import argparse import collections.abc as collections import pprint from pathlib import Path from types import SimpleNamespace from typing import Dict, List, Optional, Union import cv2 import h5py import numpy as np import PIL.Image import torch import torchvision.transforms.functional as F from tqdm import tqdm from . import extractors, logger from .utils.base_model import dynamic_load from .utils.io import list_h5_names, read_image from .utils.parsers import parse_image_lists """ A set of standard configurations that can be directly selected from the command line using their name. Each is a dictionary with the following entries: - output: the name of the feature file that will be generated. - model: the model configuration, as passed to a feature extractor. - preprocessing: how to preprocess the images read from disk. """ confs = { "superpoint_aachen": { "output": "feats-superpoint-n4096-r1024", "model": { "name": "superpoint", "nms_radius": 3, "max_keypoints": 4096, "keypoint_threshold": 0.005, }, "preprocessing": { "grayscale": True, "force_resize": True, "resize_max": 1600, "width": 640, "height": 480, "dfactor": 8, }, }, # Resize images to 1600px even if they are originally smaller. # Improves the keypoint localization if the images are of good quality. "superpoint_max": { "output": "feats-superpoint-n4096-rmax1600", "model": { "name": "superpoint", "nms_radius": 3, "max_keypoints": 4096, "keypoint_threshold": 0.005, }, "preprocessing": { "grayscale": True, "force_resize": True, "resize_max": 1600, "width": 640, "height": 480, "dfactor": 8, }, }, "superpoint_inloc": { "output": "feats-superpoint-n4096-r1600", "model": { "name": "superpoint", "nms_radius": 4, "max_keypoints": 4096, "keypoint_threshold": 0.005, }, "preprocessing": { "grayscale": True, "resize_max": 1600, }, }, "r2d2": { "output": "feats-r2d2-n5000-r1024", "model": { "name": "r2d2", "max_keypoints": 5000, "reliability_threshold": 0.7, "repetability_threshold": 0.7, }, "preprocessing": { "grayscale": False, "force_resize": True, "resize_max": 1024, "width": 640, "height": 480, "dfactor": 8, }, }, "d2net-ss": { "output": "feats-d2net-ss-n5000-r1600", "model": { "name": "d2net", "multiscale": False, "max_keypoints": 5000, }, "preprocessing": { "grayscale": False, "resize_max": 1600, }, }, "d2net-ms": { "output": "feats-d2net-ms-n5000-r1600", "model": { "name": "d2net", "multiscale": True, "max_keypoints": 5000, }, "preprocessing": { "grayscale": False, "resize_max": 1600, }, }, "rord": { "output": "feats-rord-ss-n5000-r1600", "model": { "name": "rord", "multiscale": False, "max_keypoints": 5000, }, "preprocessing": { "grayscale": False, "resize_max": 1600, }, }, "rootsift": { "output": "feats-rootsift-n5000-r1600", "model": { "name": "dog", "descriptor": "rootsift", "max_keypoints": 5000, }, "preprocessing": { "grayscale": True, "force_resize": True, "resize_max": 1600, "width": 640, "height": 480, "dfactor": 8, }, }, "sift": { "output": "feats-sift-n5000-r1600", "model": { "name": "sift", "rootsift": True, "max_keypoints": 5000, }, "preprocessing": { "grayscale": True, "force_resize": True, "resize_max": 1600, "width": 640, "height": 480, "dfactor": 8, }, }, "sosnet": { "output": "feats-sosnet-n5000-r1600", "model": { "name": "dog", "descriptor": "sosnet", "max_keypoints": 5000, }, "preprocessing": { "grayscale": True, "resize_max": 1600, "force_resize": True, "width": 640, "height": 480, "dfactor": 8, }, }, "hardnet": { "output": "feats-hardnet-n5000-r1600", "model": { "name": "dog", "descriptor": "hardnet", "max_keypoints": 5000, }, "preprocessing": { "grayscale": True, "resize_max": 1600, "force_resize": True, "width": 640, "height": 480, "dfactor": 8, }, }, "disk": { "output": "feats-disk-n5000-r1600", "model": { "name": "disk", "max_keypoints": 5000, }, "preprocessing": { "grayscale": False, "resize_max": 1600, }, }, "xfeat": { "output": "feats-xfeat-n5000-r1600", "model": { "name": "xfeat", "max_keypoints": 5000, }, "preprocessing": { "grayscale": False, "resize_max": 1600, }, }, "alike": { "output": "feats-alike-n5000-r1600", "model": { "name": "alike", "max_keypoints": 5000, "use_relu": True, "multiscale": False, "detection_threshold": 0.5, "top_k": -1, "sub_pixel": False, }, "preprocessing": { "grayscale": False, "resize_max": 1600, }, }, "lanet": { "output": "feats-lanet-n5000-r1600", "model": { "name": "lanet", "keypoint_threshold": 0.1, "max_keypoints": 