dunkirk.sh
an eink camera running on an rpi zero 2 w
feat: add colmap scripts
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colmap.bat
+135
colmap.bat
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@echo off
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setlocal
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REM --- Windows Batch Version of inky/colmap.sh ---
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REM --- Modified to use CUDA for GPU acceleration ---
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REM Prerequisites:
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REM 1. ffmpeg.exe and colmap.exe must be in the system PATH or the same directory as this script.
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REM 2. The COLMAP build MUST support CUDA.
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REM 3. Appropriate NVIDIA drivers and CUDA toolkit (compatible with your COLMAP build) must be installed.
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REM Define paths (using backslashes for Windows compatibility)
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set INPUT_VIDEO=data\input.mov
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set FRAMES_DIR=data\frames
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set COLMAP_DB=data\colmap.db
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set SPARSE_DIR=data\sparse
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set DENSE_DIR=data\dense
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REM Check if input video exists
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if not exist "%INPUT_VIDEO%" (
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echo Error: Input video not found at %INPUT_VIDEO%
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exit /b 1
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)
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REM Create directories
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echo Creating directories...
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if not exist "data" mkdir "data"
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if not exist "%FRAMES_DIR%" mkdir "%FRAMES_DIR%"
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if errorlevel 1 ( echo Failed to create %FRAMES_DIR%. & exit /b 1 )
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if not exist "%SPARSE_DIR%" mkdir "%SPARSE_DIR%"
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if errorlevel 1 ( echo Failed to create %SPARSE_DIR%. & exit /b 1 )
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if not exist "%DENSE_DIR%" mkdir "%DENSE_DIR%"
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if errorlevel 1 ( echo Failed to create %DENSE_DIR%. & exit /b 1 )
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REM Step 1: Extract frames from video
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echo Extracting frames from video...
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ffmpeg -i "%INPUT_VIDEO%" -q:v 1 "%FRAMES_DIR%\frame_%%04d.jpg"
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if errorlevel 1 (
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echo Error: ffmpeg frame extraction failed. Check ffmpeg output/log.
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exit /b 1
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)
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REM Step 2: Run COLMAP feature extraction (Using CUDA GPU)
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echo Running COLMAP feature extraction (CUDA)...
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colmap feature_extractor ^
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--database_path "%COLMAP_DB%" ^
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--image_path "%FRAMES_DIR%" ^
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--ImageReader.camera_model SIMPLE_RADIAL ^
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--SiftExtraction.use_gpu 1
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if errorlevel 1 (
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echo Error: COLMAP feature_extractor failed. Check COLMAP output/log. Ensure CUDA is working.
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exit /b 1
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)
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REM Step 3: Run COLMAP feature matching (Using CUDA GPU)
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echo Running COLMAP feature matching (CUDA)...
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colmap exhaustive_matcher ^
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--database_path "%COLMAP_DB%" ^
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--SiftMatching.use_gpu 1
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if errorlevel 1 (
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echo Error: COLMAP exhaustive_matcher failed. Check COLMAP output/log. Ensure CUDA is working.
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exit /b 1
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)
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REM Step 4: Run COLMAP sparse reconstruction
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REM Note: The 'mapper' step primarily uses CPU.
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echo Running COLMAP sparse reconstruction...
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colmap mapper ^
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--database_path "%COLMAP_DB%" ^
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--image_path "%FRAMES_DIR%" ^
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--output_path "%SPARSE_DIR%"
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if errorlevel 1 (
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echo Warning: COLMAP mapper returned an error code (%ERRORLEVEL%), but proceeding. Check COLMAP output/log.
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REM Mapper might "fail" (e.g., if few matches) but sometimes produces usable results.
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REM More robust checking might be needed depending on COLMAP's specific exit codes.
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)
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REM Check if the expected output directory from mapper exists
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REM COLMAP often creates numbered subdirectories (0, 1, ...) for different models.
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REM We assume the first model (0) is the one we want.
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if not exist "%SPARSE_DIR%\0" (
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echo Error: Sparse reconstruction output directory (%SPARSE_DIR%\0) not found.
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echo Mapper likely failed to produce a valid reconstruction. Check COLMAP output/log.
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exit /b 1
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)
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REM Step 5: Run COLMAP dense reconstruction
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echo Running COLMAP image undistorter...
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colmap image_undistorter ^
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--image_path "%FRAMES_DIR%" ^
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--input_path "%SPARSE_DIR%\0" ^
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--output_path "%DENSE_DIR%"
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if errorlevel 1 (
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echo Error: COLMAP image_undistorter failed. Check COLMAP output/log.
