Merge pull request #9 from OpenPerceptionX/dev

v0.0 of tool box

Author: ChonghaoSima

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+*.mp4
+# C extensions
+#*.so
+*.txt
+*.bin
+*.json
+npy/
+npys/
+# Distribution / packaging
+.Python
+arun_log/
+test/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+test/
+val/
+arun_log/
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
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+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+.cache/
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+# cython generated cpp
+data
+.vscode
+.idea
+*.tfrecord
+# custom
+*.pkl
+*.pkl.json
+*.log.json
+*.npy
+work_dirs/
+ckpts
+notebooks
+*~
+mmdet3d/.mim
+visual/
+# Pytorch
+*.pth
+*.tmp
+# demo
+*.obj
+*.ply
+*.zip
+.DS_Store

.gitmodules

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+[submodule "mmdetection3d"]
+	path = code/mmdetection3d
+	url = git@github.com:open-mmlab/mmdetection3d.git

README.md

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+from . import data_aug
+from .init_toolbox import init_toolbox_mmdet3d

bev_toolbox/__init__.py

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+from .transforms import RandomScaleImageMultiViewImage, RandomHorizontalFlipMultiViewImage
+
+__all__ = ['RandomScaleImageMultiViewImage', 'RandomHorizontalFlipMultiViewImage']

