def __getitem__(self, index):
# index = 4
# print("get_item_index: ", index)
info = copy.deepcopy(self.kitti_infos[index])
# print(1111111111111)
sample_idx = info['point_cloud']['lidar_idx']
points = self.get_lidar(sample_idx)
if not cfg.DATA_CONFIG.TS_DATA:
calib = self.get_calib(sample_idx)
img_shape = info['image']['image_shape']
if cfg.DATA_CONFIG.FOV_POINTS_ONLY:
pts_rect = calib.lidar_to_rect(points[:, 0:3])
fov_flag = self.get_fov_flag(pts_rect, img_shape, calib)
points = points[fov_flag]
if not cfg.DATA_CONFIG.TS_DATA:
input_dict = {
'points': points,
'sample_idx': sample_idx,
'calib': calib,
}
else:
input_dict = {
'points': points,
'sample_idx': sample_idx,
}
if 'annos' in info:
annos = info['annos']
if not cfg.DATA_CONFIG.TS_DATA:
annos = common_utils.drop_info_with_name(annos, name='DontCare')
else:
annos = common_utils.drop_info_with_name(annos, name='0')
loc, dims, rots = annos['location'], annos['dimensions'], annos['rotation_y']
gt_names = annos['name']
bbox = annos['bbox']
gt_boxes = np.concatenate([loc, dims, rots[..., np.newaxis]], axis=1).astype(np.float32)
if 'gt_boxes_lidar' in annos:
gt_boxes_lidar = annos['gt_boxes_lidar']
else:
gt_boxes_lidar = box_utils.boxes3d_camera_to_lidar(gt_boxes, calib)
input_dict.update({
'gt_boxes': gt_boxes,
'gt_names': gt_names,
'gt_box2d': bbox,
'gt_boxes_lidar': gt_boxes_lidar
})
example = self.prepare_data(input_dict=input_dict, has_label='annos' in info)
example['sample_idx'] = sample_idx
if not cfg.DATA_CONFIG.TS_DATA:
example['image_shape'] = img_shape
return example
def __getitem__(self, index):
# index = 4
if self._merge_all_iters_to_one_epoch:
index = index % len(self.kitti_infos)
info = copy.deepcopy(self.kitti_infos[index])
sample_idx = info['point_cloud']['lidar_idx']
points = self.get_lidar(sample_idx)
if self.raw: #use raw data contains ground points
raw_points_fov = None
else:
raw_points_fov = self.get_lidar_raw(sample_idx)
calib = self.get_calib(sample_idx)
#kdtree = self.get_kdtree(sample_idx)
img_shape = info['image']['image_shape']
if self.dataset_cfg.FOV_POINTS_ONLY:
pts_rect = calib.lidar_to_rect(points[:, 0:3])
fov_flag = self.get_fov_flag(pts_rect, img_shape, calib)
points = points[fov_flag]
input_dict = {
'points': points,
'frame_id': sample_idx,
'calib': calib,
'raw_points': raw_points_fov
#'kd_tree': kdtree
}
if 'annos' in info:
annos = info['annos']
annos = common_utils.drop_info_with_name(annos, name='DontCare')
loc, dims, rots = annos['location'], annos['dimensions'], annos[
'rotation_y']
gt_names = annos['name']
gt_boxes_camera = np.concatenate(
[loc, dims, rots[..., np.newaxis]], axis=1).astype(np.float32)
gt_boxes_lidar = box_utils.boxes3d_kitti_camera_to_lidar(
gt_boxes_camera, calib)
input_dict.update({
'gt_names': gt_names,
'gt_boxes': gt_boxes_lidar
})
road_plane = self.get_road_plane(sample_idx)
if road_plane is not None:
input_dict['road_plane'] = road_plane
data_dict = self.prepare_data(data_dict=input_dict)
data_dict['image_shape'] = img_shape
data_dict['raw_points'] = raw_points_fov
data_dict['calib'] = calib
return data_dict
def __getitem__(self, index):
# # for debug purpose, uncomment this to fix the frame
# index = 0
# index = 300
# for index, info in enumerate(self.kitti_infos):
# if info['point_cloud']['lidar_idx'] == '100001284':
# break
# print(self.kitti_infos[index]['point_cloud']['lidar_idx'])
info = copy.deepcopy(self.kitti_infos[index])
sample_idx = info['point_cloud']['lidar_idx']
if cfg.TAG_PTS_WITH_RGB:
points = self.get_colored_lidar(sample_idx)
else:
points = self.get_lidar(sample_idx)
calib = self.get_calib(sample_idx)
img_shape = info['image']['image_shape']
if cfg.DATA_CONFIG.FOV_POINTS_ONLY:
pts_rect = calib.lidar_to_rect(points[:, 0:3])
fov_flag = self.get_fov_flag(pts_rect, img_shape, calib)
points = points[fov_flag]
input_dict = {
'points': points,
'sample_idx': sample_idx,
'calib': calib,
}
if 'annos' in info:
annos = info['annos']
annos = common_utils.drop_info_with_name(annos, name='DontCare')
loc, dims, rots = annos['location'], annos['dimensions'], annos[
'rotation_y']
gt_names = annos['name']
bbox = annos['bbox']
gt_boxes = np.concatenate([loc, dims, rots[..., np.newaxis]],
axis=1).astype(np.float32)
if 'gt_boxes_lidar' in annos:
gt_boxes_lidar = annos['gt_boxes_lidar']
else:
gt_boxes_lidar = box_utils.boxes3d_camera_to_lidar(
gt_boxes, calib)
input_dict.update({
'gt_boxes': gt_boxes,
'gt_names': gt_names,
'gt_box2d': bbox,
'gt_boxes_lidar': gt_boxes_lidar
})
# debuggin model; here we cheat by tagging if each point is in the object bbox
if cfg.TAG_PTS_IF_IN_GT_BBOXES:
points[:, 3] = 0
corners_lidar = box_utils.boxes3d_to_corners3d_lidar(
gt_boxes_lidar)
for k in range(len(gt_boxes_lidar)):
if gt_names[k] == 'Car':
flag = box_utils.in_hull(points[:, 0:3], corners_lidar[k])
points[flag, 3] = 1
input_dict['points'] = points
example = self.prepare_data(input_dict=input_dict,
has_label='annos' in info)
example['sample_idx'] = sample_idx
example['image_shape'] = img_shape
# add the bev maps if HD map is available (eg, argoverse)
if 'bev' in cfg.MODE:
bev = self.get_bev(sample_idx)
example['bev'] = bev
if cfg.INJECT_SEMANTICS or cfg.USE_PSEUDOLIDAR:
