|
|
| """
|
| Hyperspectral Dataset Pipelines for MMDetection
|
|
|
| This file contains data pipelines for various hyperspectral datasets
|
| adapted from the ARMhsi/utils/datasets_maskgen.py file.
|
| """
|
|
|
| import numpy as np
|
| from skimage.transform import resize as sk_resize
|
| import torch
|
| import torch.distributed as dist
|
| from scipy.io import loadmat
|
| import mmcv
|
| from mmcv.transforms import BaseTransform
|
| from mmcv.transforms.utils import cache_randomness
|
| from mmdet.registry import TRANSFORMS
|
| from mmdet.datasets.transforms import LoadAnnotations
|
| import mmengine
|
| from mmengine.registry import TRANSFORMS as MMENGINE_TRANSFORMS
|
| from mmengine.fileio import get
|
| import os
|
| from PIL import Image
|
| import cv2
|
| import h5py
|
| import hdf5plugin
|
| import rasterio
|
| import spectral.io.envi as envi
|
| import tifffile
|
| import random
|
| from configs.hypervision import ds_load
|
| from configs.hypervision.hsi3d import get_image_loader
|
| import cupy as cp
|
| from typing import Dict, Iterable, List, Optional, Sequence, Tuple, Union
|
| import logging
|
|
|
|
|
| from mmengine.logging import MMLogger
|
| from mmengine.runner import Runner
|
|
|
| _PIPELINE_RANK_LOGGERS: Dict[str, logging.Logger] = {}
|
|
|
|
|
| def _get_rank_logger(
|
| base_name: str = 'pipeline',
|
| logger_cache: Optional[Dict[str, logging.Logger]] = None,
|
| enable: bool = False,
|
| ) -> tuple[int, Optional[logging.Logger]]:
|
| if dist.is_available() and dist.is_initialized():
|
| rank = dist.get_rank()
|
| else:
|
| rank = 0
|
|
|
| if not enable:
|
| return rank, None
|
|
|
| cache = logger_cache if logger_cache is not None else _PIPELINE_RANK_LOGGERS
|
|
|
| logger_key = f'{base_name}.rank{rank}'
|
| logger = cache.get(logger_key)
|
| if logger is not None:
|
| return rank, logger
|
|
|
| work_dir = os.environ.get('MMDET_WORK_DIR', './work_dirs')
|
| try:
|
| runner = Runner.get_instance()
|
| if runner is not None and getattr(runner, 'work_dir', None):
|
| work_dir = runner.work_dir
|
| except Exception:
|
| pass
|
|
|
| log_dir = os.path.join(work_dir, 'rank_logs')
|
| os.makedirs(log_dir, exist_ok=True)
|
| log_path = os.path.join(log_dir, f'{base_name}_rank{rank}.log')
|
|
|
| logger = logging.getLogger(logger_key)
|
| if not logger.handlers:
|
| logger.setLevel(logging.INFO)
|
| logger.propagate = False
|
| handler = logging.FileHandler(log_path)
|
| formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
|
| handler.setFormatter(formatter)
|
| logger.addHandler(handler)
|
|
|
| cache[logger_key] = logger
|
| return rank, logger
|
|
|
| def read_HSD(filename):
|
| data = np.fromfile('%s' % filename, dtype=np.int32)
|
| height = data[0]
|
| width = data[1]
|
| SR = data[2]
|
| D = data[3]
|
|
|
| data = np.fromfile('%s' % filename, dtype=np.float32)
|
| a = 7
|
| average = data[a:a + SR.item()]
|
| a = a + SR.item()
|
| coeff = data[a:a + D.item() * SR.item()].reshape((D.item(), SR.item()))
|
| a = a + D.item() * SR.item()
|
| scoredata = data[a:a + height.item() * width.item() * D.item()].reshape((height.item() * width.item(), D.item()))
|
|
|
| temp = np.dot(scoredata, coeff)
|
| data = (temp + average).reshape((height.item(), width.item(), SR.item()))
|
|
|
|
|
|
|
| return data
|
|
|
| @TRANSFORMS.register_module()
|
| @MMENGINE_TRANSFORMS.register_module()
|
| class LoadHyperspectralImage(BaseTransform):
|
| """Load hyperspectral image from various formats.
