349 lines
18 KiB
Python
349 lines
18 KiB
Python
import os
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import json
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from utils.render import RenderUtil
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from utils.pose import PoseUtil
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from utils.pts import PtsUtil
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from utils.reconstruction import ReconstructionUtil
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from beans.predict_result import PredictResult
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from tqdm import tqdm
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import numpy as np
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import pickle
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from PytorchBoot.config import ConfigManager
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import PytorchBoot.namespace as namespace
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import PytorchBoot.stereotype as stereotype
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from PytorchBoot.factory import ComponentFactory
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from PytorchBoot.dataset import BaseDataset
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from PytorchBoot.runners.runner import Runner
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from PytorchBoot.utils import Log
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from PytorchBoot.status import status_manager
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from utils.data_load import DataLoadUtil
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@stereotype.runner("evaluate_uncertainty_guide")
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class EvaluateUncertaintyGuide(Runner):
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def __init__(self, config_path):
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super().__init__(config_path)
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self.script_path = ConfigManager.get(namespace.Stereotype.RUNNER, "blender_script_path")
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self.output_dir = ConfigManager.get(namespace.Stereotype.RUNNER, "output_dir")
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self.voxel_size = ConfigManager.get(namespace.Stereotype.RUNNER, "voxel_size")
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self.min_new_area = ConfigManager.get(namespace.Stereotype.RUNNER, "min_new_area")
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CM = 0.01
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self.min_new_pts_num = self.min_new_area * (CM / self.voxel_size) ** 2
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self.overlap_limit = ConfigManager.get(namespace.Stereotype.RUNNER, "overlap_limit")
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self.radius = 0.5
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self.output_data_root = ConfigManager.get(namespace.Stereotype.RUNNER, "output_data_root")
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self.output_data = dict()
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for scene_name in os.listdir(self.output_data_root):
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real_scene_name = scene_name[:-len("_poses.npy")]
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self.output_data[real_scene_name] = np.load(os.path.join(self.output_data_root, scene_name))
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#mport ipdb; ipdb.set_trace()
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''' Experiment '''
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self.load_experiment("nbv_evaluator")
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self.stat_result_path = os.path.join(self.output_dir, "stat.json")
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if os.path.exists(self.stat_result_path):
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with open(self.stat_result_path, "r") as f:
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self.stat_result = json.load(f)
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else:
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self.stat_result = {}
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''' Test '''
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self.test_config = ConfigManager.get(namespace.Stereotype.RUNNER, namespace.Mode.TEST)
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self.test_dataset_name_list = self.test_config["dataset_list"]
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self.test_set_list = []
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self.test_writer_list = []
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seen_name = set()
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for test_dataset_name in self.test_dataset_name_list:
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if test_dataset_name not in seen_name:
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seen_name.add(test_dataset_name)
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else:
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raise ValueError("Duplicate test dataset name: {}".format(test_dataset_name))
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test_set: BaseDataset = ComponentFactory.create(namespace.Stereotype.DATASET, test_dataset_name)
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self.test_set_list.append(test_set)
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self.print_info()
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def run(self):
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Log.info("Loading from epoch {}.".format(self.current_epoch))
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self.inference()
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Log.success("Inference finished.")
