485 lines
20 KiB
Python
485 lines
20 KiB
Python
import copy
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import pickle
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import numpy as np
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from skimage import io
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from . import kitti_utils
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from ...ops.roiaware_pool3d import roiaware_pool3d_utils
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from ...utils import box_utils, calibration_kitti, common_utils, object3d_kitti
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from ..dataset import DatasetTemplate
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class KittiDataset(DatasetTemplate):
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def __init__(self, dataset_cfg, class_names, training=True, root_path=None, logger=None):
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"""
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Args:
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root_path:
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dataset_cfg:
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class_names:
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training:
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logger:
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"""
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super().__init__(
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dataset_cfg=dataset_cfg, class_names=class_names, training=training, root_path=root_path, logger=logger
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)
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self.split = self.dataset_cfg.DATA_SPLIT[self.mode]
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self.root_split_path = self.root_path / ('training' if self.split != 'test' else 'testing')
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split_dir = self.root_path / 'ImageSets' / (self.split + '.txt')
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self.sample_id_list = [x.strip() for x in open(split_dir).readlines()] if split_dir.exists() else None
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self.kitti_infos = []
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self.include_kitti_data(self.mode)
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def include_kitti_data(self, mode):
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if self.logger is not None:
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self.logger.info('Loading KITTI dataset')
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kitti_infos = []
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for info_path in self.dataset_cfg.INFO_PATH[mode]:
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info_path = self.root_path / info_path
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if not info_path.exists():
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continue
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with open(info_path, 'rb') as f:
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infos = pickle.load(f)
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kitti_infos.extend(infos)
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self.kitti_infos.extend(kitti_infos)
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if self.logger is not None:
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self.logger.info('Total samples for KITTI dataset: %d' % (len(kitti_infos)))
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def set_split(self, split):
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super().__init__(
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dataset_cfg=self.dataset_cfg, class_names=self.class_names, training=self.training, root_path=self.root_path, logger=self.logger
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)
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self.split = split
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self.root_split_path = self.root_path / ('training' if self.split != 'test' else 'testing')
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split_dir = self.root_path / 'ImageSets' / (self.split + '.txt')
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self.sample_id_list = [x.strip() for x in open(split_dir).readlines()] if split_dir.exists() else None
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def get_lidar(self, idx):
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lidar_file = self.root_split_path / 'velodyne' / ('%s.bin' % idx)
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assert lidar_file.exists()
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return np.fromfile(str(lidar_file), dtype=np.float32).reshape(-1, 4)
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def get_image(self, idx):
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"""
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Loads image for a sample
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Args:
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idx: int, Sample index
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Returns:
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image: (H, W, 3), RGB Image
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"""
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img_file = self.root_split_path / 'image_2' / ('%s.png' % idx)
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assert img_file.exists()
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image = io.imread(img_file)
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image = image.astype(np.float32)
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image /= 255.0
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return image
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def get_image_shape(self, idx):
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img_file = self.root_split_path / 'image_2' / ('%s.png' % idx)
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assert img_file.exists()
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return np.array(io.imread(img_file).shape[:2], dtype=np.int32)
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def get_label(self, idx):
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label_file = self.root_split_path / 'label_2' / ('%s.txt' % idx)
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assert label_file.exists()
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return object3d_kitti.get_objects_from_label(label_file)
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def get_depth_map(self, idx):
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"""
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Loads depth map for a sample
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Args:
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idx: str, Sample index
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Returns:
