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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 os
import torch
import cv2
import imageio
import numpy as np
from cotracker.datasets.utils import CoTrackerData
from torchvision.transforms import ColorJitter, GaussianBlur
from PIL import Image
class CoTrackerDataset(torch.utils.data.Dataset):
def __init__(
self,
data_root,
crop_size=(384, 512),
seq_len=24,
traj_per_sample=768,
sample_vis_1st_frame=False,
use_augs=False,
):
super(CoTrackerDataset, self).__init__()
np.random.seed(0)
torch.manual_seed(0)
self.data_root = data_root
self.seq_len = seq_len
self.traj_per_sample = traj_per_sample
self.sample_vis_1st_frame = sample_vis_1st_frame
self.use_augs = use_augs
self.crop_size = crop_size
# photometric augmentation
self.photo_aug = ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.25 / 3.14)
self.blur_aug = GaussianBlur(11, sigma=(0.1, 2.0))
self.blur_aug_prob = 0.25
self.color_aug_prob = 0.25
# occlusion augmentation
self.eraser_aug_prob = 0.5
self.eraser_bounds = [2, 100]
self.eraser_max = 10
# occlusion augmentation
self.replace_aug_prob = 0.5
self.replace_bounds = [2, 100]
self.replace_max = 10
# spatial augmentations
self.pad_bounds = [0, 100]
self.crop_size = crop_size
self.resize_lim = [0.25, 2.0] # sample resizes from here
self.resize_delta = 0.2
self.max_crop_offset = 50
self.do_flip = True
self.h_flip_prob = 0.5
self.v_flip_prob = 0.5
def getitem_helper(self, index):
return NotImplementedError
def __getitem__(self, index):
gotit = False
sample, gotit = self.getitem_helper(index)
if not gotit:
print("warning: sampling failed")
# fake sample, so we can still collate
sample = CoTrackerData(
video=torch.zeros((self.seq_len, 3, self.crop_size[0], self.crop_size[1])),
trajectory=torch.zeros((self.seq_len, self.traj_per_sample, 2)),
visibility=torch.zeros((self.seq_len, self.traj_per_sample)),
valid=torch.zeros((self.seq_len, self.traj_per_sample)),
)
return sample, gotit
def add_photometric_augs(self, rgbs, trajs, visibles, eraser=True, replace=True):
T, N, _ = trajs.shape
S = len(rgbs)
H, W = rgbs[0].shape[:2]
assert S == T
if eraser:
############ eraser transform (per image after the first) ############
rgbs = [rgb.astype(np.float32) for rgb in rgbs]
for i in range(1, S):
if np.random.rand() < self.eraser_aug_prob:
for _ in range(
np.random.randint(1, self.eraser_max + 1)
): # number of times to occlude
xc = np.random.randint(0, W)
yc = np.random.randint(0, H)
dx = np.random.randint(self.eraser_bounds[0], self.eraser_bounds[1])
dy = np.random.randint(self.eraser_bounds[0], self.eraser_bounds[1])
x0 = np.clip(xc - dx / 2, 0, W - 1).round().astype(np.int32)
x1 = np.clip(xc + dx / 2, 0, W - 1).round().astype(np.int32)
y0 = np.clip(yc - dy / 2, 0, H - 1).round().astype(np.int32)
y1 = np.clip(yc + dy / 2, 0, H - 1).round().astype(np.int32)
mean_color = np.mean(rgbs[i][y0:y1, x0:x1, :].reshape(-1, 3), axis=0)
rgbs[i][y0:y1, x0:x1, :] = mean_color
occ_inds = np.logical_and(
np.logical_and(trajs[i, :, 0] >= x0, trajs[i, :, 0] < x1),
np.logical_and(trajs[i, :, 1] >= y0, trajs[i, :, 1] < y1),
