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hofee
2024-09-08 19:43:01 +08:00
parent b5d44b153a
commit 38f7f8df18
10 changed files with 142 additions and 847 deletions
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35
runners/strategy_generator.py
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@@ -22,6 +22,8 @@ class StrategyGenerator(Runner):
"app_name": "generate",
"runner_name": "strategy_generator"
}
self.to_specified_dir = ConfigManager.get("runner", "generate", "to_specified_dir")
def run(self):
dataset_name_list = ConfigManager.get("runner", "generate", "dataset_list")
@@ -31,15 +33,13 @@ 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")
model_dir = ConfigManager.get("datasets", dataset_name, "model_dir")
scene_name_list = os.listdir(root_dir)
scene_name_list = os.listdir(root_dir)[:10]
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, model_dir, scene_name,voxel_threshold, overlap_threshold)
self.generate_sequence(root_dir, dataset_name, 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))
@@ -52,20 +52,21 @@ class StrategyGenerator(Runner):
def load_experiment(self, backup_name=None):
super().load_experiment(backup_name)
def generate_sequence(self, root, model_dir, scene_name, voxel_threshold, overlap_threshold):
def generate_sequence(self, root, dataset_name, 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_pts = DataLoadUtil.load_original_model_points(model_dir, scene_name)
model_points_normals = DataLoadUtil.load_points_normals(root, scene_name)
model_pts = model_points_normals[:,:3]
down_sampled_model_pts = PtsUtil.voxel_downsample_point_cloud(model_pts, voxel_threshold)
obj_pose = DataLoadUtil.load_target_object_pose(root, scene_name)
down_sampled_transformed_model_pts = PtsUtil.transform_point_cloud(down_sampled_model_pts, obj_pose)
pts_list = []
for frame_idx in range(frame_num):
path = DataLoadUtil.get_path(root, scene_name, frame_idx)
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_point_cloud_world_from_path(path)
sampled_point_cloud = PtsUtil.voxel_downsample_point_cloud(point_cloud, voxel_threshold)
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)
if self.save_pts:
pts_dir = os.path.join(root,scene_name, "pts")
if not os.path.exists(pts_dir):
@@ -74,7 +75,7 @@ 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_transformed_model_pts, pts_list, threshold=voxel_threshold, overlap_threshold=overlap_threshold, status_info=self.status_info)
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)
data_pairs = self.generate_data_pairs(limited_useful_view)
seq_save_data = {
"data_pairs": data_pairs,
@@ -84,12 +85,18 @@ 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)}")
output_label_path = DataLoadUtil.get_label_path(root, scene_name)
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)
with open(output_label_path, 'w') as f:
json.dump(seq_save_data, f)
DataLoadUtil.save_downsampled_world_model_points(root, scene_name, down_sampled_transformed_model_pts)
DataLoadUtil.save_downsampled_world_model_points(root, scene_name, down_sampled_model_pts)
def generate_data_pairs(self, useful_view):
data_pairs = []

2
runners/view_generator.py
View File

@@ -9,7 +9,7 @@ class ViewGenerator(Runner):
self.config_path = config_path
def run(self):
subprocess.run(['blender', '-b', '-P', './blender/run_blender.py', '--', self.config_path])
subprocess.run(['blender', '-b', '-P', '../blender/run_blender.py', '--', self.config_path])
def create_experiment(self, backup_name=None):
return super().create_experiment(backup_name)