|
|
|
| This transform handles loading of hyperspectral images from different
|
| file formats (.mat, .npy, .tif, .png) and normalizes them according
|
| to dataset-specific parameters.
|
|
|
| Required Keys:
|
| - img_path
|
|
|
| Modified Keys:
|
| - img
|
| - img_shape
|
| - ori_shape
|
| - img_path
|
|
|
| Args:
|
| dataset_type (str): Type of dataset ('harvard', 'umld2015', etc.)
|
| to_float32 (bool): Whether to convert to float32. Defaults to True.
|
| append_rgb (bool): Whether to append RGB channels. Defaults to False.
|
| """
|
|
|
| def __init__(
|
| self,
|
| dataset_type='harvard',
|
| to_float32=True,
|
| append_rgb=True,
|
| enable_rank_logging=False,
|
| rank_logger_cache: Optional[Dict[str, logging.Logger]] = None,
|
| ):
|
| self.dataset_type = dataset_type
|
| self.to_float32 = to_float32
|
| self.append_rgb = append_rgb
|
| self.enable_rank_logging = enable_rank_logging
|
| self.rank_logger_cache = rank_logger_cache
|
|
|
|
|
| self._init_dataset_params()
|
|
|
|
|
| self._init_wavelength_params()
|
|
|
| def _init_dataset_params(self):
|
| """Initialize dataset-specific normalization parameters."""
|
| if self.dataset_type == 'harvard':
|
| self.mean = [0.00050713, 0.00070553, 0.0011365, 0.00149156, 0.00197441, 0.00223908,
|
| 0.00271003, 0.00345612, 0.00369303, 0.00385219, 0.00435618, 0.00506823,
|
| 0.0070418, 0.00800666, 0.0064554, 0.00628767, 0.00703461, 0.00775983,
|
| 0.00832022, 0.00929337, 0.00898094, 0.00748522, 0.00755848, 0.00718764,
|
| 0.00699057, 0.00689327, 0.00674753, 0.00671624, 0.00751943, 0.00741207,
|
| 0.00676514] + [0.485, 0.456, 0.406]
|
| self.std = [0.00035424, 0.00053509, 0.0009189, 0.00122868, 0.00164547, 0.00186258,
|
| 0.00224559, 0.00286002, 0.0030474, 0.00315195, 0.00353866, 0.00406267,
|
| 0.0055249, 0.00622435, 0.00500298, 0.00480104, 0.0052598, 0.00571489,
|
| 0.00601454, 0.00659049, 0.00634349, 0.00530685, 0.00537778, 0.00513787,
|
| 0.00498475, 0.00488729, 0.00471964, 0.00454973, 0.00490581, 0.0046964,
|
| 0.00421346] + [0.229, 0.224, 0.225]
|
| self.max_val = 1.0
|
| self.min_val = 0.0
|
| self.bands = 31
|
|
|
| elif self.dataset_type == 'umld2015':
|
| self.mean = [0.00049507, 0.00077369, 0.00086868, 0.00092949, 0.00106588, 0.00134611,
|
| 0.00148952, 0.00138134, 0.00147178, 0.00140757, 0.00146943, 0.0014896,
|
| 0.00158795, 0.0017375, 0.00171897, 0.00186404, 0.00182205, 0.00166717,
|
| 0.00176569, 0.00172996, 0.00177187, 0.00178079, 0.00172655, 0.00166295,
|
| 0.00167107, 0.0016319, 0.00167044, 0.00166259, 0.00153768, 0.0013869,
|
| 0.00148293, 0.0015571, 0.00129303] + [0.485, 0.456, 0.406]
|
| self.std = [0.0004163, 0.00053372, 0.00057629, 0.00060292, 0.00068114, 0.00085653,
