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import os
import torch
import numpy as np
from pyquaternion import Quaternion
from nuscenes.nuscenes import NuScenes
from itertools import chain
from PIL import Image
from torchvision import transforms as T
import torchvision.transforms as tvf
from torchvision.transforms.functional import to_tensor
from .splits_roddick import create_splits_scenes_roddick
from ..image import pad_image, rectify_image, resize_image
from .utils import decode_binary_labels
from ..utils import decompose_rotmat
from ...utils.io import read_image
from ...utils.wrappers import Camera
from ..schema import NuScenesDataConfiguration
class NuScenesDataset(torch.utils.data.Dataset):
def __init__(self, cfg: NuScenesDataConfiguration, split="train"):
self.cfg = cfg
self.nusc = NuScenes(version=cfg.version, dataroot=str(cfg.data_dir))
self.map_data_root = cfg.map_dir
self.split = split
self.scenes = create_splits_scenes_roddick() # custom based on Roddick et al.
scene_split = {
'v1.0-trainval': {'train': 'train', 'val': 'val', 'test': 'val'},
'v1.0-mini': {'train': 'mini_train', 'val': 'mini_val'},
}[cfg.version][split]
self.scenes = self.scenes[scene_split]
self.sample = list(filter(lambda sample: self.nusc.get(
'scene', sample['scene_token'])['name'] in self.scenes, self.nusc.sample))
self.tfs = self.get_augmentations() if split == "train" else T.Compose([])
data_tokens = []
for sample in self.sample:
data_token = sample['data']
data_token = [v for k,v in data_token.items() if k == "CAM_FRONT"]
data_tokens.append(data_token)
data_tokens = list(chain.from_iterable(data_tokens))
data = [self.nusc.get('sample_data', token) for token in data_tokens]
self.data = []
for d in data:
sample = self.nusc.get('sample', d['sample_token'])
scene = self.nusc.get('scene', sample['scene_token'])
location = self.nusc.get('log', scene['log_token'])['location']
file_name = d['filename']
ego_pose = self.nusc.get('ego_pose', d['ego_pose_token'])
calibrated_sensor = self.nusc.get(
"calibrated_sensor", d['calibrated_sensor_token'])
ego2global = np.eye(4).astype(np.float32)
ego2global[:3, :3] = Quaternion(ego_pose['rotation']).rotation_matrix
ego2global[:3, 3] = ego_pose['translation']
sensor2ego = np.eye(4).astype(np.float32)
sensor2ego[:3, :3] = Quaternion(
calibrated_sensor['rotation']).rotation_matrix
sensor2ego[:3, 3] = calibrated_sensor['translation']
sensor2global = ego2global @ sensor2ego
rotation = sensor2global[:3, :3]
roll, pitch, yaw = decompose_rotmat(rotation)
fx = calibrated_sensor['camera_intrinsic'][0][0]
fy = calibrated_sensor['camera_intrinsic'][1][1]
cx = calibrated_sensor['camera_intrinsic'][0][2]
cy = calibrated_sensor['camera_intrinsic'][1][2]
width = d['width']
height = d['height']
cam = Camera(torch.tensor(
[width, height, fx, fy, cx - 0.5, cy - 0.5])).float()
self.data.append({
'filename': file_name,
'yaw': yaw,
'pitch': pitch,
'roll': roll,
'cam': cam,
'sensor2global': sensor2global,
'token': d['token'],
'sample_token': d['sample_token'],
'location': location
})
if self.cfg.percentage < 1.0 and split == "train":
self.data = self.data[:int(len(self.data) * self.cfg.percentage)]
def get_augmentations(self):
print(f"Augmentation!", "\n" * 10)
augmentations = [
tvf.ColorJitter(
brightness=self.cfg.augmentations.brightness,
contrast=self.cfg.augmentations.contrast,
saturation=self.cfg.augmentations.saturation,
hue=self.cfg.augmentations.hue,
)
]
if self.cfg.augmentations.random_resized_crop:
augmentations.append(
tvf.RandomResizedCrop(scale=(0.8, 1.0))
) # RandomResizedCrop
if self.cfg.augmentations.gaussian_noise.enabled:
augmentations.append(
tvf.GaussianNoise(
mean=self.cfg.augmentations.gaussian_noise.mean,
std=self.cfg.augmentations.gaussian_noise.std,
)
) # Gaussian noise
if self.cfg.augmentations.brightness_contrast.enabled:
augmentations.append(
tvf.ColorJitter(
brightness=self.cfg.augmentations.brightness_contrast.brightness_factor,
contrast=self.cfg.augmentations.brightness_contrast.contrast_factor,
saturation=0, # Keep saturation at 0 for brightness and contrast adjustment
hue=0,
)
) # Brightness and contrast adjustment
return tvf.Compose(augmentations)
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
d = self.data[idx]
image = read_image(os.path.join(self.nusc.dataroot, d['filename']))
image = np.array(image)
cam = d['cam']
roll = d['roll']
pitch = d['pitch']
yaw = d['yaw']
with Image.open(self.map_data_root / f"{d['token']}.png") as semantic_image:
semantic_mask = to_tensor(semantic_image)
semantic_mask = decode_binary_labels(semantic_mask, self.cfg.num_classes + 1)
semantic_mask = torch.nn.functional.max_pool2d(semantic_mask.float(), (2, 2), stride=2) # 2 times downsample
semantic_mask = semantic_mask.permute(1, 2, 0)
semantic_mask = torch.flip(semantic_mask, [0])
visibility_mask = semantic_mask[..., -1]
semantic_mask = semantic_mask[..., :-1]
if self.cfg.class_mapping is not None:
semantic_mask = semantic_mask[..., self.cfg.class_mapping]
image = (
torch.from_numpy(np.ascontiguousarray(image))
.permute(2, 0, 1)
.float()
.div_(255)
)
if not self.cfg.gravity_align:
# Turn off gravity alignment
roll = 0.0
pitch = 0.0
image, valid = rectify_image(image, cam, roll, pitch)
else:
image, valid = rectify_image(
image, cam, roll, pitch if self.cfg.rectify_pitch else None
)
roll = 0.0
if self.cfg.rectify_pitch:
pitch = 0.0
if self.cfg.resize_image is not None:
image, _, cam, valid = resize_image(
image, self.cfg.resize_image, fn=max, camera=cam, valid=valid
)
if self.cfg.pad_to_square:
image, valid, cam = pad_image(image, self.cfg.resize_image, cam, valid)
image = self.tfs(image)
confidence_map = visibility_mask.clone().float()
confidence_map = (confidence_map - confidence_map.min()) / (confidence_map.max() - confidence_map.min())
return {
"image": image,
"roll_pitch_yaw": torch.tensor([roll, pitch, yaw]).float(),
"camera": cam,
"valid": valid,
"seg_masks": semantic_mask.float(),
"token": d['token'],
"sample_token": d['sample_token'],
'location': d['location'],
'flood_masks': visibility_mask.float(),
"confidence_map": confidence_map,
'name': d['sample_token']
}
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