mirror of
https://github.com/graphdeco-inria/gaussian-splatting
synced 2024-12-02 09:05:27 +00:00
112 lines
4.4 KiB
Python
112 lines
4.4 KiB
Python
#
|
|
# Copyright (C) 2023, Inria
|
|
# GRAPHDECO research group, https://team.inria.fr/graphdeco
|
|
# All rights reserved.
|
|
#
|
|
# This software is free for non-commercial, research and evaluation use
|
|
# under the terms of the LICENSE.md file.
|
|
#
|
|
# For inquiries contact george.drettakis@inria.fr
|
|
#
|
|
# xvdp removed magick, even single threaded PIL resizes 4X faster
|
|
|
|
|
|
import os
|
|
import logging
|
|
from argparse import ArgumentParser
|
|
import shutil
|
|
|
|
from PIL import Image
|
|
|
|
# This Python script is based on the shell converter script provided in the MipNerF 360 repository.
|
|
parser = ArgumentParser("Colmap converter")
|
|
parser.add_argument("--no_gpu", action='store_true')
|
|
parser.add_argument("--skip_matching", action='store_true')
|
|
parser.add_argument("--source_path", "-s", required=True, type=str)
|
|
parser.add_argument("--camera", default="OPENCV", type=str)
|
|
parser.add_argument("--colmap_executable", default="", type=str)
|
|
parser.add_argument("--resize", action="store_true")
|
|
parser.add_argument("--magick_executable", default="", type=str)
|
|
args = parser.parse_args()
|
|
colmap_command = '"{}"'.format(args.colmap_executable) if len(args.colmap_executable) > 0 else "colmap"
|
|
|
|
use_gpu = 1 if not args.no_gpu else 0
|
|
|
|
if not args.skip_matching:
|
|
os.makedirs(args.source_path + "/distorted/sparse", exist_ok=True)
|
|
|
|
## Feature extraction
|
|
feat_extracton_cmd = colmap_command + " feature_extractor "\
|
|
"--database_path " + args.source_path + "/distorted/database.db \
|
|
--image_path " + args.source_path + "/input \
|
|
--ImageReader.single_camera 1 \
|
|
--ImageReader.camera_model " + args.camera + " \
|
|
--SiftExtraction.use_gpu " + str(use_gpu)
|
|
exit_code = os.system(feat_extracton_cmd)
|
|
if exit_code != 0:
|
|
logging.error(f"Feature extraction failed with code {exit_code}. Exiting.")
|
|
exit(exit_code)
|
|
|
|
## Feature matching
|
|
feat_matching_cmd = colmap_command + " exhaustive_matcher \
|
|
--database_path " + args.source_path + "/distorted/database.db \
|
|
--SiftMatching.use_gpu " + str(use_gpu)
|
|
exit_code = os.system(feat_matching_cmd)
|
|
if exit_code != 0:
|
|
logging.error(f"Feature matching failed with code {exit_code}. Exiting.")
|
|
exit(exit_code)
|
|
|
|
### Bundle adjustment
|
|
# The default Mapper tolerance is unnecessarily large,
|
|
# decreasing it speeds up bundle adjustment steps.
|
|
mapper_cmd = (colmap_command + " mapper \
|
|
--database_path " + args.source_path + "/distorted/database.db \
|
|
--image_path " + args.source_path + "/input \
|
|
--output_path " + args.source_path + "/distorted/sparse \
|
|
--Mapper.ba_global_function_tolerance=0.000001")
|
|
exit_code = os.system(mapper_cmd)
|
|
if exit_code != 0:
|
|
logging.error(f"Mapper failed with code {exit_code}. Exiting.")
|
|
exit(exit_code)
|
|
|
|
### Image undistortion
|
|
## We need to undistort our images into ideal pinhole intrinsics.
|
|
img_undist_cmd = (colmap_command + " image_undistorter \
|
|
--image_path " + args.source_path + "/input \
|
|
--input_path " + args.source_path + "/distorted/sparse/0 \
|
|
--output_path " + args.source_path + "\
|
|
--output_type COLMAP")
|
|
exit_code = os.system(img_undist_cmd)
|
|
if exit_code != 0:
|
|
logging.error(f"Mapper failed with code {exit_code}. Exiting.")
|
|
exit(exit_code)
|
|
|
|
files = os.listdir(args.source_path + "/sparse")
|
|
os.makedirs(args.source_path + "/sparse/0", exist_ok=True)
|
|
# Copy each file from the source directory to the destination directory
|
|
for file in files:
|
|
if file == '0':
|
|
continue
|
|
source_file = os.path.join(args.source_path, "sparse", file)
|
|
destination_file = os.path.join(args.source_path, "sparse", "0", file)
|
|
shutil.move(source_file, destination_file)
|
|
|
|
if args.resize:
|
|
print("Copying and resizing...")
|
|
|
|
# Resize images.
|
|
for div in [2,4,8]:
|
|
os.makedirs(args.source_path + f"/images_{div}", exist_ok=True)
|
|
# Get the list of files in the source directory
|
|
files = os.listdir(args.source_path + "/images")
|
|
# Copy each file from the source directory to the destination directory
|
|
for j, file in enumerate(files):
|
|
source_file = os.path.join(args.source_path, "images", file)
|
|
im = Image.open(source_file)
|
|
logging.info(f"processing image [{j}/{len(files)}] {source_file}")
|
|
for div in [2,4,8]:
|
|
destination_file = os.path.join(args.source_path, f"images_{div}", file)
|
|
im.resize([round(i/div) for i in im.size], Image.BICUBIC).save(destination_file, quality=100)
|
|
|
|
print("Done.")
|