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debug strategy_generator

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hofee
2024-09-10 20:12:46 +08:00
parent 38f7f8df18
commit fd96b97d7b
6 changed files with 82 additions and 38 deletions
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35
runners/strategy_generator.py
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@@ -23,6 +23,10 @@ class StrategyGenerator(Runner):
"runner_name": "strategy_generator"
}
self.to_specified_dir = ConfigManager.get("runner", "generate", "to_specified_dir")
self.save_best_combined_pts = ConfigManager.get("runner", "generate", "save_best_combined_points")
self.save_mesh = ConfigManager.get("runner", "generate", "save_mesh")
self.filter_degree = ConfigManager.get("runner", "generate", "filter_degree")
def run(self):
@@ -33,13 +37,14 @@ class StrategyGenerator(Runner):
dataset_name = dataset_name_list[dataset_idx]
status_manager.set_progress("generate", "strategy_generator", "dataset", dataset_idx, len(dataset_name_list))
root_dir = ConfigManager.get("datasets", dataset_name, "root_dir")
scene_name_list = os.listdir(root_dir)[:10]
model_dir = ConfigManager.get("datasets", dataset_name, "model_dir")
scene_name_list = os.listdir(root_dir)
cnt = 0
total = len(scene_name_list)
for scene_name in scene_name_list:
Log.info(f"({dataset_name})Processing [{cnt}/{total}]: {scene_name}")
status_manager.set_progress("generate", "strategy_generator", "scene", cnt, total)
self.generate_sequence(root_dir, dataset_name, scene_name,voxel_threshold, overlap_threshold, )
self.generate_sequence(root_dir, model_dir, scene_name,voxel_threshold, overlap_threshold)
cnt += 1
status_manager.set_progress("generate", "strategy_generator", "scene", total, total)
status_manager.set_progress("generate", "strategy_generator", "dataset", len(dataset_name_list), len(dataset_name_list))
@@ -52,7 +57,7 @@ class StrategyGenerator(Runner):
def load_experiment(self, backup_name=None):
super().load_experiment(backup_name)
def generate_sequence(self, root, dataset_name, scene_name, voxel_threshold, overlap_threshold):
def generate_sequence(self, root, model_dir, scene_name, voxel_threshold, overlap_threshold):
status_manager.set_status("generate", "strategy_generator", "scene", scene_name)
frame_num = DataLoadUtil.get_scene_seq_length(root, scene_name)
model_points_normals = DataLoadUtil.load_points_normals(root, scene_name)
@@ -66,7 +71,9 @@ class StrategyGenerator(Runner):
cam_params = DataLoadUtil.load_cam_info(path, binocular=True)
status_manager.set_progress("generate", "strategy_generator", "loading frame", frame_idx, frame_num)
point_cloud = DataLoadUtil.get_target_point_cloud_world_from_path(path, binocular=True)
sampled_point_cloud = ReconstructionUtil.filter_points(point_cloud, model_points_normals, cam_pose=cam_params["cam_to_world"], voxel_size=voxel_threshold, theta=45)
#display_table = None #DataLoadUtil.get_target_point_cloud_world_from_path(path, binocular=True, target_mask_label=()) #TODO
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)
if self.save_pts:
pts_dir = os.path.join(root,scene_name, "pts")
if not os.path.exists(pts_dir):
@@ -75,7 +82,7 @@ class StrategyGenerator(Runner):
pts_list.append(sampled_point_cloud)
status_manager.set_progress("generate", "strategy_generator", "loading frame", frame_num, frame_num)
limited_useful_view, _ = 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)
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)
data_pairs = self.generate_data_pairs(limited_useful_view)
seq_save_data = {
"data_pairs": data_pairs,
@@ -85,17 +92,19 @@ class StrategyGenerator(Runner):
status_manager.set_status("generate", "strategy_generator", "max_coverage_rate", limited_useful_view[-1][1])
Log.success(f"Scene <{scene_name}> Finished, Max Coverage Rate: {limited_useful_view[-1][1]}, Best Sequence length: {len(limited_useful_view)}")
if self.to_specified_dir:
output_dir = ConfigManager.get("datasets", dataset_name,"output_dir")
output_label_path = os.path.join(output_dir, f"{scene_name}.json")
if not os.path.exists(output_dir):
os.makedirs(output_dir)
else:
output_label_path = DataLoadUtil.get_label_path(root, scene_name)
output_label_path = DataLoadUtil.get_label_path(root, scene_name)
output_best_reconstructed_pts_path = os.path.join(root,scene_name, f"best_reconstructed_pts.txt")
with open(output_label_path, 'w') as f:
json.dump(seq_save_data, f)
if self.save_best_combined_pts:
np.savetxt(output_best_reconstructed_pts_path, best_combined_pts)
if self.save_mesh:
DataLoadUtil.save_target_mesh_at_world_space(root, model_dir, scene_name)
DataLoadUtil.save_downsampled_world_model_points(root, scene_name, down_sampled_model_pts)
def generate_data_pairs(self, useful_view):