5000, }, "preprocessing": { "grayscale": False, "resize_max": 1600, }, }, "darkfeat": { "output": "feats-darkfeat-n5000-r1600", "model": { "name": "darkfeat", "max_keypoints": 5000, "reliability_threshold": 0.7, "repetability_threshold": 0.7, }, "preprocessing": { "grayscale": False, "force_resize": True, "resize_max": 1600, "width": 640, "height": 480, "dfactor": 8, }, }, "dedode": { "output": "feats-dedode-n5000-r1600", "model": { "name": "dedode", "max_keypoints": 5000, }, "preprocessing": { "grayscale": False, "force_resize": True, "resize_max": 1600, "width": 768, "height": 768, "dfactor": 8, }, }, "example": { "output": "feats-example-n2000-r1024", "model": { "name": "example", "keypoint_threshold": 0.1, "max_keypoints": 2000, "model_name": "model.pth", }, "preprocessing": { "grayscale": False, "force_resize": True, "resize_max": 1024, "width": 768, "height": 768, "dfactor": 8, }, }, "sfd2": { "output": "feats-sfd2-n4096-r1600", "model": { "name": "sfd2", "max_keypoints": 4096, }, "preprocessing": { "grayscale": False, "force_resize": True, "resize_max": 1600, "width": 640, "height": 480, "conf_th": 0.001, "multiscale": False, "scales": [1.0], }, }, # Global descriptors "dir": { "output": "global-feats-dir", "model": {"name": "dir"}, "preprocessing": {"resize_max": 1024}, }, "netvlad": { "output": "global-feats-netvlad", "model": {"name": "netvlad"}, "preprocessing": {"resize_max": 1024}, }, "openibl": { "output": "global-feats-openibl", "model": {"name": "openibl"}, "preprocessing": {"resize_max": 1024}, }, "cosplace": { "output": "global-feats-cosplace", "model": {"name": "cosplace"}, "preprocessing": {"resize_max": 1024}, }, } def resize_image(image, size, interp): if interp.startswith("cv2_"): interp = getattr(cv2, "INTER_" + interp[len("cv2_") :].upper()) h, w = image.shape[:2] if interp == cv2.INTER_AREA and (w < size[0] or h < size[1]): interp = cv2.INTER_LINEAR resized = cv2.resize(image, size, interpolation=interp) elif interp.startswith("pil_"): interp = getattr(PIL.Image, interp[len("pil_") :].upper()) resized = PIL.Image.fromarray(image.astype(np.uint8)) resized = resized.resize(size, resample=interp) resized = np.asarray(resized, dtype=image.dtype) else: raise ValueError(f"Unknown interpolation {interp}.") return resized class ImageDataset(torch.utils.data.Dataset): default_conf = { "globs": ["*.jpg", "*.png", "*.jpeg", "*.JPG", "*.PNG"], "grayscale": False, "resize_max": None, "force_resize": False, "interpolation": "cv2_area", # pil_linear is more accurate but slower } def __init__(self, root, conf, paths=None): self.conf = conf = SimpleNamespace(**{**self.default_conf, **conf}) self.root = root if paths is None: paths = [] for g in conf.globs: paths += list(Path(root).glob("**/" + g)) if len(paths) == 0: raise ValueError(f"Could not find any image in root: {root}.") paths = sorted(list(set(paths))) self.names = [i.relative_to(root).as_posix() for i in paths] logger.info(f"Found {len(self.names)} images in root {root}.") else: if isinstance(paths, (Path, str)): self.names = parse_image_lists(paths) elif isinstance(paths, collections.Iterable): self.names = [ p.as_posix() if isinstance(p, Path) else p for p in paths ] else: raise ValueError(f"Unknown format for path argument {paths}.") for name in self.names: if not (root / name).exists(): raise ValueError( f"Image {name} does not exists in root: {root}." ) def __getitem__(self, idx): name = self.names[idx] image = read_image(self.root / name, self.conf.grayscale) image = image.astype(np.float32) size = image.shape[:2][::-1] if self.conf.resize_max and ( self.conf.force_resize or max(size) > self.conf.resize_max ): scale = self.conf.resize_max / max(size) size_new = tuple(int(round(x * scale)) for x in size) image = resize_image(image, size_new, self.conf.interpolation) if self.conf.grayscale: image = image[None] else: image = image.transpose((2, 0, 1)) # HxWxC to CxHxW image = image / 255.0 data = { "image": image, "original_size": np.array(size), } return data def __len__(self): return len(self.names) def extract(model, image_0, conf): default_conf = { "grayscale": True, "resize_max": 1024, "dfactor": 8, "cache_images": False, "force_resize": False, "width": 320, "height": 240, "interpolation": "cv2_area", } conf = SimpleNamespace(**{**default_conf, **conf}) device = "cuda" if torch.cuda.is_available() else "cpu" def preprocess(image: