File size: 14,954 Bytes
6a176cb | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 | # ------------------------------------------------------------------------
# Copyright (c) 2022 megvii-model. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from DETR3D (https://github.com/WangYueFt/detr3d)
# Copyright (c) 2021 Wang, Yue
# ------------------------------------------------------------------------
# Modified from mmdetection3d (https://github.com/open-mmlab/mmdetection3d)
# Copyright (c) OpenMMLab. All rights reserved.
# ------------------------------------------------------------------------
# Modified by Shihao Wang
# ------------------------------------------------------------------------
import numpy as np
import mmcv
from mmdet.datasets.builder import PIPELINES
import torch
from PIL import Image
@PIPELINES.register_module()
class PadMultiViewImage():
"""Pad the multi-view image.
There are two padding modes: (1) pad to a fixed size and (2) pad to the
minimum size that is divisible by some number.
Added keys are "pad_shape", "pad_fixed_size", "pad_size_divisor",
Args:
size (tuple, optional): Fixed padding size.
size_divisor (int, optional): The divisor of padded size.
pad_val (float, optional): Padding value, 0 by default.
"""
def __init__(self, size=None, size_divisor=None, pad_val=0):
self.size = size
self.size_divisor = size_divisor
self.pad_val = pad_val
assert size is not None or size_divisor is not None
assert size_divisor is None or size is None
def _pad_img(self, results):
"""Pad images according to ``self.size``."""
if self.size is not None:
padded_img = [mmcv.impad(img,
shape = self.size, pad_val=self.pad_val) for img in results['img']]
elif self.size_divisor is not None:
padded_img = [mmcv.impad_to_multiple(img,
self.size_divisor, pad_val=self.pad_val) for img in results['img']]
results['img_shape'] = [img.shape for img in results['img']]
results['img'] = padded_img
results['pad_shape'] = [img.shape for img in padded_img]
results['pad_fix_size'] = self.size
results['pad_size_divisor'] = self.size_divisor
def __call__(self, results):
"""Call function to pad images, masks, semantic segmentation maps.
Args:
results (dict): Result dict from loading pipeline.
Returns:
dict: Updated result dict.
"""
self._pad_img(results)
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += f'(size={self.size}, '
repr_str += f'size_divisor={self.size_divisor}, '
repr_str += f'pad_val={self.pad_val})'
return repr_str
@PIPELINES.register_module()
class NormalizeMultiviewImage(object):
"""Normalize the image.
Added key is "img_norm_cfg".
Args:
mean (sequence): Mean values of 3 channels.
std (sequence): Std values of 3 channels.
to_rgb (bool): Whether to convert the image from BGR to RGB,
default is true.
"""
def __init__(self, mean, std, to_rgb=True):
self.mean = np.array(mean, dtype=np.float32)
self.std = np.array(std, dtype=np.float32)
self.to_rgb = to_rgb
def __call__(self, results):
"""Call function to normalize images.
Args:
results (dict): Result dict from loading pipeline.
Returns:
dict: Normalized results, 'img_norm_cfg' key is added into
result dict.
"""
results['img'] = [mmcv.imnormalize(
img, self.mean, self.std, self.to_rgb) for img in results['img']]
results['img_norm_cfg'] = dict(
mean=self.mean, std=self.std, to_rgb=self.to_rgb)
return results
def __repr__(self):
repr_str = self.__class__.__name__
repr_str += f'(mean={self.mean}, std={self.std}, to_rgb={self.to_rgb})'
return repr_str
@PIPELINES.register_module()
class ResizeCropFlipRotImage():
def __init__(self, data_aug_conf=None, with_2d=True, filter_invisible=True, training=True):
self.data_aug_conf = data_aug_conf
self.training = training
self.min_size = 2.0
self.with_2d = with_2d
self.filter_invisible = filter_invisible
def __call__(self, results):
imgs = results['img']
N = len(imgs)
new_imgs = []
new_gt_bboxes = []
new_centers2d = []
new_gt_labels = []
new_depths = []
assert self.data_aug_conf['rot_lim'] == (0.0, 0.0), "Rotation is not currently supported"