|
| 0.00095103, 0.0008941, 0.00095387, 0.00092446, 0.00096502, 0.00096958,
|
| 0.00102551, 0.00111695, 0.00109679, 0.00118504, 0.00117788, 0.00107841,
|
| 0.00114765, 0.00113261, 0.00116322, 0.00116857, 0.0011256, 0.0010963,
|
| 0.00110381, 0.00108571, 0.00111103, 0.00111677, 0.00103602, 0.00092566,
|
| 0.0009724, 0.00101692, 0.00085337] + [0.229, 0.224, 0.225]
|
| self.max_val = 795.4633697273729
|
| self.min_val = -1.2406029247200091e-05
|
| self.bands = 33
|
|
|
| elif self.dataset_type == 'umns2002':
|
|
|
| self.mean = [0.00050713, 0.00070553, 0.0011365, 0.00149156, 0.00197441, 0.00223908,
|
| 0.00271003, 0.00345612, 0.00369303, 0.00385219, 0.00435618, 0.00506823,
|
| 0.0070418, 0.00800666, 0.0064554, 0.00628767, 0.00703461, 0.00775983,
|
| 0.00832022, 0.00929337, 0.00898094, 0.00748522, 0.00755848, 0.00718764,
|
| 0.00699057, 0.00689327, 0.00674753, 0.00671624, 0.00751943, 0.00741207,
|
| 0.00676514] + [0.485, 0.456, 0.406]
|
| self.std = [0.00035424, 0.00053509, 0.0009189, 0.00122868, 0.00164547, 0.00186258,
|
| 0.00224559, 0.00286002, 0.0030474, 0.00315195, 0.00353866, 0.00406267,
|
| 0.0055249, 0.00622435, 0.00500298, 0.00480104, 0.0052598, 0.00571489,
|
| 0.00601454, 0.00659049, 0.00634349, 0.00530685, 0.00537778, 0.00513787,
|
| 0.00498475, 0.00488729, 0.00471964, 0.00454973, 0.00490581, 0.0046964,
|
| 0.00421346] + [0.229, 0.224, 0.225]
|
| self.max_val = 1.0
|
| self.min_val = 0.0
|
| self.bands = 31
|
|
|
| elif self.dataset_type == 'umns2004':
|
|
|
| self.mean = [0.00049507, 0.00077369, 0.00086868, 0.00092949, 0.00106588, 0.00134611,
|
| 0.00148952, 0.00138134, 0.00147178, 0.00140757, 0.00146943, 0.0014896,
|
| 0.00158795, 0.0017375, 0.00171897, 0.00186404, 0.00182205, 0.00166717,
|
| 0.00176569, 0.00172996, 0.00177187, 0.00178079, 0.00172655, 0.00166295,
|
| 0.00167107, 0.0016319, 0.00167044, 0.00166259, 0.00153768, 0.0013869,
|
| 0.00148293, 0.0015571, 0.00129303] + [0.485, 0.456, 0.406]
|
| self.std = [0.0004163, 0.00053372, 0.00057629, 0.00060292, 0.00068114, 0.00085653,
|
| 0.00095103, 0.0008941, 0.00095387, 0.00092446, 0.00096502, 0.00096958,
|
| 0.00102551, 0.00111695, 0.00109679, 0.00118504, 0.00117788, 0.00107841,
|
| 0.00114765, 0.00113261, 0.00116322, 0.00116857, 0.0011256, 0.0010963,
|
| 0.00110381, 0.00108571, 0.00111103, 0.00111677, 0.00103602, 0.00092566,
|
| 0.0009724, 0.00101692, 0.00085337] + [0.229, 0.224, 0.225]
|
| self.max_val = 795.4633697273729
|
| self.min_val = -1.2406029247200091e-05
|
| self.bands = 33
|
|
|
| elif self.dataset_type == 'umos':
|
|
|
| self.mean = [0.00050713, 0.00070553, 0.0011365, 0.00149156, 0.00197441, 0.00223908,
|