# todo: check the ordering of the imagenet mean and std. is it rgb or bgr?
img = self.get_image(sample_idx)
# preprocessing for the image to work for hrnet which works on cityscapes
# if on kitti (not on argoverse), some of the images don't have the same size,
# so we must scale them to the same size, then during voxelization, rescale
# to the right size for projecting lidar pts onto image plane to work
# if the height is not set, then we scale it according to the width
if cfg.INJECT_SEMANTICS_HEIGHT == 0:
img = image_resize(img, cfg.INJECT_SEMANTICS_WIDTH)
else:
img = cv2.resize(
img,
(cfg.INJECT_SEMANTICS_WIDTH, cfg.INJECT_SEMANTICS_HEIGHT),
interpolation=cv2.INTER_LINEAR)
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
img = input_transform(img, mean, std) # normalize wrt to imagenet
img = img.transpose(
(2, 0, 1)) # change dim ordering to c, h, w for torch
example['img'] = img
example['pseudolidar_image_shape'] = img.shape[-2:]
# if using pseudolidar from depth estimation, we need to crop out the sky and the road
if cfg.USE_PSEUDOLIDAR:
# first scale down, then crop out sky and roads
oh, ow = img.shape[-2:]
bottom_p, top_p = .15, cfg.DEPTH_MAP_TOP_MARGIN_PCT
top = oh * top_p
bottom = oh - (oh * bottom_p)
top = int(top)
bottom = int(bottom)
img_cropped = img[:, top:bottom, :]
example['img'] = img_cropped
return example
def __getitem__(self, index):
# index = 4
if self._merge_all_iters_to_one_epoch:
index = index % len(self.kitti_infos)
info = copy.deepcopy(self.kitti_infos[index])
sample_idx = info['point_cloud']['lidar_idx']
points = self.get_lidar(sample_idx)
raw_points_fov = self.get_lidar_raw(sample_idx)
calib = self.get_calib(sample_idx)
#kdtree = self.get_kdtree(sample_idx)
img_shape = info['image']['image_shape']
if self.dataset_cfg.FOV_POINTS_ONLY:
pts_rect = calib.lidar_to_rect(points[:, 0:3])
fov_flag = self.get_fov_flag(pts_rect, img_shape, calib)
points = points[fov_flag]
#pts_rect = calib.lidar_to_rect(raw_points[:, 0:3])
#fov_flag = self.get_fov_flag(pts_rect, img_shape, calib)
#raw_points_fov = raw_points_fov[fov_flag]
img_path = '/media/ddd/data2/3d_MOTS_Ex./Code/OpenPCDet/data/kitti/training/image_2/' + info['image']['image_idx'] + '.png'
img = cv2.imread(img_path)
image = np.float32(img)
image *= 1. / 255
img = cv2.cvtColor(image, cv2.COLOR_BGR2Lab)
'''
calib_result = calib.lidar_to_img(points) # [N,3] in lidar to [N,2] in img
points_color = self.painted_point_cloud(calib_result,img,points)
'''
input_dict = {
'points': points,
'frame_id': sample_idx,
'calib': calib,
'raw_points': raw_points_fov
#'kd_tree': kdtree
}
if 'annos' in info:
annos = info['annos']
annos = common_utils.drop_info_with_name(annos, name='DontCare')
loc, dims, rots = annos['location'], annos['dimensions'], annos['rotation_y']
gt_names = annos['name']
gt_boxes_camera = np.concatenate([loc, dims, rots[..., np.newaxis]], axis=1).astype(np.float32)
gt_boxes_lidar = box_utils.boxes3d_kitti_camera_to_lidar(gt_boxes_camera, calib)
input_dict.update({
'gt_names': gt_names,
'gt_boxes': gt_boxes_lidar
})
road_plane = self.get_road_plane(sample_idx)
if road_plane is not None:
input_dict['road_plane'] = road_plane
data_dict = self.prepare_data(data_dict=input_dict)
data_dict['image_shape'] = img_shape
data_dict['raw_points'] = raw_points_fov
#img_path = '/media/ddd/data2/3d_MOTS_Ex./Code/OpenPCDet/data/kitti/training/image_2/' + info['image']['image_idx'] + '.png'
#img = cv2.imread(img_path)
#image = np.float32(img)
#image *= 1. / 255
#img = cv2.cvtColor(image, cv2.COLOR_BGR2Lab)
data_dict['calib'] = calib
#data_dict['image'] = img
return data_dict
