def _calculate_num_points_in_gt(data_path, infos, relative_path, remove_outside=True, num_features=4): for info in infos: if relative_path: v_path = str(pathlib.Path(data_path) / info["velodyne_path"]) else: v_path = info["velodyne_path"] points_v = np.fromfile( v_path, dtype=np.float32, count=-1).reshape([-1, num_features]) rect = info['calib/R0_rect'] Trv2c = info['calib/Tr_velo_to_cam'] P2 = info['calib/P2'] if remove_outside: points_v = box_np_ops.remove_outside_points(points_v, rect, Trv2c, P2, info["img_shape"]) # points_v = points_v[points_v[:, 0] > 0] annos = info['annos'] num_obj = len([n for n in annos['name'] if n != 'DontCare']) # annos = kitti.filter_kitti_anno(annos, ['DontCare']) dims = annos['dimensions'][:num_obj] loc = annos['location'][:num_obj] rots = annos['rotation_y'][:num_obj] gt_boxes_camera = np.concatenate( [loc, dims, rots[..., np.newaxis]], axis=1) gt_boxes_lidar = box_np_ops.box_camera_to_lidar( gt_boxes_camera, rect, Trv2c) indices = box_np_ops.points_in_rbbox(points_v[:, :3], gt_boxes_lidar) num_points_in_gt = indices.sum(0) num_ignored = len(annos['dimensions']) - num_obj num_points_in_gt = np.concatenate( [num_points_in_gt, -np.ones([num_ignored])]) annos["num_points_in_gt"] = num_points_in_gt.astype(np.int32)
def inference_by_idx(): global BACKEND instance = request.json response = {"status": "normal"} if BACKEND.root_path is None: return error_response("root path is not set") if BACKEND.kitti_infos is None: return error_response("kitti info is not loaded") if BACKEND.inference_ctx is None: return error_response("inference_ctx is not loaded") image_idx = instance["image_idx"] idx = BACKEND.image_idxes.index(image_idx) kitti_info = BACKEND.kitti_infos[idx] v_path = str(Path(BACKEND.root_path) / kitti_info['velodyne_path']) num_features = 4 points = np.fromfile( str(v_path), dtype=np.float32, count=-1).reshape([-1, num_features]) rect = kitti_info['calib/R0_rect'] P2 = kitti_info['calib/P2'] Trv2c = kitti_info['calib/Tr_velo_to_cam'] if 'img_shape' in kitti_info: image_shape = kitti_info['img_shape'] points = box_np_ops.remove_outside_points( points, rect, Trv2c, P2, image_shape) print(points.shape[0]) t = time.time() inputs = BACKEND.inference_ctx.get_inference_input_dict( kitti_info, points) print("input preparation time:", time.time() - t) t = time.time() with BACKEND.inference_ctx.ctx(): dt_annos = BACKEND.inference_ctx.inference(inputs)[0] print("detection time:", time.time() - t) dims = dt_annos['dimensions'] num_obj = dims.shape[0] loc = dt_annos['location'] rots = dt_annos['rotation_y'] labels = dt_annos['name'] dt_boxes_camera = np.concatenate( [loc, dims, rots[..., np.newaxis]], axis=1) dt_boxes = box_np_ops.box_camera_to_lidar( dt_boxes_camera, rect, Trv2c) box_np_ops.change_box3d_center_(dt_boxes, src=[0.5, 0.5, 0], dst=[0.5, 0.5, 0.5]) locs = dt_boxes[:, :3] dims = dt_boxes[:, 3:6] rots = np.concatenate([np.zeros([num_obj, 2], dtype=np.float32), -dt_boxes[:, 6:7]], axis=1) response["dt_locs"] = locs.tolist() response["dt_dims"] = dims.tolist() response["dt_rots"] = rots.tolist() response["dt_labels"] = labels.tolist() response["dt_scores"] = dt_annos["score"].tolist() response = jsonify(results=[response]) response.headers['Access-Control-Allow-Headers'] = '*' return response
def kitti_cam_to_lidar(self, kitti_anno): rect = self.calib_info['calib/R0_rect'] Tr_velo_to_cam = self.calib_info['calib/Tr_velo_to_cam'] dims = kitti_anno['dimensions'] loc = kitti_anno['location'] rots = kitti_anno['rotation_y'] boxes_camera = np.concatenate([loc, dims, rots[..., np.newaxis]], axis=1) boxes_lidar = box_np_ops.box_camera_to_lidar(boxes_camera, rect, Tr_velo_to_cam) return boxes_lidar
def kitti_anno_to_corners(info, annos=None): rect = info['calib/R0_rect'] P2 = info['calib/P2'] Tr_velo_to_cam = info['calib/Tr_velo_to_cam'] if annos is None: annos = info['annos'] dims = annos['dimensions'] loc = annos['location'] rots = annos['rotation_y'] scores = None if 'score' in annos: scores = annos['score'] boxes_camera = np.concatenate([loc, dims, rots[..., np.newaxis]], axis=1) boxes_lidar = box_np_ops.box_camera_to_lidar(boxes_camera, rect, Tr_velo_to_cam) boxes_corners = box_np_ops.center_to_corner_box3d(boxes_lidar[:, :3], boxes_lidar[:, 3:6], boxes_lidar[:, 6], origin=[0.5, 0.5, 0], axis=2) return boxes_corners, scores, boxes_lidar
def __getitem__(self, idx): """ you need to create a input dict in this function for network inference. format: { anchors voxels num_points coordinates ground_truth: { gt_boxes gt_names [optional]difficulty [optional]group_ids } [optional]anchors_mask, slow in SECOND v1.5, don't use this. [optional]metadata, in kitti, image index is saved in metadata } """ info = self._kitti_infos[idx] kitti.convert_to_kitti_info_version2(info) pc_info = info["point_cloud"] if "points" not in pc_info: velo_path = pathlib.Path(pc_info['velodyne_path']) if not velo_path.is_absolute(): velo_path = pathlib.Path( self._root_path) / pc_info['velodyne_path'] velo_reduced_path = velo_path.parent.parent / ( velo_path.parent.stem + '_reduced') / velo_path.name if velo_reduced_path.exists(): velo_path = velo_reduced_path points = np.fromfile(str(velo_path), dtype=np.float32, count=-1).reshape( [-1, self._num_point_features]) input_dict = { 'points': points, } if "image" in info: input_dict["image"] = info["image"] if "calib" in info: calib = info["calib"] calib_dict = { 'rect': calib['R0_rect'], 'Trv2c': calib['Tr_velo_to_cam'], 'P2': calib['P2'], } input_dict["calib"] = calib_dict if 'annos' in info: annos = info['annos'] annos_dict = {} # we need other objects to avoid collision when sample annos = kitti.remove_dontcare(annos) loc = annos["location"] dims = annos["dimensions"] rots = annos["rotation_y"] gt_names = annos["name"] gt_boxes = np.concatenate([loc, dims, rots[..., np.newaxis]], axis=1).astype(np.float32) if "calib" in info: calib = info["calib"] gt_boxes = box_np_ops.box_camera_to_lidar( gt_boxes, calib["R0_rect"], calib["Tr_velo_to_cam"]) # only center format is allowed. so we need to convert # kitti [0.5, 0.5, 0] center to [0.5, 0.5, 0.5] box_np_ops.change_box3d_center_(gt_boxes, [0.5, 0.5, 0], [0.5, 0.5, 0.5]) gt_dict = { 'gt_boxes': gt_boxes, 'gt_names': gt_names, } if 'difficulty' in annos: gt_dict['difficulty'] = annos["difficulty"] if 'group_ids' in annos: gt_dict['group_ids'] = annos["group_ids"] input_dict["ground_truth"] = gt_dict example = self._prep_func(input_dict=input_dict) example["metadata"] = {} if "image" in info: example["metadata"]["image"] = input_dict["image"] if "anchors_mask" in example: example["anchors_mask"] = example["anchors_mask"].astype(np.uint8) return example
def create_groundtruth_database(data_path, info_path=None, used_classes=None, database_save_path=None, db_info_save_path=None, relative_path=True, lidar_only=False, bev_only=False, coors_range=None): RGB_embedding = True root_path = pathlib.Path(data_path) if info_path is None: info_path = root_path / 'kitti_infos_train.pkl' if database_save_path is None: database_save_path = root_path / 'gt_database' else: database_save_path = pathlib.Path(database_save_path) if db_info_save_path is None: if RGB_embedding: db_info_save_path = root_path / "kitti_dbinfos_train_RGB.pkl" else: db_info_save_path = root_path / "kitti_dbinfos_train.pkl" database_save_path.mkdir(parents=True, exist_ok=True) with open(info_path, 'rb') as f: kitti_infos = pickle.load(f) all_db_infos = {} if used_classes is None: used_classes = list(kitti.get_classes()) used_classes.pop(used_classes.index('DontCare')) for name in used_classes: all_db_infos[name] = [] group_counter = 0 for info in prog_bar(kitti_infos): velodyne_path = info['velodyne_path'] if relative_path: # velodyne_path = str(root_path / velodyne_path) + "_reduced" velodyne_path = str(root_path / velodyne_path) num_features = 4 if 'pointcloud_num_features' in info: num_features = info['pointcloud_num_features'] points = np.fromfile(velodyne_path, dtype=np.float32, count=-1).reshape([-1, num_features]) image_idx = info["image_idx"] rect = info['calib/R0_rect'] P2 = info['calib/P2'] Trv2c = info['calib/Tr_velo_to_cam'] if not lidar_only: points = box_np_ops.remove_outside_points(points, rect, Trv2c, P2, info["img_shape"]) annos = info["annos"] names = annos["name"] bboxes = annos["bbox"] difficulty = annos["difficulty"] gt_idxes = annos["index"] num_obj = np.sum(annos["index"] >= 0) rbbox_cam = kitti.anno_to_rbboxes(annos)[:num_obj] rbbox_lidar = box_np_ops.box_camera_to_lidar(rbbox_cam, rect, Trv2c) if bev_only: # set z and h to limits assert coors_range is not None rbbox_lidar[:, 2] = coors_range[2] rbbox_lidar[:, 5] = coors_range[5] - coors_range[2] if RGB_embedding: RGB_image = cv2.imread(str(root_path / info['img_path'])) points_camera = box_np_ops.box_lidar_to_camera( points[:, :3], rect, Trv2c) points_to_image_idx = box_np_ops.project_to_image( points_camera, P2) points_to_image_idx = points_to_image_idx.astype(int) mask = box_np_ops.remove_points_outside_image( RGB_image, points_to_image_idx) points = points[mask] points_to_image_idx = points_to_image_idx[mask] BGR = RGB_image[points_to_image_idx[:, 1], points_to_image_idx[:, 0]] points = np.concatenate((points, BGR), axis=1) group_dict = {} group_ids = np.full([bboxes.shape[0]], -1, dtype=np.int64) if "group_ids" in annos: group_ids = annos["group_ids"] else: group_ids = np.arange(bboxes.shape[0], dtype=np.int64) point_indices = box_np_ops.points_in_rbbox(points, rbbox_lidar) for i in range(num_obj): if RGB_embedding: filename = f"{image_idx}_{names[i]}_{gt_idxes[i]}_RGB.bin" else: filename = f"{image_idx}_{names[i]}_{gt_idxes[i]}.bin" filepath = database_save_path / filename gt_points = points[point_indices[:, i]] gt_points[:, :3] -= rbbox_lidar[i, :3] with open(filepath, 'w') as f: gt_points.tofile(f) if names[i] in used_classes: if relative_path: db_path = str(database_save_path.stem + "/" + filename) else: db_path = str(filepath) db_info = { "name": names[i], "path": db_path, "image_idx": image_idx, "gt_idx": gt_idxes[i], "box3d_lidar": rbbox_lidar[i], "num_points_in_gt": gt_points.shape[0], "difficulty": difficulty[i], # "group_id": -1, # "bbox": bboxes[i], } local_group_id = group_ids[i] # if local_group_id >= 0: if local_group_id not in group_dict: group_dict[local_group_id] = group_counter group_counter += 1 db_info["group_id"] = group_dict[local_group_id] if "score" in annos: db_info["score"] = annos["score"][i] all_db_infos[names[i]].append(db_info) for k, v in all_db_infos.items(): print(f"load {len(v)} {k} database infos") with open(db_info_save_path, 'wb') as f: pickle.dump(all_db_infos, f)