)
visibles[i, occ_inds] = 0
rgbs = [rgb.astype(np.uint8) for rgb in rgbs]
if replace:
rgbs_alt = [
np.array(self.photo_aug(Image.fromarray(rgb)), dtype=np.uint8) for rgb in rgbs
]
rgbs_alt = [
np.array(self.photo_aug(Image.fromarray(rgb)), dtype=np.uint8) for rgb in rgbs_alt
]
############ replace transform (per image after the first) ############
rgbs = [rgb.astype(np.float32) for rgb in rgbs]
rgbs_alt = [rgb.astype(np.float32) for rgb in rgbs_alt]
for i in range(1, S):
if np.random.rand() < self.replace_aug_prob:
for _ in range(
np.random.randint(1, self.replace_max + 1)
): # number of times to occlude
xc = np.random.randint(0, W)
yc = np.random.randint(0, H)
dx = np.random.randint(self.replace_bounds[0], self.replace_bounds[1])
dy = np.random.randint(self.replace_bounds[0], self.replace_bounds[1])
x0 = np.clip(xc - dx / 2, 0, W - 1).round().astype(np.int32)
x1 = np.clip(xc + dx / 2, 0, W - 1).round().astype(np.int32)
y0 = np.clip(yc - dy / 2, 0, H - 1).round().astype(np.int32)
y1 = np.clip(yc + dy / 2, 0, H - 1).round().astype(np.int32)
wid = x1 - x0
hei = y1 - y0
y00 = np.random.randint(0, H - hei)
x00 = np.random.randint(0, W - wid)
fr = np.random.randint(0, S)
rep = rgbs_alt[fr][y00 : y00 + hei, x00 : x00 + wid, :]
rgbs[i][y0:y1, x0:x1, :] = rep
occ_inds = np.logical_and(
np.logical_and(trajs[i, :, 0] >= x0, trajs[i, :, 0] < x1),
np.logical_and(trajs[i, :, 1] >= y0, trajs[i, :, 1] < y1),
)
visibles[i, occ_inds] = 0
rgbs = [rgb.astype(np.uint8) for rgb in rgbs]
############ photometric augmentation ############
if np.random.rand() < self.color_aug_prob:
# random per-frame amount of aug
rgbs = [np.array(self.photo_aug(Image.fromarray(rgb)), dtype=np.uint8) for rgb in rgbs]
if np.random.rand() < self.blur_aug_prob:
# random per-frame amount of blur
rgbs = [np.array(self.blur_aug(Image.fromarray(rgb)), dtype=np.uint8) for rgb in rgbs]
return rgbs, trajs, visibles
def add_spatial_augs(self, rgbs, trajs, visibles):
T, N, __ = trajs.shape
S = len(rgbs)
H, W = rgbs[0].shape[:2]
assert S == T
rgbs = [rgb.astype(np.float32) for rgb in rgbs]
############ spatial transform ############
# padding
pad_x0 = np.random.randint(self.pad_bounds[0], self.pad_bounds[1])
pad_x1 = np.random.randint(self.pad_bounds[0], self.pad_bounds[1])
pad_y0 = np.random.randint(self.pad_bounds[0], self.pad_bounds[1])
pad_y1 = np.random.randint(self.pad_bounds[0], self.pad_bounds[1])
rgbs = [np.pad(rgb, ((pad_y0, pad_y1), (pad_x0, pad_x1), (0, 0))) for rgb in rgbs]
trajs[:, :, 0] += pad_x0
trajs[:, :, 1] += pad_y0
H, W = rgbs[0].shape[:2]
# scaling + stretching
scale = np.random.uniform(self.resize_lim[0], self.resize_lim[1])
scale_x = scale
scale_y = scale
H_new = H
W_new = W
scale_delta_x = 0.0
scale_delta_y = 0.0
rgbs_scaled = []
for s in range(S):
if s == 1:
scale_delta_x = np.random.uniform(-self.resize_delta, self.resize_delta)
scale_delta_y = np.random.uniform(-self.resize_delta, self.resize_delta)
elif s > 1:
scale_delta_x = (
scale_delta_x * 0.8
+ np.random.uniform(-self.resize_delta, self.resize_delta) * 0.2
)
scale_delta_y = (
