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Fix - setting cropping parameters in analyze_videos for PyTorch models #2849

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Merged
maximpavliv merged 3 commits into from
Jan 27, 2025
Merged

Fix - setting cropping parameters in analyze_videos for PyTorch models #2849

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6c5cb58
fix cropping in video analysis; bug fix for MPS prediction
n-poulsen Jan 27, 2025
295575c
remove debug prints
n-poulsen Jan 27, 2025
71d0c7e
improve cropping for video iterator
n-poulsen Jan 27, 2025
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13 changes: 11 additions & 2 deletions deeplabcut/pose_estimation_pytorch/apis/analyze_videos.py
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Original file line number Diff line number Diff line change
Expand Up @@ -50,6 +50,14 @@ def __init__(
if self._crop:
self.set_bbox(*cropping)

def set_crop(self, cropping: list[int] | None = None) -> None:
"""Sets the cropping parameters for the video."""
self._crop = cropping is not None
if self._crop:
self.set_bbox(*cropping)
else:
self.set_bbox(0, 1, 0, 1, relative=True)

def get_context(self) -> list[dict[str, Any]] | None:
if self._context is None:
return None
Expand Down Expand Up @@ -157,6 +165,8 @@ def video_inference(
"""
if not isinstance(video, VideoIterator):
video = VideoIterator(str(video), cropping=cropping)
elif cropping is not None:
video.set_crop(cropping)

n_frames = video.get_n_frames(robust=robust_nframes)
vid_w, vid_h = video.dimensions
Expand Down Expand Up @@ -421,7 +431,7 @@ def analyze_videos(
output_prefix = video.stem + dlc_scorer
output_pkl = output_path / f"{output_prefix}_full.pickle"

video_iterator = VideoIterator(video)
video_iterator = VideoIterator(video, cropping=cropping)

shelf_writer = None
if use_shelve:
Expand All @@ -439,7 +449,6 @@ def analyze_videos(
video=video_iterator,
pose_runner=pose_runner,
detector_runner=detector_runner,
cropping=cropping,
shelf_writer=shelf_writer,
robust_nframes=robust_nframes,
)
Expand Down
4 changes: 2 additions & 2 deletions deeplabcut/pose_estimation_pytorch/models/predictors/single_predictor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -143,7 +143,7 @@ def get_pose_prediction(

batch_size, num_joints = x.shape

dz = torch.zeros((batch_size, 1, num_joints, 3)).to(x.device)
dz = torch.zeros((batch_size, 1, num_joints, 3), device=heatmap.device)
for b in range(batch_size):
for j in range(num_joints):
dz[b, 0, j, 2] = heatmap[b, y[b, j], x[b, j], j]
Expand All @@ -155,7 +155,7 @@ def get_pose_prediction(
x = x * scale_factors[1] + 0.5 * scale_factors[1] + dz[:, :, :, 0]
y = y * scale_factors[0] + 0.5 * scale_factors[0] + dz[:, :, :, 1]

pose = torch.empty((batch_size, 1, num_joints, 3))
pose = torch.zeros((batch_size, 1, num_joints, 3), device=heatmap.device)
pose[:, :, :, 0] = x
pose[:, :, :, 1] = y
pose[:, :, :, 2] = dz[:, :, :, 2]
Expand Down