312 lines
11 KiB
Python
312 lines
11 KiB
Python
"""
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Simply load images from a folder or nested folders (does not have any split),
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and apply homographic adaptations to it. Yields an image pair without border
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artifacts.
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"""
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import argparse
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import logging
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import shutil
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import tarfile
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from pathlib import Path
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import cv2
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import matplotlib.pyplot as plt
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import numpy as np
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import omegaconf
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import torch
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from omegaconf import OmegaConf
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from tqdm import tqdm
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from ..geometry.homography import (
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compute_homography,
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sample_homography_corners,
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warp_points,
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)
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from ..models.cache_loader import CacheLoader, pad_local_features
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from ..settings import DATA_PATH
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from ..utils.image import read_image
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from ..utils.tools import fork_rng
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from ..visualization.viz2d import plot_image_grid
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from .augmentations import IdentityAugmentation, augmentations
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from .base_dataset import BaseDataset
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logger = logging.getLogger(__name__)
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def sample_homography(img, conf: dict, size: list):
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data = {}
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H, _, coords, _ = sample_homography_corners(img.shape[:2][::-1], **conf)
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data["image"] = cv2.warpPerspective(img, H, tuple(size))
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data["H_"] = H.astype(np.float32)
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data["coords"] = coords.astype(np.float32)
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data["image_size"] = np.array(size, dtype=np.float32)
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return data
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class HomographyDataset(BaseDataset):
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default_conf = {
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# image search
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"data_dir": "revisitop1m", # the top-level directory
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"image_dir": "jpg/", # the subdirectory with the images
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"image_list": "revisitop1m.txt", # optional: list or filename of list
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"glob": ["*.jpg", "*.png", "*.jpeg", "*.JPG", "*.PNG"],
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# splits
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"train_size": 100,
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"val_size": 10,
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"shuffle_seed": 0, # or None to skip
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# image loading
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"grayscale": False,
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"triplet": False,
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"right_only": False, # image0 is orig (rescaled), image1 is right
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"reseed": False,
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"homography": {
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"difficulty": 0.8,
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"translation": 1.0,
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"max_angle": 60,
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"n_angles": 10,
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"patch_shape": [640, 480],
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"min_convexity": 0.05,
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},
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"photometric": {
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"name": "dark",
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"p": 0.75,
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# 'difficulty': 1.0, # currently unused
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},
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# feature loading
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"load_features": {
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"do": False,
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**CacheLoader.default_conf,
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"collate": False,
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"thresh": 0.0,
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"max_num_keypoints": -1,
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"force_num_keypoints": False,
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},
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}
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def _init(self, conf):
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data_dir = DATA_PATH / conf.data_dir
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if not data_dir.exists():
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if conf.data_dir == "revisitop1m":
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logger.info("Downloading the revisitop1m dataset.")
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self.download_revisitop1m()
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else:
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raise FileNotFoundError(data_dir)
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image_dir = data_dir / conf.image_dir
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images = []
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if conf.image_list is None:
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glob = [conf.glob] if isinstance(conf.glob, str) else conf.glob
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for g in glob:
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images += list(image_dir.glob("**/" + g))
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if len(images) == 0:
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raise ValueError(f"Cannot find any image in folder: {image_dir}.")
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images = [i.relative_to(image_dir).as_posix() for i in images]
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images = sorted(images) # for deterministic behavior
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logger.info("Found %d images in folder.", len(images))
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elif isinstance(conf.image_list, (str, Path)):
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image_list = data_dir / conf.image_list
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if not image_list.exists():
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raise FileNotFoundError(f"Cannot find image list {image_list}.")
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images = image_list.read_text().rstrip("\n").split("\n")
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for image in images:
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if not (image_dir / image).exists():
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raise FileNotFoundError(image_dir / image)
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logger.info("Found %d images in list file.", len(images))
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elif isinstance(conf.image_list, omegaconf.listconfig.ListConfig):
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images = conf.image_list.to_container()
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for image in images:
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if not (image_dir / image).exists():
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raise FileNotFoundError(image_dir / image)
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else:
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raise ValueError(conf.image_list)
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if conf.shuffle_seed is not None:
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np.random.RandomState(conf.shuffle_seed).shuffle(images)
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train_images = images[: conf.train_size]
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val_images = images[conf.train_size : conf.train_size + conf.val_size]
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self.images = {"train": train_images, "val": val_images}
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def download_revisitop1m(self):
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data_dir = DATA_PATH / self.conf.data_dir
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tmp_dir = data_dir.parent / "revisitop1m_tmp"
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if tmp_dir.exists(): # The previous download failed.
