154 lines
6.9 KiB
Python
154 lines
6.9 KiB
Python
import numpy as np
|
|
from PytorchBoot.dataset import BaseDataset
|
|
import PytorchBoot.namespace as namespace
|
|
import PytorchBoot.stereotype as stereotype
|
|
from PytorchBoot.utils.log_util import Log
|
|
import torch
|
|
import os
|
|
import sys
|
|
sys.path.append(r"/home/data/hofee/project/nbv_rec/nbv_reconstruction")
|
|
|
|
from utils.data_load import DataLoadUtil
|
|
from utils.pose import PoseUtil
|
|
from utils.pts import PtsUtil
|
|
|
|
@stereotype.dataset("old_seq_nbv_reconstruction_dataset")
|
|
class SeqNBVReconstructionDataset(BaseDataset):
|
|
def __init__(self, config):
|
|
super(SeqNBVReconstructionDataset, self).__init__(config)
|
|
self.type = config["type"]
|
|
if self.type != namespace.Mode.TEST:
|
|
Log.error("Dataset <seq_nbv_reconstruction_dataset> Only support test mode", terminate=True)
|
|
self.config = config
|
|
self.root_dir = config["root_dir"]
|
|
self.split_file_path = config["split_file"]
|
|
self.scene_name_list = self.load_scene_name_list()
|
|
self.datalist = self.get_datalist()
|
|
self.pts_num = config["pts_num"]
|
|
|
|
self.model_dir = config["model_dir"]
|
|
self.filter_degree = config["filter_degree"]
|
|
self.load_from_preprocess = config.get("load_from_preprocess", False)
|
|
|
|
|
|
def load_scene_name_list(self):
|
|
scene_name_list = []
|
|
with open(self.split_file_path, "r") as f:
|
|
for line in f:
|
|
scene_name = line.strip()
|
|
scene_name_list.append(scene_name)
|
|
return scene_name_list
|
|
|
|
def get_datalist(self):
|
|
datalist = []
|
|
for scene_name in self.scene_name_list:
|
|
seq_num = DataLoadUtil.get_label_num(self.root_dir, scene_name)
|
|
scene_max_coverage_rate = 0
|
|
scene_max_cr_idx = 0
|
|
|
|
for seq_idx in range(seq_num):
|
|
label_path = DataLoadUtil.get_label_path(self.root_dir, scene_name, seq_idx)
|
|
label_data = DataLoadUtil.load_label(label_path)
|
|
max_coverage_rate = label_data["max_coverage_rate"]
|
|
if max_coverage_rate > scene_max_coverage_rate:
|
|
scene_max_coverage_rate = max_coverage_rate
|
|
scene_max_cr_idx = seq_idx
|
|
|
|
label_path = DataLoadUtil.get_label_path(self.root_dir, scene_name, scene_max_cr_idx)
|
|
label_data = DataLoadUtil.load_label(label_path)
|
|
first_frame = label_data["best_sequence"][0]
|
|
best_seq_len = len(label_data["best_sequence"])
|
|
datalist.append({
|
|
"scene_name": scene_name,
|
|
"first_frame": first_frame,
|
|
"max_coverage_rate": scene_max_coverage_rate,
|
|
"best_seq_len": best_seq_len,
|
|
"label_idx": scene_max_cr_idx,
|
|
})
|
|
return datalist
|
|
|
|
def __getitem__(self, index):
|
|
data_item_info = self.datalist[index]
|
|
first_frame_idx = data_item_info["first_frame"][0]
|
|
first_frame_coverage = data_item_info["first_frame"][1]
|
|
max_coverage_rate = data_item_info["max_coverage_rate"]
|
|
scene_name = data_item_info["scene_name"]
|
|
first_cam_info = DataLoadUtil.load_cam_info(DataLoadUtil.get_path(self.root_dir, scene_name, first_frame_idx), binocular=True)
|
|
first_view_path = DataLoadUtil.get_path(self.root_dir, scene_name, first_frame_idx)
|
|
first_left_cam_pose = first_cam_info["cam_to_world"]
|
|
first_center_cam_pose = first_cam_info["cam_to_world_O"]
|
|