resize, resize_dims, crop, flip, rotate = self._sample_augmentation()
for i in range(N):
img = Image.fromarray(np.uint8(imgs[i]))
img, ida_mat = self._img_transform(
img,
resize=resize,
resize_dims=resize_dims,
crop=crop,
flip=flip,
rotate=rotate,
)
if self.training and self.with_2d: # sync_2d bbox labels
gt_bboxes = results['gt_bboxes'][i]
centers2d = results['centers2d'][i]
gt_labels = results['gt_labels'][i]
depths = results['depths'][i]
if len(gt_bboxes) != 0:
gt_bboxes, centers2d, gt_labels, depths = self._bboxes_transform(
gt_bboxes,
centers2d,
gt_labels,
depths,
resize=resize,
crop=crop,
flip=flip,
)
if len(gt_bboxes) != 0 and self.filter_invisible:
gt_bboxes, centers2d, gt_labels, depths = self._filter_invisible(gt_bboxes, centers2d, gt_labels, depths)
new_gt_bboxes.append(gt_bboxes)
new_centers2d.append(centers2d)
new_gt_labels.append(gt_labels)
new_depths.append(depths)
new_imgs.append(np.array(img).astype(np.float32))
results['intrinsics'][i][:3, :3] = ida_mat @ results['intrinsics'][i][:3, :3]
results['gt_bboxes'] = new_gt_bboxes
results['centers2d'] = new_centers2d
results['gt_labels'] = new_gt_labels
results['depths'] = new_depths
results['img'] = new_imgs
results['lidar2img'] = [results['intrinsics'][i] @ results['extrinsics'][i] for i in range(len(results['extrinsics']))]
return results
def _bboxes_transform(self, bboxes, centers2d, gt_labels, depths,resize, crop, flip):
assert len(bboxes) == len(centers2d) == len(gt_labels) == len(depths)
fH, fW = self.data_aug_conf["final_dim"]
bboxes = bboxes * resize
bboxes[:, 0] = bboxes[:, 0] - crop[0]
bboxes[:, 1] = bboxes[:, 1] - crop[1]
bboxes[:, 2] = bboxes[:, 2] - crop[0]
bboxes[:, 3] = bboxes[:, 3] - crop[1]
bboxes[:, 0] = np.clip(bboxes[:, 0], 0, fW)
bboxes[:, 2] = np.clip(bboxes[:, 2], 0, fW)
bboxes[:, 1] = np.clip(bboxes[:, 1], 0, fH)
bboxes[:, 3] = np.clip(bboxes[:, 3], 0, fH)
keep = ((bboxes[:, 2] - bboxes[:, 0]) >= self.min_size) & ((bboxes[:, 3] - bboxes[:, 1]) >= self.min_size)
if flip:
x0 = bboxes[:, 0].copy()
x1 = bboxes[:, 2].copy()
bboxes[:, 2] = fW - x0
bboxes[:, 0] = fW - x1
bboxes = bboxes[keep]
centers2d = centers2d * resize
centers2d[:, 0] = centers2d[:, 0] - crop[0]
centers2d[:, 1] = centers2d[:, 1] - crop[1]
centers2d[:, 0] = np.clip(centers2d[:, 0], 0, fW)
centers2d[:, 1] = np.clip(centers2d[:, 1], 0, fH)
if flip:
centers2d[:, 0] = fW - centers2d[:, 0]
centers2d = centers2d[keep]
gt_labels = gt_labels[keep]
depths = depths[keep]
return bboxes, centers2d, gt_labels, depths
def _filter_invisible(self, bboxes, centers2d, gt_labels, depths):
# filter invisible 2d bboxes
assert len(bboxes) == len(centers2d) == len(gt_labels) == len(depths)
fH, fW = self.data_aug_conf["final_dim"]
indices_maps = np.zeros((fH,fW))
tmp_bboxes = np.zeros_like(bboxes)
tmp_bboxes[:, :2] = np.ceil(bboxes[:, :2])
tmp_bboxes[:, 2:] = np.floor(bboxes[:, 2:])
tmp_bboxes = tmp_bboxes.astype(np.int64)
sort_idx = np.argsort(-depths, axis=0, kind='stable')
tmp_bboxes = tmp_bboxes[sort_idx]
bboxes = bboxes[sort_idx]
depths = depths[sort_idx]
centers2d = centers2d[sort_idx]
gt_labels = gt_labels[sort_idx]
for i in range(bboxes.shape[0]):
u1, v1, u2, v2 = tmp_bboxes[i]
indices_maps[v1:v2, u1:u2] = i
indices_res = np.unique(indices_maps).astype(np.int64)
bboxes = bboxes[indices_res]
depths = depths[indices_res]
centers2d = centers2d[indices_res]
gt_labels = gt_labels[indices_res]
return bboxes, centers2d, gt_labels, depths
def _get_rot(self, h):
return torch.Tensor(
[
[np.cos(h), np.sin(h)],
[-np.sin(h), np.cos(h)],
]
)
def _img_transform(self, img, resize, resize_dims, crop, flip, rotate):
ida_rot = torch.eye(2)
ida_tran = torch.zeros(2)
# adjust image
img = img.resize(resize_dims)
img = img.crop(crop)
if flip:
img = img.transpose(method=Image.FLIP_LEFT_RIGHT)
img = img.rotate(rotate)
# post-homography transformation
ida_rot *= resize
ida_tran -= torch.Tensor(crop[:2])
if flip:
A = torch.Tensor([[-1, 0], [0, 1]])
b = torch.Tensor([crop[2] - crop[0], 0])
ida_rot = A.matmul(ida_rot)