scale_delta_y * 0.8
+ np.random.uniform(-self.resize_delta, self.resize_delta) * 0.2
)
scale_x = scale_x + scale_delta_x
scale_y = scale_y + scale_delta_y
# bring h/w closer
scale_xy = (scale_x + scale_y) * 0.5
scale_x = scale_x * 0.5 + scale_xy * 0.5
scale_y = scale_y * 0.5 + scale_xy * 0.5
# don't get too crazy
scale_x = np.clip(scale_x, 0.2, 2.0)
scale_y = np.clip(scale_y, 0.2, 2.0)
H_new = int(H * scale_y)
W_new = int(W * scale_x)
# make it at least slightly bigger than the crop area,
# so that the random cropping can add diversity
H_new = np.clip(H_new, self.crop_size[0] + 10, None)
W_new = np.clip(W_new, self.crop_size[1] + 10, None)
# recompute scale in case we clipped
scale_x = (W_new - 1) / float(W - 1)
scale_y = (H_new - 1) / float(H - 1)
rgbs_scaled.append(cv2.resize(rgbs[s], (W_new, H_new), interpolation=cv2.INTER_LINEAR))
trajs[s, :, 0] *= scale_x
trajs[s, :, 1] *= scale_y
rgbs = rgbs_scaled
ok_inds = visibles[0, :] > 0
vis_trajs = trajs[:, ok_inds] # S,?,2
if vis_trajs.shape[1] > 0:
mid_x = np.mean(vis_trajs[0, :, 0])
mid_y = np.mean(vis_trajs[0, :, 1])
else:
mid_y = self.crop_size[0]
mid_x = self.crop_size[1]
x0 = int(mid_x - self.crop_size[1] // 2)
y0 = int(mid_y - self.crop_size[0] // 2)
offset_x = 0
offset_y = 0
for s in range(S):
# on each frame, shift a bit more
if s == 1:
offset_x = np.random.randint(-self.max_crop_offset, self.max_crop_offset)
offset_y = np.random.randint(-self.max_crop_offset, self.max_crop_offset)
elif s > 1:
offset_x = int(
offset_x * 0.8
+ np.random.randint(-self.max_crop_offset, self.max_crop_offset + 1) * 0.2
)
offset_y = int(
offset_y * 0.8
+ np.random.randint(-self.max_crop_offset, self.max_crop_offset + 1) * 0.2
)
x0 = x0 + offset_x
y0 = y0 + offset_y
H_new, W_new = rgbs[s].shape[:2]
if H_new == self.crop_size[0]:
y0 = 0
else:
y0 = min(max(0, y0), H_new - self.crop_size[0] - 1)
if W_new == self.crop_size[1]:
x0 = 0
else:
x0 = min(max(0, x0), W_new - self.crop_size[1] - 1)
rgbs[s] = rgbs[s][y0 : y0 + self.crop_size[0], x0 : x0 + self.crop_size[1]]
trajs[s, :, 0] -= x0
trajs[s, :, 1] -= y0
H_new = self.crop_size[0]
W_new = self.crop_size[1]
# flip
h_flipped = False
v_flipped = False
if self.do_flip:
# h flip
if np.random.rand() < self.h_flip_prob:
h_flipped = True
rgbs = [rgb[:, ::-1] for rgb in rgbs]
# v flip
if np.random.rand() < self.v_flip_prob:
v_flipped = True
rgbs = [rgb[::-1] for rgb in rgbs]
if h_flipped:
trajs[:, :, 0] = W_new - trajs[:, :, 0]
if v_flipped:
trajs[:, :, 1] = H_new - trajs[:, :, 1]
return rgbs, trajs
def crop(self, rgbs, trajs):
T, N, _ = trajs.shape
S = len(rgbs)
H, W = rgbs[0].shape[:2]
assert S == T
############ spatial transform ############
H_new = H
W_new = W
# simple random crop
y0 = 0 if self.crop_size[0] >= H_new else np.random.randint(0, H_new - self.crop_size[0])
x0 = 0 if self.crop_size[1] >= W_new else np.random.randint(0, W_new - self.crop_size[1])
rgbs = [rgb[y0 : y0 + self.crop_size[0], x0 : x0 + self.crop_size[1]] for rgb in rgbs]
trajs[:, :, 0] -= x0
trajs[:, :, 1] -= y0
return rgbs, trajs
class KubricMovifDataset(CoTrackerDataset):