| 0.00271003, 0.00345612, 0.00369303, 0.00385219, 0.00435618, 0.00506823,
|
| 0.0070418, 0.00800666, 0.0064554, 0.00628767, 0.00703461, 0.00775983,
|
| 0.00832022, 0.00929337, 0.00898094, 0.00748522, 0.00755848, 0.00718764,
|
| 0.00699057, 0.00689327, 0.00674753, 0.00671624, 0.00751943, 0.00741207,
|
| 0.00676514] + [0.485, 0.456, 0.406]
|
| self.std = [0.00035424, 0.00053509, 0.0009189, 0.00122868, 0.00164547, 0.00186258,
|
| 0.00224559, 0.00286002, 0.0030474, 0.00315195, 0.00353866, 0.00406267,
|
| 0.0055249, 0.00622435, 0.00500298, 0.00480104, 0.0052598, 0.00571489,
|
| 0.00601454, 0.00659049, 0.00634349, 0.00530685, 0.00537778, 0.00513787,
|
| 0.00498475, 0.00488729, 0.00471964, 0.00454973, 0.00490581, 0.0046964,
|
| 0.00421346] + [0.229, 0.224, 0.225]
|
| self.max_val = 1.0
|
| self.min_val = 0.0
|
| self.bands = 31
|
|
|
| elif self.dataset_type == 'umri2015':
|
|
|
| self.mean = [0.00049507, 0.00077369, 0.00086868, 0.00092949, 0.00106588, 0.00134611,
|
| 0.00148952, 0.00138134, 0.00147178, 0.00140757, 0.00146943, 0.0014896,
|
| 0.00158795, 0.0017375, 0.00171897, 0.00186404, 0.00182205, 0.00166717,
|
| 0.00176569, 0.00172996, 0.00177187, 0.00178079, 0.00172655, 0.00166295,
|
| 0.00167107, 0.0016319, 0.00167044, 0.00166259, 0.00153768, 0.0013869,
|
| 0.00148293, 0.0015571, 0.00129303] + [0.485, 0.456, 0.406]
|
| self.std = [0.0004163, 0.00053372, 0.00057629, 0.00060292, 0.00068114, 0.00085653,
|
| 0.00095103, 0.0008941, 0.00095387, 0.00092446, 0.00096502, 0.00096958,
|
| 0.00102551, 0.00111695, 0.00109679, 0.00118504, 0.00117788, 0.00107841,
|
| 0.00114765, 0.00113261, 0.00116322, 0.00116857, 0.0011256, 0.0010963,
|
| 0.00110381, 0.00108571, 0.00111103, 0.00111677, 0.00103602, 0.00092566,
|
| 0.0009724, 0.00101692, 0.00085337] + [0.229, 0.224, 0.225]
|
| self.max_val = 795.4633697273729
|
| self.min_val = -1.2406029247200091e-05
|
| self.bands = 33
|
|
|
| elif self.dataset_type == 'umemm':
|
| self.mean = [0.01173168, 0.01234073, 0.01300494, 0.01367329, 0.01436883, 0.0151207,
|
| 0.0155524, 0.01612679, 0.01647242, 0.0169334, 0.01737261, 0.01776505,
|
| 0.01821689, 0.01851488, 0.01895346, 0.01905013, 0.01976628, 0.02014966,
|
| 0.02034537, 0.02077717, 0.02100478, 0.02121398, 0.0215768, 0.02184028,
|
| 0.02216735, 0.02231407, 0.02260189, 0.02286339, 0.02325497, 0.0233728,
|
| 0.02365622, 0.02380282, 0.02444496] + [0.485, 0.456, 0.406]
|
| self.std = [0.00483212, 0.00528087, 0.00574404, 0.00611767, 0.00647407, 0.00695638,
|
| 0.0072416, 0.00756005, 0.00780453, 0.00815911, 0.00851377, 0.00878704,
|
| 0.00913599, 0.0093712, 0.0096884, 0.00983615, 0.01027443, 0.01053299,
|
| 0.01056043, 0.01087872, 0.01099751, 0.01115679, 0.01137956, 0.01154608,
