Adds evaluation on challenging WxBS and EVD datasets #52
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,126 @@ | ||
| """ | ||
| Simply load images from a folder or nested folders (does not have any split). | ||
| """ | ||
| import argparse | ||
| import logging | ||
| import zipfile | ||
| import os | ||
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| import matplotlib.pyplot as plt | ||
| import numpy as np | ||
| import torch | ||
| from omegaconf import OmegaConf | ||
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| from ..settings import DATA_PATH | ||
| from ..utils.image import ImagePreprocessor, load_image | ||
| from ..utils.tools import fork_rng | ||
| from ..visualization.viz2d import plot_image_grid | ||
| from .base_dataset import BaseDataset | ||
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| logger = logging.getLogger(__name__) | ||
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| def read_homography(path): | ||
| with open(path, 'r') as hf: | ||
| lines = hf.readlines() | ||
| H = [] | ||
| for l in lines: | ||
| H.append([float(x) for x in l.replace('\t',' ').strip().split(' ') if len(x) > 0]) | ||
| H = np.array(H) | ||
| H = H / H[2, 2] | ||
| return H | ||
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| class EVD(BaseDataset, torch.utils.data.Dataset): | ||
| default_conf = { | ||
| "preprocessing": ImagePreprocessor.default_conf, | ||
| "data_dir": "EVD", | ||
| "subset": None, | ||
| "grayscale": False, | ||
| } | ||
| url = "http://cmp.felk.cvut.cz/wbs/datasets/EVD.zip" | ||
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| def _init(self, conf): | ||
| assert conf.batch_size == 1 | ||
| self.preprocessor = ImagePreprocessor(conf.preprocessing) | ||
| self.root = DATA_PATH / conf.data_dir | ||
| if not self.root.exists(): | ||
| logger.info("Downloading the EVD dataset.") | ||
| self.download() | ||
| self.pairs = self.index_dataset() | ||
| if not self.pairs: | ||
| raise ValueError("No image found!") | ||
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| def download(self): | ||
| data_dir = self.root.parent | ||
| data_dir.mkdir(exist_ok=True, parents=True) | ||
| zip_path = data_dir / self.url.rsplit("/", 1)[-1] | ||
| torch.hub.download_url_to_file(self.url, zip_path) | ||
| with zipfile.ZipFile(zip_path, 'r') as z: | ||
| z.extractall(data_dir) | ||
| os.unlink(zip_path) | ||
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| def index_dataset(self): | ||
| sets = sorted([x for x in os.listdir(os.path.join(self.root, '1'))]) | ||
| img_pairs_list = [] | ||
| for s in sets: | ||
| if s == '.DS_Store': | ||
| continue | ||
| img_pairs_list.append(((os.path.join(self.root, '1', s)), | ||
| (os.path.join(self.root, '2', s)), | ||
| (os.path.join(self.root, 'h', s.replace('png', 'txt'))))) | ||
| return img_pairs_list | ||
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| def __getitem__(self, idx): | ||
| imgfname1, imgfname2, h_fname = self.pairs[idx] | ||
| H = read_homography(h_fname) | ||
| data0 = self.preprocessor(load_image(imgfname1)) | ||
| data1 = self.preprocessor(load_image(imgfname2)) | ||
| H = data1["transform"] @ H @ np.linalg.inv(data0["transform"]) | ||
| pair_name = imgfname1.split('/')[-1].split('.')[0] | ||
| return { | ||
| "H_0to1": H.astype(np.float32), | ||
| "scene": pair_name, | ||
| "view0": data0, | ||
| "view1": data1, | ||
| "idx": idx, | ||
| "name": pair_name, | ||
| } | ||
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| def __len__(self): | ||
| return len(self.pairs) | ||
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| def get_dataset(self, split): | ||
| return self | ||
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| def visualize(args): | ||
| conf = { | ||
| "batch_size": 1, | ||
| "num_workers": 8, | ||
| "prefetch_factor": 1, | ||
| } | ||
| conf = OmegaConf.merge(conf, OmegaConf.from_cli(args.dotlist)) | ||
| dataset = EVD(conf) | ||
| loader = dataset.get_data_loader("test") | ||
| logger.info("The dataset has %d elements.", len(loader)) | ||
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| with fork_rng(seed=dataset.conf.seed): | ||
| images = [] | ||
| for _, data in zip(range(args.num_items), loader): | ||
| images.append( | ||
| [data[f"view{i}"]["image"][0].permute(1, 2, 0) for i in range(2)] | ||
| ) | ||
| plot_image_grid(images, dpi=args.dpi) | ||
| plt.tight_layout() | ||
| plt.show() | ||
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| if __name__ == "__main__": | ||
| from .. import logger # overwrite the logger | ||
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| parser = argparse.ArgumentParser() | ||
| parser.add_argument("--num_items", type=int, default=8) | ||
| parser.add_argument("--dpi", type=int, default=100) | ||
| parser.add_argument("dotlist", nargs="*") | ||
| args = parser.parse_intermixed_args() | ||
| visualize(args) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,148 @@ | ||
| """ | ||
| Simply load images from a folder or nested folders (does not have any split). | ||
| """ | ||
|
|
||
| import argparse | ||
| import logging | ||
|
|
||
| import numpy as np | ||
| import torch | ||
| import torchvision | ||
| import os | ||
|
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||
| import matplotlib.pyplot as plt | ||
| import numpy as np | ||
| import torch | ||
| import zipfile | ||
| from omegaconf import OmegaConf | ||
| from pathlib import Path | ||
