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| import numpy as np | |
| import torch | |
| from torchvision.transforms import functional as tfn | |
| import torchvision.transforms.functional as tvf | |
| from ..utils import decompose_rotmat | |
| from ..image import pad_image, rectify_image, resize_image | |
| from ...utils.wrappers import Camera | |
| from ..schema import KITTIDataConfiguration | |
| class BEVTransform: | |
| def __init__(self, | |
| cfg: KITTIDataConfiguration, augmentations): | |
| self.cfg = cfg | |
| self.augmentations = augmentations | |
| def _compact_labels(msk, cat, iscrowd): | |
| ids = np.unique(msk) | |
| if 0 not in ids: | |
| ids = np.concatenate((np.array([0], dtype=np.int32), ids), axis=0) | |
| ids_to_compact = np.zeros((ids.max() + 1,), dtype=np.int32) | |
| ids_to_compact[ids] = np.arange(0, ids.size, dtype=np.int32) | |
| msk = ids_to_compact[msk] | |
| cat = cat[ids] | |
| iscrowd = iscrowd[ids] | |
| return msk, cat, iscrowd | |
| def __call__(self, img, bev_msk=None, bev_plabel=None, fv_msk=None, bev_weights_msk=None, | |
| bev_cat=None, bev_iscrowd=None, fv_cat=None, fv_iscrowd=None, | |
| fv_intrinsics=None, ego_pose=None): | |
| # Wrap in np.array | |
| if bev_cat is not None: | |
| bev_cat = np.array(bev_cat, dtype=np.int32) | |
| if bev_iscrowd is not None: | |
| bev_iscrowd = np.array(bev_iscrowd, dtype=np.uint8) | |
| if ego_pose is not None: | |
| ego_pose = np.array(ego_pose, dtype=np.float32) | |
| roll, pitch, yaw = decompose_rotmat(ego_pose[:3, :3]) | |
| # Image transformations | |
| img = tfn.to_tensor(img) | |
| # img = [self._normalize_image(rgb) for rgb in img] | |
| fx = fv_intrinsics[0][0] | |
| fy = fv_intrinsics[1][1] | |
| cx = fv_intrinsics[0][2] | |
| cy = fv_intrinsics[1][2] | |
| width = img.shape[2] | |
| height = img.shape[1] | |
| cam = Camera(torch.tensor( | |
| [width, height, fx, fy, cx - 0.5, cy - 0.5])).float() | |
| if not self.cfg.gravity_align: | |
| # Turn off gravity alignment | |
| roll = 0.0 | |
| pitch = 0.0 | |
| img, valid = rectify_image(img, cam, roll, pitch) | |
| else: | |
| img, valid = rectify_image( | |
| img, cam, roll, pitch if self.cfg.rectify_pitch else None | |
| ) | |
| roll = 0.0 | |
| if self.cfg.rectify_pitch: | |
| pitch = 0.0 | |
| if self.cfg.target_focal_length is not None: | |
| # Resize to a canonical focal length | |
| factor = self.cfg.target_focal_length / cam.f.numpy() | |
| size = (np.array(img.shape[-2:][::-1]) * factor).astype(int) | |
| img, _, cam, valid = resize_image(img, size, camera=cam, valid=valid) | |
| size_out = self.cfg.resize_image | |
| if size_out is None: | |
| # Round the edges up such that they are multiple of a factor | |
| stride = self.cfg.pad_to_multiple | |
| size_out = (np.ceil((size / stride)) * stride).astype(int) | |
| # Crop or pad such that both edges are of the given size | |
| img, valid, cam = pad_image( | |
| img, size_out, cam, valid, crop_and_center=False | |
| ) | |
| elif self.cfg.resize_image is not None: | |
| img, _, cam, valid = resize_image( | |
| img, self.cfg.resize_image, fn=max, camera=cam, valid=valid | |
| ) | |
| if self.cfg.pad_to_square: | |
| # Pad such that both edges are of the given size | |
| img, valid, cam = pad_image(img, self.cfg.resize_image, cam, valid) | |
| # Label transformations, | |
| if bev_msk is not None: | |
| bev_msk = np.expand_dims( | |
| np.array(bev_msk, dtype=np.int32, copy=False), | |
| axis=0 | |
| ) | |
| bev_msk, bev_cat, bev_iscrowd = self._compact_labels( | |
| bev_msk, bev_cat, bev_iscrowd | |
| ) | |
| bev_msk = torch.from_numpy(bev_msk) | |
| bev_cat = torch.from_numpy(bev_cat) | |
| rotated_mask = torch.rot90(bev_msk, dims=(1, 2)) | |
| cropped_mask = rotated_mask[:, :672, (rotated_mask.size(2) - 672) // 2:-(rotated_mask.size(2) - 672) // 2] | |
| bev_msk = cropped_mask.squeeze(0) | |
| seg_masks = bev_cat[bev_msk] | |
| seg_masks_onehot = seg_masks.clone() | |
| seg_masks_onehot[seg_masks_onehot == 255] = 0 | |
| seg_masks_onehot = torch.nn.functional.one_hot( | |
| seg_masks_onehot.to(torch.int64), | |
| num_classes=self.cfg.num_classes | |
| ) | |
| seg_masks_onehot[seg_masks == 255] = 0 | |
| seg_masks_onehot = seg_masks_onehot.permute(2, 0, 1) | |
| seg_masks_down = tvf.resize(seg_masks_onehot, (100, 100)) | |
| seg_masks_down = seg_masks_down.permute(1, 2, 0) | |
| if self.cfg.class_mapping is not None: | |
| seg_masks_down = seg_masks_down[:, :, self.cfg.class_mapping] | |
| img = self.augmentations(img) | |
| flood_masks = torch.all(seg_masks_down == 0, dim=2).float() | |
| ret = { | |
| "image": img, | |
| "valid": valid, | |
| "camera": cam, | |
| "seg_masks": (seg_masks_down).float().contiguous(), | |
| "flood_masks": flood_masks, | |
| "roll_pitch_yaw": torch.tensor((roll, pitch, yaw)).float(), | |
| "confidence_map": flood_masks, | |
| } | |
| for key, value in ret.items(): | |
| if isinstance(value, np.ndarray): | |
| ret[key] = torch.from_numpy(value) | |
| return ret | |