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colmap_script.py
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colmap_script.py
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import logging
import subprocess
import os
from pathlib import Path
import numpy as np
from skimage.io import imsave
from dataset.database import BaseDatabase, get_database_split
from utils.colmap_database import COLMAPDatabase
from utils.read_write_model import CAMERA_MODEL_NAMES
def run_sfm(colmap_path, model_path, database_path, image_dir):
logging.info('Running the triangulation...')
model_path.mkdir(exist_ok=True, parents=True)
cmd = [
str(colmap_path), 'mapper',
'--database_path', str(database_path),
'--image_path', str(image_dir),
'--output_path', str(model_path),
]
logging.info(' '.join(cmd))
subprocess.run(cmd, check=True)
def run_patch_match(colmap_path, sparse_model: Path, image_dir: Path, dense_model: Path):
logging.info('Running patch match...')
assert sparse_model.exists()
dense_model.mkdir(parents=True, exist_ok=True)
cmd = [str(colmap_path), 'image_undistorter', '--input_path', str(sparse_model), '--image_path', str(image_dir), '--output_path', str(dense_model),]
logging.info(' '.join(cmd))
subprocess.run(cmd, check=True)
cmd = [str(colmap_path), 'patch_match_stereo','--workspace_path', str(dense_model),]
logging.info(' '.join(cmd))
subprocess.run(cmd, check=True)
def run_depth_fusion(colmap_path, dense_model: Path, ply_path: Path):
logging.info('Running patch match...')
dense_model.mkdir(parents=True, exist_ok=True)
cmd = [str(colmap_path), 'stereo_fusion',
'--workspace_path', str(dense_model),
'--workspace_format', 'COLMAP',
'--input_type', 'geometric',
'--output_path', str(ply_path),]
logging.info(' '.join(cmd))
subprocess.run(cmd, check=True)
def dump_images(database, ref_ids, image_path: Path):
image_path.mkdir(parents=True, exist_ok=True)
for ref_id in ref_ids:
if (image_path / f'{ref_id}.jpg').exists():
continue
else:
imsave(str(image_path / f'{ref_id}.jpg'),database.get_image(ref_id))
def extract_and_match_sift(colmap_path, database_path, image_dir):
cmd = [
str(colmap_path), 'feature_extractor',
'--database_path', str(database_path),
'--image_path', str(image_dir),
]
logging.info(' '.join(cmd))
subprocess.run(cmd, check=True)
cmd = [
str(colmap_path), 'exhaustive_matcher',
'--database_path', str(database_path),
]
logging.info(' '.join(cmd))
subprocess.run(cmd, check=True)
def create_db_from_database(database, ref_ids, database_path: Path):
if database_path.exists():
logging.warning('Database already exists. we will skip db creation.')
return
db = COLMAPDatabase.connect(database_path)
db.create_tables()
for ri, ref_id in enumerate(ref_ids):
img = database.get_image(ref_id)
h, w = img.shape[:2]
model_id = CAMERA_MODEL_NAMES["SIMPLE_RADIAL"].model_id
db.add_camera(model_id, float(w), float(h), np.asarray([np.sqrt(h**2+w**2), w/2.0, h/2.0, 0.0],np.float64), camera_id=ri+1)
db.add_image(f'{ref_id}.jpg', ri+1, image_id=ri+1)
db.commit()
db.close()
def build_colmap_model_no_pose(database: BaseDatabase, colmap_path='colmap'):
colmap_root = Path('data') / database.database_name / 'colmap'
colmap_root.mkdir(exist_ok=True, parents=True)
image_path = colmap_root / 'images'
database_path = colmap_root / 'database.db'
ref_ids, _ = get_database_split(database, 'all')
dump_images(database, ref_ids, image_path)
create_db_from_database(database, ref_ids, database_path)
extract_and_match_sift(colmap_path, database_path, image_path)
sparse_model_path = colmap_root / f'sparse'
dense_model_path = colmap_root / f'dense'
ply_path = colmap_root / f'pointcloud.ply'
run_sfm(colmap_path, sparse_model_path, database_path, image_path)
run_patch_match(colmap_path, sparse_model_path / '0', image_path, dense_model_path)
run_depth_fusion(colmap_path, dense_model_path, ply_path)
def clean_colmap_project(database, split_name):
extractor_name = 'colmap_default'
matcher_name = 'colmap_default'
colmap_root = Path('data/colmap_projects') / database.database_name / f'colmap-{split_name}' / f'{extractor_name}-{matcher_name}'
image_path = colmap_root / 'images'
database_path = colmap_root / 'database.db'
empty_model_path = colmap_root / 'empty'
sparse_model_path = colmap_root / f'sparse'
dense_model_path = colmap_root / f'dense'
os.system(f'rm {str(sparse_model_path)} -r')
os.system(f'rm {str(database_path)} -r')
os.system(f'rm {str(image_path)} -r')
os.system(f'rm {str(empty_model_path)} -r')
os.system(f'rm {str(dense_model_path / "images")} -r')
os.system(f'rm {str(dense_model_path / "sparse")} -r')
os.system(f'rm {str(dense_model_path / "stereo" / "normal_maps")} -r')
os.system(f'rm {str(dense_model_path / "stereo" / "depth_maps")}/*.photometric.bin')