with_selector(self):
with temp_video(60, 300, 300, 5, video_codec="mpeg4") as (fname, data):
video_list = [fname]
random.seed(0)
frame_selector = RandomKFramesSelector(3)
dataset = VideoKeyframeDataset(video_list, frame_selector)
self.assertEqual(len(dataset), 1)
data1 = dataset[0]
self.assertEqual(data1.shape, torch.Size((3, 300, 300, 3)))
self.assertEqual(data1.dtype, torch.uint8)
return
self.assertTrue(False)
def test_read_keyframes_all(self):
with temp_video(60, 300, 300, 5, video_codec="mpeg4") as (fname, data):
video_list = [fname]
category_list = [None]
dataset = VideoKeyframeDataset(video_list, category_list)
self.assertEqual(len(dataset), 1)
data1, categories1 = dataset[0]["images"], dataset[0]["categories"]
self.assertEqual(data1.shape, torch.Size((5, 3, 300, 300)))
self.assertEqual(data1.dtype, torch.float32)
self.assertIsNone(categories1[0])
return
self.assertTrue(False)
def test_read_keyframes_with_selector_with_transform(self):
with temp_video(60, 300, 300, 5, video_codec="mpeg4") as (fname, data):
video_list = [fname]
random.seed(0)
frame_selector = RandomKFramesSelector(1)
transform = ImageResizeTransform()
dataset = VideoKeyframeDataset(video_list, frame_selector,
transform)
data1 = dataset[0]
self.assertEqual(len(dataset), 1)
self.assertEqual(data1.shape, torch.Size((1, 3, 800, 800)))
self.assertEqual(data1.dtype, torch.float32)
return
self.assertTrue(False)