add global_feat
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@@ -7,12 +7,11 @@ from PytorchBoot.utils.log_util import Log
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import torch
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import os
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import sys
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sys.path.append(r"/media/hofee/data/project/python/nbv_reconstruction/nbv_reconstruction")
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sys.path.append(r"/home/data/hofee/project/nbv_rec/nbv_reconstruction")
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from utils.data_load import DataLoadUtil
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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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@stereotype.dataset("nbv_reconstruction_dataset")
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@@ -35,7 +34,7 @@ class NBVReconstructionDataset(BaseDataset):
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self.model_dir = config["model_dir"]
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self.filter_degree = config["filter_degree"]
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if self.type == namespace.Mode.TRAIN:
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scale_ratio = 1
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scale_ratio = 10
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self.datalist = self.datalist*scale_ratio
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if self.cache:
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expr_root = ConfigManager.get("runner", "experiment", "root_dir")
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@@ -56,20 +55,34 @@ class NBVReconstructionDataset(BaseDataset):
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def get_datalist(self):
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datalist = []
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for scene_name in self.scene_name_list:
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label_path = DataLoadUtil.get_label_path_old(self.root_dir, scene_name)
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label_data = DataLoadUtil.load_label(label_path)
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for data_pair in label_data["data_pairs"]:
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scanned_views = data_pair[0]
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next_best_view = data_pair[1]
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seq_num = DataLoadUtil.get_label_num(self.root_dir, scene_name)
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scene_max_coverage_rate = 0
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max_coverage_rate_list = []
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for seq_idx in range(seq_num):
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label_path = DataLoadUtil.get_label_path(self.root_dir, scene_name, seq_idx)
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label_data = DataLoadUtil.load_label(label_path)
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max_coverage_rate = label_data["max_coverage_rate"]
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datalist.append(
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{
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"scanned_views": scanned_views,
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"next_best_view": next_best_view,
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"max_coverage_rate": max_coverage_rate,
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"scene_name": scene_name,
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}
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)
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if max_coverage_rate > scene_max_coverage_rate:
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scene_max_coverage_rate = max_coverage_rate
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max_coverage_rate_list.append(max_coverage_rate)
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mean_coverage_rate = np.mean(max_coverage_rate_list)
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for seq_idx in range(seq_num):
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label_path = DataLoadUtil.get_label_path(self.root_dir, scene_name, seq_idx)
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label_data = DataLoadUtil.load_label(label_path)
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if max_coverage_rate_list[seq_idx] > mean_coverage_rate - 0.1:
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for data_pair in label_data["data_pairs"]:
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scanned_views = data_pair[0]
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next_best_view = data_pair[1]
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datalist.append({
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"scanned_views": scanned_views,
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"next_best_view": next_best_view,
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"seq_max_coverage_rate": max_coverage_rate,
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"scene_name": scene_name,
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"label_idx": seq_idx,
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"scene_max_coverage_rate": scene_max_coverage_rate
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})
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return datalist
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def preprocess_cache(self):
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@@ -102,7 +115,7 @@ class NBVReconstructionDataset(BaseDataset):
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data_item_info = self.datalist[index]
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scanned_views = data_item_info["scanned_views"]
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nbv = data_item_info["next_best_view"]
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max_coverage_rate = data_item_info["max_coverage_rate"]
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max_coverage_rate = data_item_info["seq_max_coverage_rate"]
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scene_name = data_item_info["scene_name"]
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scanned_views_pts, scanned_coverages_rate, scanned_n_to_world_pose = [], [], []
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@@ -151,13 +164,18 @@ class NBVReconstructionDataset(BaseDataset):
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best_to_world_6d = PoseUtil.matrix_to_rotation_6d_numpy(np.asarray(best_frame_to_world[:3,:3]))
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best_to_world_trans = best_frame_to_world[:3,3]
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best_to_world_9d = np.concatenate([best_to_world_6d, best_to_world_trans], axis=0)
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combined_scanned_views_pts = np.concatenate(scanned_views_pts, axis=0)
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voxel_downsampled_combined_scanned_pts_np = PtsUtil.voxel_downsample_point_cloud(combined_scanned_views_pts, 0.002)
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random_downsampled_combined_scanned_pts_np = PtsUtil.random_downsample_point_cloud(voxel_downsampled_combined_scanned_pts_np, self.pts_num)
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data_item = {
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"scanned_pts": np.asarray(scanned_views_pts,dtype=np.float32),
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"combined_scanned_pts": np.asarray(random_downsampled_combined_scanned_pts_np,dtype=np.float32),
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"scanned_coverage_rate": scanned_coverages_rate,
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"scanned_n_to_world_pose_9d": np.asarray(scanned_n_to_world_pose,dtype=np.float32),
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"best_coverage_rate": nbv_coverage_rate,
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"best_to_world_pose_9d": np.asarray(best_to_world_9d,dtype=np.float32),
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"max_coverage_rate": max_coverage_rate,
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"seq_max_coverage_rate": max_coverage_rate,
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"scene_name": scene_name
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}
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@@ -195,10 +213,11 @@ class NBVReconstructionDataset(BaseDataset):
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collate_data["scanned_pts"] = [torch.tensor(item['scanned_pts']) for item in batch]
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collate_data["scanned_n_to_world_pose_9d"] = [torch.tensor(item['scanned_n_to_world_pose_9d']) for item in batch]
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collate_data["best_to_world_pose_9d"] = torch.stack([torch.tensor(item['best_to_world_pose_9d']) for item in batch])
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collate_data["combined_scanned_pts"] = torch.stack([torch.tensor(item['combined_scanned_pts']) for item in batch])
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if "first_frame_to_world" in batch[0]:
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collate_data["first_frame_to_world"] = torch.stack([torch.tensor(item["first_frame_to_world"]) for item in batch])
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for key in batch[0].keys():
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if key not in ["scanned_pts", "scanned_n_to_world_pose_9d", "best_to_world_pose_9d", "first_frame_to_world"]:
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if key not in ["scanned_pts", "scanned_n_to_world_pose_9d", "best_to_world_pose_9d", "first_frame_to_world", "combined_scanned_pts"]:
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collate_data[key] = [item[key] for item in batch]
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return collate_data
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return collate_fn
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@@ -211,11 +230,11 @@ if __name__ == "__main__":
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torch.manual_seed(seed)
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np.random.seed(seed)
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config = {
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"root_dir": "/media/hofee/repository/nbv_reconstruction_data_512",
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"model_dir": "/media/hofee/data/data/scaled_object_meshes",
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"root_dir": "/home/data/hofee/project/nbv_rec/data/nbv_rec_data_512_preproc_npy",
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"model_dir": "/home/data/hofee/project/nbv_rec/data/scaled_object_meshes",
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"source": "nbv_reconstruction_dataset",
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"split_file": "/media/hofee/data/project/python/nbv_reconstruction/sample_for_training/OmniObject3d_train.txt",
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"load_from_preprocess": False,
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"split_file": "/home/data/hofee/project/nbv_rec/data/OmniObject3d_test.txt",
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"load_from_preprocess": True,
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"ratio": 0.5,
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"batch_size": 2,
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"filter_degree": 75,
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