|
| 0.01175272, 0.01184787, 0.01195576, 0.01215307, 0.0123245, 0.01232338,
|
| 0.01237378, 0.01234926, 0.01252923] + [0.229, 0.224, 0.225]
|
| self.max_val = 4.380079501083187
|
| self.min_val = -0.08767047645618645
|
| self.bands = 33
|
|
|
| elif self.dataset_type == 'hyperblood':
|
|
|
| self.mean = [0.11792768, 0.12286566, 0.13354243, 0.12907951, 0.09845191, 0.13444436,
|
| 0.14597324, 0.14484807, 0.12660842, 0.08799646, 0.11882777, 0.13161479,
|
| 0.10589161, 0.09883164, 0.08022733, 0.09555449, 0.10218862, 0.09988686,
|
| 0.09676628, 0.08561739, 0.1227139, 0.13198491, 0.13013612, 0.12793193,
|
| 0.09031978] + [0.485, 0.456, 0.406]
|
| self.std = [0.06492269, 0.06635557, 0.07162475, 0.07012394, 0.05701815, 0.07323762,
|
| 0.0766644, 0.07810237, 0.07141274, 0.05436147, 0.06862938, 0.07280301,
|
| 0.0626632, 0.05946415, 0.05017714, 0.05620537, 0.0591019, 0.05838145,
|
| 0.05704834, 0.05148091, 0.06747658, 0.07073647, 0.07083215, 0.07006211,
|
| 0.05280771] + [0.229, 0.224, 0.225]
|
| self.max_val = 4.0
|
| self.min_val = 0.0
|
| self.bands = 113
|
|
|
| elif self.dataset_type == 'hsidrive20':
|
| self.mean = [0.11792768, 0.12286566, 0.13354243, 0.12907951, 0.09845191, 0.13444436,
|
| 0.14597324, 0.14484807, 0.12660842, 0.08799646, 0.11882777, 0.13161479,
|
| 0.10589161, 0.09883164, 0.08022733, 0.09555449, 0.10218862, 0.09988686,
|
| 0.09676628, 0.08561739, 0.1227139, 0.13198491, 0.13013612, 0.12793193,
|
| 0.09031978] + [0.485, 0.456, 0.406]
|
| self.std = [0.06492269, 0.06635557, 0.07162475, 0.07012394, 0.05701815, 0.07323762,
|
| 0.0766644, 0.07810237, 0.07141274, 0.05436147, 0.06862938, 0.07280301,
|
| 0.0626632, 0.05946415, 0.05017714, 0.05620537, 0.0591019, 0.05838145,
|
| 0.05704834, 0.05148091, 0.06747658, 0.07073647, 0.07083215, 0.07006211,
|
| 0.05280771] + [0.229, 0.224, 0.225]
|
| self.max_val = 4.0
|
| self.min_val = 0.0
|
| self.bands = 25
|
|
|
| elif self.dataset_type == 'hotrednir':
|
| self.mean = [0.28415901, 0.21461861, 0.18719398, 0.19470588, 0.20235114, 0.20367749,
|
| 0.2163216, 0.21995637, 0.2168878, 0.20578036, 0.20192309, 0.217628,
|
| 0.17708894, 0.17544539, 0.16722978] + [0.485, 0.456, 0.406]
|
| self.std = [0.2247296, 0.17951354, 0.15692467, 0.15598073, 0.16016452, 0.15911849,
|
| 0.16180973, 0.15985215, 0.15849979, 0.15031793, 0.146697, 0.14847991,
|
| 0.13463819, 0.13324966, 0.12782383] + [0.229, 0.224, 0.225]
|
| self.max_val = 254.0
|
| self.min_val = 0.0
|
| self.bands = 15
|
|
|
| elif self.dataset_type == 'arad_1k_31':
|
|
|
| self.mean = [0.00050713, 0.00070553, 0.0011365, 0.00149156, 0.00197441, 0.00223908,
|
