v0.4.0

What's Changed

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New feature : create_calibrator function that allows to create calibrator from name and kwargs is added to alonet.torch2trt to optimize imports in Tensorrt scripts.

from alonet.torch2trt import create_calibrator, DataBatchStreamer

cache_file = "calib.bin"
data_streamer = DataBatchStreamer(...)

calibrator = create_calibrator("minmax", data_streamer, cache_file)

by @Data-Iab in #211

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• Fix bug : aloscene.read_image is now supported in the Jetsonnx.

by @Data-Iab in #209

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• Fix bug : Change the way of representing an augmented tensor. Clearer separation between property with new separator and without unnecessary ones.

by @Ardorax in #218

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• Fix bug : The default value (10) for calibration batches does not allow the use of the whole calibration dataset. Default value has been changed to None.

by @Data-Iab in #216

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• Fix bug : Change all the links to the documentation due to the change of name of the repository.

by @Ardorax in #223

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• New feature : Added a setup.py to facilitate the installation of aloception.

by @tflahaul in #220

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• New feature : Kitti Dataset (Stereo, Flow, Scene Flow, Depth, Odometry, Object, Tracking, Road, Semantics)

Kitti Depth : How to use Kitti Depth

date = "2011_09_26"
idsOfDrives = [
    "0001",  # sample from training subset
    "0002",  # sample from validation subset
]
custom_drives = {date: idsOfDrives}
kitti_ds = KittiDepth(
    subset="all",
    return_depth=True,
    custom_drives=custom_drives,
)

for f, frames in enumerate(kitti_ds.train_loader(batch_size=2)):
    frames = Frame.batch_list(frames)

Kitti Semantic : The semantic class

dataset = KittiSemanticDataset()
obj = dataset.getitem(0)
obj.get_view().render()

How to use the remaining task of the dataset : Dataset's class list : KittiStereoFlow2012, KittiStereoFlowSFlow2015, KittiOdometryDataset, KittiObjectDataset, KittiTrackingDataset, KittiRoadDataset

dataset = DATASET_CLASS(right_frame=False)
obj = dataset.getitem(0)
obj["left"].get_view().render()

Scene Flow: dimensions : Error with shape of occlusion mask.

by @Ardorax in #213

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• Fix bug : Fix calibrator import issue. All TensorRT and prod package are now optional.

by @thibo73800 in #215

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Support for the kumler_bauer projection in coords2rtheta

• New feature : Add support for the kumler_bauer projection in coords2rtheta

coords2rtheta(..., distortion=(0.25, 0.45), projection="kumler_bauer")

coords2rtheta(..., distortion=0.25, projection="equidistant") # API doesn't change for other projections

by @tflahaul in #227

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• New feature : Add WoodScape dataset.

WooodScapeDataset.

from alodatset import WooodScapeDataset

woodscape =  WooodScapeDataset(
        labels=[],
        cameras=[],
        fragment=1.,
        )
frame = woodscape[222]
frame.get_view().render()

WooodScapeSplitDataset : WoodScape dataset with train and validation fractions.

from alodatset import WoodScapeSplitDataset, Split

woodscapeTrain =  WoodScapeSplitDataset(split=Split.TRAIN)
frame = woodscapeTrain[222]
frame.get_view().render()

by @Data-Iab in #226

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• Fix bug: kumler-bauer projection support for aloscene.Depth

• as_planar, as_euclidean: Assert error because the missing of "kumler_bauer" in verification condition.
• as_points3d: missing of the calculation for distorted focal length for kumler_bauer.

by @anhtu293 in #235

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• New feature : Better handle distortion coef for equidistant projection: both float and list are accepted.

# Python code snippet showing how to use it.
import torch
from aloscene import Depth

x = torch.rand(size=(1, 1, 20, 20))
depth1 = Depth(x, projection="equidistant", distortion=[0.5])
depth2 = Depth(x, projection="equidistant", distortion=0.5)

by @anhtu293 in #238

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• New feature : 3 different implementations of focus blur augmentation

from alodataset.transforms import RandomFocusBlur, RandomFocusBlurV2, RandomFocusBlurV3

import aloscene
import torch

frame = aloscene.Frame(torch.rand((3, 300, 300)))
blured_frame1 = RandomFocusBlur()(frame)
blured_frame2 = RandomFocusBlurV2()(frame)
blured_frame3 = RandomFocusBlurV3()(frame)

• New feature : Motion blur augmentation from optical flow

## Motion blur from RAFT-flow
from alonet.raft.raft import RAFT

flow_model = RAFT(weights="raft-things")
flow_model = model.eval()

frame_t0_t1 = aloscene.Frame(torch.ones((2, 3, 300, 300)), names=tuple("TCHW"))
frame_t0 = frame_t0_t1[0]
frame_t1 = frame_t0_t1[1]

blured_t1 = RandomFlowMotionBlur(flow_model=flow_model)(frame_t1, p_frame=frame_t0)
blured_t1.get_view().render()

## Motion blur from ground truth optical flow
flow = aloscene.Flow(torch.ones((2, 300, 300)))
blured_t1 = RandomFlowMotionBlur()(frame_t1, flow=flow)
blured_t1.get_view().render()

• New feature : Random corner masking augmentation

from alodataset.transforms import RandomCornersMask
import aloscene
import torch

frame = aloscene.Frame(torch.ones((3, 300, 300)))
randomly_masked_frame = RandomCornersMask()(frame)

• Fix bug : CameraIntrinsic initialization with a shape of 4x4 is now possible using __init__.

by @Data-Iab in #314

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• Fix bug : Fix detr exportation to onnx & trt

by @Data-Iab in #315

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• New feature : Added title to frames displayed with get_view()

Added a "title" argument to the get_view() method to be able to directly input a title for your display.
frames.get_view(title="test").render()

by @Dee61298 in #310

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• Typing by @Ardorax in #212
• Warning gcc/g++ version for cuda ops by @Aurelien-VB in #230
• fix euclidean depth when as_point3d, better handle points behind camera by @anhtu293 in #311
• Delete github actions by @Ardorax in #313

Full Changelog: v0.3.0...v0.4.0