def temp_charades_dataset():
frame_names = [f"{str(i)}.png" for i in range(3)]
# Create csv containing 2 test frame videos.
with tempfile.NamedTemporaryFile(delete=False, suffix=".csv") as f:
f.write("original_vido_id video_id frame_id path labels\n".encode())
# Frame video 1
with temp_frame_video(frame_names) as (frame_1_video_dir, data_1):
for i, frame_name in enumerate(frame_names):
original_video_id = str(frame_1_video_dir)
video_id = "1"
frame_id = str(i)
path = pathlib.Path(frame_1_video_dir) / frame_name
label = "0"
f.write(
f"{original_video_id} {video_id} {frame_id} {path} {label}\n"
.encode())
# Frame video 2
with temp_frame_video(frame_names) as (frame_2_video_dir, data_2):
for i, frame_name in enumerate(frame_names):
original_video_id = str(frame_2_video_dir)
video_id = "2"
frame_id = str(i)
path = pathlib.Path(frame_2_video_dir) / frame_name
label = "1"
f.write(
f"{original_video_id} {video_id} {frame_id} {path} {label}\n"
.encode())
f.close()
yield f.name, data_1, data_2
def test_multiple_labels_per_frame(self):
frame_names = [f"{str(i)}.png" for i in range(3)]
# Create csv containing a test frame videos.
with tempfile.NamedTemporaryFile(delete=False, suffix=".csv") as f:
f.write(
"original_vido_id video_id frame_id path labels\n".encode())
with temp_frame_video(frame_names) as (frame_1_video_dir, data_1):
for i, frame_name in enumerate(frame_names):
original_video_id = str(frame_1_video_dir)
video_id = "1"
frame_id = str(i)
path = pathlib.Path(frame_1_video_dir) / frame_name
label = "0,100"
f.write(
f"{original_video_id} {video_id} {frame_id} {path} {label}\n"
.encode())
f.close()
clip_sampler = make_clip_sampler(
"random",
0.1, # Total duration of 3 frames at 30fps is 0.1 seconds. )
)
dataset = Charades(f.name,
clip_sampler=clip_sampler,
video_sampler=SequentialSampler)
sample = next(dataset)
self.assertEqual(sample["label"],
[[0, 100], [0, 100], [0, 100]])
self.assertTrue(sample["video"].equal(data_1))
def test_frame_video_works(self):
frame_names = [f"{str(i)}.png" for i in range(3)]
with temp_frame_video(frame_names) as (f_name, data):
frame_paths = [f_name / x for x in frame_names]
test_video = FrameVideo.from_frame_paths(frame_paths)
expected_duration = (
0.1 # Total duration of 3 frames at 30fps is 0.1 seconds.
)
self.assertEqual(test_video.duration, expected_duration)
# All frames (0 - 0.1 seconds)
clip = test_video.get_clip(0, 0.1)
frames, indices = clip["video"], clip["frame_indices"]
self.assertTrue(frames.equal(data))
self.assertEqual(indices, [0, 1, 2])
# All frames (0 - 0.1 seconds), filtred to middle frame
clip = test_video.get_clip(0, 0.1, lambda lst: lst[1:2])
frames, indices = clip["video"], clip["frame_indices"]
self.assertTrue(frames.equal(data[:, 1:2]))
self.assertEqual(indices, [1])
# 2 frames (0 - 0.066 seconds)
clip = test_video.get_clip(0, 0.066)
frames, indices = clip["video"], clip["frame_indices"]
self.assertTrue(frames.equal(data[:, :2]))
self.assertEqual(indices, [0, 1])
# No frames (3 - 5 seconds)
result = test_video.get_clip(3, 5)
self.assertEqual(result, None)
def temp_ava_dataset_2_videos():
frame_names = [f"{str(i)}.png" for i in range(90)]
# Create csv containing 2 test frame videos.
with tempfile.NamedTemporaryFile(delete=False,
suffix=".csv") as frames_file:
frames_file.write(
"original_vido_id video_id frame_id path labels\n".encode())
# Frame video 1
with temp_frame_video(frame_names) as (frame_1_video_dir, data_1):
for i, frame_name in enumerate(frame_names):
original_video_id_1 = str(frame_1_video_dir)
video_id = "1"
frame_id = str(i)
path = pathlib.Path(frame_1_video_dir) / frame_name
label = "0"
frames_file.write(
f"{original_video_id_1} {video_id} {frame_id} {path} {label}\n"
.encode())
# Frame video 2
with temp_frame_video(frame_names, height=5, width=5) as (
frame_2_video_dir,
data_2,
):
for i, frame_name in enumerate(frame_names):
original_video_id_2 = str(frame_2_video_dir)
video_id = "2"
frame_id = str(i)
path = pathlib.Path(frame_2_video_dir) / frame_name
label = "1"
frames_file.write(
f"{original_video_id_2} {video_id} {frame_id} {path} {label}\n"
.encode())
frames_file.close()
yield frames_file.name, data_1, data_2, original_video_id_1, original_video_id_2
def temp_ssv2_dataset():
frame_names = [f"{str(i)}.png" for i in range(7)]
# Create json file for label names.
labels = [
"Approaching something with your camera",
"Attaching something to something",
]
label_names = {labels[0]: "0", labels[1]: "1"}
with tempfile.NamedTemporaryFile(mode="w", delete=False,
suffix=".json") as f:
json.dump(label_names, f)
label_name_file = f.name
# Create csv containing 2 test frame videos.
with tempfile.NamedTemporaryFile(delete=False, suffix=".csv") as f:
f.write("original_vido_id video_id frame_id path labels\n".encode())
# Frame video 1
with temp_frame_video(frame_names) as (frame_1_video_dir, data_1):
for i, frame_name in enumerate(frame_names):
original_video_id = str(frame_1_video_dir)
video_id = "1"
frame_id = str(i)
path = pathlib.Path(frame_1_video_dir) / frame_name
f.write(
f"{original_video_id} {video_id} {frame_id} {path} ''\n".
encode())
# Frame video 2
with temp_frame_video(frame_names) as (frame_2_video_dir, data_2):
for i, frame_name in enumerate(frame_names):
original_video_id = str(frame_2_video_dir)
video_id = "2"
frame_id = str(i)
path = pathlib.Path(frame_2_video_dir) / frame_name
f.write(
f"{original_video_id} {video_id} {frame_id} {path} ''\n"
.encode())
f.close()
video_path_file = f.name
# Create json file for lable names.
with tempfile.NamedTemporaryFile(mode="w",
delete=False,
suffix=".json") as f:
videos = [
{
"id": str(frame_1_video_dir),
"template": labels[0]
},
{
"id": str(frame_2_video_dir),
"template": labels[1]
},
]
json.dump(videos, f)
video_label_file = f.name
yield label_name_file, video_label_file, video_path_file, data_1, data_2