File size: 11,819 Bytes
b69ec42 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 | import os
from pathlib import Path
import json
import time
import random
from typing import *
import traceback
import itertools
from numbers import Number
import io
import numpy as np
import cv2
from PIL import Image
import torch
import torchvision.transforms.v2.functional as TF
import utils3d
import pipeline
from tqdm import tqdm
from ..utils.io import *
from ..utils.geometry_numpy import harmonic_mean_numpy, norm3d, depth_occlusion_edge_numpy
from ..utils.data_augmentation import sample_perspective, warp_perspective, image_color_augmentation
class TrainDataLoaderPipeline:
def __init__(self, config: dict, batch_size: int, num_load_workers: int = 4, num_process_workers: int = 8, buffer_size: int = 8):
self.config = config
self.batch_size = batch_size
self.clamp_max_depth = config['clamp_max_depth']
self.fov_range_absolute = config.get('fov_range_absolute', 0.0)
self.fov_range_relative = config.get('fov_range_relative', 0.0)
self.center_augmentation = config.get('center_augmentation', 0.0)
self.image_augmentation = config.get('image_augmentation', [])
self.depth_interpolation = config.get('depth_interpolation', 'bilinear')
if 'image_sizes' in config:
self.image_size_strategy = 'fixed'
self.image_sizes = config['image_sizes']
elif 'aspect_ratio_range' in config and 'area_range' in config:
self.image_size_strategy = 'aspect_area'
self.aspect_ratio_range = config['aspect_ratio_range']
self.area_range = config['area_range']
else:
raise ValueError('Invalid image size configuration')
# Load datasets
self.datasets = {}
for dataset in tqdm(config['datasets'], desc='Loading datasets'):
name = dataset['name']
content = Path(dataset['path'], dataset.get('index', '.index.txt')).joinpath().read_text()
filenames = content.splitlines()
self.datasets[name] = {
**dataset,
'path': dataset['path'],
'filenames': filenames,
}
self.dataset_names = [dataset['name'] for dataset in config['datasets']]
self.dataset_weights = [dataset['weight'] for dataset in config['datasets']]
# Build pipeline
self.pipeline = pipeline.Sequential([
self._sample_batch,
pipeline.Unbatch(),
pipeline.Parallel([self._load_instance] * num_load_workers),
pipeline.Parallel([self._process_instance] * num_process_workers),
pipeline.Batch(self.batch_size),
self._collate_batch,
pipeline.Buffer(buffer_size),
])
self.invalid_instance = {
'intrinsics': np.array([[1.0, 0.0, 0.5], [0.0, 1.0, 0.5], [0.0, 0.0, 1.0]], dtype=np.float32),
'image': np.zeros((256, 256, 3), dtype=np.uint8),
'depth': np.ones((256, 256), dtype=np.float32),
'depth_mask': np.ones((256, 256), dtype=bool),
'depth_mask_inf': np.zeros((256, 256), dtype=bool),
'label_type': 'invalid',
}
def _sample_batch(self):
batch_id = 0
last_area = None
while True:
# Depending on the sample strategy, choose a dataset and a filename
batch_id += 1
batch = []
# Sample instances
for _ in range(self.batch_size):
dataset_name = random.choices(self.dataset_names, weights=self.dataset_weights)[0]
filename = random.choice(self.datasets[dataset_name]['filenames'])
path = Path(self.datasets[dataset_name]['path'], filename)
instance = {
'batch_id': batch_id,
'seed': random.randint(0, 2 ** 32 - 1),
'dataset': dataset_name,
'filename': filename,
'path': path,
'label_type': self.datasets[dataset_name]['label_type'],
}
batch.append(instance)
# Decide the image size for this batch
if self.image_size_strategy == 'fixed':
width, height = random.choice(self.config['image_sizes'])
elif self.image_size_strategy == 'aspect_area':
area = random.uniform(*self.area_range)
aspect_ratio_ranges = [self.datasets[instance['dataset']].get('aspect_ratio_range', self.aspect_ratio_range) for instance in batch]
aspect_ratio_range = (min(r[0] for r in aspect_ratio_ranges), max(r[1] for r in aspect_ratio_ranges))
aspect_ratio = random.uniform(*aspect_ratio_range)
width, height = int((area * aspect_ratio) ** 0.5), int((area / aspect_ratio) ** 0.5)
else:
raise ValueError('Invalid image size strategy')
for instance in batch:
instance['width'], instance['height'] = width, height
yield batch
def _load_instance(self, instance: dict):
try:
image = read_image(Path(instance['path'], 'image.jpg'))
depth = read_depth(Path(instance['path'], self.datasets[instance['dataset']].get('depth', 'depth.png')))
meta = read_json(Path(instance['path'], 'meta.json'))
intrinsics = np.array(meta['intrinsics'], dtype=np.float32)
data = {
'image': image,
'depth': depth,
'intrinsics': intrinsics
}
instance.update({
**data,
})
except Exception as e:
print(f"Failed to load instance {instance['dataset']}/{instance['filename']} because of exception:", e)
instance.update(self.invalid_instance)
return instance
def _process_instance(self, instance: Dict[str, Union[np.ndarray, str, float, bool]]):