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def inference(self):
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#self.pipeline.eval()
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test_set: BaseDataset
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for dataset_idx, test_set in enumerate(self.test_set_list):
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status_manager.set_progress("inference", "inferencer", f"dataset", dataset_idx, len(self.test_set_list))
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test_set_name = test_set.get_name()
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total=int(len(test_set))
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for i in tqdm(range(total), desc=f"Processing {test_set_name}", ncols=100):
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try:
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data = test_set.__getitem__(i)
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scene_name = data["scene_name"]
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inference_result_path = os.path.join(self.output_dir, test_set_name, f"{scene_name}.pkl")
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if os.path.exists(inference_result_path):
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Log.info(f"Inference result already exists for scene: {scene_name}")
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continue
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status_manager.set_progress("inference", "inferencer", f"Batch[{test_set_name}]", i+1, total)
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output = self.predict_sequence(data)
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self.save_inference_result(test_set_name, data["scene_name"], output)
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except Exception as e:
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print(e)
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Log.error(f"Error, {e}")
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continue
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status_manager.set_progress("inference", "inferencer", f"dataset", len(self.test_set_list), len(self.test_set_list))
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def get_output_data(self, scene_name, idx):
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pose_matrix = self.output_data[scene_name][idx]
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offset = np.asarray([[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]])
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pose_matrix = pose_matrix @ offset
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rot = pose_matrix[:3,:3]
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pose_6d = PoseUtil.matrix_to_rotation_6d_numpy(rot)
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# 计算相机在球面上的位置
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camera_direction = rot[:, 2] # 相机朝向球心
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translation = -self.radius * camera_direction # 相机位置在球面上
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pose_9d = np.concatenate([pose_6d, translation], axis=0).reshape(1,9)
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pose_9d = pose_9d.repeat(50, axis=0)
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#import ipdb; ipdb.set_trace()
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return {"pred_pose_9d": pose_9d}
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def predict_sequence(self, data, cr_increase_threshold=0, overlap_area_threshold=25, scan_points_threshold=10, max_iter=50, max_retry = 10, max_success=3):
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scene_name = data["scene_name"]
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Log.info(f"Processing scene: {scene_name}")
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status_manager.set_status("inference", "inferencer", "scene", scene_name)
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''' data for rendering '''
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scene_path = data["scene_path"]
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O_to_L_pose = data["O_to_L_pose"]
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voxel_threshold = self.voxel_size
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filter_degree = 75
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down_sampled_model_pts = data["gt_pts"]
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first_frame_to_world_9d = data["first_scanned_n_to_world_pose_9d"][0]
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first_frame_to_world = np.eye(4)
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first_frame_to_world[:3,:3] = PoseUtil.rotation_6d_to_matrix_numpy(first_frame_to_world_9d[:6])
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first_frame_to_world[:3,3] = first_frame_to_world_9d[6:]
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''' data for inference '''
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input_data = {}
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input_data["combined_scanned_pts"] = np.array(data["first_scanned_pts"][0], dtype=np.float32)
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input_data["scanned_pts"] = [np.array(data["first_scanned_pts"][0], dtype=np.float32)]
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input_data["scanned_pts_mask"] = [np.zeros(input_data["combined_scanned_pts"].shape[0], dtype=np.bool_)]
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input_data["scanned_n_to_world_pose_9d"] = [np.array(data["first_scanned_n_to_world_pose_9d"], dtype=np.float32)]
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input_data["mode"] = namespace.Mode.TEST
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input_pts_N = input_data["combined_scanned_pts"].shape[0]
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root = os.path.dirname(scene_path)
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display_table_info = DataLoadUtil.get_display_table_info(root, scene_name)
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radius = display_table_info["radius"]
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scan_points = np.asarray(ReconstructionUtil.generate_scan_points(display_table_top=0,display_table_radius=radius))
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first_frame_target_pts, first_frame_target_normals, first_frame_scan_points_indices = RenderUtil.render_pts(first_frame_to_world, scene_path, self.script_path, scan_points, voxel_threshold=voxel_threshold, filter_degree=filter_degree, nO_to_nL_pose=O_to_L_pose)
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scanned_view_pts = [first_frame_target_pts]
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history_indices = [first_frame_scan_points_indices]
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last_pred_cr, added_pts_num = self.compute_coverage_rate(scanned_view_pts, None, down_sampled_model_pts, threshold=voxel_threshold)
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retry_duplication_pose = []
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retry_no_pts_pose = []
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retry_overlap_pose = []
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retry = 0
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pred_cr_seq = [last_pred_cr]
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success = 0
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last_pts_num = PtsUtil.voxel_downsample_point_cloud(data["first_scanned_pts"][0], voxel_threshold).shape[0]
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#import time
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for i in range(len(self.output_data[scene_name])):
