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from typing import Literal, Optional
from io import IOBase
import os
from pathlib import Path
import shutil
import subprocess
from rich.progress import Progress
from rich.console import Console
console = Console()
class FailedProcess(Exception):
pass
def colmap_feature_extraction(
database_path: Path,
image_path: Path,
camera: Literal["OPENCV"],
colmap_command: str = "colmap",
use_gpu: bool = True,
stream_file: Optional[IOBase] = None
):
total = len(list(image_path.glob("*.jpg")))
with Progress(console=console) as progress:
task = progress.add_task("Feature Extraction", total=total)
database_path.parent.mkdir(parents=True, exist_ok=True)
cmd = [
colmap_command,
"feature_extractor",
"--database_path", database_path.as_posix(),
"--image_path", image_path.as_posix(),
"--ImageReader.single_camera", "1",
"--ImageReader.camera_model", camera,
"--SiftExtraction.use_gpu", "1" if use_gpu else "0",
# "--SiftExtraction.domain_size_pooling", "1",
# "--SiftExtraction.estimate_affine_shape", "1"
]
console.log(f"💻 Executing command: {' '.join(cmd)}")
_stdout = stream_file if stream_file else subprocess.PIPE
with subprocess.Popen(cmd, stdout=_stdout, stderr=subprocess.STDOUT, text=True) as process:
if process.stdout:
for line in process.stdout:
if line.startswith("Processed file "):
line_process = line\
.replace("Processed file [", "")\
.replace("]", "")\
.replace("\n", "")
current, total = line_process.split("/")
progress.update(task, completed=int(current), total=int(total), refresh=True)
progress.update(task, completed=int(total), refresh=True)
return_code = process.returncode
if return_code == 0:
console.log(f'Feature stored in {database_path.as_posix()}.')
console.log('✅ Feature extraction completed.')
else:
raise FailedProcess("Feature extraction failed.")
def colmap_feature_matching(
database_path: Path,
image_path: Path,
colmap_command: str = "colmap",
use_gpu: bool = True,
stream_file: Optional[IOBase] = None
):
total = len(list(image_path.glob("*.jpg")))
with Progress(console=console) as progress:
task = progress.add_task("Feature Matching", total=total)
database_path
cmd = [
colmap_command,
"exhaustive_matcher",
"--database_path", database_path.as_posix(),
"--SiftMatching.use_gpu", "1" if use_gpu else "0"
]
console.log(f"💻 Executing command: {' '.join(cmd)}")
_stdout = stream_file if stream_file else subprocess.PIPE
with subprocess.Popen(cmd, stdout=_stdout, stderr=subprocess.STDOUT, text=True) as process:
if process.stdout:
for line in process.stdout:
pass
progress.update(task, completed=int(total), refresh=True)
return_code = process.returncode
if return_code == 0:
console.log('✅ Feature matching completed.')
else:
raise FailedProcess("Feature matching failed.")
def colmap_bundle_adjustment(
database_path: Path,
image_path: Path,
sparse_path: Path,
colmap_command: str = "colmap",
stream_file: Optional[IOBase] = None
):
total = len(list(image_path.glob("*.jpg")))
with Progress(console=console) as progress:
task = progress.add_task("Bundle Adjustment", total=total)
cmd = [
colmap_command,
"mapper",
"--database_path", database_path.as_posix(),
"--image_path", image_path.as_posix(),
"--output_path", sparse_path.as_posix(),
"--Mapper.ba_global_function_tolerance=0.000001"
# "--Mapper.ba_local_max_num_iterations", "40",
# "--Mapper.ba_global_max_num_iterations", "100",
# "--Mapper.ba_local_max_refinements", "3",
# "--Mapper.ba_global_max_refinements", "5"
]
console.log(f"💻 Executing command: {' '.join(cmd)}")
sparse_path.mkdir(parents=True, exist_ok=True)
_stdout = stream_file if stream_file else subprocess.PIPE
with subprocess.Popen(cmd, stdout=_stdout, stderr=subprocess.STDOUT, text=True) as process:
if process.stdout:
for line in process.stdout:
print(line)
if line.startswith("Registering image #"):
line_process = line\
.replace("Registering image #", "")\
.replace("\n", "")
*_, current = line_process.split("(")
current, *_ = current.split(")")
progress.update(task, completed=int(current), refresh=True)
progress.update(task, completed=int(total), refresh=True)
return_code = process.returncode
if return_code == 0:
console.log('✅ Bundle adjustment completed.')
