120 lines
6.3 KiB
Python
120 lines
6.3 KiB
Python
import os
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import json
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import numpy as np
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from PytorchBoot.runners.runner import Runner
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from PytorchBoot.config import ConfigManager
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from PytorchBoot.utils import Log
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import PytorchBoot.stereotype as stereotype
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from PytorchBoot.status import status_manager
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from utils.data_load import DataLoadUtil
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from utils.reconstruction import ReconstructionUtil
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from utils.pts import PtsUtil
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@stereotype.runner("strategy_generator")
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class StrategyGenerator(Runner):
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def __init__(self, config):
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super().__init__(config)
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self.load_experiment("generate")
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self.status_info = {
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"status_manager": status_manager,
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"app_name": "generate",
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"runner_name": "strategy_generator"
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}
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self.to_specified_dir = ConfigManager.get("runner", "generate", "to_specified_dir")
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self.save_best_combined_pts = ConfigManager.get("runner", "generate", "save_best_combined_points")
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self.save_mesh = ConfigManager.get("runner", "generate", "save_mesh")
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self.filter_degree = ConfigManager.get("runner", "generate", "filter_degree")
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def run(self):
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dataset_name_list = ConfigManager.get("runner", "generate", "dataset_list")
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voxel_threshold, overlap_threshold = ConfigManager.get("runner","generate","voxel_threshold"), ConfigManager.get("runner","generate","overlap_threshold")
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self.save_pts = ConfigManager.get("runner","generate","save_points")
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for dataset_idx in range(len(dataset_name_list)):
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dataset_name = dataset_name_list[dataset_idx]
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status_manager.set_progress("generate", "strategy_generator", "dataset", dataset_idx, len(dataset_name_list))
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root_dir = ConfigManager.get("datasets", dataset_name, "root_dir")
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model_dir = ConfigManager.get("datasets", dataset_name, "model_dir")
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scene_name_list = os.listdir(root_dir)
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cnt = 0
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total = len(scene_name_list)
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for scene_name in scene_name_list:
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Log.info(f"({dataset_name})Processing [{cnt}/{total}]: {scene_name}")
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status_manager.set_progress("generate", "strategy_generator", "scene", cnt, total)
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self.generate_sequence(root_dir, model_dir, scene_name,voxel_threshold, overlap_threshold)
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cnt += 1
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status_manager.set_progress("generate", "strategy_generator", "scene", total, total)
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status_manager.set_progress("generate", "strategy_generator", "dataset", len(dataset_name_list), len(dataset_name_list))
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def create_experiment(self, backup_name=None):
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super().create_experiment(backup_name)
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output_dir = os.path.join(str(self.experiment_path), "output")
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os.makedirs(output_dir)
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def load_experiment(self, backup_name=None):
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super().load_experiment(backup_name)
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def generate_sequence(self, root, model_dir, scene_name, voxel_threshold, overlap_threshold):
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status_manager.set_status("generate", "strategy_generator", "scene", scene_name)
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frame_num = DataLoadUtil.get_scene_seq_length(root, scene_name)
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model_points_normals = DataLoadUtil.load_points_normals(root, scene_name)
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model_pts = model_points_normals[:,:3]
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down_sampled_model_pts = PtsUtil.voxel_downsample_point_cloud(model_pts, voxel_threshold)
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pts_list = []
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for frame_idx in range(frame_num):
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path = DataLoadUtil.get_path(root, scene_name, frame_idx)
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cam_params = DataLoadUtil.load_cam_info(path, binocular=True)
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status_manager.set_progress("generate", "strategy_generator", "loading frame", frame_idx, frame_num)
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point_cloud = DataLoadUtil.get_target_point_cloud_world_from_path(path, binocular=True)
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#display_table = None #DataLoadUtil.get_target_point_cloud_world_from_path(path, binocular=True, target_mask_label=()) #TODO
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sampled_point_cloud = ReconstructionUtil.filter_points(point_cloud, model_points_normals, cam_pose=cam_params["cam_to_world"], voxel_size=voxel_threshold, theta=self.filter_degree)
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if self.save_pts:
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pts_dir = os.path.join(root,scene_name, "pts")
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if not os.path.exists(pts_dir):
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os.makedirs(pts_dir)
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np.savetxt(os.path.join(pts_dir, f"{frame_idx}.txt"), sampled_point_cloud)
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pts_list.append(sampled_point_cloud)
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status_manager.set_progress("generate", "strategy_generator", "loading frame", frame_num, frame_num)
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limited_useful_view, _, best_combined_pts = ReconstructionUtil.compute_next_best_view_sequence_with_overlap(down_sampled_model_pts, pts_list, threshold=voxel_threshold, overlap_threshold=overlap_threshold, status_info=self.status_info)
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data_pairs = self.generate_data_pairs(limited_useful_view)
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seq_save_data = {
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"data_pairs": data_pairs,
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"best_sequence": limited_useful_view,
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"max_coverage_rate": limited_useful_view[-1][1]
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}
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status_manager.set_status("generate", "strategy_generator", "max_coverage_rate", limited_useful_view[-1][1])
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Log.success(f"Scene <{scene_name}> Finished, Max Coverage Rate: {limited_useful_view[-1][1]}, Best Sequence length: {len(limited_useful_view)}")
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output_label_path = DataLoadUtil.get_label_path(root, scene_name)
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output_best_reconstructed_pts_path = os.path.join(root,scene_name, f"best_reconstructed_pts.txt")
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with open(output_label_path, 'w') as f:
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json.dump(seq_save_data, f)
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if self.save_best_combined_pts:
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np.savetxt(output_best_reconstructed_pts_path, best_combined_pts)
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if self.save_mesh:
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DataLoadUtil.save_target_mesh_at_world_space(root, model_dir, scene_name)
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DataLoadUtil.save_downsampled_world_model_points(root, scene_name, down_sampled_model_pts)
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def generate_data_pairs(self, useful_view):
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data_pairs = []
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for next_view_idx in range(1, len(useful_view)):
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scanned_views = useful_view[:next_view_idx]
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next_view = useful_view[next_view_idx]
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data_pairs.append((scanned_views, next_view))
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return data_pairs
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