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exit /b 1
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)
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echo Running COLMAP patch matching (Using CUDA GPU)...
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REM Use --PatchMatchStereo.gpu_index 0 to select the first CUDA device. Change if needed.
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colmap patch_match_stereo ^
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--workspace_path "%DENSE_DIR%" ^
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--PatchMatchStereo.gpu_index 0
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if errorlevel 1 (
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echo Error: COLMAP patch_match_stereo failed. Check COLMAP output/log. Ensure CUDA is working.
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exit /b 1
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)
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echo Running COLMAP stereo fusion...
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REM Note: Stereo fusion is primarily CPU-bound.
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colmap stereo_fusion ^
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--workspace_path "%DENSE_DIR%" ^
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--output_path "%DENSE_DIR%\fused.ply"
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if errorlevel 1 (
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echo Error: COLMAP stereo_fusion failed. Check COLMAP output/log.
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exit /b 1
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)
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REM Output camera pose information
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echo Extracting camera pose information...
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colmap model_converter ^
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--input_path "%SPARSE_DIR%\0" ^
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--output_path "%SPARSE_DIR%\txt" ^
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--output_type TXT
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if errorlevel 1 (
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echo Error: COLMAP model_converter failed. Check COLMAP output/log.
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exit /b 1
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)
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echo COLMAP processing complete!
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echo Camera poses are in: %SPARSE_DIR%\txt\images.txt
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echo Dense point cloud is in: %DENSE_DIR%\fused.ply
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echo Script completed successfully
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endlocal
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exit /b 0
+68
colmap.sh
+68
colmap.sh
···
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#!/usr/bin/env -S nix shell --impure nixpkgs#ffmpeg nixpkgs#colmap nixpkgs#opencv4 --command bash
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set -euo pipefail
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# Define paths
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INPUT_VIDEO="data/input.mov"
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FRAMES_DIR="data/frames"
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COLMAP_DB="data/colmap.db"
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SPARSE_DIR="data/sparse"
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DENSE_DIR="data/dense"
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# Create directories
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mkdir -p "$FRAMES_DIR" "$SPARSE_DIR" "$DENSE_DIR"
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# Step 1: Extract frames from video
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echo "Extracting frames from video..."
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ffmpeg -i "$INPUT_VIDEO" -q:v 1 "$FRAMES_DIR/frame_%04d.jpg"
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# Step 2: Run COLMAP feature extraction (CPU only)
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echo "Running COLMAP feature extraction..."
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colmap feature_extractor \
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--database_path "$COLMAP_DB" \
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--image_path "$FRAMES_DIR" \
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--ImageReader.camera_model SIMPLE_RADIAL \
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--SiftExtraction.use_gpu 0
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# Step 3: Run COLMAP feature matching (CPU only)
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echo "Running COLMAP feature matching..."
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colmap exhaustive_matcher \
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--database_path "$COLMAP_DB" \
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--SiftMatching.use_gpu 0
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# Step 4: Run COLMAP sparse reconstruction
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echo "Running COLMAP sparse reconstruction..."
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colmap mapper \
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--database_path "$COLMAP_DB" \
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--image_path "$FRAMES_DIR" \
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--output_path "$SPARSE_DIR"
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# Step 5: Run COLMAP dense reconstruction
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echo "Running COLMAP image undistorter..."
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colmap image_undistorter \
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--image_path "$FRAMES_DIR" \
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--input_path "$SPARSE_DIR/0" \
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--output_path "$DENSE_DIR"
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echo "Running COLMAP patch matching (CPU only)..."
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colmap patch_match_stereo \
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--workspace_path "$DENSE_DIR" \
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--PatchMatchStereo.gpu_index -1
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echo "Running COLMAP stereo fusion..."
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colmap stereo_fusion \
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--workspace_path "$DENSE_DIR" \
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--output_path "$DENSE_DIR/fused.ply"
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# Output camera pose information for dithering
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echo "Extracting camera pose information..."
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colmap model_converter \
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--input_path "$SPARSE_DIR/0" \
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--output_path "$SPARSE_DIR/txt" \
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--output_type TXT
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echo "COLMAP processing complete!"
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echo "Camera poses are in: $SPARSE_DIR/txt/images.txt"
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echo "Point cloud is in: $DENSE_DIR/fused.ply"
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echo "Script completed successfully"