raw_image, raw_depth, raw_intrinsics, label_type = instance['image'], instance['depth'], instance['intrinsics'], instance['label_type']
raw_normal, raw_normal_mask = utils3d.np.depth_map_to_normal_map(raw_depth, intrinsics=raw_intrinsics, mask=np.isfinite(raw_depth), edge_threshold=88)
raw_normal = np.where(raw_normal_mask[..., None], raw_normal, np.nan)
depth_unit = self.datasets[instance['dataset']].get('depth_unit', None)
raw_height, raw_width = raw_image.shape[:2]
raw_fov_x, raw_fov_y = utils3d.np.intrinsics_to_fov(raw_intrinsics)
tgt_width, tgt_height = instance['width'], instance['height']
tgt_aspect = tgt_width / tgt_height
rng = np.random.default_rng(instance['seed'])
# Sample perspective transformation
tgt_intrinsics, R = sample_perspective(
raw_intrinsics,
tgt_aspect=tgt_aspect,
center_augmentation=self.datasets[instance['dataset']].get('center_augmentation', self.center_augmentation),
fov_range_absolute=self.datasets[instance['dataset']].get('fov_range_absolute', self.fov_range_absolute),
fov_range_relative=self.datasets[instance['dataset']].get('fov_range_relative', self.fov_range_relative),
rng=rng
)
# Warp
transform = tgt_intrinsics @ R @ np.linalg.inv(raw_intrinsics)
# - Warp image
tgt_image = warp_perspective(raw_image, transform, tgt_size=(tgt_height, tgt_width), interpolation='lanczos')
# - Warp depth
depth_edge_mask = utils3d.np.depth_map_edge(raw_depth, mask=np.isfinite(raw_depth), kernel_size=5, ltol=0.01)
depth_bilinear_mask = np.isfinite(raw_depth) & ~depth_edge_mask
warped_depth_bilinear_mask = warp_perspective(depth_bilinear_mask.astype(np.float32), transform, (tgt_height, tgt_width), interpolation='bilinear')
warped_depth_nearest = warp_perspective(raw_depth, transform, (tgt_height, tgt_width), interpolation='nearest', sparse_mask=~np.isnan(raw_depth))
warped_depth_bilinear = 1 / warp_perspective(1 / raw_depth, transform, (tgt_height, tgt_width), interpolation='bilinear') # NOTE: Bilinear intepolation in disparity space maintains planar surfaces.
warped_depth = np.where(warped_depth_bilinear_mask == 1., warped_depth_bilinear, warped_depth_nearest)
tgt_uvhomo = np.concatenate([utils3d.np.uv_map((tgt_height, tgt_width)), np.ones((tgt_height, tgt_width, 1), dtype=np.float32)], axis=-1)
tgt_depth = warped_depth / np.dot(tgt_uvhomo, np.linalg.inv(transform)[2, :])
# - Warp normal
warped_normal = warp_perspective(raw_normal, transform, (tgt_height, tgt_width), interpolation='bilinear')
tgt_normal = warped_normal @ R.T
# always make sure that mask is not empty
if np.isfinite(tgt_depth).sum() / tgt_depth.size < 0.001:
tgt_depth = np.ones_like(tgt_depth)
instance['label_type'] = 'invalid'
# Flip augmentation
if rng.choice([True, False]):
tgt_image = np.flip(tgt_image, axis=1).copy()
tgt_depth = np.flip(tgt_depth, axis=1).copy()
tgt_normal = np.flip(tgt_normal, axis=1).copy() * [-1, 1, 1]
# NOTE: if cx != 0.5, flip intrinsics accordingly.
# Color augmentation
image_augmentation = self.datasets[instance['dataset']].get('image_augmentation', self.image_augmentation)
tgt_image = image_color_augmentation(
tgt_image,
augmentations=image_augmentation,
rng=rng,
depth=tgt_depth,
)
# Set metric flag if depth is in metric unit
if depth_unit is not None:
tgt_depth *= depth_unit
instance['is_metric'] = True
else:
instance['is_metric'] = False
# Clip maximum depth
max_depth = np.nanquantile(np.where(np.isfinite(tgt_depth), tgt_depth, np.nan), 0.01) * self.clamp_max_depth
tgt_depth = np.where(np.isfinite(tgt_depth), np.clip(tgt_depth, 0, max_depth), tgt_depth)
tgt_depth_mask_inf = np.isinf(tgt_depth)
if self.datasets[instance['dataset']].get('finite_depth_mask', None) == "only_known":
tgt_depth_mask_fin = np.isfinite(tgt_depth)
else:
tgt_depth_mask_fin = ~tgt_depth_mask_inf
instance.update({
'image': torch.from_numpy(tgt_image.astype(np.float32) / 255.0).permute(2, 0, 1),
'depth': torch.from_numpy(tgt_depth).float(),
'depth_mask_fin': torch.from_numpy(tgt_depth_mask_fin).bool(),
'depth_mask_inf': torch.from_numpy(tgt_depth_mask_inf).bool(),
"normal": torch.from_numpy(tgt_normal).float(),
'intrinsics': torch.from_numpy(tgt_intrinsics).float(),
})
return instance
def _collate_batch(self, instances: List[Dict[str, Any]]):
batch = {k: torch.stack([instance[k] for instance in instances], dim=0) for k in ['image', 'depth', 'depth_mask_fin', 'depth_mask_inf', 'normal', 'intrinsics']}
batch = {
'label_type': [instance['label_type'] for instance in instances],
'is_metric': [instance['is_metric'] for instance in instances],
'info': [{'dataset': instance['dataset'], 'filename': instance['filename']} for instance in instances],
**batch,
}
return batch
def get(self) -> Dict[str, Union[torch.Tensor, str]]:
return self.pipeline.get()
def start(self):
self.pipeline.start()
def stop(self):
self.pipeline.stop()
def __enter__(self):
self.start()
return self
def __exit__(self, exc_type, exc_value, traceback):
self.pipeline.stop()
return False
|