def evaluation_from_kitti_dets(self, dt_annos, output_dir): if "annos" not in self._kitti_infos[0]: return None gt_annos = [info["annos"] for info in self._kitti_infos] # firstly convert standard detection to kitti-format dt annos z_axis = 1 # KITTI camera format use y as regular "z" axis. z_center = 1.0 # KITTI camera box's center is [0.5, 1, 0.5] # for regular raw lidar data, z_axis = 2, z_center = 0.5. result_official_dict = get_official_eval_result( gt_annos, dt_annos, self._class_names, z_axis=z_axis, z_center=z_center) result_coco = get_coco_eval_result( gt_annos, dt_annos, self._class_names, z_axis=z_axis, z_center=z_center) # feature extraction for info, det in tqdm(zip(self._kitti_infos, dt_annos), desc="feature", total=len(dt_annos)): pc_info = info["point_cloud"] image_info = info["image"] calib = info["calib"] num_features = pc_info["num_features"] v_path = self._root_path / pc_info["velodyne_path"] v_path = str(v_path.parent.parent / (v_path.parent.stem + "_reduced") / v_path.name) points_v = np.fromfile( v_path, dtype=np.float32, count=-1).reshape([-1, num_features]) rect = calib['R0_rect'] Trv2c = calib['Tr_velo_to_cam'] P2 = calib['P2'] if False: # No longer you need remove outside image-rect (*_reduced pointcloud is already filtered.) points_v = box_np_ops.remove_outside_points( points_v, rect, Trv2c, P2, image_info["image_shape"]) annos = det num_obj = len([n for n in annos['name'] if n != 'DontCare']) # annos = kitti.filter_kitti_anno(annos, ['DontCare']) dims = annos['dimensions'][:num_obj] loc = annos['location'][:num_obj] rots = annos['rotation_y'][:num_obj] gt_boxes_camera = np.concatenate([loc, dims, rots[..., np.newaxis]], axis=1) gt_boxes_lidar = box_np_ops.box_camera_to_lidar( gt_boxes_camera, rect, Trv2c) indices = box_np_ops.points_in_rbbox(points_v[:, :3], gt_boxes_lidar) num_points_in_gt = indices.sum(0) num_ignored = len(annos['dimensions']) - num_obj num_points_in_gt = np.concatenate( [num_points_in_gt, -np.ones([num_ignored])]) annos["num_points_in_det"] = num_points_in_gt.astype(np.int32) return { "results": { "official": result_official_dict["result"], "coco": result_coco["result"], }, "detail": { "eval.kitti": { "official": result_official_dict["detail"], "coco": result_coco["detail"] } }, "result_kitti": result_official_dict["detections"], }
def get_sensor_data(self, query): read_image = False idx = query if isinstance(query, dict): read_image = "cam" in query assert "lidar" in query idx = query["lidar"]["idx"] info = self._kitti_infos[idx] res = { "lidar": { "type": "lidar", "points": None, }, "metadata": { "image_idx": info["image"]["image_idx"], "image_shape": info["image"]["image_shape"], }, "calib": None, "cam": {} } pc_info = info["point_cloud"] velo_path = Path(pc_info['velodyne_path']) if not velo_path.is_absolute(): velo_path = Path(self._root_path) / pc_info['velodyne_path'] velo_reduced_path = velo_path.parent.parent / ( velo_path.parent.stem + '_reduced') / velo_path.name if velo_reduced_path.exists(): velo_path = velo_reduced_path points = np.fromfile(str(velo_path), dtype=np.float32, count=-1).reshape([-1, self.NumPointFeatures]) res["lidar"]["points"] = points image_info = info["image"] image_path = image_info['image_path'] if read_image: image_path = self._root_path / image_path with open(str(image_path), 'rb') as f: image_str = f.read() res["cam"] = { "type": "camera", "data": image_str, "datatype": image_path.suffix[1:], } calib = info["calib"] calib_dict = { 'rect': calib['R0_rect'], 'Trv2c': calib['Tr_velo_to_cam'], 'P2': calib['P2'], } res["calib"] = calib_dict if 'annos' in info: annos = info['annos'] # we need other objects to avoid collision when sample annos = kitti.remove_dontcare(annos) locs = annos["location"] dims = annos["dimensions"] rots = annos["rotation_y"] gt_names = annos["name"] # rots = np.concatenate([np.zeros([locs.shape[0], 2], dtype=np.float32), rots], axis=1) gt_boxes = np.concatenate([locs, dims, rots[..., np.newaxis]], axis=1).astype(np.float32) calib = info["calib"] gt_boxes = box_np_ops.box_camera_to_lidar(gt_boxes, calib["R0_rect"], calib["Tr_velo_to_cam"]) # only center format is allowed. so we need to convert # kitti [0.5, 0.5, 0] center to [0.5, 0.5, 0.5] box_np_ops.change_box3d_center_(gt_boxes, [0.5, 0.5, 0], [0.5, 0.5, 0.5]) res["lidar"]["annotations"] = { 'boxes': gt_boxes, 'names': gt_names, } res["cam"]["annotations"] = { 'boxes': annos["bbox"], 'names': gt_names, } return res
def get_sensor_data(self, query): read_image = False colored_lidar = False create_init_lidar = True idx = query if isinstance(query, dict): colored_lidar = "colored" in query["lidar"] read_image = ("cam" in query) or colored_lidar create_init_lidar = "init_lidar" in query if create_init_lidar: assert "num_beams" in query["init_lidar"] assert "lidar" in query idx = query["lidar"]["idx"] info = self._vkitti_infos[idx] res = { "lidar": { "type": "lidar", "points": None, "colored": False }, "metadata": { "image_idx": info["image"]["image_idx"], "image_shape": info["image"]["image_shape"], }, "calib": None, "cam": {}, "depth": { "type": "depth_map", "image": None }, "init_lidar": { "num_beams": None, "points": None } } image_info = info["image"] image_path = image_info['image_path'] if read_image: image_path = self._root_path / image_path with open(str(image_path), 'rb') as f: image_str = f.read() res["cam"] = { "type": "camera", "data": image_str, "datatype": image_path.suffix[1:], } # Will always produce depth, needed for lidar. depth_info = info["depth"] depth_path = Path(depth_info["depth_path"]) if not depth_path.is_absolute(): depth_path = Path(self._root_path) / depth_info["depth_path"] np_depth_image = np.array( Image.open(depth_path)) # (W, H) dtype=np.int32 # Convert centimeters (int32) to meters. np_depth_image = np_depth_image.astype( np.float32) / 100 # (W, H) dtype=np.float32 res["depth"] = {"type": "depth_map", "image": np_depth_image} if colored_lidar: # Concatenate depth map with colors. np_rgb_image = np.array(Image.open( io.BytesIO(image_str))) # (H, W, 3) H, W = np_depth_image.shape np_depth_image = np.concatenate( (np_depth_image.reshape(H, W, 1), np_rgb_image), axis=2) # (H, W, 4) res["lidar"]["colored"] = True else: np_depth_image = np_depth_image[..., np.newaxis] # (H, W, 1) np_depth_image = np.concatenate( (np_depth_image, np.ones_like(np_depth_image)), axis=2) # (H, W, 2) points = cam_transforms.depth_map_to_point_cloud( np_depth_image, self.lc_device.CAMERA_PARAMS['matrix']) # (N, 4) or (N, 6) # Convert from camera frame to velo frame. cam2velo = self.lc_device.TRANSFORMS[ 'cTw'] # inverse extrinsics matrix xyz_velo = np.hstack( (points[:, :3], np.ones([len(points), 1], dtype=points.dtype))) xyz_velo = xyz_velo @ cam2velo.T points = np.hstack((xyz_velo[:, :3], points[:, 3:])) # Simulated LiDAR points. if create_init_lidar and query["init_lidar"]["num_beams"] > 1: num_beams = query["init_lidar"]["num_beams"] if self.mb_lidar_mask is None or self.mb_lidar_mask[ "num_beams"] != num_beams: self.create_mb_lidar_mask(num_beams) init_lidar_points = points[ self.mb_lidar_mask["1d_mask"]] # (R, 4) or (R, 6) init_lidar_points = init_lidar_points[:, :3] # (R, 3) # Only select points with less than MAX_RANGE value. init_lidar_points = init_lidar_points[ init_lidar_points[:, 0] < self._MAX_RANGE, :] res["init_lidar"]["points"] = init_lidar_points # Only select points with less than MAX_RANGE value. points = points[points[:, 0] < self._MAX_RANGE, :] res["lidar"]["points"] = points # Compute single-beam lidar. if create_init_lidar and query["init_lidar"]["num_beams"] == 1: init_lidar_points = self.convert_points_to_sb_lidar(points) init_lidar_points = init_lidar_points[:, :3] # (N, 3) res["init_lidar"]["points"] = init_lidar_points R0_rect = np.eye( 4, dtype=np.float32) # there is no rectification in vkitti Tr_velo_to_cam = self.lc_device.TRANSFORMS["wTc"] # Extended intrinsics matrix: shape= (4, 4). P2 = np.eye(4, dtype=np.float32) P2[:3, :3] = self.lc_device.CAMERA_PARAMS["matrix"] calib_dict = { 'R0_rect': R0_rect, 'Tr_velo_to_cam': Tr_velo_to_cam, 'P2': P2 } res["calib"] = calib_dict if 'annos' in info: annos = info['annos'] # we need other objects to avoid collision when sample annos = vkitti.remove_dontcare(annos) locs = annos["location"] dims = annos["dimensions"] rots = annos["rotation_y"] gt_names = annos["name"] gt_boxes = np.concatenate([locs, dims, rots[..., np.newaxis]], axis=1).astype(np.float32) calib = info["calib"] gt_boxes = box_np_ops.box_camera_to_lidar(gt_boxes, r_rect=R0_rect, velo2cam=Tr_velo_to_cam) # only center format is allowed. so we need to convert # kitti [0.5, 0.5, 0] center to [0.5, 0.5, 0.5] box_np_ops.change_box3d_center_(gt_boxes, [0.5, 0.5, 0], [0.5, 0.5, 0.5]) res["lidar"]["annotations"] = { 'boxes': gt_boxes, 'names': gt_names, } res["cam"]["annotations"] = { 'boxes': annos["bbox"], 'names': gt_names, } return res
