Add Metashape support (#989)

Add metashape support
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Matthew Tancik authored and GitHub committed 2022-11-20 14:55:41 -08:00
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commit 4065bb4733
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@@ -16,6 +16,7 @@ from typing_extensions import Annotated, Literal
from nerfstudio.process_data import (
colmap_utils,
insta360_utils,
metashape_utils,
polycam_utils,
process_data_utils,
record3d_utils,
@@ -487,10 +488,102 @@ class ProcessPolycam:
CONSOLE.rule()
@dataclass
class ProcessMetashape:
"""Process Metashape data into a nerfstudio dataset.
This script assumes that cameras have been aligned using Metashape. After alignment, it is necessary to export the
camera poses as a `.xml` file. This option can be found under `File > Export > Export Cameras`.
This script does the following:
1. Scales images to a specified size.
2. Converts Metashape poses into the nerfstudio format.
"""
data: Path
"""Path to a folder of images."""
xml: Path
"""Path to the Metashape xml file."""
output_dir: Path
"""Path to the output directory."""
num_downscales: int = 3
"""Number of times to downscale the images. Downscales by 2 each time. For example a value of 3
will downscale the images by 2x, 4x, and 8x."""
max_dataset_size: int = 600
"""Max number of images to train on. If the dataset has more, images will be sampled approximately evenly. If -1,
use all images."""
verbose: bool = False
"""If True, print extra logging."""
def main(self) -> None:
"""Process images into a nerfstudio dataset."""
self.output_dir.mkdir(parents=True, exist_ok=True)
image_dir = self.output_dir / "images"
image_dir.mkdir(parents=True, exist_ok=True)
summary_log = []
# Copy images to output directory
image_filenames = []
for f in self.data.iterdir():
image_filenames.append(f)
image_filenames = sorted(image_filenames, key=lambda fn: fn.stem)
num_images = len(image_filenames)
idx = np.arange(num_images)
if self.max_dataset_size != -1 and num_images > self.max_dataset_size:
idx = np.round(np.linspace(0, num_images - 1, self.max_dataset_size)).astype(int)
image_filenames = list(np.array(image_filenames)[idx])
# Copy images to output directory
copied_image_paths = process_data_utils.copy_images_list(
image_filenames,
image_dir=image_dir,
verbose=self.verbose,
)
num_frames = len(copied_image_paths)
copied_image_paths = [Path("images/" + copied_image_path.name) for copied_image_path in copied_image_paths]
original_names = [image_path.stem for image_path in image_filenames]
image_filename_map = dict(zip(original_names, copied_image_paths))
if self.max_dataset_size > 0 and num_frames != num_images:
summary_log.append(f"Started with {num_frames} images out of {num_images} total")
summary_log.append(
"To change the size of the dataset add the argument --max_dataset_size to larger than the "
f"current value ({self.max_dataset_size}), or -1 to use all images."
)
else:
summary_log.append(f"Started with {num_frames} images")
# Downscale images
summary_log.append(process_data_utils.downscale_images(image_dir, self.num_downscales, verbose=self.verbose))
# Save json
if num_frames == 0:
CONSOLE.print("[bold red]No images found, exiting")
sys.exit(1)
summary_log.append(
metashape_utils.metashape_to_json(
image_filename_map=image_filename_map,
xml_filename=self.xml,
output_dir=self.output_dir,
)
)
CONSOLE.rule("[bold green]:tada: :tada: :tada: All DONE :tada: :tada: :tada:")
for summary in summary_log:
CONSOLE.print(summary, justify="center")
CONSOLE.rule()
Commands = Union[
Annotated[ProcessImages, tyro.conf.subcommand(name="images")],
Annotated[ProcessVideo, tyro.conf.subcommand(name="video")],
Annotated[ProcessPolycam, tyro.conf.subcommand(name="polycam")],
Annotated[ProcessMetashape, tyro.conf.subcommand(name="metashape")],
Annotated[ProcessInsta360, tyro.conf.subcommand(name="insta360")],
Annotated[ProcessRecord3D, tyro.conf.subcommand(name="record3d")],
]