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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn.functional as F
from cotracker.models.core.model_utils import smart_cat, get_points_on_a_grid
from cotracker.models.build_cotracker import build_cotracker
class CoTrackerPredictor(torch.nn.Module):
def __init__(self, checkpoint="./checkpoints/cotracker2.pth"):
super().__init__()
self.support_grid_size = 6
model = build_cotracker(checkpoint)
self.interp_shape = model.model_resolution
self.model = model
self.model.eval()
@torch.no_grad()
def forward(
self,
video, # (B, T, 3, H, W)
# input prompt types:
# - None. Dense tracks are computed in this case. You can adjust *query_frame* to compute tracks starting from a specific frame.
# *backward_tracking=True* will compute tracks in both directions.
# - queries. Queried points of shape (B, N, 3) in format (t, x, y) for frame index and pixel coordinates.
# - grid_size. Grid of N*N points from the first frame. if segm_mask is provided, then computed only for the mask.
# You can adjust *query_frame* and *backward_tracking* for the regular grid in the same way as for dense tracks.
queries: torch.Tensor = None,
segm_mask: torch.Tensor = None, # Segmentation mask of shape (B, 1, H, W)
grid_size: int = 0,
grid_query_frame: int = 0, # only for dense and regular grid tracks
backward_tracking: bool = False,
):
if queries is None and grid_size == 0:
tracks, visibilities = self._compute_dense_tracks(
video,
grid_query_frame=grid_query_frame,
backward_tracking=backward_tracking,
)
else:
tracks, visibilities = self._compute_sparse_tracks(
video,
queries,
segm_mask,
grid_size,
add_support_grid=(grid_size == 0 or segm_mask is not None),
grid_query_frame=grid_query_frame,
backward_tracking=backward_tracking,
)
return tracks, visibilities
def _compute_dense_tracks(self, video, grid_query_frame, grid_size=80, backward_tracking=False):
*_, H, W = video.shape
grid_step = W // grid_size
grid_width = W // grid_step
grid_height = H // grid_step
tracks = visibilities = None
grid_pts = torch.zeros((1, grid_width * grid_height, 3)).to(video.device)
grid_pts[0, :, 0] = grid_query_frame
for offset in range(grid_step * grid_step):
print(f"step {offset} / {grid_step * grid_step}")
ox = offset % grid_step
oy = offset // grid_step
grid_pts[0, :, 1] = torch.arange(grid_width).repeat(grid_height) * grid_step + ox
grid_pts[0, :, 2] = (
torch.arange(grid_height).repeat_interleave(grid_width) * grid_step + oy
)
tracks_step, visibilities_step = self._compute_sparse_tracks(
video=video,
queries=grid_pts,
backward_tracking=backward_tracking,
)
tracks = smart_cat(tracks, tracks_step, dim=2)
visibilities = smart_cat(visibilities, visibilities_step, dim=2)
return tracks, visibilities
def _compute_sparse_tracks(
self,
video,
queries,
segm_mask=None,
grid_size=0,
add_support_grid=False,
grid_query_frame=0,
backward_tracking=False,
):
B, T, C, H, W = video.shape
video = video.reshape(B * T, C, H, W)
video = F.interpolate(video, tuple(self.interp_shape), mode="bilinear", align_corners=True)
video = video.reshape(B, T, 3, self.interp_shape[0], self.interp_shape[1])
if queries is not None:
B, N, D = queries.shape
assert D == 3
queries = queries.clone()
queries[:, :, 1:] *= queries.new_tensor(
[
(self.interp_shape[1] - 1) / (W - 1),
(self.interp_shape[0] - 1) / (H - 1),
]
)
elif grid_size > 0:
grid_pts = get_points_on_a_grid(grid_size, self.interp_shape, device=video.device)
if segm_mask is not None:
segm_mask = F.interpolate(segm_mask, tuple(self.interp_shape), mode="nearest")
point_mask = segm_mask[0, 0][
(grid_pts[0, :, 1]).round().long().cpu(),
(grid_pts[0, :, 0]).round().long().cpu(),
].bool()
grid_pts = grid_pts[:, point_mask]
queries = torch.cat(
[torch.ones_like(grid_pts[:, :, :1]) * grid_query_frame, grid_pts],
dim=2,
).repeat(B, 1, 1)
if add_support_grid:
grid_pts = get_points_on_a_grid(
