OpenMVG Command CheatSheet

openmvg-command-cheatsheet.sh

# This are "easy to use" instructions how to create point cloud data files
# extracting from a set of images using a technique called SfM or Structure from Motion:
# http://openmvg.readthedocs.io/en/latest/software/SfM/SfM/

# FFMPEG HELPERS:
# ffmpeg -i video.webm -vf fps=1 image%07d.jpg

export SFM_OUT_DIR=".sfm"

# Init SfM with OpenMVG
mkdir $SFM_OUT_DIR
openMVG_main_SfMInit_ImageListing --imageDirectory=. -o $SFM_OUT_DIR \
-d ../../sensor_width_camera_database.txt

# With manual focal value
#openMVG_main_SfMInit_ImageListing --imageDirectory=. -o $SFM_OUT_DIR -f 1714.285714
# Calculation of focal
# -f|--focal = ( std::max ( image_width, image_height ) * exifReader->getFocal() ) / ccdw;
# With camera databse -d ../sensor_database_detailed.csv (???) don't seemed to work...

# Filter distorted images
# This application export undistorted images from known camera parameter intrinsic.
echo -e "Filtering image distortion...\n"
mkdir $SFM_OUT_DIR/filter
openMVG_main_ExportUndistortedImages -i $SFM_OUT_DIR/sfm_data.json -o $SFM_OUT_DIR/filter -r 1 -n 2
#echo -e "Deleting old SfM Data file...\n"
#rm $SFM_OUT_DIR/sfm_data.json
#openMVG_main_SfMInit_ImageListing --imageDirectory=$SFM_OUT_DIR/filter -o $SFM_OUT_DIR -f 2758

# Compute features
mkdir $SFM_OUT_DIR/matches
openMVG_main_ComputeFeatures -i $SFM_OUT_DIR/sfm_data.json -o $SFM_OUT_DIR/matches -p NORMAL -n 2 -u 1
#openMVG_main_ComputeFeatures -i $SFM_OUT_DIR/sfm_data.json -o $SFM_OUT_DIR/matches -p HIGH -n 1
#openMVG_main_ComputeFeatures -i $SFM_OUT_DIR/sfm_data.json -o $SFM_OUT_DIR/matches -p ULTRA -n 1
#
# [-m|--describerMethod]
#   (method to use to describe an image):
#    SIFT (default),
#    SIFT_ANATOMY,
#    AKAZE_FLOAT: AKAZE with floating point descriptors,
#    AKAZE_MLDB:  AKAZE with binary descriptors
#
# [-u|--upright] Use Upright feature 0 or 1
#
# [-p|--describerPreset]
#   (used to control the Image_describer configuration):
#    NORMAL (default),
#    HIGH,
#    ULTRA: !!Can take long time!!
# [-n|--numThreads] number of parallel computations

# Compute matches
openMVG_main_ComputeMatches -i $SFM_OUT_DIR/sfm_data.json -o $SFM_OUT_DIR/matches -n ANNL2 -g e -v 2
# [-n|--nearest_matching_method]
#  AUTO: auto choice from regions type,
#  For Scalar based regions descriptor:
#    BRUTEFORCEL2: L2 BruteForce matching,
#    ANNL2: L2 Approximate Nearest Neighbor matching,
#    CASCADEHASHINGL2: L2 Cascade Hashing matching.
#    FASTCASCADEHASHINGL2: (default)
#      L2 Cascade Hashing with precomputed hashed regions
#     (faster than CASCADEHASHINGL2 but use more memory).
#  For Binary based descriptor:
#    BRUTEFORCEHAMMING: BruteForce Hamming matching.
#
# [-g|--geometric_model]
#   (pairwise correspondences filtering thanks to robust model estimation):
#   f: (default) fundamental matrix,
#   e: essential matrix,
#   h: homography matrix.
#   a: essential matrix with an angular parametrization,
#   o: orthographic essential matrix.
#
# [-v|--video_mode_matching]
#   (sequence matching with an overlap of X images)
#   X: with match 0 with (1->X), ...]
#   2: will match 0 with (1,2), 1 with (2,3), ...
#   3: will match 0 with (1,2,3), 1 with (2,3,4), ...
#
# [-m|--guided_matching]
#   use the found model to improve the pairwise correspondences.
#
# [-r|--ratio] Distance ratio to discard non meaningful matches 0.8: (default).

# Generate Incremental SfM
mkdir $SFM_OUT_DIR/incremental
openMVG_main_IncrementalSfM \
-i $SFM_OUT_DIR/sfm_data.json \
-m $SFM_OUT_DIR/matches/ \
-o $SFM_OUT_DIR/incremental/ \
-M $SFM_OUT_DIR/matches/matches.putative.bin

# Generate Global SfM
mkdir $SFM_OUT_DIR/global
openMVG_main_GlobalSfM \
-i $SFM_OUT_DIR/sfm_data.json \
-m $SFM_OUT_DIR/matches/ \
-M $SFM_OUT_DIR/matches/matches.putative.bin \
-o $SFM_OUT_DIR/global/ \
-r 2 -t 3

# ^^^^^ This does not work right now for me.

# Compute the color of the Structure of a sfm_data scene.
mkdir $SFM_OUT_DIR/out
openMVG_main_ComputeSfM_DataColor \
-i $SFM_OUT_DIR/incremental/sfm_data.bin \
-o $SFM_OUT_DIR/out/model_color.ply

# ComputeStructureFromKnownPoses
openMVG_main_ComputeStructureFromKnownPoses \
-i $SFM_OUT_DIR/incremental/sfm_data.bin \
-m $SFM_OUT_DIR/matches/ \
-f $SFM_OUT_DIR/matches/matches.putative.bin \
-o $SFM_OUT_DIR/out/robust.bin

# Compute the color of the Structure of a robust scene.
openMVG_main_ComputeSfM_DataColor \
-i $SFM_OUT_DIR/out/robust.bin \
-o $SFM_OUT_DIR/out/model_color_robust.ply

# Export to WebGL
mkdir $SFM_OUT_DIR/out/html
openMVG_main_openMVG2WebGL \
-i $SFM_OUT_DIR/sfm_data.json \
-o $SFM_OUT_DIR/out/html

# Convert to PCD
pcl_ply2pcd $SFM_OUT_DIR/out/model_color_robust.ply $SFM_OUT_DIR/out/model_color_robust.pcd
pcl_ply2pcd $SFM_OUT_DIR/out/model_color.ply $SFM_OUT_DIR/out/model_color.pcd
pcl_ply2pcd $SFM_OUT_DIR/out/robust.ply $SFM_OUT_DIR/out/robust.pcd

# Convert to VTK
pcl_ply2vtk $SFM_OUT_DIR/out/model_color_robust.ply $SFM_OUT_DIR/out/model_color_robust.vtk
pcl_ply2vtk $SFM_OUT_DIR/out/model_color.ply $SFM_OUT_DIR/out/model_color.vtk
pcl_ply2vtk $SFM_OUT_DIR/out/robust.ply $SFM_OUT_DIR/out/robust.vtk

# MVE - Convert the openMVG SfM scene to the MVE format
mkdir $SFM_OUT_DIR/mve && cd $SFM_OUT_DIR/mve
openMVG_main_openMVG2MVE2 -i ../sfm_data.json -o .
dmrecon -s2 MVE/
scene2pset -ddepth-L2 -iundist-L2 -n -s -c MVE/ ../out/model_color_robust.ply
fssrecon ../out/model_color_robust.ply output.ply
meshclean output.ply output_clean.ply
cd ..
ls -alh $SFM_OUT_DIR/mve