def get_pointcloud(): global BACKEND instance = request.json response = {"status": "normal"} if BACKEND.root_path is None: return error_response("root path is not set") if BACKEND.kitti_infos is None: return error_response("kitti info is not loaded") image_idx = instance["image_idx"] enable_int16 = instance["enable_int16"] idx = BACKEND.image_idxes.index(image_idx) kitti_info = BACKEND.kitti_infos[idx] pc_info = kitti_info["point_cloud"] image_info = kitti_info["image"] calib = kitti_info["calib"] rect = calib['R0_rect'] Trv2c = calib['Tr_velo_to_cam'] P2 = calib['P2'] img_shape = image_info["image_shape"] # hw wh = np.array(img_shape[::-1]) whwh = np.tile(wh, 2) if 'annos' in kitti_info: annos = kitti_info['annos'] labels = annos['name'] num_obj = len([n for n in annos['name'] if n != 'DontCare']) dims = annos['dimensions'][:num_obj] loc = annos['location'][:num_obj] rots = annos['rotation_y'][:num_obj] bbox = annos['bbox'][:num_obj] / whwh gt_boxes_camera = np.concatenate([loc, dims, rots[..., np.newaxis]], axis=1) gt_boxes = box_np_ops.box_camera_to_lidar(gt_boxes_camera, rect, Trv2c) box_np_ops.change_box3d_center_(gt_boxes, src=[0.5, 0.5, 0], dst=[0.5, 0.5, 0.5]) locs = gt_boxes[:, :3] dims = gt_boxes[:, 3:6] rots = np.concatenate( [np.zeros([num_obj, 2], dtype=np.float32), -gt_boxes[:, 6:7]], axis=1) frontend_annos = {} response["locs"] = locs.tolist() response["dims"] = dims.tolist() response["rots"] = rots.tolist() response["bbox"] = bbox.tolist() response["labels"] = labels[:num_obj].tolist() response["num_features"] = pc_info["num_features"] v_path = str(Path(BACKEND.root_path) / pc_info['velodyne_path']) points = np.fromfile(v_path, dtype=np.float32, count=-1).reshape([-1, pc_info["num_features"]]) if instance['remove_outside']: if 'image_shape' in image_info: image_shape = image_info['image_shape'] points = box_np_ops.remove_outside_points(points, rect, Trv2c, P2, image_shape) else: points = points[points[..., 2] > 0] if enable_int16: int16_factor = instance["int16_factor"] points *= int16_factor points = points.astype(np.int16) pc_str = base64.b64encode(points.tobytes()) response["pointcloud"] = pc_str.decode("utf-8") if "with_det" in instance and instance["with_det"]: if BACKEND.dt_annos is None: return error_response("det anno is not loaded") dt_annos = BACKEND.dt_annos[idx] dims = dt_annos['dimensions'] num_obj = dims.shape[0] loc = dt_annos['location'] rots = dt_annos['rotation_y'] bbox = dt_annos['bbox'] / whwh labels = dt_annos['name'] dt_boxes_camera = np.concatenate([loc, dims, rots[..., np.newaxis]], axis=1) dt_boxes = box_np_ops.box_camera_to_lidar(dt_boxes_camera, rect, Trv2c) box_np_ops.change_box3d_center_(dt_boxes, src=[0.5, 0.5, 0], dst=[0.5, 0.5, 0.5]) locs = dt_boxes[:, :3] dims = dt_boxes[:, 3:6] rots = np.concatenate( [np.zeros([num_obj, 2], dtype=np.float32), -dt_boxes[:, 6:7]], axis=1) response["dt_locs"] = locs.tolist() response["dt_dims"] = dims.tolist() response["dt_rots"] = rots.tolist() response["dt_labels"] = labels.tolist() response["dt_bbox"] = bbox.tolist() response["dt_scores"] = dt_annos["score"].tolist() # if "score" in annos: # response["score"] = score.tolist() response = jsonify(results=[response]) response.headers['Access-Control-Allow-Headers'] = '*' print("send response with size {}!".format(len(pc_str))) return response
def prep_pointcloud(input_dict, root_path, voxel_generator, target_assigner, db_sampler=None, max_voxels=20000, class_names=['Car'], remove_outside_points=False, training=True, create_targets=True, shuffle_points=False, reduce_valid_area=False, remove_unknown=False, gt_rotation_noise=[-np.pi / 3, np.pi / 3], gt_loc_noise_std=[1.0, 1.0, 1.0], global_rotation_noise=[-np.pi / 4, np.pi / 4], global_scaling_noise=[0.95, 1.05], global_loc_noise_std=(0.2, 0.2, 0.2), global_random_rot_range=[0.78, 2.35], generate_bev=False, without_reflectivity=False, num_point_features=4, anchor_area_threshold=1, gt_points_drop=0.0, gt_drop_max_keep=10, remove_points_after_sample=True, anchor_cache=None, remove_environment=False, random_crop=False, reference_detections=None, add_rgb_to_points=False, lidar_input=False, unlabeled_db_sampler=None, out_size_factor=2, min_gt_point_dict=None, bev_only=False, use_group_id=False, out_dtype=np.float32): """convert point cloud to voxels, create targets if ground truths exists. """ # 这部分用来读取某一帧数据 points = input_dict["points"] # velodyne_reduced, array(N*4) if training: gt_boxes = input_dict["gt_boxes"] # 真值框,位置,尺寸,绝对转角,N*1,一个真值框一行 gt_names = input_dict["gt_names"] difficulty = input_dict["difficulty"] group_ids = None if use_group_id and "group_ids" in input_dict: # False group_ids = input_dict["group_ids"] rect = input_dict["rect"] Trv2c = input_dict["Trv2c"] P2 = input_dict["P2"] unlabeled_training = unlabeled_db_sampler is not None image_idx = input_dict["image_idx"] if reference_detections is not None: # None C, R, T = box_np_ops.projection_matrix_to_CRT_kitti(P2) frustums = box_np_ops.get_frustum_v2(reference_detections, C) frustums -= T # frustums = np.linalg.inv(R) @ frustums.T frustums = np.einsum('ij, akj->aki', np.linalg.inv(R), frustums) frustums = box_np_ops.camera_to_lidar(frustums, rect, Trv2c) surfaces = box_np_ops.corner_to_surfaces_3d_jit(frustums) masks = points_in_convex_polygon_3d_jit(points, surfaces) points = points[masks.any(-1)] if remove_outside_points and not lidar_input: # False image_shape = input_dict["image_shape"] points = box_np_ops.remove_outside_points(points, rect, Trv2c, P2, image_shape) if remove_environment is True and training: # False selected = kitti.keep_arrays_by_name(gt_names, class_names) gt_boxes = gt_boxes[selected] gt_names = gt_names[selected] difficulty = difficulty[selected] if group_ids is not None: # None group_ids = group_ids[selected] points = prep.remove_points_outside_boxes(points, gt_boxes) if training: # 先去掉真值内的DontCare selected = kitti.drop_arrays_by_name(gt_names, ["DontCare"]) # 去掉DontCare gt_boxes = gt_boxes[selected] gt_names = gt_names[selected] difficulty = difficulty[selected] if group_ids is not None: # None group_ids = group_ids[selected] gt_boxes = box_np_ops.box_camera_to_lidar(gt_boxes, rect, Trv2c) # 相机坐标下的真值框转换成激光雷达坐标下[xyz_lidar,w,l,h,r],一个对象一行 if remove_unknown: # False remove_mask = difficulty == -1 """ gt_boxes_remove = gt_boxes[remove_mask] gt_boxes_remove[:, 3:6] += 0.25 points = prep.remove_points_in_boxes(points, gt_boxes_remove) """ keep_mask = np.logical_not(remove_mask) gt_boxes = gt_boxes[keep_mask] gt_names = gt_names[keep_mask] difficulty = difficulty[keep_mask] if group_ids is not None: # None group_ids = group_ids[keep_mask] gt_boxes_mask = np.array( # 目标类别的对象标签,布尔类型,同样一行一个对象 [n in class_names for n in gt_names], dtype=np.bool_) # 下面用来对去掉DontCare的真值进行采样补充 if db_sampler is not None: # not None # 数据库预处理类里的方法,返回的是该帧数据内各类别用来补充的采样真值数据,包括真值内出现的非目标类别 sampled_dict = db_sampler.sample_all( root_path, gt_boxes, gt_names, num_point_features, random_crop, gt_group_ids=group_ids, # None rect=rect, Trv2c=Trv2c, P2=P2) if sampled_dict is not None: # 下面将原始数据与补充采样数据合并 sampled_gt_names = sampled_dict["gt_names"] sampled_gt_boxes = sampled_dict["gt_boxes"] sampled_points = sampled_dict["points"] sampled_gt_masks = sampled_dict["gt_masks"] # gt_names = gt_names[gt_boxes_mask].tolist() gt_names = np.concatenate([gt_names, sampled_gt_names], axis=0) # gt_names += [s["name"] for s in sampled] gt_boxes = np.concatenate([gt_boxes, sampled_gt_boxes]) gt_boxes_mask = np.concatenate( [gt_boxes_mask, sampled_gt_masks], axis=0) # 真值框是目标类别的标志 if group_ids is not None: # None sampled_group_ids = sampled_dict["group_ids"] group_ids = np.concatenate([group_ids, sampled_group_ids]) if remove_points_after_sample: # False,将采样框所占位置点云去除 points = prep.remove_points_in_boxes( points, sampled_gt_boxes) points = np.concatenate([sampled_points, points], axis=0) # 合并原始点云与采样点云 # unlabeled_mask = np.zeros((gt_boxes.shape[0], ), dtype=np.bool_) if without_reflectivity: # False used_point_axes = list(range(num_point_features)) used_point_axes.pop(3) points = points[:, used_point_axes] pc_range = voxel_generator.point_cloud_range if bev_only: # set z and h to limits, False gt_boxes[:, 2] = pc_range[2] gt_boxes[:, 5] = pc_range[5] - pc_range[2] prep.noise_per_object_v3_( # 每一个对象添加扰动,实际上就是修改每个对象真值框坐标与内部点云坐标 gt_boxes, # 补充过采样数据的真值框 points, # 补充过采样数据的点云 gt_boxes_mask, # 真值框标志(不区分原始数据与采样数据) rotation_perturb=gt_rotation_noise, # 旋转角度范围,平均分布 center_noise_std=gt_loc_noise_std, # 位置正态分布方差 global_random_rot_range=global_random_rot_range, # 全局旋转[0.0,0.0] group_ids=group_ids, # None num_try=100) # should remove unrelated objects after noise per object gt_boxes = gt_boxes[gt_boxes_mask] # 添加扰动后筛选目标类别真值框 gt_names = gt_names[gt_boxes_mask] if group_ids is not None: # Fasle group_ids = group_ids[gt_boxes_mask] gt_classes = np.array( # (gt_num) [class_names.index(n) + 1 for n in gt_names], dtype=np.int32) gt_boxes, points = prep.random_flip(gt_boxes, points) # 随机翻转 gt_boxes, points = prep.global_rotation( # 全局旋转 gt_boxes, points, rotation=global_rotation_noise) gt_boxes, points = prep.global_scaling_v2(gt_boxes, points, # 全局缩放 *global_scaling_noise) # Global translation # 全局定位扰动 gt_boxes, points = prep.global_translate(gt_boxes, points, global_loc_noise_std) bv_range = voxel_generator.point_cloud_range[[0, 1, 3, 4]] mask = prep.filter_gt_box_outside_range(gt_boxes, bv_range) # 过滤超出鸟瞰图范围的真值框 gt_boxes = gt_boxes[mask] gt_classes = gt_classes[mask] if group_ids is not None: # None group_ids = group_ids[mask] # limit rad to [-pi, pi] gt_boxes[:, 6] = box_np_ops.limit_period( # 调整真值转角至目标范围 gt_boxes[:, 6], offset=0.5, period=2 * np.pi) if shuffle_points: # True,打乱全局点云数据 # shuffle is a little slow. np.random.shuffle(points) voxel_size = voxel_generator.voxel_size # [0.16, 0.16, 4] pc_range = voxel_generator.point_cloud_range # [0, -39.68, -3, 69.12, 39.68, 1] grid_size = voxel_generator.grid_size # [432, 496, 1] x,y,z # 生成体素 """ Returns: voxels:[num_voxels, 100, 4] 体素索引映射全局体素特征 coordinates:[num_voxels, 3] 体素索引映射体素坐标 num_points:(num_voxels,) 体素索引映射体素内的点数 """ voxels, coordinates, num_points = voxel_generator.generate( points, max_voxels) example = { 'voxels': voxels, 'num_points': num_points, 'coordinates': coordinates, "num_voxels": np.array([voxels.shape[0]], dtype=np.int64) } example.update({ 'rect': rect, 'Trv2c': Trv2c, 'P2': P2, }) # if not lidar_input: feature_map_size = grid_size[:2] // out_size_factor # [216 248] feature_map_size = [*feature_map_size, 1][::-1] # [1,248,216] if anchor_cache is not None: anchors = anchor_cache["anchors"] anchors_bv = anchor_cache["anchors_bv"] matched_thresholds = anchor_cache["matched_thresholds"] unmatched_thresholds = anchor_cache["unmatched_thresholds"] else: ret = target_assigner.generate_anchors(feature_map_size) anchors = ret["anchors"] anchors = anchors.reshape([-1, 7]) matched_thresholds = ret["matched_thresholds"] unmatched_thresholds = ret["unmatched_thresholds"] anchors_bv = box_np_ops.rbbox2d_to_near_bbox( anchors[:, [0, 1, 3, 4, 6]]) example["anchors"] = anchors # print("debug", anchors.shape, matched_thresholds.shape) # anchors_bv = anchors_bv.reshape([-1, 4]) anchors_mask = None if anchor_area_threshold >= 0: # True coors = coordinates dense_voxel_map = box_np_ops.sparse_sum_for_anchors_mask( # 某处体素是否被采样,否为0,是为1.array[496,432] y,x coors, tuple(grid_size[::-1][1:])) dense_voxel_map = dense_voxel_map.cumsum(0) # 元素值为按列累加到元素的和 dense_voxel_map = dense_voxel_map.cumsum(1) # 元素值为按行累加到元素的和,相当于统计了元素两个维度上之前元素的和 anchors_area = box_np_ops.fused_get_anchors_area( # 统计每个锚框内的体素数量 dense_voxel_map, anchors_bv, voxel_size, pc_range, grid_size) anchors_mask = anchors_area > anchor_area_threshold # 标记超过一个体素的锚框 # example['anchors_mask'] = anchors_mask.astype(np.uint8) example['anchors_mask'] = anchors_mask if generate_bev: # False bev_vxsize = voxel_size.copy() bev_vxsize[:2] /= 2 bev_vxsize[2] *= 2 bev_map = points_to_bev(points, bev_vxsize, pc_range, without_reflectivity) example["bev_map"] = bev_map if not training: return example # 测试数据集不需要创建训练目标 if create_targets: # True targets_dict = target_assigner.assign( anchors, # (248*216*2,7) gt_boxes, # (gt_num, 7) anchors_mask, # (248*216*2,) gt_classes=gt_classes, # (gt_num,) matched_thresholds=matched_thresholds, # (248*216*2,) unmatched_thresholds=unmatched_thresholds) # (248*216*2,) example.update({ 'labels': targets_dict['labels'], # (total_anchors,),所有锚框对应真值类别(1,2...),无对应真值设为0,dontcare设为-1 'reg_targets': targets_dict['bbox_targets'], # (total_anchors, 7),所有锚框对应真值相对锚框的偏差编码,无对应真值设为[0,0,0,0,0,0,0] 'reg_weights': targets_dict['bbox_outside_weights'], # (total_anchors,),所有锚框的外部权重,有对应真值设为1,无对应真值设为0 }) return example