def __init__(
self,
data_root,
crop_size=(384, 512),
seq_len=24,
traj_per_sample=768,
sample_vis_1st_frame=False,
use_augs=False,
):
super(KubricMovifDataset, self).__init__(
data_root=data_root,
crop_size=crop_size,
seq_len=seq_len,
traj_per_sample=traj_per_sample,
sample_vis_1st_frame=sample_vis_1st_frame,
use_augs=use_augs,
)
self.pad_bounds = [0, 25]
self.resize_lim = [0.75, 1.25] # sample resizes from here
self.resize_delta = 0.05
self.max_crop_offset = 15
self.seq_names = [
fname
for fname in os.listdir(data_root)
if os.path.isdir(os.path.join(data_root, fname))
]
print("found %d unique videos in %s" % (len(self.seq_names), self.data_root))
def getitem_helper(self, index):
gotit = True
seq_name = self.seq_names[index]
npy_path = os.path.join(self.data_root, seq_name, seq_name + ".npy")
rgb_path = os.path.join(self.data_root, seq_name, "frames")
img_paths = sorted(os.listdir(rgb_path))
rgbs = []
for i, img_path in enumerate(img_paths):
rgbs.append(imageio.v2.imread(os.path.join(rgb_path, img_path)))
rgbs = np.stack(rgbs)
annot_dict = np.load(npy_path, allow_pickle=True).item()
traj_2d = annot_dict["coords"]
visibility = annot_dict["visibility"]
# random crop
assert self.seq_len <= len(rgbs)
if self.seq_len < len(rgbs):
start_ind = np.random.choice(len(rgbs) - self.seq_len, 1)[0]
rgbs = rgbs[start_ind : start_ind + self.seq_len]
traj_2d = traj_2d[:, start_ind : start_ind + self.seq_len]
visibility = visibility[:, start_ind : start_ind + self.seq_len]
traj_2d = np.transpose(traj_2d, (1, 0, 2))
visibility = np.transpose(np.logical_not(visibility), (1, 0))
if self.use_augs:
rgbs, traj_2d, visibility = self.add_photometric_augs(rgbs, traj_2d, visibility)
rgbs, traj_2d = self.add_spatial_augs(rgbs, traj_2d, visibility)
else:
rgbs, traj_2d = self.crop(rgbs, traj_2d)
visibility[traj_2d[:, :, 0] > self.crop_size[1] - 1] = False
visibility[traj_2d[:, :, 0] < 0] = False
visibility[traj_2d[:, :, 1] > self.crop_size[0] - 1] = False
visibility[traj_2d[:, :, 1] < 0] = False
visibility = torch.from_numpy(visibility)
traj_2d = torch.from_numpy(traj_2d)
visibile_pts_first_frame_inds = (visibility[0]).nonzero(as_tuple=False)[:, 0]
if self.sample_vis_1st_frame:
visibile_pts_inds = visibile_pts_first_frame_inds
else:
visibile_pts_mid_frame_inds = (visibility[self.seq_len // 2]).nonzero(as_tuple=False)[
:, 0
]
visibile_pts_inds = torch.cat(
(visibile_pts_first_frame_inds, visibile_pts_mid_frame_inds), dim=0
)
point_inds = torch.randperm(len(visibile_pts_inds))[: self.traj_per_sample]
if len(point_inds) < self.traj_per_sample:
gotit = False
visible_inds_sampled = visibile_pts_inds[point_inds]
trajs = traj_2d[:, visible_inds_sampled].float()
visibles = visibility[:, visible_inds_sampled]
valids = torch.ones((self.seq_len, self.traj_per_sample))
rgbs = torch.from_numpy(np.stack(rgbs)).permute(0, 3, 1, 2).float()
sample = CoTrackerData(
video=rgbs,
trajectory=trajs,
visibility=visibles,
valid=valids,
seq_name=seq_name,
)
return sample, gotit
def __len__(self):
return len(self.seq_names)
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