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#import ipdb; ipdb.set_trace()
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Log.green(f"iter: {len(pred_cr_seq)}, retry: {retry}/{max_retry}, success: {success}/{max_success}")
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combined_scanned_pts = np.vstack(scanned_view_pts)
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voxel_downsampled_combined_scanned_pts_np, inverse = self.voxel_downsample_with_mapping(combined_scanned_pts, voxel_threshold)
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output = self.get_output_data(scene_name, i)
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pred_pose_9d = output["pred_pose_9d"]
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pred_pose = np.eye(4)
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predict_result = PredictResult(pred_pose_9d, input_pts=input_data["combined_scanned_pts"], cluster_params=dict(eps=0.25, min_samples=3))
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# -----------------------
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# import ipdb; ipdb.set_trace()
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# predict_result.visualize()
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# -----------------------
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pred_pose_9d_candidates = predict_result.candidate_9d_poses
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#import ipdb; ipdb.set_trace()
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for pred_pose_9d in pred_pose_9d_candidates:
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#import ipdb; ipdb.set_trace()
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pred_pose_9d = np.array(pred_pose_9d, dtype=np.float32)
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pred_pose[:3,:3] = PoseUtil.rotation_6d_to_matrix_numpy(pred_pose_9d[:6])
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pred_pose[:3,3] = pred_pose_9d[6:]
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try:
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new_target_pts, new_target_normals, new_scan_points_indices = RenderUtil.render_pts(pred_pose, scene_path, self.script_path, scan_points, voxel_threshold=voxel_threshold, filter_degree=filter_degree, nO_to_nL_pose=O_to_L_pose)
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#import ipdb; ipdb.set_trace()
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if not ReconstructionUtil.check_scan_points_overlap(history_indices, new_scan_points_indices, scan_points_threshold):
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curr_overlap_area_threshold = overlap_area_threshold
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else:
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curr_overlap_area_threshold = overlap_area_threshold * 0.5
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downsampled_new_target_pts = PtsUtil.voxel_downsample_point_cloud(new_target_pts, voxel_threshold)
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#import ipdb; ipdb.set_trace()
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if self.overlap_limit:
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overlap, _ = ReconstructionUtil.check_overlap(downsampled_new_target_pts, voxel_downsampled_combined_scanned_pts_np, overlap_area_threshold = curr_overlap_area_threshold, voxel_size=voxel_threshold, require_new_added_pts_num = True)
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if not overlap:
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Log.yellow("no overlap!")
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retry += 1
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retry_overlap_pose.append(pred_pose.tolist())
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continue
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history_indices.append(new_scan_points_indices)
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except Exception as e:
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Log.error(f"Error in scene {scene_path}, {e}")
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print("current pose: ", pred_pose)
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print("curr_pred_cr: ", last_pred_cr)
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retry_no_pts_pose.append(pred_pose.tolist())
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retry += 1
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continue
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if new_target_pts.shape[0] == 0:
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Log.red("no pts in new target")
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retry_no_pts_pose.append(pred_pose.tolist())
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retry += 1
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continue
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pred_cr, _ = self.compute_coverage_rate(scanned_view_pts, new_target_pts, down_sampled_model_pts, threshold=voxel_threshold)
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Log.yellow(f"{pred_cr}, {last_pred_cr}, max: , {data['seq_max_coverage_rate']}")
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if pred_cr >= data["seq_max_coverage_rate"] - 1e-3:
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print("max coverage rate reached!: ", pred_cr)
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pred_cr_seq.append(pred_cr)
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scanned_view_pts.append(new_target_pts)
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pred_pose_9d = pred_pose_9d.reshape(1, -1)
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input_data["scanned_n_to_world_pose_9d"] = [np.concatenate([input_data["scanned_n_to_world_pose_9d"][0], pred_pose_9d], axis=0)]
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combined_scanned_pts = np.vstack(scanned_view_pts)
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voxel_downsampled_combined_scanned_pts_np = PtsUtil.voxel_downsample_point_cloud(combined_scanned_pts, voxel_threshold)
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random_downsampled_combined_scanned_pts_np = PtsUtil.random_downsample_point_cloud(voxel_downsampled_combined_scanned_pts_np, input_pts_N)
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input_data["combined_scanned_pts"] = np.array(random_downsampled_combined_scanned_pts_np, dtype=np.float32)
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input_data["scanned_pts"] = [np.concatenate([input_data["scanned_pts"][0], np.array(random_downsampled_combined_scanned_pts_np, dtype=np.float32)], axis=0)]
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last_pred_cr = pred_cr
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pts_num = voxel_downsampled_combined_scanned_pts_np.shape[0]
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Log.info(f"delta pts num:,{pts_num - last_pts_num },{pts_num}, {last_pts_num}")
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if pts_num - last_pts_num < self.min_new_pts_num and pred_cr <= data["seq_max_coverage_rate"] - 1e-2:
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retry += 1
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retry_duplication_pose.append(pred_pose.tolist())
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Log.red(f"delta pts num < {self.min_new_pts_num}:, {pts_num}, {last_pts_num}")
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elif pts_num - last_pts_num < self.min_new_pts_num and pred_cr > data["seq_max_coverage_rate"] - 1e-2:
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success += 1
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Log.success(f"delta pts num < {self.min_new_pts_num}:, {pts_num}, {last_pts_num}")
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last_pts_num = pts_num