bev_toolbox/data_aug/__init__.py

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+######################################################################
+# Copyright (c) 2022 OpenPerceptionX. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+######################################################################
+
+######################################################################
+# This file includes concrete implementation for different data augmentation
+# methods in transforms.py.
+######################################################################
+
+import numpy as np
+import cv2
+from typing import List, Tuple
+
+#  Available interpolation modes (opencv)
+cv2_interp_codes = {
+    'nearest': cv2.INTER_NEAREST,
+    'bilinear': cv2.INTER_LINEAR,
+    'bicubic': cv2.INTER_CUBIC,
+    'area': cv2.INTER_AREA,
+    'lanczos': cv2.INTER_LANCZOS4
+}
+
+
+def scale_image_multiple_view(imgs: List[np.ndarray],
+                              cam_intrinsics: List[np.ndarray],
+                              cam_extrinsics: List[np.ndarray],
+                              lidar2img: List[np.ndarray],
+                              rand_scale: float,
+                              interpolation='bilinear') -> Tuple[List[np.ndarray], List[np.ndarray], List[np.ndarray]]:
+    """Resize the multiple-view images with the same scale selected randomly.
+    Notably used in :class:`.transforms.RandomScaleImageMultiViewImage_naive
+    Args:
+        imgs (list of numpy.array): Multiple-view images to be resized. len(img) is the number of cameras.
+                    img shape: [H, W, 3].
+        cam_intrinsics (list of numpy.array): Intrinsic parameters of different cameras. Transformations from camera 
+                    to image. len(cam_intrinsics) is the number of camera. For each camera, shape is 4 * 4.
+        cam_extrinsics (list of numpy.array): Extrinsic parameters of different cameras. Transformations from
+                lidar to cameras. len(cam_extrinsics) is the number of camera. For each camera, shape is 4 * 4.
+        lidar2img (list of numpy.array): Transformations from lidar to images. len(lidar2img) is the number
+                of camera. For each camera, shape is 4 * 4.
+        rand_scale (float): resize ratio
+        interpolation (string): mode for interpolation in opencv.
+    Returns:
+        imgs_new (list of numpy.array): Updated multiple-view images
+        cam_intrinsics_new (list of numpy.array): Updated intrinsic parameters of different cameras.
+        lidar2img_new (list of numpy.array): Updated Transformations from lidar to images.
+    """
+
+    y_size = [int(img.shape[0] * rand_scale) for img in imgs]
+    x_size = [int(img.shape[1] * rand_scale) for img in imgs]
+
+    scale_factor = np.eye(4)
+    scale_factor[0, 0] *= rand_scale
+    scale_factor[1, 1] *= rand_scale
+    imgs_new = [
+        cv2.resize(img, (x_size[idx], y_size[idx]), interpolation=cv2_interp_codes[interpolation])
+        for idx, img in enumerate(imgs)
+    ]
+    cam_intrinsics_new = [scale_factor @ cam_intrinsic for cam_intrinsic in cam_intrinsics]
+    lidar2img_new = [scale_factor @ l2i for l2i in lidar2img]
+
+    return imgs_new, cam_intrinsics_new, lidar2img_new
+
+
+def horizontal_flip_image_multiview(imgs: List[np.ndarray]) -> List[np.ndarray]:
+    """Flip every image horizontally.
+    Args:
+        imgs (list of numpy.array): Multiple-view images to be resized. len(img) is the number of cameras.
+                    img shape: [H, W, 3].
+    Returns:
+        imgs_new (list of numpy.array): Flippd multiple-view images
+    """
+    imgs_new = [np.flip(img, axis=1) for img in imgs]
+    return imgs_new
+
+
+def vertical_flip_image_multiview(imgs: List[np.ndarray]) -> List[np.ndarray]:
+    """Flip every image vertically.
+    Args:
+        imgs (list of numpy.array): Multiple-view images to be resized. len(img) is the number of cameras.
+                    img shape: [H, W, 3].
+    Returns:
+        imgs_new (list of numpy.array): Flippd multiple-view images
+    """
+    imgs_new = [np.flip(img, axis=0) for img in imgs]
+    return imgs_new
+
+
+def horizontal_flip_bbox(bboxes_3d: np.ndarray, dataset: str) -> np.ndarray:
+    """Flip bounding boxes horizontally.
+    Args:
+        bboxes_3d (np.ndarray): bounding boxes of shape [N * 7], N is the number of objects.
+        dataset (string): 'waymo' coordinate system or 'nuscenes' coordinate system.
+    Returns:
+        bboxes_3d (numpy.array): Flippd bounding boxes.
+    """
+    if dataset == 'nuScenes':
+        bboxes_3d[:, 0::7] = -bboxes_3d[:, 0::7]
+        bboxes_3d[:, 6] = -bboxes_3d[:, 6]  # + np.pi
+    elif dataset == 'waymo':
+        bboxes_3d[:, 1::7] = -bboxes_3d[:, 1::7]
+        bboxes_3d[:, 6] = -bboxes_3d[:, 6] + np.pi
+    return bboxes_3d
+
+
+def horizontal_flip_cam_params(img_shape: np.ndarray, cam_intrinsics: List[np.ndarray],
+                                cam_extrinsics: List[np.ndarray], lidar2imgs: List[np.ndarray],
+                                dataset: str) -> Tuple[List[np.ndarray], List[np.ndarray], List[np.ndarray]]:
+    """Flip camera parameters horizontally.
+    Args:
+        img_shape (numpy.array) of shape [3].
+        cam_intrinsics (list of numpy.array): Intrinsic parameters of different cameras. Transformations from camera 
+                    to image. len(cam_intrinsics) is the number of camera. For each camera, shape is 4 * 4.
+        cam_extrinsics (list of numpy.array): Extrinsic parameters of different cameras. Transformations from
+                lidar to cameras. len(cam_extrinsics) is the number of camera. For each camera, shape is 4 * 4.
+        lidar2img (list of numpy.array): Transformations from lidar to images. len(lidar2img) is the number
+                of camera. For each camera, shape is 4 * 4.
+        dataset (string): Specify 'waymo' coordinate system or 'nuscenes' coordinate system.
+    Returns:
+        cam_intrinsics (list of numpy.array): Updated intrinsic parameters of different cameras.
+        cam_extrinsics (list of numpy.array): Updated extrinsic parameters of different cameras.
+        lidar2img (list of numpy.array): Updated Transformations from lidar to images.
+    """
+    flip_factor = np.eye(4)
+    lidar2imgs = []
+
+    w = img_shape[1]
+    if dataset == 'nuScenes':
+        flip_factor[0, 0] = -1
+        cam_extrinsics = [l2c @ flip_factor for l2c in cam_extrinsics]
+        for cam_intrinsic, l2c in zip(cam_intrinsics, cam_extrinsics):
+            cam_intrinsic[0, 0] = -cam_intrinsic[0, 0]
+            cam_intrinsic[0, 2] = w - cam_intrinsic[0, 2]
+            lidar2imgs.append(cam_intrinsic @ l2c)
+    elif dataset == 'waymo':
+        flip_factor[1, 1] = -1
+        cam_extrinsics = [l2c @ flip_factor for l2c in cam_extrinsics]
+        for cam_intrinsic, l2c in zip(cam_intrinsics, cam_extrinsics):
+            cam_intrinsic[0, 0] = -cam_intrinsic[0, 0]
+            cam_intrinsic[0, 2] = w - cam_intrinsic[0, 2]
+            lidar2imgs.append(cam_intrinsic @ l2c)
+    else:
+        assert False
+
+    return cam_intrinsics, cam_extrinsics, lidar2imgs
+
+
+def horizontal_flip_canbus(canbus: np.ndarray, dataset: str) -> np.ndarray:
+    """Flip can bus horizontally.
+    Args:
+        canbus (numpy.ndarray) of shape [18,]
+        dataset (string): 'waymo' or 'nuscenes'
+    Returns:
+        canbus_new (list of numpy.array): Flipped canbus.
+    """
+    if dataset == 'nuScenes':
+        # results['canbus'][1] = -results['canbus'][1]  # flip location
+        # results['canbus'][-2] = -results['canbus'][-2]  # flip direction
+        canbus[-1] = -canbus[-1]  # flip direction
+    elif dataset == 'waymo':
+        # results['canbus'][1] = -results['canbus'][-1]  # flip location
+        # results['canbus'][-2] = -results['canbus'][-2]  # flip direction
+        canbus[-1] = -canbus[-1]  # flip direction
+    else:
+        raise NotImplementedError((f"Not support {dataset} dataset"))
+    return canbus