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import argparse
import logging
from pathlib import Path
import torch
from omegaconf import OmegaConf
from ..datasets import get_dataset
from ..geometry.depth import sample_depth
from ..models import get_model
from ..settings import DATA_PATH
from ..utils.export_predictions import export_predictions
resize = 1024
n_kpts = 2048
configs = {
"sp": {
"name": f"r{resize}_SP-k{n_kpts}-nms3",
"keys": ["keypoints", "descriptors", "keypoint_scores"],
"gray": True,
"conf": {
"name": "gluefactory_nonfree.superpoint",
"nms_radius": 3,
"max_num_keypoints": n_kpts,
"detection_threshold": 0.000,
},
},
"sp_open": {
"name": f"r{resize}_SP-open-k{n_kpts}-nms3",
"keys": ["keypoints", "descriptors", "keypoint_scores"],
"gray": True,
"conf": {
"name": "extractors.superpoint_open",
"nms_radius": 3,
"max_num_keypoints": n_kpts,
"detection_threshold": 0.000,
},
},
"cv2-sift": {
"name": f"r{resize}_opencv-SIFT-k{n_kpts}",
"keys": ["keypoints", "descriptors", "keypoint_scores", "oris", "scales"],
"gray": True,
"conf": {
"name": "extractors.sift",
"max_num_keypoints": 4096,
"backend": "opencv",
},
},
"pycolmap-sift": {
"name": f"r{resize}_pycolmap-SIFT-k{n_kpts}",
"keys": ["keypoints", "descriptors", "keypoint_scores", "oris", "scales"],
"gray": True,
"conf": {
"name": "extractors.sift",
"max_num_keypoints": n_kpts,
"backend": "pycolmap",
},
},
"pycolmap-sift-gpu": {
"name": f"r{resize}_pycolmap_SIFTGPU-nms3-fixed-k{n_kpts}",
"keys": ["keypoints", "descriptors", "keypoint_scores", "oris", "scales"],
"gray": True,
"conf": {
"name": "extractors.sift",
"max_num_keypoints": n_kpts,
"backend": "pycolmap_cuda",
"nms_radius": 3,
},
},
"keynet-affnet-hardnet": {
"name": f"r{resize}_KeyNetAffNetHardNet-k{n_kpts}",
"keys": ["keypoints", "descriptors", "keypoint_scores", "oris", "scales"],
"gray": True,
"conf": {
"name": "extractors.keynet_affnet_hardnet",
"max_num_keypoints": n_kpts,
},
},
"disk": {
"name": f"r{resize}_DISK-k{n_kpts}-nms5",
"keys": ["keypoints", "descriptors", "keypoint_scores"],
"gray": False,
"conf": {
"name": "extractors.disk_kornia",
"max_num_keypoints": n_kpts,
},
},
"aliked": {
"name": f"r{resize}_ALIKED-k{n_kpts}-n16",
"keys": ["keypoints", "descriptors", "keypoint_scores"],
"gray": False,
"conf": {
"name": "extractors.aliked",
"max_num_keypoints": n_kpts,
},
},
}
def get_kp_depth(pred, data):
d, valid = sample_depth(pred["keypoints"], data["depth"])
return {"depth_keypoints": d, "valid_depth_keypoints": valid}
def run_export(feature_file, scene, args):
conf = {
"data": {
"name": "megadepth",
"views": 1,
"grayscale": configs[args.method]["gray"],
"preprocessing": {
"resize": resize,
"side": "long",
},
"batch_size": 1,
"num_workers": args.num_workers,
"read_depth": True,
"train_split": [scene],
"train_num_per_scene": None,
},
"split": "train",
"model": configs[args.method]["conf"],
}
conf = OmegaConf.create(conf)
keys = configs[args.method]["keys"]
dataset = get_dataset(conf.data.name)(conf.data)
loader = dataset.get_data_loader(conf.split or "test")
device = "cuda" if torch.cuda.is_available() else "cpu"
model = get_model(conf.model.name)(conf.model).eval().to(device)
if args.export_sparse_depth:
callback_fn = get_kp_depth # use this to store the depth of each keypoint
keys = keys + ["depth_keypoints", "valid_depth_keypoints"]
else:
callback_fn = None
export_predictions(
loader, model, feature_file, as_half=True, keys=keys, callback_fn=callback_fn
)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--export_prefix", type=str, default="")
parser.add_argument("--method", type=str, default="sp")
parser.add_argument("--scenes", type=str, default=None)
parser.add_argument("--num_workers", type=int, default=0)
parser.add_argument("--export_sparse_depth", action="store_true")
args = parser.parse_args()
export_name = configs[args.method]["name"]
data_root = Path(DATA_PATH, "megadepth/Undistorted_SfM")
export_root = Path(DATA_PATH, "exports", "megadepth-undist-depth-" + export_name)
export_root.mkdir(parents=True, exist_ok=True)
if args.scenes is None:
scenes = [p.name for p in data_root.iterdir() if p.is_dir()]
else:
with open(DATA_PATH / "megadepth" / args.scenes, "r") as f:
scenes = f.read().split()
for i, scene in enumerate(scenes):
print(f"{i} / {len(scenes)}", scene)
feature_file = export_root / (scene + ".h5")
if feature_file.exists() and False:
continue
if not (data_root / scene / "images").exists():
logging.info("Skip " + scene)
continue
logging.info(f"Export local features for scene {scene}")
run_export(feature_file, scene, args)
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