136 lines
6.1 KiB
Python
136 lines
6.1 KiB
Python
import numpy as np
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from PytorchBoot.dataset import BaseDataset
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import PytorchBoot.stereotype as stereotype
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import sys
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sys.path.append(r"C:\Document\Local 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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@stereotype.dataset("nbv_reconstruction_dataset")
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class NBVReconstructionDataset(BaseDataset):
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def __init__(self, config):
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super(NBVReconstructionDataset, self).__init__(config)
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self.config = config
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self.root_dir = config["root_dir"]
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self.split_file_path = config["split_file"]
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self.scene_name_list = self.load_scene_name_list()
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self.datalist = self.get_datalist()
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self.pts_num = 1024
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def load_scene_name_list(self):
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scene_name_list = []
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with open(self.split_file_path, "r") as f:
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for line in f:
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scene_name = line.strip()
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scene_name_list.append(scene_name)
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return scene_name_list
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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(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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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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return datalist
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def __getitem__(self, index):
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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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scene_name = data_item_info["scene_name"]
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scanned_views_pts, scanned_coverages_rate, scanned_n_to_1_pose = [], [], []
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first_frame_idx = scanned_views[0][0]
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first_frame_to_world = DataLoadUtil.load_cam_info(DataLoadUtil.get_path(self.root_dir, scene_name, first_frame_idx))["cam_to_world"]
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for view in scanned_views:
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frame_idx = view[0]
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coverage_rate = view[1]
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view_path = DataLoadUtil.get_path(self.root_dir, scene_name, frame_idx)
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depth = DataLoadUtil.load_depth(view_path)
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cam_info = DataLoadUtil.load_cam_info(view_path)
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mask = DataLoadUtil.load_seg(view_path)
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frame_curr_to_world = cam_info["cam_to_world"]
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n_to_1_pose = np.dot(np.linalg.inv(first_frame_to_world), frame_curr_to_world)
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target_point_cloud = DataLoadUtil.get_target_point_cloud(depth, cam_info["cam_intrinsic"], n_to_1_pose, mask)["points_world"]
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downsampled_target_point_cloud = PtsUtil.random_downsample_point_cloud(target_point_cloud, self.pts_num)
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scanned_views_pts.append(downsampled_target_point_cloud)
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scanned_coverages_rate.append(coverage_rate)
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n_to_1_6d = PoseUtil.matrix_to_rotation_6d_numpy(np.asarray(n_to_1_pose[:3,:3]))
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n_to_1_trans = n_to_1_pose[:3,3]
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n_to_1_9d = np.concatenate([n_to_1_6d, n_to_1_trans], axis=0)
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scanned_n_to_1_pose.append(n_to_1_9d)
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nbv_idx, nbv_coverage_rate = nbv[0], nbv[1]
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nbv_path = DataLoadUtil.get_path(self.root_dir, scene_name, nbv_idx)
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nbv_depth = DataLoadUtil.load_depth(nbv_path)
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cam_info = DataLoadUtil.load_cam_info(nbv_path)
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nbv_mask = DataLoadUtil.load_seg(nbv_path)
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best_frame_to_world = cam_info["cam_to_world"]
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best_to_1_pose = np.dot(np.linalg.inv(first_frame_to_world), best_frame_to_world)
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best_target_point_cloud = DataLoadUtil.get_target_point_cloud(nbv_depth, cam_info["cam_intrinsic"], best_to_1_pose, nbv_mask)["points_world"]
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downsampled_best_target_point_cloud = PtsUtil.random_downsample_point_cloud(best_target_point_cloud, self.pts_num)
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best_to_1_6d = PoseUtil.matrix_to_rotation_6d_numpy(np.asarray(best_to_1_pose[:3,:3]))
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best_to_1_trans = best_to_1_pose[:3,3]
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best_to_1_9d = np.concatenate([best_to_1_6d, best_to_1_trans], axis=0)
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data_item = {
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"scanned_pts": np.asarray(scanned_views_pts,dtype=np.float32),
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"scanned_coverage_rate": np.asarray(scanned_coverages_rate,dtype=np.float32),
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"scanned_n_to_1_pose_9d": np.asarray(scanned_n_to_1_pose,dtype=np.float32),
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"best_pts": np.asarray(downsampled_best_target_point_cloud,dtype=np.float32),
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"best_coverage_rate": nbv_coverage_rate,
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"best_to_1_pose_9d": best_to_1_9d,
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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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return data_item
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def __len__(self):
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return len(self.datalist)
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if __name__ == "__main__":
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import torch
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seed = 0
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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": "C:\\Document\\Local Project\\nbv_rec\\data\\sample",
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"split_file": "C:\\Document\\Local Project\\nbv_rec\\data\\OmniObject3d_train.txt",
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"ratio": 0.05,
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"batch_size": 1,
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"num_workers": 0,
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}
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ds = NBVReconstructionDataset(config)
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print(len(ds))
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dl = ds.get_loader(shuffle=True)
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for idx, data in enumerate(dl):
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cnt=0
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print(data["scene_name"])
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print(data["scanned_coverage_rate"])
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print(data["best_coverage_rate"])
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for pts in data["scanned_pts"][0]:
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#np.savetxt(f"pts_{cnt}.txt", pts)
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cnt+=1
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best_pts = data["best_pts"][0]
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#np.savetxt("best_pts.txt", best_pts)
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for key, value in data.items():
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if isinstance(value, torch.Tensor):
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print(key, ":" ,value.shape)
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print() |