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shutil.rmtree(tmp_dir)
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image_dir = tmp_dir / self.conf.image_dir
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image_dir.mkdir(exist_ok=True, parents=True)
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num_files = 100
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url_base = "http://ptak.felk.cvut.cz/revisitop/revisitop1m/"
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list_name = "revisitop1m.txt"
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torch.hub.download_url_to_file(url_base + list_name, tmp_dir / list_name)
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for n in tqdm(range(num_files), position=1):
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tar_name = "revisitop1m.{}.tar.gz".format(n + 1)
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tar_path = image_dir / tar_name
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torch.hub.download_url_to_file(url_base + "jpg/" + tar_name, tar_path)
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with tarfile.open(tar_path) as tar:
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tar.extractall(path=image_dir)
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tar_path.unlink()
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shutil.move(tmp_dir, data_dir)
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def get_dataset(self, split):
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return _Dataset(self.conf, self.images[split], split)
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class _Dataset(torch.utils.data.Dataset):
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def __init__(self, conf, image_names, split):
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self.conf = conf
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self.split = split
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self.image_names = np.array(image_names)
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self.image_dir = DATA_PATH / conf.data_dir / conf.image_dir
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aug_conf = conf.photometric
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aug_name = aug_conf.name
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assert (
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aug_name in augmentations.keys()
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), f'{aug_name} not in {" ".join(augmentations.keys())}'
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self.photo_augment = augmentations[aug_name](aug_conf)
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self.left_augment = (
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IdentityAugmentation() if conf.right_only else self.photo_augment
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)
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self.img_to_tensor = IdentityAugmentation()
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if conf.load_features.do:
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self.feature_loader = CacheLoader(conf.load_features)
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def _transform_keypoints(self, features, data):
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"""Transform keypoints by a homography, threshold them,
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and potentially keep only the best ones."""
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# Warp points
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features["keypoints"] = warp_points(
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features["keypoints"], data["H_"], inverse=False
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)
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h, w = data["image"].shape[1:3]
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valid = (
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(features["keypoints"][:, 0] >= 0)
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& (features["keypoints"][:, 0] <= w - 1)
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& (features["keypoints"][:, 1] >= 0)
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& (features["keypoints"][:, 1] <= h - 1)
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)
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features["keypoints"] = features["keypoints"][valid]
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# Threshold
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if self.conf.load_features.thresh > 0:
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valid = features["keypoint_scores"] >= self.conf.load_features.thresh
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features = {k: v[valid] for k, v in features.items()}
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# Get the top keypoints and pad
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n = self.conf.load_features.max_num_keypoints
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if n > -1:
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inds = np.argsort(-features["keypoint_scores"])
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features = {k: v[inds[:n]] for k, v in features.items()}
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if self.conf.load_features.force_num_keypoints:
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features = pad_local_features(
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features, self.conf.load_features.max_num_keypoints
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)
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return features
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def __getitem__(self, idx):
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if self.conf.reseed:
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with fork_rng(self.conf.seed + idx, False):
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return self.getitem(idx)
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else:
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return self.getitem(idx)
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def _read_view(self, img, H_conf, ps, left=False):
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data = sample_homography(img, H_conf, ps)
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if left:
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data["image"] = self.left_augment(data["image"], return_tensor=True)
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else:
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data["image"] = self.photo_augment(data["image"], return_tensor=True)
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gs = data["image"].new_tensor([0.299, 0.587, 0.114]).view(3, 1, 1)
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if self.conf.grayscale:
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data["image"] = (data["image"] * gs).sum(0, keepdim=True)
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if self.conf.load_features.do:
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features = self.feature_loader({k: [v] for k, v in data.items()})
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features = self._transform_keypoints(features, data)
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data["cache"] = features
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return data
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def getitem(self, idx):
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name = self.image_names[idx]
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img = read_image(self.image_dir / name, False)
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if img is None:
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logging.warning("Image %s could not be read.", name)
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img = np.zeros((1024, 1024) + (() if self.conf.grayscale else (3,)))
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img = img.astype(np.float32) / 255.0
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size = img.shape[:2][::-1]
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ps = self.conf.homography.patch_shape
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left_conf = omegaconf.OmegaConf.to_container(self.conf.homography)
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if self.conf.right_only:
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left_conf["difficulty"] = 0.0
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data0 = self._read_view(img, left_conf, ps, left=True)
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data1 = self._read_view(img, self.conf.homography, ps, left=False)
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H = compute_homography(data0["coords"], data1["coords"], [1, 1])
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data = {
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"name": name,
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"original_image_size": np.array(size),
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"H_0to1": H.astype(np.float32),
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"idx": idx,
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"view0": data0,
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"view1": data1,
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}
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if self.conf.triplet:
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# Generate third image
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data2 = self._read_view(img, self.conf.homography, ps, left=False)
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H02 = compute_homography(data0["coords"], data2["coords"], [1, 1])
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H12 = compute_homography(data1["coords"], data2["coords"], [1, 1])
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data = {
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"H_0to2": H02.astype(np.float32),
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"H_1to2": H12.astype(np.float32),
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"view2": data2,
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**data,
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}
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return data
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def __len__(self):
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return len(self.image_names)
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def visualize(args):
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conf = {
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"batch_size": 1,
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"num_workers": 1,
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"prefetch_factor": 1,
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}
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conf = OmegaConf.merge(conf, OmegaConf.from_cli(args.dotlist))
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dataset = HomographyDataset(conf)
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loader = dataset.get_data_loader("train")
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logger.info("The dataset has %d elements.", len(loader))
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with fork_rng(seed=dataset.conf.seed):
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images = []
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for _, data in zip(range(args.num_items), loader):
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images.append(
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(data[f"view{i}"]["image"][0].permute(1, 2, 0) for i in range(2))
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)
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plot_image_grid(images, dpi=args.dpi)
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plt.tight_layout()
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plt.show()
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if __name__ == "__main__":
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from .. import logger # overwrite the logger
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parser = argparse.ArgumentParser()
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parser.add_argument("--num_items", type=int, default=8)
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parser.add_argument("--dpi", type=int, default=100)
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parser.add_argument("dotlist", nargs="*")
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args = parser.parse_intermixed_args()
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visualize(args)
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