298 lines
9.6 KiB
Python
298 lines
9.6 KiB
Python
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"""
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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 mercantile
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import rasterio
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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 PIL import Image
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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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from .sat_utils import get_random_tiff_patch
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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 SatelliteDataset(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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"metadata_dir": None,
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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 = conf.data_dir
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coordinates_file = conf.metadata_dir
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with open(coordinates_file, "r") as cf:
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coordinates = cf.readlines()
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parsed_coordinates = []
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for coordinate in coordinates:
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lat_part, lon_part = coordinate.split(',')
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lat = float(lat_part.split(':')[-1].strip())
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lon = float(lon_part.split(':')[-1].strip())
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parsed_coordinates.append((lat, lon))
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# Split into train and val 20% val
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train_images = parsed_coordinates[:int(len(parsed_coordinates) * 0.8)]
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val_images = parsed_coordinates[int(len(parsed_coordinates) * 0.8):]
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self.images = {"train": train_images, "val": val_images}
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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.left_augment = (
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# IdentityAugmentation() if conf.right_only else self.photo_augment
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#)
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self.photo_augment = augmentations[aug_name](aug_conf)
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self.left_augment = augmentations[aug_name](aug_conf)
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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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# Generate a list of coordinates, based on the coordinate do the split
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lat, lon = self.image_names[idx]
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img = get_random_tiff_patch(lat, lon, self.conf.data_dir)
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if img is None:
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logging.warning("Image %f %f could not be read.", lat, lon)
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img = np.zeros((1024, 1024) + (() if self.conf.grayscale else (3,)))
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img = img.transpose(1,2,0)
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img = img.astype(np.float32) / 255.0
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#write_status = cv2.imwrite(f"/mnt/drive/{str(idx)}.jpg", img)
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#if write_status == True:
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# print("Writing success")
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#else:
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# print("fuck")
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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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print("Data0 homography_matrix", data0["H_"])
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print("Data1 homography matrix", data1["H_"])
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# image_1 = data0["image"]
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# image_1 = image_1.numpy()
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# image_1 = image_1.transpose(1,2,0)
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# image_1 = np.uint8(image_1 * 255)
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#
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# image_2 = data1["image"]
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# image_2 = image_2.numpy()
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# image_2 = image_2.transpose(1,2,0)
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# image_2 = np.uint8(image_2 * 255)
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#
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# PIL_IMAGE = Image.fromarray(image_1)
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# PIL_IMAGE.save(f"/mnt/drive/{str(idx)}.jpg")
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# PIL_IMAGE = Image.fromarray(image_2)
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# PIL_IMAGE.save(f"/mnt/drive/{str(idx)}-s.jpg")
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#
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# exit()
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#
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H = compute_homography(data0["coords"], data1["coords"], [1, 1])
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print("COmputed homography", H)
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name = f"lat{lat}_lon{lon}"
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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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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 = SatelliteDataset(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.imsave("implot.png")
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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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