np.ndarray, conf: SimpleNamespace): image = image.astype(np.float32, copy=False) size = image.shape[:2][::-1] scale = np.array([1.0, 1.0]) if conf.resize_max: scale = conf.resize_max / max(size) if scale < 1.0: size_new = tuple(int(round(x * scale)) for x in size) image = resize_image(image, size_new, "cv2_area") scale = np.array(size) / np.array(size_new) if conf.force_resize: image = resize_image(image, (conf.width, conf.height), "cv2_area") size_new = (conf.width, conf.height) scale = np.array(size) / np.array(size_new) if conf.grayscale: assert image.ndim == 2, image.shape image = image[None] else: image = image.transpose((2, 0, 1)) # HxWxC to CxHxW image = torch.from_numpy(image / 255.0).float() # assure that the size is divisible by dfactor size_new = tuple( map( lambda x: int(x // conf.dfactor * conf.dfactor), image.shape[-2:], ) ) image = F.resize(image, size=size_new, antialias=True) input_ = image.to(device, non_blocking=True)[None] data = { "image": input_, "image_orig": image_0, "original_size": np.array(size), "size": np.array(image.shape[1:][::-1]), } return data # convert to grayscale if needed if len(image_0.shape) == 3 and conf.grayscale: image0 = cv2.cvtColor(image_0, cv2.COLOR_RGB2GRAY) else: image0 = image_0 # comment following lines, image is always RGB mode # if not conf.grayscale and len(image_0.shape) == 3: # image0 = image_0[:, :, ::-1] # BGR to RGB data = preprocess(image0, conf) pred = model({"image": data["image"]}) pred["image_size"] = data["original_size"] pred = {**pred, **data} return pred @torch.no_grad() def main( conf: Dict, image_dir: Path, export_dir: Optional[Path] = None, as_half: bool = True, image_list: Optional[Union[Path, List[str]]] = None, feature_path: Optional[Path] = None, overwrite: bool = False, ) -> Path: logger.info( "Extracting local features with configuration:" f"\n{pprint.pformat(conf)}" ) dataset = ImageDataset(image_dir, conf["preprocessing"], image_list) if feature_path is None: feature_path = Path(export_dir, conf["output"] + ".h5") feature_path.parent.mkdir(exist_ok=True, parents=True) skip_names = set( list_h5_names(feature_path) if feature_path.exists() and not overwrite else () ) dataset.names = [n for n in dataset.names if n not in skip_names] if len(dataset.names) == 0: logger.info("Skipping the extraction.") return feature_path device = "cuda" if torch.cuda.is_available() else "cpu" Model = dynamic_load(extractors, conf["model"]["name"]) model = Model(conf["model"]).eval().to(device) loader = torch.utils.data.DataLoader( dataset, num_workers=1, shuffle=False, pin_memory=True ) for idx, data in enumerate(tqdm(loader)): name = dataset.names[idx] pred = model({"image": data["image"].to(device, non_blocking=True)}) pred = {k: v[0].cpu().numpy() for k, v in pred.items()} pred["image_size"] = original_size = data["original_size"][0].numpy() if "keypoints" in pred: size = np.array(data["image"].shape[-2:][::-1]) scales = (original_size / size).astype(np.float32) pred["keypoints"] = (pred["keypoints"] + 0.5) * scales[None] - 0.5 if "scales" in pred: pred["scales"] *= scales.mean() # add keypoint uncertainties scaled to the original resolution uncertainty = getattr(model, "detection_noise", 1) * scales.mean() if as_half: for k in pred: dt = pred[k].dtype if (dt == np.float32) and (dt != np.float16): pred[k] = pred[k].astype(np.float16) with h5py.File(str(feature_path), "a", libver="latest") as fd: try: if name in fd: del fd[name] grp = fd.create_group(name) for k, v in pred.items(): grp.create_dataset(k, data=v) if "keypoints" in pred: grp["keypoints"].attrs["uncertainty"] = uncertainty except OSError as error: if "No space left on device" in error.args[0]: logger.error( "Out of disk space: storing features on disk can take " "significant space, did you enable the as_half flag?" ) del grp, fd[name] raise error del pred logger.info("Finished exporting features.") return feature_path if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--image_dir", type=Path, required=True) parser.add_argument("--export_dir", type=Path, required=True) parser.add_argument( "--conf", type=str, default="superpoint_aachen", choices=list(confs.keys()), ) parser.add_argument("--as_half", action="store_true") parser.add_argument("--image_list", type=Path) parser.add_argument("--feature_path", type=Path) args = parser.parse_args() main(confs[args.conf], args.image_dir, args.export_dir, args.as_half)