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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
import time
from tqdm import tqdm
from models.spatracker.models.core.spatracker.spatracker import get_points_on_a_grid
from models.spatracker.models.core.model_utils import smart_cat
from models.spatracker.models.build_spatracker import (
build_spatracker,
)
from models.spatracker.models.core.model_utils import (
meshgrid2d, bilinear_sample2d, smart_cat
)
import spaces
class SpaTrackerPredictor(torch.nn.Module):
def __init__(
self, checkpoint="cotracker/checkpoints/cotracker_stride_4_wind_8.pth",
interp_shape=(384, 512),
seq_length=16
):
super().__init__()
self.interp_shape = interp_shape
self.support_grid_size = 6
model = build_spatracker(checkpoint, seq_length=seq_length)
self.model = model
self.model.eval()
@spaces.GPU
@torch.no_grad()
def forward(
self,
video, # (1, T, 3, H, W)
video_depth = None, # (T, 1, 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 (1, 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,
depth_predictor=None,
wind_length: int = 8,
progressive_tracking: bool = False,
):
if queries is None and grid_size == 0:
tracks, visibilities, T_Firsts = self._compute_dense_tracks(
video,
grid_query_frame=grid_query_frame,
backward_tracking=backward_tracking,
video_depth=video_depth,
depth_predictor=depth_predictor,
wind_length=wind_length,
)
else:
tracks, visibilities, T_Firsts = self._compute_sparse_tracks(
video,
queries,
segm_mask,
grid_size,
add_support_grid=False, #(grid_size == 0 or segm_mask is not None),
grid_query_frame=grid_query_frame,
backward_tracking=backward_tracking,
video_depth=video_depth,
depth_predictor=depth_predictor,
wind_length=wind_length,
)
return tracks, visibilities, T_Firsts
@spaces.GPU
def _compute_dense_tracks(
self, video, grid_query_frame, grid_size=30, backward_tracking=False,
depth_predictor=None, video_depth=None, wind_length=8
):
*_, H, W = video.shape
grid_step = W // grid_size
grid_width = W // grid_step
grid_height = H // grid_step
tracks = visibilities = T_Firsts = None
grid_pts = torch.zeros((1, grid_width * grid_height, 3)).to(video.device)
grid_pts[0, :, 0] = grid_query_frame
for offset in tqdm(range(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, T_First_step = self._compute_sparse_tracks(
video=video,
queries=grid_pts,
backward_tracking=backward_tracking,
wind_length=wind_length,
video_depth=video_depth,
depth_predictor=depth_predictor,
)
tracks = smart_cat(tracks, tracks_step, dim=2)
visibilities = smart_cat(visibilities, visibilities_step, dim=2)
T_Firsts = smart_cat(T_Firsts, T_First_step, dim=1)
return tracks, visibilities, T_Firsts
@spaces.GPU
def _compute_sparse_tracks(
self,
video,
queries,
segm_mask=None,
grid_size=0,
add_support_grid=False,
grid_query_frame=0,
backward_tracking=False,
depth_predictor=None,
video_depth=None,
wind_length=8,
):
B, T, C, H, W = video.shape
assert B == 1
video = video.reshape(B * T, C, H, W)
video = F.interpolate(video, tuple(self.interp_shape), mode="bilinear")
video = video.reshape(B, T, 3, self.interp_shape[0], self.interp_shape[1])
if queries is not None:
queries = queries.clone()
B, N, D = queries.shape
assert D == 3
queries[:, :, 1] *= self.interp_shape[1] / W
queries[:, :, 2] *= self.interp_shape[0] / H
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_extra = grid_pts[:, point_mask]
else:
grid_pts_extra = None
if grid_pts_extra is not None:
total_num = int(grid_pts_extra.shape[1])
total_num = min(800, total_num)
pick_idx = torch.randperm(grid_pts_extra.shape[1])[:total_num]
grid_pts_extra = grid_pts_extra[:, pick_idx]
queries_extra = torch.cat(
[
torch.ones_like(grid_pts_extra[:, :, :1]) * grid_query_frame,
grid_pts_extra,
],
dim=2,
)
queries = torch.cat(
[torch.zeros_like(grid_pts[:, :, :1]), grid_pts],
dim=2,
)
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
)
queries = torch.cat([queries, grid_pts], dim=1)
## ----------- estimate the video depth -----------##
if video_depth is None:
with torch.no_grad():
if video[0].shape[0]>30:
vidDepths = []
for i in range(video[0].shape[0]//30+1):
if (i+1)*30 > video[0].shape[0]:
end_idx = video[0].shape[0]
else:
end_idx = (i+1)*30
if end_idx == i*30:
break
video_ = video[0][i*30:end_idx]
vidDepths.append(depth_predictor.infer(video_/255))
video_depth = torch.cat(vidDepths, dim=0)
else:
video_depth = depth_predictor.infer(video[0]/255)
video_depth = F.interpolate(video_depth,
tuple(self.interp_shape), mode="nearest")
# from PIL import Image
# import numpy
# depth_frame = video_depth[0].detach().cpu()
# depth_frame = depth_frame.squeeze(0)
# print(depth_frame)
# print(depth_frame.min(), depth_frame.max())
# depth_img = (depth_frame * 255).numpy().astype(numpy.uint8)
# depth_img = Image.fromarray(depth_img, mode='L')
# depth_img.save('outputs/depth_map.png')
# frame = video[0, 0].detach().cpu()
# frame = frame.permute(1, 2, 0)
# frame = (frame * 255).numpy().astype(numpy.uint8)
# frame = Image.fromarray(frame, mode='RGB')
# frame.save('outputs/frame.png')
depths = video_depth
rgbds = torch.cat([video, depths[None,...]], dim=2)
# get the 3D queries
depth_interp=[]
for i in range(queries.shape[1]):
depth_interp_i = bilinear_sample2d(video_depth[queries[:, i:i+1, 0].long()],
queries[:, i:i+1, 1], queries[:, i:i+1, 2])
depth_interp.append(depth_interp_i)
depth_interp = torch.cat(depth_interp, dim=1)
queries = smart_cat(queries, depth_interp,dim=-1)
#NOTE: free the memory of depth_predictor
del depth_predictor
torch.cuda.empty_cache()
t0 = time.time()
tracks, __, visibilities = self.model(rgbds=rgbds, queries=queries, iters=6, wind_S=wind_length)
print("Time taken for inference: ", time.time()-t0)
if backward_tracking:
tracks, visibilities = self._compute_backward_tracks(
rgbds, 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
T_First = queries[..., :tracks.size(2), 0].to(torch.uint8)
tracks[:, :, :, 0] *= W / float(self.interp_shape[1])
tracks[:, :, :, 1] *= H / float(self.interp_shape[0])
return tracks, visibilities, T_First
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(
rgbds=inv_video, queries=queries, iters=6
)
inv_tracks = inv_tracks.flip(1)
inv_visibilities = inv_visibilities.flip(1)
mask = tracks == 0
tracks[mask] = inv_tracks[mask]
visibilities[mask[:, :, :, 0]] = inv_visibilities[mask[:, :, :, 0]]
return tracks, visibilities |