else:
raise FailedProcess("Bundle adjustment failed.")
def colmap_image_undistortion(
image_path: Path,
sparse0_path: Path,
source_path: Path,
colmap_command: str = "colmap",
stream_file: Optional[IOBase] = None
):
total = len(list(image_path.glob("*.jpg")))
with Progress(console=console) as progress:
task = progress.add_task("Image Undistortion", total=total)
cmd = [
colmap_command,
"image_undistorter",
"--image_path", image_path.as_posix(),
"--input_path", sparse0_path.as_posix(),
"--output_path", source_path.as_posix(),
"--output_type", "COLMAP"
]
console.log(f"💻 Executing command: {' '.join(cmd)}")
_stdout = stream_file if stream_file else subprocess.PIPE
with subprocess.Popen(cmd, stdout=_stdout, stderr=subprocess.STDOUT, text=True) as process:
if process.stdout:
for line in process.stdout:
if line.startswith("Undistorting image ["):
line_process = line\
.replace("Undistorting image [", "")\
.replace("]", "")\
.replace("\n", "")
current, total = line_process.split("/")
progress.update(task, completed=int(current), total=int(total), refresh=True)
progress.update(task, completed=int(total), refresh=True)
return_code = process.returncode
if return_code == 0:
console.log('✅ Image undistortion completed.')
else:
raise FailedProcess("Image undistortion failed.")
def colmap(
source_path: Path,
camera: Literal["OPENCV"] = "OPENCV",
colmap_command: str = "colmap",
use_gpu: bool = True,
skip_matching: bool = False,
stream_file: Optional[IOBase] = None
):
image_path = source_path / "input"
if not image_path.exists():
raise Exception(f"Image path {image_path} does not exist. Exiting.")
total = len(list(image_path.glob("*.jpg")))
if total == 0:
raise Exception(f"No images found in {image_path}. Exiting.")
database_path = source_path / "distorted" / "database.db"
sparse_path = source_path / "distorted" / "sparse"
if not skip_matching:
colmap_feature_extraction(database_path, image_path, camera, colmap_command, use_gpu, stream_file)
colmap_feature_matching(database_path, image_path, colmap_command, use_gpu, stream_file)
colmap_bundle_adjustment(database_path, image_path, sparse_path, colmap_command, stream_file)
colmap_image_undistortion(image_path, sparse_path / "0", source_path, colmap_command, stream_file)
origin_path = source_path / "sparse"
destination_path = source_path / "sparse" / "0"
destination_path.mkdir(exist_ok=True)
console.log(f"🌟 Moving files from {origin_path} to {destination_path}")
for file in os.listdir(origin_path):
if file == '0':
continue
source_file = os.path.join(origin_path, file)
destination_file = os.path.join(destination_path, file)
shutil.copy(source_file, destination_file)
if __name__ == "__main__":
import tempfile
with tempfile.NamedTemporaryFile(mode='w+t') as temp_file:
print(f"Using temp file: {temp_file.name}")
try:
colmap(
source_path = Path("/home/europe/Desktop/gaussian-splatting-kit/test/"),
camera = "OPENCV",
colmap_command = "colmap",
use_gpu = True,
skip_matching = False,
stream_file = open("/home/europe/Desktop/gaussian-splatting-kit/test.log", "w+t")
)
except FailedProcess:
console.log("🚨 Error executing colmap.")
temp_file.seek(0)
print(temp_file.read()) |