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input_data["scanned_n_to_world_pose_9d"] = input_data["scanned_n_to_world_pose_9d"][0].tolist()
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result = {
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"pred_pose_9d_seq": input_data["scanned_n_to_world_pose_9d"],
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"combined_scanned_pts": input_data["combined_scanned_pts"],
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"target_pts_seq": scanned_view_pts,
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"coverage_rate_seq": pred_cr_seq,
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"max_coverage_rate": data["seq_max_coverage_rate"],
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"pred_max_coverage_rate": max(pred_cr_seq),
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"scene_name": scene_name,
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"retry_no_pts_pose": retry_no_pts_pose,
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"retry_duplication_pose": retry_duplication_pose,
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"retry_overlap_pose": retry_overlap_pose,
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"best_seq_len": data["best_seq_len"],
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}
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self.stat_result[scene_name] = {
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"coverage_rate_seq": pred_cr_seq,
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"pred_max_coverage_rate": max(pred_cr_seq),
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"pred_seq_len": len(pred_cr_seq),
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}
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print('success rate: ', max(pred_cr_seq))
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return result
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def voxel_downsample_with_mapping(self, point_cloud, voxel_size=0.003):
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voxel_indices = np.floor(point_cloud / voxel_size).astype(np.int32)
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unique_voxels, inverse, counts = np.unique(voxel_indices, axis=0, return_inverse=True, return_counts=True)
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idx_sort = np.argsort(inverse)
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idx_unique = idx_sort[np.cumsum(counts)-counts]
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downsampled_points = point_cloud[idx_unique]
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return downsampled_points, inverse
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def compute_coverage_rate(self, scanned_view_pts, new_pts, model_pts, threshold=0.005):
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if new_pts is not None:
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new_scanned_view_pts = scanned_view_pts + [new_pts]
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else:
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new_scanned_view_pts = scanned_view_pts
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combined_point_cloud = np.vstack(new_scanned_view_pts)
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down_sampled_combined_point_cloud = PtsUtil.voxel_downsample_point_cloud(combined_point_cloud,threshold)
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return ReconstructionUtil.compute_coverage_rate(model_pts, down_sampled_combined_point_cloud, threshold)
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def voxel_downsample_with_mapping(self, point_cloud, voxel_size=0.003):
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voxel_indices = np.floor(point_cloud / voxel_size).astype(np.int32)
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unique_voxels, inverse, counts = np.unique(voxel_indices, axis=0, return_inverse=True, return_counts=True)
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idx_sort = np.argsort(inverse)
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idx_unique = idx_sort[np.cumsum(counts)-counts]
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downsampled_points = point_cloud[idx_unique]
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return downsampled_points, inverse
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def save_inference_result(self, dataset_name, scene_name, output):
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dataset_dir = os.path.join(self.output_dir, dataset_name)
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if not os.path.exists(dataset_dir):
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os.makedirs(dataset_dir)
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output_path = os.path.join(dataset_dir, f"{scene_name}.pkl")
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pickle.dump(output, open(output_path, "wb"))
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with open(self.stat_result_path, "w") as f:
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json.dump(self.stat_result, f)
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def get_checkpoint_path(self, is_last=False):
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return os.path.join(self.experiment_path, namespace.Direcotry.CHECKPOINT_DIR_NAME,
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"Epoch_{}.pth".format(
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self.current_epoch if self.current_epoch != -1 and not is_last else "last"))
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def load_experiment(self, backup_name=None):
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super().load_experiment(backup_name)
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self.current_epoch = self.experiments_config["epoch"]
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#self.load_checkpoint(is_last=(self.current_epoch == -1))
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def create_experiment(self, backup_name=None):
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super().create_experiment(backup_name)
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def load(self, path):
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# 如果仍然需要加载某些数据，可以使用numpy的load方法
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pass
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def print_info(self):
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def print_dataset(dataset: BaseDataset):
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config = dataset.get_config()
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name = dataset.get_name()
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Log.blue(f"Dataset: {name}")
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for k,v in config.items():
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Log.blue(f"\t{k}: {v}")
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super().print_info()
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table_size = 70
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Log.blue(f"{'+' + '-' * (table_size // 2)} Pipeline {'-' * (table_size // 2)}" + '+')
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#Log.blue(self.pipeline)
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Log.blue(f"{'+' + '-' * (table_size // 2)} Datasets {'-' * (table_size // 2)}" + '+')
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for i, test_set in enumerate(self.test_set_list):
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Log.blue(f"test dataset {i}: ")
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print_dataset(test_set)
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Log.blue(f"{'+' + '-' * (table_size // 2)}----------{'-' * (table_size // 2)}" + '+')
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