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from logging import root |
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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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import cv2 |
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import numpy as np |
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import os |
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import glob |
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from skimage.morphology import binary_dilation, disk |
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import argparse |
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import trimesh |
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from pathlib import Path |
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import subprocess |
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from evaluate_single_scene import cull_scan |
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Offical_DTU_Dataset = "./Offical_DTU_Dataset" |
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scans = [24, 37, 40, 55, 63, 65, 69, 83, 97, 105, 106, 110, 114, 118, 122] |
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out_dir_prefix = "evaluation/" |
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Path(out_dir_prefix).mkdir(parents=True, exist_ok=True) |
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evaluation_txt_file = "evaluation/DTU.csv" |
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evaluation_txt_file = open(evaluation_txt_file, 'w') |
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root_dir = '../exps/' |
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exp_names =["dtu_3views"] |
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for exp in exp_names: |
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for scan in scans: |
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out_dir = os.path.join(out_dir_prefix, str(scan)) |
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Path(out_dir).mkdir(parents=True, exist_ok=True) |
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vis_out_dir = os.path.join(out_dir_prefix, exp) |
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Path(vis_out_dir).mkdir(parents=True, exist_ok=True) |
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cur_root = os.path.join(root_dir, f"{exp}_{scan}") |
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files = list(filter(os.path.isfile, glob.glob(os.path.join(cur_root, "*/plots/*.ply")))) |
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files.sort(key=lambda x:os.path.getmtime(x)) |
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for ply_file in files[-1:]: |
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iter_num = Path(ply_file).stem |
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cur_vis_out_dir = os.path.join(out_dir_prefix, exp) |
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Path(cur_vis_out_dir).mkdir(parents=True, exist_ok=True) |
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print(ply_file) |
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result_mesh_file = os.path.join(out_dir, f"{exp}_{iter_num}.ply") |
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cull_scan(scan, ply_file, result_mesh_file) |
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cmd = f"python eval.py --data {result_mesh_file} --scan {scan} --mode mesh --dataset_dir {Offical_DTU_Dataset} --vis_out_dir {cur_vis_out_dir}" |
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print(cmd) |
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output = subprocess.check_output(cmd, shell=True).decode("utf-8") |
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output = output.replace(" ", ",") |
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evaluation_txt_file.write(f"{exp},{scan},{iter_num},{output}") |
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evaluation_txt_file.flush() |
