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