|
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||
|
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| from ..settings import DATA_PATH | ||
| from ..utils.image import ImagePreprocessor, load_image | ||
| from .base_dataset import BaseDataset | ||
| from ..utils.tools import fork_rng | ||
| from ..visualization.viz2d import plot_image_grid | ||
| from ..geometry.homography import warp_points | ||
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| logger = logging.getLogger(__name__) | ||
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| class WxBSDataset(BaseDataset, torch.utils.data.Dataset): | ||
| """Wide multiple baselines stereo dataset.""" | ||
| url = 'http://cmp.felk.cvut.cz/wbs/datasets/WxBS_v1.1.zip' | ||
| zip_fname = 'WxBS_v1.1.zip' | ||
| validation_pairs = ['kyiv_dolltheater2', 'petrzin'] | ||
| default_conf = { | ||
| "preprocessing": ImagePreprocessor.default_conf, | ||
| "data_dir": "WxBS", | ||
| "subset": None, | ||
| "grayscale": False, | ||
| } | ||
| def _init(self, conf): | ||
| self.preprocessor = ImagePreprocessor(conf.preprocessing) | ||
| self.root = DATA_PATH / conf.data_dir | ||
| if not self.root.exists(): | ||
| logger.info("Downloading the WxBS dataset.") | ||
| self.download() | ||
| self.pairs = self.index_dataset() | ||
| if not self.pairs: | ||
| raise ValueError("No image found!") | ||
|
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||
| def __len__(self): | ||
| return len(self.pairs) | ||
|
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| def download(self): | ||
| data_dir = self.root | ||
| data_dir.mkdir(exist_ok=True, parents=True) | ||
| zip_path = data_dir / self.url.rsplit("/", 1)[-1] | ||
| torch.hub.download_url_to_file(self.url, zip_path) | ||
| with zipfile.ZipFile(zip_path, 'r') as z: | ||
| z.extractall(data_dir) | ||
| os.unlink(zip_path) | ||
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| def index_dataset(self): | ||
| sets = sorted([x for x in os.listdir(self.root) if os.path.isdir(os.path.join(self.root, x))]) | ||
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| img_pairs_list = [] | ||
| for s in sets[::-1]: | ||
| if s == '.DS_Store': | ||
| continue | ||
| ss = os.path.join(self.root, s) | ||
| pairs = os.listdir(ss) | ||
| for p in sorted(pairs): | ||
| if p == '.DS_Store': | ||
| continue | ||
| cur_dir = os.path.join(ss, p) | ||
| if os.path.isfile(os.path.join(cur_dir, '01.png')): | ||
| img_pairs_list.append((os.path.join(cur_dir, '01.png'), | ||
| os.path.join(cur_dir, '02.png'), | ||
| os.path.join(cur_dir, 'corrs.txt'), | ||
| os.path.join(cur_dir, 'crossval_errors.txt'))) | ||
| elif os.path.isfile(os.path.join(cur_dir, '01.jpg')): | ||
| img_pairs_list.append((os.path.join(cur_dir, '01.jpg'), | ||
| os.path.join(cur_dir, '02.jpg'), | ||
| os.path.join(cur_dir, 'corrs.txt'), | ||
| os.path.join(cur_dir, 'crossval_errors.txt'))) | ||
| else: | ||
| continue | ||
| return img_pairs_list | ||
|
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| def __getitem__(self, idx): | ||
| imgfname1, imgfname2, pts_fname, err_fname = self.pairs[idx] | ||
| data0 = self.preprocessor(load_image(imgfname1)) | ||
| data1 = self.preprocessor(load_image(imgfname2)) | ||
| a = load_image(imgfname1) | ||
| pts = np.loadtxt(pts_fname) | ||
| pts[:, :2] = warp_points(pts[:, :2], data0["transform"], False) | ||
| pts[:, 2:] = warp_points(pts[:, 2:], data1["transform"], False) | ||
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| crossval_errors = np.loadtxt(err_fname) | ||
| pair_name = '/'.join(pts_fname.split('/')[-3:-1]).replace('/', '_') | ||
| scene_name = '/'.join(pts_fname.split('/')[-3:-2]) | ||
| out = { | ||
| "pts_0to1": pts, | ||
| "scene": scene_name, | ||
| "view0": data0, | ||
| "view1": data1, | ||
| "idx": idx, | ||
| "name": pair_name, | ||
| "crossval_errors": crossval_errors} | ||
| return out | ||
|
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| def get_dataset(self, split): | ||
| assert split in ['val', 'test'] | ||
| return self | ||
|
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| def visualize(args): | ||
| conf = { | ||
| "batch_size": 1, | ||
| "num_workers": 8, | ||
| "prefetch_factor": 1, | ||
| } | ||
| conf = OmegaConf.merge(conf, OmegaConf.from_cli(args.dotlist)) | ||
| dataset = WxBSDataset(conf) | ||
| loader = dataset.get_data_loader("test") | ||
| logger.info("The dataset has %d elements.", len(loader)) | ||
|
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| with fork_rng(seed=dataset.conf.seed): | ||
| images = [] | ||
| for _, data in zip(range(args.num_items), loader): | ||
| images.append( | ||
| [data[f"view{i}"]["image"][0].permute(1, 2, 0) for i in range(2)] | ||
| ) | ||
| plot_image_grid(images, dpi=args.dpi) | ||
| plt.tight_layout() | ||
| plt.show() | ||
|
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| if __name__ == "__main__": | ||
| from .. import logger # overwrite the logger | ||
|
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| parser = argparse.ArgumentParser() | ||
| parser.add_argument("--num_items", type=int, default=8) | ||
| parser.add_argument("--dpi", type=int, default=100) | ||
| parser.add_argument("dotlist", nargs="*") | ||
| args = parser.parse_intermixed_args() | ||
| visualize(args) |
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you can also run this with
python -m gluefactory.eval.evd --conf superpoint+lightglue-official