ida_tran = A.matmul(ida_tran) + b
A = self._get_rot(rotate / 180 * np.pi)
b = torch.Tensor([crop[2] - crop[0], crop[3] - crop[1]]) / 2
b = A.matmul(-b) + b
ida_rot = A.matmul(ida_rot)
ida_tran = A.matmul(ida_tran) + b
ida_mat = torch.eye(3)
ida_mat[:2, :2] = ida_rot
ida_mat[:2, 2] = ida_tran
return img, ida_mat
def _sample_augmentation(self):
H, W = self.data_aug_conf["H"], self.data_aug_conf["W"]
fH, fW = self.data_aug_conf["final_dim"]
if self.training:
resize = np.random.uniform(*self.data_aug_conf["resize_lim"])
resize_dims = (int(W * resize), int(H * resize))
newW, newH = resize_dims
crop_h = int((1 - np.random.uniform(*self.data_aug_conf["bot_pct_lim"])) * newH) - fH
crop_w = int(np.random.uniform(0, max(0, newW - fW)))
crop = (crop_w, crop_h, crop_w + fW, crop_h + fH)
flip = False
if self.data_aug_conf["rand_flip"] and np.random.choice([0, 1]):
flip = True
rotate = np.random.uniform(*self.data_aug_conf["rot_lim"])
else:
resize = max(fH / H, fW / W)
resize_dims = (int(W * resize), int(H * resize))
newW, newH = resize_dims
crop_h = int((1 - np.mean(self.data_aug_conf["bot_pct_lim"])) * newH) - fH
crop_w = int(max(0, newW - fW) / 2)
crop = (crop_w, crop_h, crop_w + fW, crop_h + fH)
flip = False
rotate = 0
return resize, resize_dims, crop, flip, rotate
@PIPELINES.register_module()
class GlobalRotScaleTransImage():
def __init__(
self,
rot_range=[-0.3925, 0.3925],
scale_ratio_range=[0.95, 1.05],
translation_std=[0, 0, 0],
reverse_angle=False,
training=True,
):
self.rot_range = rot_range
self.scale_ratio_range = scale_ratio_range
self.translation_std = translation_std
self.reverse_angle = reverse_angle
self.training = training
def __call__(self, results):
# random rotate
translation_std = np.array(self.translation_std, dtype=np.float32)
rot_angle = np.random.uniform(*self.rot_range)
scale_ratio = np.random.uniform(*self.scale_ratio_range)
trans = np.random.normal(scale=translation_std, size=3).T
self._rotate_bev_along_z(results, rot_angle)
if self.reverse_angle:
rot_angle = rot_angle * -1
results["gt_bboxes_3d"].rotate(
np.array(rot_angle)
)
# random scale
self._scale_xyz(results, scale_ratio)
results["gt_bboxes_3d"].scale(scale_ratio)
#random translate
self._trans_xyz(results, trans)
results["gt_bboxes_3d"].translate(trans)
return results
def _trans_xyz(self, results, trans):
trans_mat = torch.eye(4, 4)
trans_mat[:3, -1] = torch.from_numpy(trans).reshape(1, 3)
trans_mat_inv = torch.inverse(trans_mat)
num_view = len(results["lidar2img"])
results['ego_pose'] = (torch.tensor(results["ego_pose"]).float() @ trans_mat_inv).numpy()
results['ego_pose_inv'] = (trans_mat.float() @ torch.tensor(results["ego_pose_inv"])).numpy()
for view in range(num_view):
results["lidar2img"][view] = (torch.tensor(results["lidar2img"][view]).float() @ trans_mat_inv).numpy()
def _rotate_bev_along_z(self, results, angle):
rot_cos = torch.cos(torch.tensor(angle))
rot_sin = torch.sin(torch.tensor(angle))
rot_mat = torch.tensor([[rot_cos, rot_sin, 0, 0], [-rot_sin, rot_cos, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]])
rot_mat_inv = torch.inverse(rot_mat)
results['ego_pose'] = (torch.tensor(results["ego_pose"]).float() @ rot_mat_inv).numpy()
results['ego_pose_inv'] = (rot_mat.float() @ torch.tensor(results["ego_pose_inv"])).numpy()
num_view = len(results["lidar2img"])
for view in range(num_view):
results["lidar2img"][view] = (torch.tensor(results["lidar2img"][view]).float() @ rot_mat_inv).numpy()
def _scale_xyz(self, results, scale_ratio):
scale_mat = torch.tensor(
[
[scale_ratio, 0, 0, 0],
[0, scale_ratio, 0, 0],
[0, 0, scale_ratio, 0],
[0, 0, 0, 1],
]
)
scale_mat_inv = torch.inverse(scale_mat)
results['ego_pose'] = (torch.tensor(results["ego_pose"]).float() @ scale_mat_inv).numpy()
results['ego_pose_inv'] = (scale_mat @ torch.tensor(results["ego_pose_inv"]).float()).numpy()
num_view = len(results["lidar2img"])
for view in range(num_view):
results["lidar2img"][view] = (torch.tensor(results["lidar2img"][view]).float() @ scale_mat_inv).numpy()
|