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depth: (H, W), Depth map
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"""
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depth_file = self.root_split_path / 'depth_2' / ('%s.png' % idx)
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assert depth_file.exists()
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depth = io.imread(depth_file)
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depth = depth.astype(np.float32)
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depth /= 256.0
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return depth
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def get_calib(self, idx):
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calib_file = self.root_split_path / 'calib' / ('%s.txt' % idx)
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assert calib_file.exists()
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return calibration_kitti.Calibration(calib_file)
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def get_road_plane(self, idx):
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plane_file = self.root_split_path / 'planes' / ('%s.txt' % idx)
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if not plane_file.exists():
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return None
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with open(plane_file, 'r') as f:
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lines = f.readlines()
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lines = [float(i) for i in lines[3].split()]
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plane = np.asarray(lines)
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# Ensure normal is always facing up, this is in the rectified camera coordinate
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if plane[1] > 0:
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plane = -plane
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norm = np.linalg.norm(plane[0:3])
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plane = plane / norm
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return plane
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@staticmethod
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def get_fov_flag(pts_rect, img_shape, calib):
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"""
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Args:
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pts_rect:
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img_shape:
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calib:
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Returns:
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"""
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pts_img, pts_rect_depth = calib.rect_to_img(pts_rect)
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val_flag_1 = np.logical_and(pts_img[:, 0] >= 0, pts_img[:, 0] < img_shape[1])
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val_flag_2 = np.logical_and(pts_img[:, 1] >= 0, pts_img[:, 1] < img_shape[0])
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val_flag_merge = np.logical_and(val_flag_1, val_flag_2)
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pts_valid_flag = np.logical_and(val_flag_merge, pts_rect_depth >= 0)
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return pts_valid_flag
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def get_infos(self, num_workers=4, has_label=True, count_inside_pts=True, sample_id_list=None):
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import concurrent.futures as futures
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def process_single_scene(sample_idx):
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print('%s sample_idx: %s' % (self.split, sample_idx))
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info = {}
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pc_info = {'num_features': 4, 'lidar_idx': sample_idx}
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info['point_cloud'] = pc_info
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image_info = {'image_idx': sample_idx, 'image_shape': self.get_image_shape(sample_idx)}
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info['image'] = image_info
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calib = self.get_calib(sample_idx)
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P2 = np.concatenate([calib.P2, np.array([[0., 0., 0., 1.]])], axis=0)
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R0_4x4 = np.zeros([4, 4], dtype=calib.R0.dtype)
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R0_4x4[3, 3] = 1.
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R0_4x4[:3, :3] = calib.R0
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V2C_4x4 = np.concatenate([calib.V2C, np.array([[0., 0., 0., 1.]])], axis=0)
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calib_info = {'P2': P2, 'R0_rect': R0_4x4, 'Tr_velo_to_cam': V2C_4x4}
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info['calib'] = calib_info
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if has_label:
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obj_list = self.get_label(sample_idx)
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annotations = {}
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annotations['name'] = np.array([obj.cls_type for obj in obj_list])
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annotations['truncated'] = np.array([obj.truncation for obj in obj_list])
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annotations['occluded'] = np.array([obj.occlusion for obj in obj_list])
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annotations['alpha'] = np.array([obj.alpha for obj in obj_list])
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annotations['bbox'] = np.concatenate([obj.box2d.reshape(1, 4) for obj in obj_list], axis=0)
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annotations['dimensions'] = np.array([[obj.l, obj.h, obj.w] for obj in obj_list]) # lhw(camera) format
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annotations['location'] = np.concatenate([obj.loc.reshape(1, 3) for obj in obj_list], axis=0)
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annotations['rotation_y'] = np.array([obj.ry for obj in obj_list])
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annotations['score'] = np.array([obj.score for obj in obj_list])
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annotations['difficulty'] = np.array([obj.level for obj in obj_list], np.int32)
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num_objects = len([obj.cls_type for obj in obj_list if obj.cls_type != 'DontCare'])
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num_gt = len(annotations['name'])
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index = list(range(num_objects)) + [-1] * (num_gt - num_objects)
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annotations['index'] = np.array(index, dtype=np.int32)