def prep_pointcloud( input_dict, root_path, # voxel_generator, fv_generator, target_assigner, db_sampler=None, max_voxels=20000, class_names=['Car'], remove_outside_points=False, training=True, create_targets=True, shuffle_points=False, reduce_valid_area=False, remove_unknown=False, gt_rotation_noise=[-np.pi / 3, np.pi / 3], gt_loc_noise_std=[1.0, 1.0, 1.0], global_rotation_noise=[-np.pi / 4, np.pi / 4], global_scaling_noise=[0.95, 1.05], global_loc_noise_std=(0.2, 0.2, 0.2), global_random_rot_range=[0.78, 2.35], generate_bev=False, without_reflectivity=False, num_point_features=4, anchor_area_threshold=1, gt_points_drop=0.0, gt_drop_max_keep=10, remove_points_after_sample=False, anchor_cache=None, remove_environment=False, random_crop=False, reference_detections=None, add_rgb_to_points=False, lidar_input=False, unlabeled_db_sampler=None, out_size_factor=2, min_gt_point_dict=None, bev_only=False, use_group_id=False, out_dtype=np.float32, num_classes=1, RGB_embedding=False): """convert point cloud to voxels, create targets if ground truths exists. """ # prep_pointcloud_start = time.time() points = input_dict["points"] # if training: gt_boxes = input_dict["gt_boxes"] gt_names = input_dict["gt_names"] difficulty = input_dict["difficulty"] group_ids = None if use_group_id and "group_ids" in input_dict: group_ids = input_dict["group_ids"] rect = input_dict["rect"] Trv2c = input_dict["Trv2c"] P2 = input_dict["P2"] unlabeled_training = unlabeled_db_sampler is not None image_idx = input_dict["image_idx"] # t1 = time.time() - prep_pointcloud_start if shuffle_points: # shuffle is a little slow. np.random.shuffle(points) # t2 = time.time() - prep_pointcloud_start # print("t2-t1: ", t2-t1) # 0.035 if reference_detections is not None: C, R, T = box_np_ops.projection_matrix_to_CRT_kitti(P2) frustums = box_np_ops.get_frustum_v2(reference_detections, C) frustums -= T # frustums = np.linalg.inv(R) @ frustums.T frustums = np.einsum('ij, akj->aki', np.linalg.inv(R), frustums) frustums = box_np_ops.camera_to_lidar(frustums, rect, Trv2c) surfaces = box_np_ops.corner_to_surfaces_3d_jit(frustums) masks = points_in_convex_polygon_3d_jit(points, surfaces) points = points[masks.any(-1)] if remove_outside_points: # and not lidar_input: image_shape = input_dict["image_shape"] points = box_np_ops.remove_outside_points(points, rect, Trv2c, P2, image_shape) if remove_environment is True: # and training: selected = kitti.keep_arrays_by_name(gt_names, class_names) gt_boxes = gt_boxes[selected] gt_names = gt_names[selected] difficulty = difficulty[selected] if group_ids is not None: group_ids = group_ids[selected] points = prep.remove_points_outside_boxes(points, gt_boxes) # if training: # print(gt_names) selected = kitti.drop_arrays_by_name(gt_names, ["DontCare"]) gt_boxes = gt_boxes[selected] gt_names = gt_names[selected] difficulty = difficulty[selected] if group_ids is not None: group_ids = group_ids[selected] # t3 = time.time() - prep_pointcloud_start # print("t3-t2: ", t3 - t2) # 0.0002 gt_boxes = box_np_ops.box_camera_to_lidar(gt_boxes, rect, Trv2c) if remove_unknown: remove_mask = difficulty == -1 """ gt_boxes_remove = gt_boxes[remove_mask] gt_boxes_remove[:, 3:6] += 0.25 points = prep.remove_points_in_boxes(points, gt_boxes_remove) """ keep_mask = np.logical_not(remove_mask) gt_boxes = gt_boxes[keep_mask] gt_names = gt_names[keep_mask] difficulty = difficulty[keep_mask] if group_ids is not None: group_ids = group_ids[keep_mask] gt_boxes_mask = np.array([n in class_names for n in gt_names], dtype=np.bool_) # t4 = time.time() - prep_pointcloud_start # print("t4-t3: ", t4 - t3) # 0.001 if RGB_embedding: RGB_image = cv2.imread(input_dict['image_path']) points_camera = box_np_ops.box_lidar_to_camera(points[:, :3], rect, Trv2c) points_to_image_idx = box_np_ops.project_to_image(points_camera, P2) points_to_image_idx = points_to_image_idx.astype(int) mask = box_np_ops.remove_points_outside_image(RGB_image, points_to_image_idx) points = points[mask] points_to_image_idx = points_to_image_idx[mask] BGR = RGB_image[points_to_image_idx[:, 1], points_to_image_idx[:, 0]] points = np.concatenate((points, BGR), axis=1) # test_mask = points_camera[mask][:, 0] < 0 # test_image_idx = points_to_image_idx[test_mask] # RGB_image[test_image_idx[:, 1], test_image_idx[:, 0]] = [255, 0, 0] # test_mask = points_camera[mask][:, 0] >= 0 # test_image_idx = points_to_image_idx[test_mask] # RGB_image[test_image_idx[:, 1], test_image_idx[:, 0]] = [0, 0, 255] # print() # t5 = time.time() - prep_pointcloud_start # print("t5-t4: ", t5 - t4) # 0.019 # TODO if db_sampler is not None and training: # and not RGB_embedding: if RGB_embedding: num_point_features += 3 fg_points_mask = box_np_ops.points_in_rbbox(points, gt_boxes) fg_points_list = [] bg_points_mask = np.zeros((points.shape[0]), dtype=bool) for i in range(fg_points_mask.shape[1]): fg_points_list.append(points[fg_points_mask[:, i]]) bg_points_mask = np.logical_or(bg_points_mask, fg_points_mask[:, i]) bg_points_mask = np.logical_not(bg_points_mask) sampled_dict = db_sampler.sample_all(root_path, points[bg_points_mask], gt_boxes, gt_names, fg_points_list, num_point_features, random_crop, gt_group_ids=group_ids, rect=rect, Trv2c=Trv2c, P2=P2) # sampled_dict = db_sampler.sample_all( # root_path, # gt_boxes, # gt_names, # num_point_features, # random_crop, # gt_group_ids=group_ids, # rect=rect, # Trv2c=Trv2c, # P2=P2) # t_sample_all = time.time() - prep_pointcloud_start # print("t_sample_all - t5: ", t_sample_all - t5) # 3.83 if sampled_dict is not None: sampled_gt_names = sampled_dict["gt_names"] sampled_gt_boxes = sampled_dict["gt_boxes"] points = sampled_dict["points"] sampled_gt_masks = sampled_dict["gt_masks"] remained_boxes_idx = sampled_dict["remained_boxes_idx"] # gt_names = gt_names[gt_boxes_mask].tolist() gt_names = np.concatenate([gt_names, sampled_gt_names], axis=0) # gt_names += [s["name"] for s in sampled] gt_boxes = np.concatenate([gt_boxes, sampled_gt_boxes]) gt_boxes_mask = np.concatenate([gt_boxes_mask, sampled_gt_masks], axis=0) gt_names = gt_names[remained_boxes_idx] gt_boxes = gt_boxes[remained_boxes_idx] gt_boxes_mask = gt_boxes_mask[remained_boxes_idx] if group_ids is not None: sampled_group_ids = sampled_dict["group_ids"] group_ids = np.concatenate([group_ids, sampled_group_ids]) group_ids = group_ids[remained_boxes_idx] # if remove_points_after_sample: # # points = prep.remove_points_in_boxes( # # points, sampled_gt_boxes) # locs = sampled_gt_boxes[:, 0:3] # dims = sampled_gt_boxes[:, 3:6] # angles = sampled_gt_boxes[:, 6] # camera_box_origin = [0.5, 0.5, 0] # # box_corners = box_np_ops.center_to_corner_box3d( # locs, dims, angles, camera_box_origin, axis=2) # box_corners_in_image = box_np_ops.project_to_fv_image( # box_corners, example['grid_size'][i], example['meta'][i]) # box_centers_in_image = box_np_ops.project_to_fv_image( # locs, example['grid_size'][i], example['meta'][i]) # t_sample_all2 = time.time() - prep_pointcloud_start # print("t_sample_all2 - t_sample_all: ", t_sample_all2 - t_sample_all) # 0.0002 # unlabeled_mask = np.zeros((gt_boxes.shape[0], ), dtype=np.bool_) # if without_reflectivity and training: # used_point_axes = list(range(num_point_features)) # used_point_axes.pop(3) # points = points[:, used_point_axes] # pc_range = voxel_generator.point_cloud_range # bev_only = False # if bev_only: # set z and h to limits # gt_boxes[:, 2] = pc_range[2] # gt_boxes[:, 5] = pc_range[5] - pc_range[2] if training: gt_loc_noise_std = [0.0, 0.0, 0.0] prep.noise_per_object_v3_( gt_boxes, points, gt_boxes_mask, rotation_perturb=gt_rotation_noise, center_noise_std=gt_loc_noise_std, global_random_rot_range=global_random_rot_range, group_ids=group_ids, num_try=100) # t_noise = time.time() - prep_pointcloud_start # print("t_noise - t_sample_all2: ", t_noise - t_sample_all2) # 12.01 # should remove unrelated objects after noise per object gt_boxes = gt_boxes[gt_boxes_mask] gt_names = gt_names[gt_boxes_mask] if group_ids is not None: group_ids = group_ids[gt_boxes_mask] gt_classes = np.array([class_names.index(n) + 1 for n in gt_names], dtype=np.int32) # t6 = time.time() - prep_pointcloud_start # print("t6-t5: ", t6 - t5) # 16.0 if training: gt_boxes, points = prep.random_flip(gt_boxes, points) # gt_boxes, points = prep.global_rotation( # gt_boxes, points, rotation=global_rotation_noise) gt_boxes, points = prep.global_scaling_v2(gt_boxes, points, *global_scaling_noise) # Global translation # gt_boxes, points = prep.global_translate(gt_boxes, points, global_loc_noise_std) # bv_range = voxel_generator.point_cloud_range[[0, 1, 3, 4]] bv_range = [0, -40, 70.4, 40] mask = prep.filter_gt_box_outside_range(gt_boxes, bv_range) gt_boxes = gt_boxes[mask] gt_classes = gt_classes[mask] if group_ids is not None: group_ids = group_ids[mask] # limit rad to [-pi, pi] gt_boxes[:, 6] = box_np_ops.limit_period(gt_boxes[:, 6], offset=0.5, period=2 * np.pi) # TODO # if shuffle_points: # # shuffle is a little slow. # np.random.shuffle(points) # # t7 = time.time() - prep_pointcloud_start # # print("t7-t6: ", t7 - t6) # 1.95 # voxels, coordinates, num_points = voxel_generator.generate( # points, max_voxels, RGB_embedding=RGB_embedding) # # t8 = time.time() - prep_pointcloud_start # # print("t8-t7: ", t8 - t7) # 2.0 # voxel_size = voxel_generator.voxel_size # grid_size = voxel_generator.grid_size # pc_range = copy.deepcopy(voxel_generator.point_cloud_range) # grid_size = voxel_generator.grid_size # phi_min = voxel_generator.phi_min # theta_min = voxel_generator.theta_min # image_h, image_w = grid_size[1], grid_size[0] # c = np.array([image_w / 2., image_h / 2.]) # s = np.array([image_w, image_h], dtype=np.int32) # meta = {'c': c, 's': s, 'calib': P2, 'phi_min': phi_min, 'theta_min': theta_min} # t7 = time.time() - prep_pointcloud_start # print("t7-t6: ", t7 - t6) # 1.95 fv_image, points_mask = fv_generator.generate(points, RGB_embedding=RGB_embedding, occupancy_embedding=False) # t8 = time.time() - prep_pointcloud_start # print("t8-t7: ", t8 - t7) # 2.0 fv_dim = fv_generator.fv_dim pc_range = copy.deepcopy(fv_generator.spherical_coord_range) grid_size = fv_generator.grid_size phi_min = fv_generator.phi_min theta_min = fv_generator.theta_min image_h, image_w = fv_dim[1], fv_dim[0] c = np.array([image_w / 2., image_h / 2.]) s = np.array([image_w, image_h], dtype=np.int32) meta = { 'c': c, 's': s, 'calib': P2, 'phi_min': phi_min, 'theta_min': theta_min } fv_image = np.transpose(fv_image, [2, 1, 0]) max_objs = 50 num_objs = min(gt_boxes.shape[0], max_objs) box_np_ops.change_box3d_center_(gt_boxes, src=[0.5, 0.5, 0], dst=[0.5, 0.5, 0.5]) spherical_gt_boxes = np.zeros((max_objs, gt_boxes.shape[1])) spherical_gt_boxes[:num_objs, :] = gt_boxes[:num_objs, :] spherical_gt_boxes[:num_objs, :] = convert_to_spherical_coor( gt_boxes[:num_objs, :]) spherical_gt_boxes[:num_objs, 0] -= phi_min spherical_gt_boxes[:num_objs, 1] -= theta_min spherical_gt_boxes[:num_objs, 0] /= grid_size[0] spherical_gt_boxes[:num_objs, 1] /= grid_size[1] spherical_gt_boxes, num_objs = filter_outside_range( spherical_gt_boxes, num_objs, fv_dim) # t9 = time.time() - prep_pointcloud_start # print("t9-t8: ", t9 - t8) # 0.0005 example = { 'fv_image': fv_image, 'grid_size': grid_size, 'pc_range': pc_range, 'meta': meta, 'spherical_gt_boxes': spherical_gt_boxes, 'resized_image_shape': grid_size } example.update({'rect': rect, 'Trv2c': Trv2c, 'P2': P2}) if RGB_embedding: RGB_image = cv2.resize(RGB_image, (image_w, image_h)) example.update({'RGB_image': RGB_image}) if training: hm = np.zeros((num_classes, image_h, image_w), dtype=np.float32) reg = np.zeros((max_objs, 2), dtype=np.float32) dep = np.zeros((max_objs, 1), dtype=np.float32) rotbin = np.zeros((max_objs, 2), dtype=np.int64) rotres = np.zeros((max_objs, 2), dtype=np.float32) dim = np.zeros((max_objs, 3), dtype=np.float32) ind = np.zeros((max_objs), dtype=np.int64) reg_mask = np.zeros((max_objs), dtype=np.uint8) rot_mask = np.zeros((max_objs), dtype=np.uint8) # # hm = np.zeros((num_classes, image_h, image_w), dtype=np.float32) # reg = np.zeros((image_w, image_h, 2), dtype=np.float32) # dep = np.zeros((image_w, image_h, 1), dtype=np.float32) # rotbin = np.zeros((image_w, image_h, 2), dtype=np.int64) # rotres = np.zeros((image_w, image_h, 2), dtype=np.float32) # dim = np.zeros((image_w, image_h, 3), dtype=np.float32) # # ind = np.zeros((max_objs), dtype=np.int64) # fg_mask = np.zeros((image_w, image_h), dtype=np.uint8) # # rot_mask = np.zeros((max_objs), dtype=np.uint8) draw_gaussian = draw_umich_gaussian # center heatmap radius = int(image_h / 30) # radius = int(image_h / 25) for k in range(num_objs): gt_3d_box = spherical_gt_boxes[k] cls_id = 0 # print('heatmap gaussian radius: ' + str(radius)) ct = np.array([gt_3d_box[0], gt_3d_box[1]], dtype=np.float32) ct_int = ct.astype(np.int32) draw_gaussian(hm[cls_id], ct, radius) # depth(distance), wlh dep[k] = gt_3d_box[2] dim[k] = gt_3d_box[3:6] # dep[ct_int[0], ct_int[1], 0] = gt_3d_box[2] # dim[ct_int[0], ct_int[1], :] = gt_3d_box[3:6] # reg, ind, mask reg[k] = ct - ct_int ind[k] = ct_int[1] * image_w + ct_int[0] reg_mask[k] = rot_mask[k] = 1 # fg_mask[ct_int[0], ct_int[1]] = 1 # ry ry = gt_3d_box[6] if ry < np.pi / 6. or ry > 5 * np.pi / 6.: rotbin[k, 0] = 1 rotres[k, 0] = ry - (-0.5 * np.pi) # rotbin[ct_int[0], ct_int[1], 0] = 1 # rotres[ct_int[0], ct_int[1], 0] = ry - (-0.5 * np.pi) if ry > -np.pi / 6. or ry < -5 * np.pi / 6.: rotbin[k, 1] = 1 rotres[k, 1] = ry - (0.5 * np.pi) # rotbin[ct_int[0], ct_int[1], 1] = 1 # rotres[ct_int[0], ct_int[1], 1] = ry - (0.5 * np.pi) example.update({ 'hm': hm, 'dep': dep, 'dim': dim, 'ind': ind, 'rotbin': rotbin, 'rotres': rotres, 'reg_mask': reg_mask, 'rot_mask': rot_mask, 'reg': reg }) # example.update({ # 'hm': hm, 'dep': dep, 'dim': dim, # 'rotbin': rotbin, 'rotres': rotres, # 'fg_mask': fg_mask, 'reg': reg # }) # t10 = time.time() - prep_pointcloud_start # print("total: ", t10) # 19.58 return example
def prep_pointcloud(input_dict, root_path, voxel_generator, target_assigner, db_sampler=None, max_voxels=20000, class_names=['Car'], remove_outside_points=False, training=True, create_targets=True, shuffle_points=False, reduce_valid_area=False, remove_unknown=False, gt_rotation_noise=[-np.pi / 3, np.pi / 3], gt_loc_noise_std=[1.0, 1.0, 1.0], global_rotation_noise=[-np.pi / 4, np.pi / 4], global_scaling_noise=[0.95, 1.05], global_random_rot_range=[0.78, 2.35], generate_bev=False, without_reflectivity=False, num_point_features=4, anchor_area_threshold=1, gt_points_drop=0.0, gt_drop_max_keep=10, remove_points_after_sample=True, anchor_cache=None, remove_environment=False, random_crop=False, reference_detections=None, add_rgb_to_points=False, lidar_input=False, unlabeled_db_sampler=None, out_size_factor=2, min_gt_point_dict=None, bev_only=False, use_group_id=False, out_dtype=np.float32): """convert point cloud to voxels, create targets if ground truths exists. """ points = input_dict["points"] if training: gt_boxes = input_dict["gt_boxes"] gt_names = input_dict["gt_names"] difficulty = input_dict["difficulty"] group_ids = None if use_group_id and "group_ids" in input_dict: group_ids = input_dict["group_ids"] rect = input_dict["rect"] Trv2c = input_dict["Trv2c"] P2 = input_dict["P2"] unlabeled_training = unlabeled_db_sampler is not None #image_idx = input_dict["image_idx"] if reference_detections is not None: C, R, T = box_np_ops.projection_matrix_to_CRT_kitti(P2) frustums = box_np_ops.get_frustum_v2(reference_detections, C) frustums -= T # frustums = np.linalg.inv(R) @ frustums.T frustums = np.einsum('ij, akj->aki', np.linalg.inv(R), frustums) frustums = box_np_ops.camera_to_lidar(frustums, rect, Trv2c) surfaces = box_np_ops.corner_to_surfaces_3d_jit(frustums) masks = points_in_convex_polygon_3d_jit(points, surfaces) points = points[masks.any(-1)] if remove_outside_points and not lidar_input: image_shape = input_dict["image_shape"] points = box_np_ops.remove_outside_points(points, rect, Trv2c, P2, image_shape) if remove_environment is True and training: selected = kitti.keep_arrays_by_name(gt_names, class_names) gt_boxes = gt_boxes[selected] gt_names = gt_names[selected] difficulty = difficulty[selected] if group_ids is not None: group_ids = group_ids[selected] points = prep.remove_points_outside_boxes(points, gt_boxes) if training: # print(gt_names) selected = kitti.drop_arrays_by_name(gt_names, ["DontCare"]) gt_boxes = gt_boxes[selected] gt_names = gt_names[selected] difficulty = difficulty[selected] if group_ids is not None: group_ids = group_ids[selected] gt_boxes = box_np_ops.box_camera_to_lidar(gt_boxes, rect, Trv2c) if remove_unknown: remove_mask = difficulty == -1 """ gt_boxes_remove = gt_boxes[remove_mask] gt_boxes_remove[:, 3:6] += 0.25 points = prep.remove_points_in_boxes(points, gt_boxes_remove) """ keep_mask = np.logical_not(remove_mask) gt_boxes = gt_boxes[keep_mask] gt_names = gt_names[keep_mask] difficulty = difficulty[keep_mask] if group_ids is not None: group_ids = group_ids[keep_mask] gt_boxes_mask = np.array([n in class_names for n in gt_names], dtype=np.bool_) if db_sampler is not None: sampled_dict = db_sampler.sample_all(root_path, gt_boxes, gt_names, num_point_features, random_crop, gt_group_ids=group_ids, rect=rect, Trv2c=Trv2c, P2=P2) if sampled_dict is not None: sampled_gt_names = sampled_dict["gt_names"] sampled_gt_boxes = sampled_dict["gt_boxes"] sampled_points = sampled_dict["points"] sampled_gt_masks = sampled_dict["gt_masks"] # gt_names = gt_names[gt_boxes_mask].tolist() gt_names = np.concatenate([gt_names, sampled_gt_names], axis=0) # gt_names += [s["name"] for s in sampled] gt_boxes = np.concatenate([gt_boxes, sampled_gt_boxes]) gt_boxes_mask = np.concatenate( [gt_boxes_mask, sampled_gt_masks], axis=0) if group_ids is not None: sampled_group_ids = sampled_dict["group_ids"] group_ids = np.concatenate([group_ids, sampled_group_ids]) if remove_points_after_sample: points = prep.remove_points_in_boxes( points, sampled_gt_boxes) points = np.concatenate([sampled_points, points], axis=0) # unlabeled_mask = np.zeros((gt_boxes.shape[0], ), dtype=np.bool_) if without_reflectivity: used_point_axes = list(range(num_point_features)) used_point_axes.pop(3) points = points[:, used_point_axes] pc_range = voxel_generator.point_cloud_range if bev_only: # set z and h to limits gt_boxes[:, 2] = pc_range[2] gt_boxes[:, 5] = pc_range[5] - pc_range[2] prep.noise_per_object_v3_( gt_boxes, points, gt_boxes_mask, rotation_perturb=gt_rotation_noise, center_noise_std=gt_loc_noise_std, global_random_rot_range=global_random_rot_range, group_ids=group_ids, num_try=100) # should remove unrelated objects after noise per object gt_boxes = gt_boxes[gt_boxes_mask] gt_names = gt_names[gt_boxes_mask] if group_ids is not None: group_ids = group_ids[gt_boxes_mask] gt_classes = np.array([class_names.index(n) + 1 for n in gt_names], dtype=np.int32) gt_boxes, points = prep.random_flip(gt_boxes, points) gt_boxes, points = prep.global_rotation(gt_boxes, points, rotation=global_rotation_noise) gt_boxes, points = prep.global_scaling_v2(gt_boxes, points, *global_scaling_noise) bv_range = voxel_generator.point_cloud_range[[0, 1, 3, 4]] mask = prep.filter_gt_box_outside_range(gt_boxes, bv_range) gt_boxes = gt_boxes[mask] gt_classes = gt_classes[mask] gt_names = gt_names[mask] if group_ids is not None: group_ids = group_ids[mask] # limit rad to [-pi, pi] gt_boxes[:, 6] = box_np_ops.limit_period(gt_boxes[:, 6], offset=0.5, period=2 * np.pi) if shuffle_points: # shuffle is a little slow. np.random.shuffle(points) # [0, -40, -3, 70.4, 40, 1] voxel_size = voxel_generator.voxel_size pc_range = voxel_generator.point_cloud_range grid_size = voxel_generator.grid_size # [352, 400] voxels, coordinates, num_points = voxel_generator.generate( points, max_voxels) example = { 'voxels': voxels, 'num_points': num_points, 'coordinates': coordinates, "num_voxels": np.array([voxels.shape[0]], dtype=np.int64) } example.update({ 'rect': rect, 'Trv2c': Trv2c, 'P2': P2, }) # if not lidar_input: feature_map_size = grid_size[:2] // out_size_factor feature_map_size = [*feature_map_size, 1][::-1] if anchor_cache is not None: anchors = anchor_cache["anchors"] anchors_bv = anchor_cache["anchors_bv"] matched_thresholds = anchor_cache["matched_thresholds"] unmatched_thresholds = anchor_cache["unmatched_thresholds"] anchors_dict = anchor_cache["anchors_dict"] else: ret = target_assigner.generate_anchors(feature_map_size) anchors = ret["anchors"] anchors = anchors.reshape([-1, 7]) matched_thresholds = ret["matched_thresholds"] unmatched_thresholds = ret["unmatched_thresholds"] anchors_dict = target_assigner.generate_anchors_dict(feature_map_size) anchors_bv = box_np_ops.rbbox2d_to_near_bbox(anchors[:, [0, 1, 3, 4, 6]]) example["anchors"] = anchors # print("debug", anchors.shape, matched_thresholds.shape) # anchors_bv = anchors_bv.reshape([-1, 4]) anchors_mask = None if anchor_area_threshold >= 0: coors = coordinates dense_voxel_map = box_np_ops.sparse_sum_for_anchors_mask( coors, tuple(grid_size[::-1][1:])) dense_voxel_map = dense_voxel_map.cumsum(0) dense_voxel_map = dense_voxel_map.cumsum(1) anchors_area = box_np_ops.fused_get_anchors_area( dense_voxel_map, anchors_bv, voxel_size, pc_range, grid_size) anchors_mask = anchors_area > anchor_area_threshold # example['anchors_mask'] = anchors_mask.astype(np.uint8) example['anchors_mask'] = anchors_mask if not training: return example if create_targets: targets_dict = target_assigner.assign_v2(anchors_dict, gt_boxes, anchors_mask, gt_classes=gt_classes, gt_names=gt_names) example.update({ 'labels': targets_dict['labels'], 'reg_targets': targets_dict['bbox_targets'], 'reg_weights': targets_dict['bbox_outside_weights'], }) return example
def create_groundtruth_database( data_path='/mrtstorage/datasets/kitti/object_detection', info_path='/home/zhwang/second.pytorch/second/data/sets/kitti_second' '/kitti_infos_train.pkl', used_classes=None, database_save_path='/home/zhwang/second.pytorch/second/data/sets/kitti_second' '/gt_database', db_info_save_path='/home/zhwang/second.pytorch/second/data/sets/kitti_second' '/kitti_dbinfos_train.pkl', relative_path=True, lidar_only=False, bev_only=False, coors_range=None): root_path = pathlib.Path(data_path) if info_path is None: info_path = root_path / 'kitti_infos_train.pkl' if database_save_path is None: database_save_path = root_path / 'gt_database' else: database_save_path = pathlib.Path(database_save_path) if db_info_save_path is None: db_info_save_path = root_path / "kitti_dbinfos_train.pkl" database_save_path.mkdir(parents=True, exist_ok=True) with open(info_path, 'rb') as f: kitti_infos = pickle.load(f) all_db_infos = {} if used_classes is None: used_classes = list(kitti.get_classes()) used_classes.pop(used_classes.index('DontCare')) for name in used_classes: all_db_infos[name] = [] group_counter = 0 for info in prog_bar(kitti_infos): velodyne_path = info['velodyne_path'] if relative_path: # velodyne_path = str(root_path / velodyne_path) + "_reduced" velodyne_path = str(root_path / velodyne_path) num_features = 4 if 'pointcloud_num_features' in info: num_features = info['pointcloud_num_features'] points = np.fromfile(velodyne_path, dtype=np.float32, count=-1).reshape([-1, num_features]) image_idx = info["image_idx"] rect = info['calib/R0_rect'] P2 = info['calib/P2'] Trv2c = info['calib/Tr_velo_to_cam'] if not lidar_only: points = box_np_ops.remove_outside_points(points, rect, Trv2c, P2, info["img_shape"]) """ fusion camera data to lidar """ image_label = _get_semantic_segmentation_result( image_idx, data_dir='/home/zhwang/semantic-segmentation' '/kitti_train_results') points = _add_class_score(image_label, points, rect, Trv2c, P2) annos = info["annos"] names = annos["name"] bboxes = annos["bbox"] difficulty = annos["difficulty"] gt_idxes = annos["index"] num_obj = np.sum(annos["index"] >= 0) rbbox_cam = kitti.anno_to_rbboxes(annos)[:num_obj] rbbox_lidar = box_np_ops.box_camera_to_lidar(rbbox_cam, rect, Trv2c) if bev_only: # set z and h to limits assert coors_range is not None rbbox_lidar[:, 2] = coors_range[2] rbbox_lidar[:, 5] = coors_range[5] - coors_range[2] group_dict = {} group_ids = np.full([bboxes.shape[0]], -1, dtype=np.int64) if "group_ids" in annos: group_ids = annos["group_ids"] else: group_ids = np.arange(bboxes.shape[0], dtype=np.int64) point_indices = box_np_ops.points_in_rbbox(points, rbbox_lidar) for i in range(num_obj): filename = f"{image_idx}_{names[i]}_{gt_idxes[i]}.bin" filepath = database_save_path / filename gt_points = points[point_indices[:, i]] gt_points[:, :3] -= rbbox_lidar[i, :3] with open(filepath, 'w') as f: gt_points.tofile(f) if names[i] in used_classes: if relative_path: db_path = str(database_save_path.stem + "/" + filename) else: db_path = str(filepath) db_info = { "name": names[i], "path": db_path, "image_idx": image_idx, "gt_idx": gt_idxes[i], "box3d_lidar": rbbox_lidar[i], "num_points_in_gt": gt_points.shape[0], "difficulty": difficulty[i], # "group_id": -1, # "bbox": bboxes[i], } local_group_id = group_ids[i] # if local_group_id >= 0: if local_group_id not in group_dict: group_dict[local_group_id] = group_counter group_counter += 1 db_info["group_id"] = group_dict[local_group_id] if "score" in annos: db_info["score"] = annos["score"][i] all_db_infos[names[i]].append(db_info) for k, v in all_db_infos.items(): print(f"load {len(v)} {k} database infos") with open(db_info_save_path, 'wb') as f: pickle.dump(all_db_infos, f)
def create_groundtruth_database(data_path, train_or_test, info_path=None, used_classes=None, database_save_path=None, db_info_save_path=None, relative_path=True, lidar_only=False, bev_only=False, coors_range=None): # custom dataset parameter custom_dataset = True # if true, it indicates that we are not operating on the kitti data sample_val_dataset_mode = True if train_or_test == "test" else False # if true, we are creating a gt database for the test set (instead of the train set) # ------------------------------------------------------------------------------------------------------ # create path where the gt boxes are stored # ----------------------------------------------------------------------------------------------------- root_path = pathlib.Path(data_path) if info_path is None: if sample_val_dataset_mode: info_path = root_path / 'kitti_infos_val.pkl' else: info_path = root_path / 'kitti_infos_train.pkl' if database_save_path is None: if sample_val_dataset_mode: database_save_path = root_path / 'gt_database_val' else: database_save_path = root_path / 'gt_database' else: database_save_path = pathlib.Path(database_save_path) if db_info_save_path is None: if sample_val_dataset_mode: db_info_save_path = root_path / "kitti_dbinfos_val.pkl" else: db_info_save_path = root_path / "kitti_dbinfos_train.pkl" database_save_path.mkdir(parents=True, exist_ok=True) # ------------------------------------------------------------------------------------------------------ # load kitti infos # ----------------------------------------------------------------------------------------------------- with open(info_path, 'rb') as f: kitti_infos = pickle.load(f) all_db_infos = {} # get the classnames we are intered in if used_classes is None: used_classes = list(kitti.get_classes()) used_classes.pop(used_classes.index('DontCare')) for name in used_classes: all_db_infos[name] = [] group_counter = 0 # ------------------------------------------------------------------------------------------------------ # iterate over kitti_infos # ----------------------------------------------------------------------------------------------------- for info in prog_bar(kitti_infos): # ------------------------------------------------------------------------------------------------------ # load pc # ----------------------------------------------------------------------------------------------------- velodyne_path = info['velodyne_path'] if relative_path: # velodyne_path = str(root_path / velodyne_path) + "_reduced" velodyne_path = str(root_path / velodyne_path) num_features = 4 if 'pointcloud_num_features' in info: num_features = info['pointcloud_num_features'] if custom_dataset: with open(str(velodyne_path)[:-3] + "pkl", 'rb') as file: points = pickle.load(file, encoding='latin1') else: points = np.fromfile(str(velodyne_path), dtype=np.float32, count=-1).reshape([-1, 4]) image_idx = info["image_idx"] rect = info['calib/R0_rect'] P2 = info['calib/P2'] Trv2c = info['calib/Tr_velo_to_cam'] # ------------------------------------------------------------------------------------------------------ # remove boxes outside the frustum of the image # ----------------------------------------------------------------------------------------------------- if not lidar_only and not custom_dataset: points = box_np_ops.remove_outside_points(points, rect, Trv2c, P2, info["img_shape"]) # ------------------------------------------------------------------------------------------------------ # get the bboxes and transform (annos not points) # ----------------------------------------------------------------------------------------------------- annos = info["annos"] names = annos["name"] bboxes = annos["bbox"] difficulty = annos["difficulty"] gt_idxes = annos["index"] num_obj = np.sum(annos["index"] >= 0) rbbox_cam = kitti.anno_to_rbboxes(annos)[:num_obj] rbbox_lidar = box_np_ops.box_camera_to_lidar(rbbox_cam, rect, Trv2c) if bev_only: # set z and h to limits assert coors_range is not None rbbox_lidar[:, 2] = coors_range[2] rbbox_lidar[:, 5] = coors_range[5] - coors_range[2] # ------------------------------------------------------------------------------------------------------ # other stuff # ----------------------------------------------------------------------------------------------------- group_dict = {} group_ids = np.full([bboxes.shape[0]], -1, dtype=np.int64) if "group_ids" in annos: group_ids = annos["group_ids"] else: group_ids = np.arange(bboxes.shape[0], dtype=np.int64) # ------------------------------------------------------------------------------------------------------ # test which points are in bboxes # ----------------------------------------------------------------------------------------------------- point_indices = box_np_ops.points_in_rbbox(points, rbbox_lidar) # ------------------------------------------------------------------------------------------------------ # iterate over all objects in the annos # ----------------------------------------------------------------------------------------------------- for i in range(num_obj): filename = f"{image_idx}_{names[i]}_{gt_idxes[i]}.bin" filepath = database_save_path / filename gt_points = points[point_indices[:, i]] gt_points[:, :3] -= rbbox_lidar[i, :3] # ------------------------------------------------------------------------------------------------------ # save points of gt boxes to files # ----------------------------------------------------------------------------------------------------- with open(str(filepath)[:-3] + "pkl", 'wb') as file: pickle.dump(np.array(gt_points), file, 2) # debug # with open("/home/makr/Documents/uni/TU/3.Master/experiments/own/tf_3dRGB_pc/debug_rviz/points/bbox_pixels.pkl", 'wb') as file: # pickle.dump(np.array(gt_points), file, 2) # ------------------------------------------------------------------------------------------------------ # save infos of gt boxes to single file # ----------------------------------------------------------------------------------------------------- if names[i] in used_classes: if relative_path: db_path = str(database_save_path.stem + "/" + filename) else: db_path = str(filepath) db_info = { "name": names[i], "path": db_path, "image_idx": image_idx, "gt_idx": gt_idxes[i], "box3d_lidar": rbbox_lidar[i], "num_points_in_gt": gt_points.shape[0], "difficulty": difficulty[i], # "group_id": -1, # "bbox": bboxes[i], } local_group_id = group_ids[i] # if local_group_id >= 0: if local_group_id not in group_dict: group_dict[local_group_id] = group_counter group_counter += 1 db_info["group_id"] = group_dict[local_group_id] if "score" in annos: db_info["score"] = annos["score"][i] all_db_infos[names[i]].append(db_info) for k, v in all_db_infos.items(): print(f"load {len(v)} {k} database infos") with open(db_info_save_path, 'wb') as f: pickle.dump(all_db_infos, f)
def _calculate_num_points_in_gt(data_path, infos, relative_path, remove_outside=True, num_features=4): # custom dataset parameter custom_dataset = True # comment strating form here import pickle num_features = 3 # iterate over the datapoints for info in infos: if relative_path: v_path = str(pathlib.Path(data_path) / info["velodyne_path"]) else: v_path = info["velodyne_path"] if custom_dataset: with open(v_path[:-3] + "pkl", 'rb') as file: points_v = pickle.load(file, encoding='latin1') else: points_v = np.fromfile(v_path, dtype=np.float32, count=-1).reshape([-1, num_features]) rect = info['calib/R0_rect'] Trv2c = info['calib/Tr_velo_to_cam'] P2 = info['calib/P2'] # debug # with open("/home/makr/Documents/uni/TU/3.Master/experiments/own/tf_3dRGB_pc/debug_rviz/points/points.pkl", 'wb') as file: # pickle.dump(np.array(points_v), file, 2) # remove points outside a frustum defined by the shape of the image if remove_outside and not custom_dataset: points_v = box_np_ops.remove_outside_points( points_v, rect, Trv2c, P2, info["img_shape"]) # points_v = points_v[points_v[:, 0] > 0] annos = info['annos'] # filter DontCare num_obj = len([n for n in annos['name'] if n != 'DontCare']) # annos = kitti.filter_kitti_anno(annos, ['DontCare']) dims = annos['dimensions'][:num_obj] loc = annos['location'][:num_obj] rots = annos['rotation_y'][:num_obj] # get gt boxes in cameras and lidar coords gt_boxes_camera = np.concatenate([loc, dims, rots[..., np.newaxis]], axis=1) gt_boxes_lidar = box_np_ops.box_camera_to_lidar( gt_boxes_camera, rect, Trv2c) # check which points are in the gt boxes indices = box_np_ops.points_in_rbbox(points_v[:, :3], gt_boxes_lidar) num_points_in_gt = indices.sum(0) # fill the list up with -1s for the DontCares num_ignored = len(annos['dimensions']) - num_obj num_points_in_gt = np.concatenate( [num_points_in_gt, -np.ones([num_ignored])]) annos["num_points_in_gt"] = num_points_in_gt.astype(np.int32)
def prep_pointcloud(input_dict, root_path, voxel_generator, target_assigner, db_sampler=None, max_voxels=20000, class_names=['Car', "Cyclist", "Pedestrian"], remove_outside_points=False, training=True, create_targets=True, shuffle_points=False, reduce_valid_area=False, remove_unknown=False, gt_rotation_noise=[-np.pi / 3, np.pi / 3], gt_loc_noise_std=[1.0, 1.0, 1.0], global_rotation_noise=[-np.pi / 4, np.pi / 4], global_scaling_noise=[0.95, 1.05], global_loc_noise_std=(0.2, 0.2, 0.2), global_random_rot_range=[0.78, 2.35], generate_bev=False, without_reflectivity=False, num_point_features=4, anchor_area_threshold=1, gt_points_drop=0.0, gt_drop_max_keep=10, remove_points_after_sample=True, anchor_cache=None, remove_environment=False, random_crop=False, reference_detections=None, add_rgb_to_points=False, lidar_input=False, unlabeled_db_sampler=None, out_size_factor=2, min_gt_point_dict=None, bev_only=False, use_group_id=False, out_dtype=np.float32, max_objs=300, length=248, width=216): """convert point cloud to voxels, create targets if ground truths exists. """ points = input_dict["points"] pc_range = voxel_generator.point_cloud_range hist, bin_edges = np.histogram(points[:, 2], bins=10, range=(pc_range[2], pc_range[5])) idx = np.argmax(hist) ground = (bin_edges[idx] + bin_edges[idx + 1]) / 2 if training: gt_boxes = input_dict["gt_boxes"] gt_names = input_dict["gt_names"] difficulty = input_dict["difficulty"] group_ids = None if use_group_id and "group_ids" in input_dict: group_ids = input_dict["group_ids"] rect = input_dict["rect"] Trv2c = input_dict["Trv2c"] P2 = input_dict["P2"] unlabeled_training = unlabeled_db_sampler is not None image_idx = input_dict["image_idx"] if reference_detections is not None: C, R, T = box_np_ops.projection_matrix_to_CRT_kitti(P2) frustums = box_np_ops.get_frustum_v2(reference_detections, C) frustums -= T # frustums = np.linalg.inv(R) @ frustums.T frustums = np.einsum('ij, akj->aki', np.linalg.inv(R), frustums) frustums = box_np_ops.camera_to_lidar(frustums, rect, Trv2c) surfaces = box_np_ops.corner_to_surfaces_3d_jit(frustums) masks = points_in_convex_polygon_3d_jit(points, surfaces) points = points[masks.any(-1)] if remove_outside_points and not lidar_input: image_shape = input_dict["image_shape"] points = box_np_ops.remove_outside_points(points, rect, Trv2c, P2, image_shape) if remove_environment is True and training: selected = kitti.keep_arrays_by_name(gt_names, class_names) gt_boxes = gt_boxes[selected] gt_names = gt_names[selected] difficulty = difficulty[selected] if group_ids is not None: group_ids = group_ids[selected] points = prep.remove_points_outside_boxes(points, gt_boxes) if training: # print(gt_names) selected = kitti.drop_arrays_by_name(gt_names, ["DontCare"]) gt_boxes = gt_boxes[selected] gt_names = gt_names[selected] difficulty = difficulty[selected] if group_ids is not None: group_ids = group_ids[selected] gt_boxes = box_np_ops.box_camera_to_lidar(gt_boxes, rect, Trv2c) if remove_unknown: remove_mask = difficulty == -1 """ gt_boxes_remove = gt_boxes[remove_mask] gt_boxes_remove[:, 3:6] += 0.25 points = prep.remove_points_in_boxes(points, gt_boxes_remove) """ keep_mask = np.logical_not(remove_mask) gt_boxes = gt_boxes[keep_mask] gt_names = gt_names[keep_mask] difficulty = difficulty[keep_mask] if group_ids is not None: group_ids = group_ids[keep_mask] gt_boxes_mask = np.array([n in class_names for n in gt_names], dtype=np.bool_) if db_sampler is not None: sampled_dict = db_sampler.sample_all(root_path, gt_boxes, gt_names, num_point_features, random_crop, gt_group_ids=group_ids, rect=rect, Trv2c=Trv2c, P2=P2) if sampled_dict is not None: sampled_gt_names = sampled_dict["gt_names"] sampled_gt_boxes = sampled_dict["gt_boxes"] sampled_points = sampled_dict["points"] sampled_gt_masks = sampled_dict["gt_masks"] # gt_names = gt_names[gt_boxes_mask].tolist() gt_names = np.concatenate([gt_names, sampled_gt_names], axis=0) # gt_names += [s["name"] for s in sampled] gt_boxes = np.concatenate([gt_boxes, sampled_gt_boxes]) gt_boxes_mask = np.concatenate( [gt_boxes_mask, sampled_gt_masks], axis=0) if group_ids is not None: sampled_group_ids = sampled_dict["group_ids"] group_ids = np.concatenate([group_ids, sampled_group_ids]) if remove_points_after_sample: points = prep.remove_points_in_boxes( points, sampled_gt_boxes) points = np.concatenate([sampled_points, points], axis=0) # unlabeled_mask = np.zeros((gt_boxes.shape[0], ), dtype=np.bool_) if without_reflectivity: used_point_axes = list(range(num_point_features)) used_point_axes.pop(3) points = points[:, used_point_axes] if bev_only: # set z and h to limits gt_boxes[:, 2] = pc_range[2] gt_boxes[:, 5] = pc_range[5] - pc_range[2] prep.noise_per_object_v3_( gt_boxes, points, gt_boxes_mask, rotation_perturb=gt_rotation_noise, center_noise_std=gt_loc_noise_std, global_random_rot_range=global_random_rot_range, group_ids=group_ids, num_try=100) # should remove unrelated objects after noise per object gt_boxes = gt_boxes[gt_boxes_mask] gt_names = gt_names[gt_boxes_mask] if group_ids is not None: group_ids = group_ids[gt_boxes_mask] gt_classes = np.array([class_names.index(n) + 1 for n in gt_names], dtype=np.int32) gt_boxes, points = prep.random_flip(gt_boxes, points) gt_boxes, points = prep.global_rotation(gt_boxes, points, rotation=global_rotation_noise) gt_boxes, points = prep.global_scaling_v2(gt_boxes, points, *global_scaling_noise) # Global translation gt_boxes, points = prep.global_translate(gt_boxes, points, global_loc_noise_std) bv_range = voxel_generator.point_cloud_range[[0, 1, 3, 4]] mask = prep.filter_gt_box_outside_range(gt_boxes, bv_range) gt_boxes = gt_boxes[mask] gt_classes = gt_classes[mask] if group_ids is not None: group_ids = group_ids[mask] # limit rad to [-pi, pi] gt_boxes[:, 6] = box_np_ops.limit_period(gt_boxes[:, 6], offset=0.5, period=2 * np.pi) if shuffle_points: # shuffle is a little slow. np.random.shuffle(points) voxel_size = voxel_generator.voxel_size pc_range = voxel_generator.point_cloud_range grid_size = voxel_generator.grid_size voxels, coordinates, num_points = voxel_generator.generate( points, max_voxels) example = { 'voxels': voxels, 'num_points': num_points, 'coordinates': coordinates, "num_voxels": np.array([voxels.shape[0]], dtype=np.int64), "ground": ground } example.update({ 'rect': rect, 'Trv2c': Trv2c, 'P2': P2, }) if generate_bev: bev_vxsize = voxel_size.copy() bev_vxsize[:2] /= 2 bev_vxsize[2] *= 2 bev_map = points_to_bev(points, bev_vxsize, pc_range, without_reflectivity) example["bev_map"] = bev_map #============================ NEW CODE =================================== if training: num_classes = len(class_names) hm = np.zeros((num_classes, length, width), dtype=np.float32) # wh = np.zeros((max_objs, 2), dtype=np.float32) reg = np.zeros((max_objs, 2), dtype=np.float32) rotbin = np.zeros((max_objs, 2), dtype=np.int64) rotres = np.zeros((max_objs, 2), dtype=np.float32) dim = np.zeros((max_objs, 3), dtype=np.float32) ind = np.zeros((max_objs), dtype=np.int64) reg_mask = np.zeros((max_objs), dtype=np.uint8) rot_mask = np.zeros((max_objs), dtype=np.uint8) num_objs = min(len(gt_boxes), max_objs) draw_gaussian = draw_msra_gaussian # if self.opt.mse_loss else draw_umich_gaussian gt_det = [] xmin, ymin, _, xmax, ymax, _ = pc_range for k in range(num_objs): box = gt_boxes[k] box[0] = np.clip(box[0], xmin, xmax) box[1] = np.clip(box[1], ymin, ymax) alpha = box[6] - np.arctan2(-box[1], box[0]) cls_id = gt_classes[k] - 1 cx = (box[0] - xmin) * (width - 1) / (xmax - xmin) cy = (box[1] - ymin) * (length - 1) / (ymax - ymin) lx = box[4] * (width - 1) / (xmax - xmin) ly = box[3] * (length - 1) / (ymax - ymin) if lx > 0 and ly > 0: radius = gaussian_radius((ly, lx)) radius = max(0, int(radius)) ct = np.array([cx, cy], dtype=np.float32) ct_int = ct.astype(np.int32) if cls_id < 0: ignore_id = [_ for _ in range(num_classes)] \ if cls_id == - 1 else [- cls_id - 2] for cc in ignore_id: draw_gaussian(hm[cc], ct, radius) hm[ignore_id, ct_int[1], ct_int[0]] = 0.9999 continue draw_gaussian(hm[cls_id], ct, radius) if alpha < np.pi / 6. or alpha > 5 * np.pi / 6.: rotbin[k, 0] = 1 rotres[k, 0] = alpha - (-0.5 * np.pi) if alpha > -np.pi / 6. or alpha < -5 * np.pi / 6.: rotbin[k, 1] = 1 rotres[k, 1] = alpha - (0.5 * np.pi) dim[k] = box[3:6] #w,l,h ind[k] = ct_int[1] * width + ct_int[0] reg[k] = ct - ct_int reg_mask[k] = 1 #if not training else 0 rot_mask[k] = 1 example.update({ 'hm': hm, 'dim': dim, 'ind': ind, 'rotbin': rotbin, 'rotres': rotres, 'reg_mask': reg_mask, 'rot_mask': rot_mask, 'reg': reg }) #============================ NEW CODE =================================== return example
def __getitem__(self, idx): """ Args: idx: (int) this is the index of the frame, from 0 to len(Video) - 1 """ frame_info = self.video_info[idx] # frame info root_path = Path(os.environ["DATADIR"]) / "synthia" res = { "depth": None, # np.ndarray, dtype=np.float32, shape=(H, W) "points": None, "cam": { "image_str": None, # str, image string "datatype": None, # str, suffix type }, "metadata": { "frameid": frame_info["frameid"], "image_shape": frame_info["image"]["image_shape"] }, "calib": frame_info["calib"], "annos": None } # -------------------------------------------------------------------------------------------------------------- # depth # -------------------------------------------------------------------------------------------------------------- depth_path = Path(frame_info["depth"]["depth_path"]) if not depth_path.is_absolute(): depth_path = root_path / depth_path # synthia depth formula: "Depth = 5000 * (R + G*256 + B*256*256) / (256*256*256 - 1)" np_depth_image = np.array( Image.open(depth_path)) # (H, W, 4) dtype=np.uint8 R, G, B = [np_depth_image[:, :, e].astype(np.int64) for e in range(3)] # (H, W), dtype=np.int64 np_depth_image = 5000 * (R + G * 256 + B * 256 * 256) / ( 256 * 256 * 256 - 1) # (H, W) dtype=np.float64 np_depth_image = np_depth_image.astype( np.float32) # (H, W) dtype=np.float32 res["depth"] = np_depth_image # -------------------------------------------------------------------------------------------------------------- # cam # -------------------------------------------------------------------------------------------------------------- if self.cam or self.colored_pc: image_path = Path(frame_info['image']['image_path']) if not image_path.is_absolute(): image_path = root_path / image_path with open(str(image_path), 'rb') as f: image_str = f.read() res["cam"]["image_str"] = image_str res["cam"]["datatype"] = image_path.suffix[1:] # -------------------------------------------------------------------------------------------------------------- # points # -------------------------------------------------------------------------------------------------------------- np_depth_image = np_depth_image[..., np.newaxis] # (H, W, 1) if self.colored_pc: # concatenate depth map with colors np_rgb_image = np.array(Image.open( io.BytesIO(image_str))) # (H, W, 4) np_rgb_image = np_rgb_image[:, :, :3] # (H, W, 3) np_depth_image = np.concatenate([np_depth_image, np_rgb_image], axis=2) # (H, W, 4) # points in cam frame P2 = frame_info['calib']['P2'] # intrinsics matrix if P2.shape == (4, 4): P2 = P2[:3, :3] else: assert P2.shape == (3, 3) points = cam_transforms.depth_map_to_point_cloud( np_depth_image, P2) # (N, 3) or (N, 6) # points in velo frame Tr_velo_to_cam = frame_info['calib'][ 'Tr_velo_to_cam'] # extrinsics matrix Tr_cam_to_velo = np.linalg.inv(Tr_velo_to_cam) xyz1_cam = np.hstack( (points[:, :3], np.ones([len(points), 1], dtype=points.dtype))) # (N, 4) xyz1_velo = xyz1_cam @ Tr_cam_to_velo.T # (N, 4) points = np.hstack((xyz1_velo[:, :3], points[:, 3:])) # (N, 3) or (N, 6) # points within MAX_DEPTH points = points[points[:, 0] < self._MAX_DEPTH, :] # (M, 3) or (M, 6) res["points"] = points # -------------------------------------------------------------------------------------------------------------- # annos # -------------------------------------------------------------------------------------------------------------- annos = frame_info['annos'] annos = self._remove_dontcare(annos) locs = annos["location"] dims = annos["dimensions"] rots = annos["rotation_y"] gt_names = annos["name"] gt_boxes = np.concatenate([locs, dims, rots[..., np.newaxis]], axis=1).astype(np.float32) gt_boxes = box_np_ops.box_camera_to_lidar( gt_boxes, r_rect=frame_info["calib"]["R0_rect"], velo2cam=frame_info["calib"]["Tr_velo_to_cam"]) # only center format is allowed. so we need to convert kitti [0.5, 0.5, 0] center to [0.5, 0.5, 0.5] box_np_ops.change_box3d_center_(gt_boxes, [0.5, 0.5, 0], [0.5, 0.5, 0.5]) res["annos"] = { 'names': gt_names, 'boxes': gt_boxes, 'boxes2d': annos["bbox"] } return res
def get_pointcloud(): global BACKEND instance = request.json response = {"status": "normal"} if BACKEND.root_path is None: return error_response("root path is not set") if BACKEND.kitti_infos is None: return error_response("kitti info is not loaded") image_idx = instance["image_idx"] print("image_idx",image_idx) idx = BACKEND.image_idxes.index(image_idx) kitti_info = BACKEND.kitti_infos[idx] rect = kitti_info['calib/R0_rect'] P2 = kitti_info['calib/P2'] Trv2c = kitti_info['calib/Tr_velo_to_cam'] img_shape = kitti_info["img_shape"] # hw wh = np.array(img_shape[::-1]) whwh = np.tile(wh, 2) if 'annos' in kitti_info: annos = kitti_info['annos'] labels = annos['name'] num_obj = len([n for n in annos['name'] if n != 'DontCare']) dims = annos['dimensions'][:num_obj] loc = annos['location'][:num_obj] rots = annos['rotation_y'][:num_obj] bbox = annos['bbox'][:num_obj] / whwh gt_boxes_camera = np.concatenate( [loc, dims, rots[..., np.newaxis]], axis=1) gt_boxes = box_np_ops.box_camera_to_lidar( gt_boxes_camera, rect, Trv2c) box_np_ops.change_box3d_center_(gt_boxes, src=[0.5, 0.5, 0], dst=[0.5, 0.5, 0.5]) locs = gt_boxes[:, :3] dims = gt_boxes[:, 3:6] rots = np.concatenate([np.zeros([num_obj, 2], dtype=np.float32), -gt_boxes[:, 6:7]], axis=1) frontend_annos = {} response["locs"] = locs.tolist() response["dims"] = dims.tolist() response["rots"] = rots.tolist() response["bbox"] = bbox.tolist() response["labels"] = labels[:num_obj].tolist() v_path = str(Path(BACKEND.root_path) / kitti_info['velodyne_path']) print("v_path:",v_path) with open(v_path, 'rb') as f: pc_str = base64.encodebytes(f.read()) response["pointcloud"] = pc_str.decode("utf-8") if "with_det" in instance and instance["with_det"]: if BACKEND.dt_annos is None: return error_response("det anno is not loaded") dt_annos = BACKEND.dt_annos[idx] dims = dt_annos['dimensions'] num_obj = dims.shape[0] loc = dt_annos['location'] rots = dt_annos['rotation_y'] bbox = dt_annos['bbox'] / whwh labels = dt_annos['name'] dt_boxes_camera = np.concatenate( [loc, dims, rots[..., np.newaxis]], axis=1) dt_boxes = box_np_ops.box_camera_to_lidar( dt_boxes_camera, rect, Trv2c) box_np_ops.change_box3d_center_(dt_boxes, src=[0.5, 0.5, 0], dst=[0.5, 0.5, 0.5]) locs = dt_boxes[:, :3] dims = dt_boxes[:, 3:6] rots = np.concatenate([np.zeros([num_obj, 2], dtype=np.float32), -dt_boxes[:, 6:7]], axis=1) response["dt_locs"] = locs.tolist() response["dt_dims"] = dims.tolist() response["dt_rots"] = rots.tolist() response["dt_labels"] = labels.tolist() response["dt_bbox"] = bbox.tolist() response["dt_scores"] = dt_annos["score"].tolist() # if "score" in annos: # response["score"] = score.tolist() response = jsonify(results=[response]) response.headers['Access-Control-Allow-Headers'] = '*' print("send response!") return response
def create_inference_dataset(det_anno_path, calib_path, velodyne_path, box3d_expansion, bbox_expansion, output_file): # get annos annos = get_label_annos(det_anno_path) # create examples examples = [] for anno in tqdm(annos): # continue if zero object detected if anno['image_idx'].shape[0] == 0: continue # get scene index scene_idx = str(anno['image_idx'][0]).zfill(6) # get calib calib = get_kitti_calib(os.path.join(calib_path, scene_idx + '.txt'), True) # get box3d_camera box3d_camera = anno_to_rbboxes(anno) box3d_lidar = box_camera_to_lidar(box3d_camera, calib["R0_rect"], calib["Tr_velo_to_cam"]) box3d_lidar[:, 2] += box3d_lidar[:, 5] / 2 # get expanded box3d box3d_lidar_expanded = expand_box3d(box3d_lidar, box3d_expansion) # get bbox bbox = box3d_to_bbox(box3d_lidar_expanded, calib["R0_rect"], calib["Tr_velo_to_cam"], calib['P2']) bbox_expanded = expand_bbox(bbox, bbox_expansion).astype(np.int) bbox_expanded[:, 0] = np.clip(bbox_expanded[:, 0], 0, 1242) bbox_expanded[:, 1] = np.clip(bbox_expanded[:, 1], 0, 375) bbox_expanded[:, 2] = np.clip(bbox_expanded[:, 2], 0, 1242) bbox_expanded[:, 3] = np.clip(bbox_expanded[:, 3], 0, 375) # read scene pts pts = read_bin(os.path.join(velodyne_path, scene_idx + '.bin'))[:, :3] # create example for idx in range(box3d_lidar_expanded.shape[0]): filtered_pts = points_in_rbbox( pts, box3d_lidar_expanded[idx][np.newaxis, ...]) res = { 'rgb': {}, 'point': {}, 'calib': calib, } res['rgb']['bbox'] = bbox_expanded[idx] res['rgb']['img_id'] = anno['image_idx'][0] res['point']['box3d_lidar'] = box3d_lidar_expanded[idx] res['point']['point_dict'] = { 'source': [pts[filtered_pts.reshape(-1)]], 'gt': [], 3: [], 4: [], 5: [], 7: [], 9: [], } examples.append(res) # write to file with open(output_file, 'wb') as f: pickle.dump(examples, f)