self.support_grid_size, self.interp_shape, device=video.device
)
grid_pts = torch.cat([torch.zeros_like(grid_pts[:, :, :1]), grid_pts], dim=2)
grid_pts = grid_pts.repeat(B, 1, 1)
queries = torch.cat([queries, grid_pts], dim=1)
tracks, visibilities, __ = self.model.forward(video=video, queries=queries, iters=6)
if backward_tracking:
tracks, visibilities = self._compute_backward_tracks(
video, queries, tracks, visibilities
)
if add_support_grid:
queries[:, -self.support_grid_size**2 :, 0] = T - 1
if add_support_grid:
tracks = tracks[:, :, : -self.support_grid_size**2]
visibilities = visibilities[:, :, : -self.support_grid_size**2]
thr = 0.9
visibilities = visibilities > thr
# correct query-point predictions
# see https://github.com/facebookresearch/co-tracker/issues/28
# TODO: batchify
for i in range(len(queries)):
queries_t = queries[i, : tracks.size(2), 0].to(torch.int64)
arange = torch.arange(0, len(queries_t))
# overwrite the predictions with the query points
tracks[i, queries_t, arange] = queries[i, : tracks.size(2), 1:]
# correct visibilities, the query points should be visible
visibilities[i, queries_t, arange] = True
tracks *= tracks.new_tensor(
[(W - 1) / (self.interp_shape[1] - 1), (H - 1) / (self.interp_shape[0] - 1)]
)
return tracks, visibilities
def _compute_backward_tracks(self, video, queries, tracks, visibilities):
inv_video = video.flip(1).clone()
inv_queries = queries.clone()
inv_queries[:, :, 0] = inv_video.shape[1] - inv_queries[:, :, 0] - 1
inv_tracks, inv_visibilities, __ = self.model(video=inv_video, queries=inv_queries, iters=6)
inv_tracks = inv_tracks.flip(1)
inv_visibilities = inv_visibilities.flip(1)
arange = torch.arange(video.shape[1], device=queries.device)[None, :, None]
mask = (arange < queries[:, None, :, 0]).unsqueeze(-1).repeat(1, 1, 1, 2)
tracks[mask] = inv_tracks[mask]
visibilities[mask[:, :, :, 0]] = inv_visibilities[mask[:, :, :, 0]]
return tracks, visibilities
class CoTrackerOnlinePredictor(torch.nn.Module):
def __init__(self, checkpoint="./checkpoints/cotracker2.pth"):
super().__init__()
self.support_grid_size = 6
model = build_cotracker(checkpoint)
self.interp_shape = model.model_resolution
self.step = model.window_len // 2
self.model = model
self.model.eval()
@torch.no_grad()
def forward(
self,
video_chunk,
is_first_step: bool = False,
queries: torch.Tensor = None,
grid_size: int = 10,
grid_query_frame: int = 0,
add_support_grid=False,
):
B, T, C, H, W = video_chunk.shape
# Initialize online video processing and save queried points
# This needs to be done before processing *each new video*
if is_first_step:
self.model.init_video_online_processing()
if queries is not None:
B, N, D = queries.shape
assert D == 3
queries = queries.clone()
queries[:, :, 1:] *= queries.new_tensor(
[
(self.interp_shape[1] - 1) / (W - 1),
(self.interp_shape[0] - 1) / (H - 1),
]
)
elif grid_size > 0:
grid_pts = get_points_on_a_grid(
grid_size, self.interp_shape, device=video_chunk.device
)
queries = torch.cat(
[torch.ones_like(grid_pts[:, :, :1]) * grid_query_frame, grid_pts],
dim=2,
)
if add_support_grid:
grid_pts = get_points_on_a_grid(
self.support_grid_size, self.interp_shape, device=video_chunk.device
)
grid_pts = torch.cat([torch.zeros_like(grid_pts[:, :, :1]), grid_pts], dim=2)
queries = torch.cat([queries, grid_pts], dim=1)
self.queries = queries
return (None, None)
video_chunk = video_chunk.reshape(B * T, C, H, W)
video_chunk = F.interpolate(
video_chunk, tuple(self.interp_shape), mode="bilinear", align_corners=True
)
video_chunk = video_chunk.reshape(B, T, 3, self.interp_shape[0], self.interp_shape[1])
tracks, visibilities, __ = self.model(
video=video_chunk,
queries=self.queries,
iters=6,
is_online=True,
)
thr = 0.9
return (
tracks
* tracks.new_tensor(
[
(W - 1) / (self.interp_shape[1] - 1),
(H - 1) / (self.interp_shape[0] - 1),
]
),
visibilities > thr,
)
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