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loc = annotations['location'][:num_objects]
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dims = annotations['dimensions'][:num_objects]
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rots = annotations['rotation_y'][:num_objects]
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loc_lidar = calib.rect_to_lidar(loc)
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l, h, w = dims[:, 0:1], dims[:, 1:2], dims[:, 2:3]
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loc_lidar[:, 2] += h[:, 0] / 2
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gt_boxes_lidar = np.concatenate([loc_lidar, l, w, h, -(np.pi / 2 + rots[..., np.newaxis])], axis=1)
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annotations['gt_boxes_lidar'] = gt_boxes_lidar
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info['annos'] = annotations
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if count_inside_pts:
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points = self.get_lidar(sample_idx)
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calib = self.get_calib(sample_idx)
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pts_rect = calib.lidar_to_rect(points[:, 0:3])
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fov_flag = self.get_fov_flag(pts_rect, info['image']['image_shape'], calib)
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pts_fov = points[fov_flag]
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corners_lidar = box_utils.boxes_to_corners_3d(gt_boxes_lidar)
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num_points_in_gt = -np.ones(num_gt, dtype=np.int32)
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for k in range(num_objects):
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flag = box_utils.in_hull(pts_fov[:, 0:3], corners_lidar[k])
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num_points_in_gt[k] = flag.sum()
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annotations['num_points_in_gt'] = num_points_in_gt
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return info
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sample_id_list = sample_id_list if sample_id_list is not None else self.sample_id_list
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with futures.ThreadPoolExecutor(num_workers) as executor:
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infos = executor.map(process_single_scene, sample_id_list)
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return list(infos)
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def create_groundtruth_database(self, info_path=None, used_classes=None, split='train'):
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import torch
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database_save_path = Path(self.root_path) / ('gt_database' if split == 'train' else ('gt_database_%s' % split))
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db_info_save_path = Path(self.root_path) / ('kitti_dbinfos_%s.pkl' % split)
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database_save_path.mkdir(parents=True, exist_ok=True)
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all_db_infos = {}
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with open(info_path, 'rb') as f:
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infos = pickle.load(f)
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for k in range(len(infos)):
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print('gt_database sample: %d/%d' % (k + 1, len(infos)))
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info = infos[k]
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sample_idx = info['point_cloud']['lidar_idx']
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points = self.get_lidar(sample_idx)
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annos = info['annos']
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names = annos['name']
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difficulty = annos['difficulty']
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bbox = annos['bbox']
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gt_boxes = annos['gt_boxes_lidar']
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num_obj = gt_boxes.shape[0]
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point_indices = roiaware_pool3d_utils.points_in_boxes_cpu(
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torch.from_numpy(points[:, 0:3]), torch.from_numpy(gt_boxes)
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).numpy() # (nboxes, npoints)
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for i in range(num_obj):
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filename = '%s_%s_%d.bin' % (sample_idx, names[i], i)
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filepath = database_save_path / filename
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gt_points = points[point_indices[i] > 0]
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gt_points[:, :3] -= gt_boxes[i, :3]
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with open(filepath, 'w') as f:
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gt_points.tofile(f)
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if (used_classes is None) or names[i] in used_classes:
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db_path = str(filepath.relative_to(self.root_path)) # gt_database/xxxxx.bin
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db_info = {'name': names[i], 'path': db_path, 'image_idx': sample_idx, 'gt_idx': i,
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'box3d_lidar': gt_boxes[i], 'num_points_in_gt': gt_points.shape[0],
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'difficulty': difficulty[i], 'bbox': bbox[i], 'score': annos['score'][i]}
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if names[i] in all_db_infos:
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all_db_infos[names[i]].append(db_info)
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else:
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all_db_infos[names[i]] = [db_info]
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for k, v in all_db_infos.items():
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print('Database %s: %d' % (k, len(v)))
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with open(db_info_save_path, 'wb') as f:
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pickle.dump(all_db_infos, f)
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@staticmethod
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def generate_prediction_dicts(batch_dict, pred_dicts, class_names, output_path=None):
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"""
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Args:
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batch_dict:
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frame_id:
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pred_dicts: list of pred_dicts