first_target_point_cloud = DataLoadUtil.load_from_preprocessed_pts(first_view_path)
|
|
first_pts_num = first_target_point_cloud.shape[0]
|
|
first_downsampled_target_point_cloud = PtsUtil.random_downsample_point_cloud(first_target_point_cloud, self.pts_num)
|
|
first_to_world_rot_6d = PoseUtil.matrix_to_rotation_6d_numpy(np.asarray(first_left_cam_pose[:3,:3]))
|
|
first_to_world_trans = first_left_cam_pose[:3,3]
|
|
first_to_world_9d = np.concatenate([first_to_world_rot_6d, first_to_world_trans], axis=0)
|
|
diag = DataLoadUtil.get_bbox_diag(self.model_dir, scene_name)
|
|
voxel_threshold = diag*0.02
|
|
first_O_to_first_L_pose = np.dot(np.linalg.inv(first_left_cam_pose), first_center_cam_pose)
|
|
scene_path = os.path.join(self.root_dir, scene_name)
|
|
model_points_normals = DataLoadUtil.load_points_normals(self.root_dir, scene_name)
|
|
|
|
data_item = {
|
|
"first_pts_num": np.asarray(
|
|
first_pts_num, dtype=np.int32
|
|
),
|
|
"first_pts": np.asarray([first_downsampled_target_point_cloud],dtype=np.float32),
|
|
"combined_scanned_pts": np.asarray(first_downsampled_target_point_cloud,dtype=np.float32),
|
|
"first_to_world_9d": np.asarray([first_to_world_9d],dtype=np.float32),
|
|
"scene_name": scene_name,
|
|
"max_coverage_rate": max_coverage_rate,
|
|
"voxel_threshold": voxel_threshold,
|
|
"filter_degree": self.filter_degree,
|
|
"O_to_L_pose": first_O_to_first_L_pose,
|
|
"first_frame_coverage": first_frame_coverage,
|
|
"scene_path": scene_path,
|
|
"model_points_normals": model_points_normals,
|
|
"best_seq_len": data_item_info["best_seq_len"],
|
|
"first_frame_id": first_frame_idx,
|
|
}
|
|
return data_item
|
|
|
|
def __len__(self):
|
|
return len(self.datalist)
|
|
|
|
def get_collate_fn(self):
|
|
def collate_fn(batch):
|
|
collate_data = {}
|
|
collate_data["first_pts"] = [torch.tensor(item['first_pts']) for item in batch]
|
|
collate_data["first_to_world_9d"] = [torch.tensor(item['first_to_world_9d']) for item in batch]
|
|
collate_data["combined_scanned_pts"] = torch.stack([torch.tensor(item['combined_scanned_pts']) for item in batch])
|
|
for key in batch[0].keys():
|
|
if key not in ["first_pts", "first_to_world_9d", "combined_scanned_pts"]:
|
|
collate_data[key] = [item[key] for item in batch]
|
|
return collate_data
|
|
return collate_fn
|
|
|
|
# -------------- Debug ---------------- #
|
|
if __name__ == "__main__":
|
|
import torch
|
|
seed = 0
|
|
torch.manual_seed(seed)
|
|
np.random.seed(seed)
|
|
config = {
|
|
"root_dir": "/home/data/hofee/project/nbv_rec/data/nbv_rec_data_512_preproc_npy",
|
|
"split_file": "/home/data/hofee/project/nbv_rec/data/OmniObject3d_train.txt",
|
|
"model_dir": "/home/data/hofee/project/nbv_rec/data/scaled_object_meshes",
|
|
"ratio": 0.005,
|
|
"batch_size": 2,
|
|
"filter_degree": 75,
|
|
"num_workers": 0,
|
|
"pts_num": 32684,
|
|
"type": namespace.Mode.TEST,
|
|
"load_from_preprocess": True
|
|
}
|
|
ds = SeqNBVReconstructionDataset(config)
|
|
print(len(ds))
|
|
#ds.__getitem__(10)
|
|
dl = ds.get_loader(shuffle=True)
|
|
for idx, data in enumerate(dl):
|
|
data = ds.process_batch(data, "cuda:0")
|
|
print(data)
|
|
# ------ Debug Start ------
|
|
import ipdb;ipdb.set_trace()
|
|
# ------ Debug End ------+ |