From 152ff1fff21abff0400bfa6df9cc9ee50c9b9694 Mon Sep 17 00:00:00 2001
From: ingra14m <1261238025@qq.com>
Date: Mon, 27 Nov 2023 14:47:16 +0800
Subject: [PATCH] Remove unrelated code

---
 utils/preprocess.py | 79 ---------------------------------------------
 1 file changed, 79 deletions(-)
 delete mode 100644 utils/preprocess.py

diff --git a/utils/preprocess.py b/utils/preprocess.py
deleted file mode 100644
index caf35b9..0000000
--- a/utils/preprocess.py
+++ /dev/null
@@ -1,79 +0,0 @@
-# @title Configure dataset directories
-import os
-from pathlib import Path
-
-# @markdown The base directory for all captures. This can be anything if you're running this notebook on your own Jupyter runtime.
-save_dir = '/data00/yzy/Git_Project/data/dynamic/mine/'  # @param {type: 'string'}
-capture_name = 'lemon'  # @param {type: 'string'}
-# The root directory for this capture.
-root_dir = Path(save_dir, capture_name)
-# Where to save RGB images.
-rgb_dir = root_dir / 'rgb'
-rgb_raw_dir = root_dir / 'rgb-raw'
-# Where to save the COLMAP outputs.
-colmap_dir = root_dir / 'colmap'
-colmap_db_path = colmap_dir / 'database.db'
-colmap_out_path = colmap_dir / 'sparse'
-
-colmap_out_path.mkdir(exist_ok=True, parents=True)
-rgb_raw_dir.mkdir(exist_ok=True, parents=True)
-
-print(f"""Directories configured:
-  root_dir = {root_dir}
-  rgb_raw_dir = {rgb_raw_dir}
-  rgb_dir = {rgb_dir}
-  colmap_dir = {colmap_dir}
-""")
-
-# ==================== colmap =========================
-# @title Extract features.
-# @markdown Computes SIFT features and saves them to the COLMAP DB.
-share_intrinsics = True  # @param {type: 'boolean'}
-assume_upright_cameras = True  # @param {type: 'boolean'}
-
-# @markdown This sets the scale at which we will run COLMAP. A scale of 1 will be more accurate but will be slow.
-colmap_image_scale = 4  # @param {type: 'number'}
-colmap_rgb_dir = rgb_dir / f'{colmap_image_scale}x'
-
-# @markdown Check this if you want to re-process SfM.
-overwrite = False  # @param {type: 'boolean'}
-
-if overwrite and colmap_db_path.exists():
-    colmap_db_path.unlink()
-
-os.system('colmap feature_extractor \
---SiftExtraction.use_gpu 0 \
---SiftExtraction.upright {int(assume_upright_cameras)} \
---ImageReader.camera_model OPENCV \
---ImageReader.single_camera {int(share_intrinsics)} \
---database_path "{str(colmap_db_path)}" \
---image_path "{str(colmap_rgb_dir)}"')
-
-# @title Match features.
-# @markdown Match the SIFT features between images. Use `exhaustive` if you only have a few images and use `vocab_tree` if you have a lot of images.
-
-match_method = 'exhaustive'  # @param ["exhaustive", "vocab_tree"]
-
-if match_method == 'exhaustive':
-    os.system('colmap exhaustive_matcher \
-    --SiftMatching.use_gpu 0 \
-    --database_path "{str(colmap_db_path)}"')
-
-# @title Reconstruction.
-# @markdown Run structure-from-motion to compute camera parameters.
-
-refine_principal_point = True  # @param {type:"boolean"}
-min_num_matches = 32  # @param {type: 'number'}
-filter_max_reproj_error = 2  # @param {type: 'number'}
-tri_complete_max_reproj_error = 2  # @param {type: 'number'}
-
-os.system('colmap mapper \
-  --Mapper.ba_refine_principal_point {int(refine_principal_point)} \
-  --Mapper.filter_max_reproj_error $filter_max_reproj_error \
-  --Mapper.tri_complete_max_reproj_error $tri_complete_max_reproj_error \
-  --Mapper.min_num_matches $min_num_matches \
-  --database_path "{str(colmap_db_path)}" \
-  --image_path "{str(colmap_rgb_dir)}" \
-  --export_path "{str(colmap_out_path)}"')
-
-print("debug")