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pred_boxes: (N, 7), Tensor
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pred_scores: (N), Tensor
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pred_labels: (N), Tensor
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class_names:
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output_path:
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Returns:
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"""
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def get_template_prediction(num_samples):
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ret_dict = {
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'name': np.zeros(num_samples), 'truncated': np.zeros(num_samples),
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'occluded': np.zeros(num_samples), 'alpha': np.zeros(num_samples),
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'bbox': np.zeros([num_samples, 4]), 'dimensions': np.zeros([num_samples, 3]),
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'location': np.zeros([num_samples, 3]), 'rotation_y': np.zeros(num_samples),
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'score': np.zeros(num_samples), 'boxes_lidar': np.zeros([num_samples, 7])
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}
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return ret_dict
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def generate_single_sample_dict(batch_index, box_dict):
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pred_scores = box_dict['pred_scores'].cpu().numpy()
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pred_boxes = box_dict['pred_boxes'].cpu().numpy()
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pred_labels = box_dict['pred_labels'].cpu().numpy()
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pred_dict = get_template_prediction(pred_scores.shape[0])
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if pred_scores.shape[0] == 0:
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return pred_dict
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calib = batch_dict['calib'][batch_index]
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image_shape = batch_dict['image_shape'][batch_index].cpu().numpy()
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pred_boxes_camera = box_utils.boxes3d_lidar_to_kitti_camera(pred_boxes, calib)
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pred_boxes_img = box_utils.boxes3d_kitti_camera_to_imageboxes(
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pred_boxes_camera, calib, image_shape=image_shape
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)
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pred_dict['name'] = np.array(class_names)[pred_labels - 1]
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pred_dict['alpha'] = -np.arctan2(-pred_boxes[:, 1], pred_boxes[:, 0]) + pred_boxes_camera[:, 6]
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pred_dict['bbox'] = pred_boxes_img
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pred_dict['dimensions'] = pred_boxes_camera[:, 3:6]
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pred_dict['location'] = pred_boxes_camera[:, 0:3]
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pred_dict['rotation_y'] = pred_boxes_camera[:, 6]
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pred_dict['score'] = pred_scores
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pred_dict['boxes_lidar'] = pred_boxes
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return pred_dict
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annos = []
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for index, box_dict in enumerate(pred_dicts):
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frame_id = batch_dict['frame_id'][index]
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single_pred_dict = generate_single_sample_dict(index, box_dict)
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single_pred_dict['frame_id'] = frame_id
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annos.append(single_pred_dict)
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if output_path is not None:
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cur_det_file = output_path / ('%s.txt' % frame_id)
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with open(cur_det_file, 'w') as f:
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bbox = single_pred_dict['bbox']
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loc = single_pred_dict['location']
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dims = single_pred_dict['dimensions'] # lhw -> hwl
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for idx in range(len(bbox)):
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print('%s -1 -1 %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f %.4f'
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% (single_pred_dict['name'][idx], single_pred_dict['alpha'][idx],
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bbox[idx][0], bbox[idx][1], bbox[idx][2], bbox[idx][3],
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dims[idx][1], dims[idx][2], dims[idx][0], loc[idx][0],
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loc[idx][1], loc[idx][2], single_pred_dict['rotation_y'][idx],
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single_pred_dict['score'][idx]), file=f)
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return annos
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def evaluation(self, det_annos, class_names, **kwargs):
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if 'annos' not in self.kitti_infos[0].keys():
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return None, {}
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from .kitti_object_eval_python import eval as kitti_eval
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eval_det_annos = copy.deepcopy(det_annos)
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eval_gt_annos = [copy.deepcopy(info['annos']) for info in self.kitti_infos]
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ap_result_str, ap_dict = kitti_eval.get_official_eval_result(eval_gt_annos, eval_det_annos, class_names)
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return ap_result_str, ap_dict
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def __len__(self):
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if self._merge_all_iters_to_one_epoch:
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return len(self.kitti_infos) * self.total_epochs
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return len(self.kitti_infos)
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def __getitem__(self, index):
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# index = 4
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if self._merge_all_iters_to_one_epoch:
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index = index % len(self.kitti_infos)
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info = copy.deepcopy(self.kitti_infos[index])