| 0.00271003, 0.00345612, 0.00369303, 0.00385219, 0.00435618, 0.00506823,
|
| 0.0070418, 0.00800666, 0.0064554, 0.00628767, 0.00703461, 0.00775983,
|
| 0.00832022, 0.00929337, 0.00898094, 0.00748522, 0.00755848, 0.00718764,
|
| 0.00699057, 0.00689327, 0.00674753, 0.00671624, 0.00751943, 0.00741207,
|
| 0.00676514] + [0.485, 0.456, 0.406]
|
| self.std = [0.00035424, 0.00053509, 0.0009189, 0.00122868, 0.00164547, 0.00186258,
|
| 0.00224559, 0.00286002, 0.0030474, 0.00315195, 0.00353866, 0.00406267,
|
| 0.0055249, 0.00622435, 0.00500298, 0.00480104, 0.0052598, 0.00571489,
|
| 0.00601454, 0.00659049, 0.00634349, 0.00530685, 0.00537778, 0.00513787,
|
| 0.00498475, 0.00488729, 0.00471964, 0.00454973, 0.00490581, 0.0046964,
|
| 0.00421346] + [0.229, 0.224, 0.225]
|
| self.max_val = 1.0
|
| self.min_val = 0.0
|
| self.bands = 31
|
|
|
| elif self.dataset_type == 'arad_1k_16':
|
|
|
| self.mean = [0.00050713, 0.00070553, 0.0011365, 0.00149156, 0.00197441, 0.00223908,
|
| 0.00271003, 0.00345612, 0.00369303, 0.00385219, 0.00435618, 0.00506823,
|
| 0.0070418, 0.00800666, 0.0064554, 0.00628767] + [0.485, 0.456, 0.406]
|
| self.std = [0.00035424, 0.00053509, 0.0009189, 0.00122868, 0.00164547, 0.00186258,
|
| 0.00224559, 0.00286002, 0.0030474, 0.00315195, 0.00353866, 0.00406267,
|
| 0.0055249, 0.00622435, 0.00500298, 0.00480104] + [0.229, 0.224, 0.225]
|
| self.max_val = 1.0
|
| self.min_val = 0.0
|
| self.bands = 16
|
|
|
| elif self.dataset_type == 'cave':
|
|
|
| self.mean = [0.00050713, 0.00070553, 0.0011365, 0.00149156, 0.00197441, 0.00223908,
|
| 0.00271003, 0.00345612, 0.00369303, 0.00385219, 0.00435618, 0.00506823,
|
| 0.0070418, 0.00800666, 0.0064554, 0.00628767, 0.00703461, 0.00775983,
|
| 0.00832022, 0.00929337, 0.00898094, 0.00748522, 0.00755848, 0.00718764,
|
| 0.00699057, 0.00689327, 0.00674753, 0.00671624, 0.00751943, 0.00741207,
|
| 0.00676514] + [0.485, 0.456, 0.406]
|
| self.std = [0.00035424, 0.00053509, 0.0009189, 0.00122868, 0.00164547, 0.00186258,
|
| 0.00224559, 0.00286002, 0.0030474, 0.00315195, 0.00353866, 0.00406267,
|
| 0.0055249, 0.00622435, 0.00500298, 0.00480104, 0.0052598, 0.00571489,
|
| 0.00601454, 0.00659049, 0.00634349, 0.00530685, 0.00537778, 0.00513787,
|
| 0.00498475, 0.00488729, 0.00471964, 0.00454973, 0.00490581, 0.0046964,
|
| 0.00421346] + [0.229, 0.224, 0.225]
|
| self.max_val = 1.0
|
| self.min_val = 0.0
|
| self.bands = 31
|
|
|
| elif self.dataset_type == 'icvl':
|
|
|
| self.mean = [0.00050713, 0.00070553, 0.0011365, 0.00149156, 0.00197441, 0.00223908,
|
| 0.00271003, 0.00345612, 0.00369303, 0.00385219, 0.00435618, 0.00506823,
|
| 0.0070418, 0.00800666, 0.0064554, 0.00628767, 0.00703461, 0.00775983,
|
