mirror of
https://github.com/graphdeco-inria/gaussian-splatting
synced 2024-11-25 21:28:17 +00:00
112 lines
4.4 KiB
Python
112 lines
4.4 KiB
Python
#
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# Copyright (C) 2023, Inria
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# GRAPHDECO research group, https://team.inria.fr/graphdeco
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# All rights reserved.
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#
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# This software is free for non-commercial, research and evaluation use
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# under the terms of the LICENSE.md file.
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#
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# For inquiries contact george.drettakis@inria.fr
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#
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# xvdp removed magick, even single threaded PIL resizes 4X faster
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import os
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import logging
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from argparse import ArgumentParser
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import shutil
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from PIL import Image
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# This Python script is based on the shell converter script provided in the MipNerF 360 repository.
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parser = ArgumentParser("Colmap converter")
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parser.add_argument("--no_gpu", action='store_true')
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parser.add_argument("--skip_matching", action='store_true')
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parser.add_argument("--source_path", "-s", required=True, type=str)
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parser.add_argument("--camera", default="OPENCV", type=str)
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parser.add_argument("--colmap_executable", default="", type=str)
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parser.add_argument("--resize", action="store_true")
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parser.add_argument("--magick_executable", default="", type=str)
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args = parser.parse_args()
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colmap_command = '"{}"'.format(args.colmap_executable) if len(args.colmap_executable) > 0 else "colmap"
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use_gpu = 1 if not args.no_gpu else 0
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if not args.skip_matching:
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os.makedirs(args.source_path + "/distorted/sparse", exist_ok=True)
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## Feature extraction
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feat_extracton_cmd = colmap_command + " feature_extractor "\
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"--database_path " + args.source_path + "/distorted/database.db \
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--image_path " + args.source_path + "/input \
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--ImageReader.single_camera 1 \
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--ImageReader.camera_model " + args.camera + " \
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--SiftExtraction.use_gpu " + str(use_gpu)
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exit_code = os.system(feat_extracton_cmd)
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if exit_code != 0:
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logging.error(f"Feature extraction failed with code {exit_code}. Exiting.")
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exit(exit_code)
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## Feature matching
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feat_matching_cmd = colmap_command + " exhaustive_matcher \
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--database_path " + args.source_path + "/distorted/database.db \
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--SiftMatching.use_gpu " + str(use_gpu)
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exit_code = os.system(feat_matching_cmd)
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if exit_code != 0:
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logging.error(f"Feature matching failed with code {exit_code}. Exiting.")
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exit(exit_code)
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### Bundle adjustment
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# The default Mapper tolerance is unnecessarily large,
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# decreasing it speeds up bundle adjustment steps.
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mapper_cmd = (colmap_command + " mapper \
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--database_path " + args.source_path + "/distorted/database.db \
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--image_path " + args.source_path + "/input \
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--output_path " + args.source_path + "/distorted/sparse \
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--Mapper.ba_global_function_tolerance=0.000001")
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exit_code = os.system(mapper_cmd)
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if exit_code != 0:
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logging.error(f"Mapper failed with code {exit_code}. Exiting.")
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exit(exit_code)
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### Image undistortion
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## We need to undistort our images into ideal pinhole intrinsics.
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img_undist_cmd = (colmap_command + " image_undistorter \
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--image_path " + args.source_path + "/input \
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--input_path " + args.source_path + "/distorted/sparse/0 \
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--output_path " + args.source_path + "\
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--output_type COLMAP")
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exit_code = os.system(img_undist_cmd)
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if exit_code != 0:
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logging.error(f"Mapper failed with code {exit_code}. Exiting.")
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exit(exit_code)
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files = os.listdir(args.source_path + "/sparse")
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os.makedirs(args.source_path + "/sparse/0", exist_ok=True)
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# Copy each file from the source directory to the destination directory
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for file in files:
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if file == '0':
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continue
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source_file = os.path.join(args.source_path, "sparse", file)
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destination_file = os.path.join(args.source_path, "sparse", "0", file)
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shutil.move(source_file, destination_file)
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if args.resize:
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print("Copying and resizing...")
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# Resize images.
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for div in [2,4,8]:
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os.makedirs(args.source_path + f"/images_{div}", exist_ok=True)
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# Get the list of files in the source directory
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files = os.listdir(args.source_path + "/images")
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# Copy each file from the source directory to the destination directory
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for j, file in enumerate(files):
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source_file = os.path.join(args.source_path, "images", file)
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im = Image.open(source_file)
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logging.info(f"processing image [{j}/{len(files)}] {source_file}")
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for div in [2,4,8]:
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destination_file = os.path.join(args.source_path, f"images_{div}", file)
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im.resize([round(i/div) for i in im.size], Image.BICUBIC).save(destination_file, quality=100)
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print("Done.")
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