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sample_idx = info['point_cloud']['lidar_idx']
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img_shape = info['image']['image_shape']
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calib = self.get_calib(sample_idx)
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get_item_list = self.dataset_cfg.get('GET_ITEM_LIST', ['points'])
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input_dict = {
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'frame_id': sample_idx,
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'calib': calib,
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}
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if 'annos' in info:
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annos = info['annos']
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annos = common_utils.drop_info_with_name(annos, name='DontCare')
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loc, dims, rots = annos['location'], annos['dimensions'], annos['rotation_y']
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gt_names = annos['name']
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gt_boxes_camera = np.concatenate([loc, dims, rots[..., np.newaxis]], axis=1).astype(np.float32)
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gt_boxes_lidar = box_utils.boxes3d_kitti_camera_to_lidar(gt_boxes_camera, calib)
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input_dict.update({
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'gt_names': gt_names,
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'gt_boxes': gt_boxes_lidar
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})
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if "gt_boxes2d" in get_item_list:
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input_dict['gt_boxes2d'] = annos["bbox"]
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road_plane = self.get_road_plane(sample_idx)
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if road_plane is not None:
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input_dict['road_plane'] = road_plane
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if "points" in get_item_list:
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points = self.get_lidar(sample_idx)
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if self.dataset_cfg.FOV_POINTS_ONLY:
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pts_rect = calib.lidar_to_rect(points[:, 0:3])
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fov_flag = self.get_fov_flag(pts_rect, img_shape, calib)
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points = points[fov_flag]
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input_dict['points'] = points
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if "images" in get_item_list:
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input_dict['images'] = self.get_image(sample_idx)
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if "depth_maps" in get_item_list:
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input_dict['depth_maps'] = self.get_depth_map(sample_idx)
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if "calib_matricies" in get_item_list:
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input_dict["trans_lidar_to_cam"], input_dict["trans_cam_to_img"] = kitti_utils.calib_to_matricies(calib)
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input_dict['calib'] = calib
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data_dict = self.prepare_data(data_dict=input_dict)
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data_dict['image_shape'] = img_shape
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return data_dict
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|
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def create_kitti_infos(dataset_cfg, class_names, data_path, save_path, workers=4):
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dataset = KittiDataset(dataset_cfg=dataset_cfg, class_names=class_names, root_path=data_path, training=False)
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train_split, val_split = 'train', 'val'
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|
|
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train_filename = save_path / ('kitti_infos_%s.pkl' % train_split)
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val_filename = save_path / ('kitti_infos_%s.pkl' % val_split)
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trainval_filename = save_path / 'kitti_infos_trainval.pkl'
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test_filename = save_path / 'kitti_infos_test.pkl'
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|
|
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print('---------------Start to generate data infos---------------')
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|
|
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dataset.set_split(train_split)
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kitti_infos_train = dataset.get_infos(num_workers=workers, has_label=True, count_inside_pts=True)
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with open(train_filename, 'wb') as f:
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pickle.dump(kitti_infos_train, f)
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print('Kitti info train file is saved to %s' % train_filename)
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|
|
|
dataset.set_split(val_split)
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kitti_infos_val = dataset.get_infos(num_workers=workers, has_label=True, count_inside_pts=True)
|
|
with open(val_filename, 'wb') as f:
|
|
pickle.dump(kitti_infos_val, f)
|
|
print('Kitti info val file is saved to %s' % val_filename)
|
|
|
|
with open(trainval_filename, 'wb') as f:
|
|
pickle.dump(kitti_infos_train + kitti_infos_val, f)
|
|
print('Kitti info trainval file is saved to %s' % trainval_filename)
|
|
|
|
dataset.set_split('test')
|
|
kitti_infos_test = dataset.get_infos(num_workers=workers, has_label=False, count_inside_pts=False)
|
|
with open(test_filename, 'wb') as f:
|
|
pickle.dump(kitti_infos_test, f)
|
|
print('Kitti info test file is saved to %s' % test_filename)
|
|
|
|
print('---------------Start create groundtruth database for data augmentation---------------')
|
|
dataset.set_split(train_split)
|
|
dataset.create_groundtruth_database(train_filename, split=train_split)
|
|
|
|
print('---------------Data preparation Done---------------')
|
|
|
|
|
|
if __name__ == '__main__':
|
|
import sys
|
|
if sys.argv.__len__() > 1 and sys.argv[1] == 'create_kitti_infos':
|
|
import yaml
|
|
from pathlib import Path
|
|
from easydict import EasyDict
|
|
dataset_cfg = EasyDict(yaml.safe_load(open(sys.argv[2])))
|
|
ROOT_DIR = (Path(__file__).resolve().parent / '../../../').resolve()
|
|
create_kitti_infos(
|
|
dataset_cfg=dataset_cfg,
|
|
class_names=['Car', 'Pedestrian', 'Cyclist'],
|
|
data_path=ROOT_DIR / 'data' / 'kitti',
|
|
save_path=ROOT_DIR / 'data' / 'kitti'
|
|
)
|