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