| 0.00832022, 0.00929337, 0.00898094, 0.00748522, 0.00755848, 0.00718764,
|
| 0.00699057, 0.00689327, 0.00674753, 0.00671624, 0.00751943, 0.00741207,
|
| 0.00676514] + [0.485, 0.456, 0.406]
|
| self.std = [0.00035424, 0.00053509, 0.0009189, 0.00122868, 0.00164547, 0.00186258,
|
| 0.00224559, 0.00286002, 0.0030474, 0.00315195, 0.00353866, 0.00406267,
|
| 0.0055249, 0.00622435, 0.00500298, 0.00480104, 0.0052598, 0.00571489,
|
| 0.00601454, 0.00659049, 0.00634349, 0.00530685, 0.00537778, 0.00513787,
|
| 0.00498475, 0.00488729, 0.00471964, 0.00454973, 0.00490581, 0.0046964,
|
| 0.00421346] + [0.229, 0.224, 0.225]
|
| self.max_val = 1.0
|
| self.min_val = 0.0
|
| self.bands = 31
|
|
|
| elif self.dataset_type == 'hs_sod':
|
|
|
| self.mean = [0.00505788, 0.00601034, 0.00747643, 0.00959971, 0.0147855, 0.01898453,
|
| 0.0239441, 0.03211231, 0.03791218, 0.04520133, 0.04938109, 0.05594452,
|
| 0.06514561, 0.07837064, 0.08795061, 0.10231026, 0.1106684, 0.12345689,
|
| 0.13252559, 0.14111, 0.15414085, 0.16602382, 0.16550444, 0.1745532,
|
| 0.18869128, 0.19148134, 0.19939337, 0.20783469, 0.20828562, 0.21840917,
|
| 0.23144286, 0.24558449, 0.25482584, 0.25775317, 0.26426578, 0.27155495,
|
| 0.27239095, 0.2683785, 0.26598797, 0.26503738, 0.26443645, 0.26481411,
|
| 0.25628452, 0.2493137, 0.25782899, 0.26621533, 0.26577412, 0.26109554,
|
| 0.26062482, 0.26170838, 0.25086012, 0.24544579, 0.2453043, 0.24627007,
|
| 0.23747638, 0.22964289, 0.22108862, 0.22013535, 0.2160363, 0.21200803,
|
| 0.21234002, 0.20738988, 0.18760026, 0.17716606, 0.18474978, 0.20061198,
|
| 0.2190922, 0.23057194, 0.20136807, 0.18552641, 0.18074632, 0.20211651,
|
| 0.22420212, 0.25190136, 0.26552015, 0.26589699, 0.23496681, 0.15726452,
|
| 0.18386361, 0.22457717, 0.23622217] + [0.485, 0.456, 0.406]
|
| self.std = [0.00335335, 0.00379358, 0.00453958, 0.0057187, 0.00848124, 0.01070781,
|
| 0.01339494, 0.01787782, 0.02111595, 0.02536344, 0.02784967, 0.03205192,
|
| 0.03756858, 0.04570729, 0.05158716, 0.06053343, 0.06596695, 0.07434336,
|
| 0.08030715, 0.0860997, 0.09462209, 0.10215146, 0.10232753, 0.10816608,
|
| 0.11646034, 0.11808529, 0.12236777, 0.12631026, 0.12541599, 0.12993119,
|
| 0.13554931, 0.14209915, 0.14627095, 0.14776726, 0.1509073, 0.15434794,
|
| 0.15478504, 0.15323047, 0.15261152, 0.15322816, 0.15360044, 0.15428015,
|
| 0.1509565, 0.14847103, 0.15334979, 0.15741342, 0.1573434, 0.15556864,
|
| 0.15630692, 0.15746275, 0.15289239, 0.1505807, 0.15081839, 0.15202881,
|
| 0.14851399, 0.1455947, 0.14142634, 0.14174904, 0.14012646, 0.13851422,
|
| 0.13887505, 0.13566844, 0.12321144, 0.11449433, 0.11628917, 0.12220737,
|
| 0.1309691, 0.13637021, 0.12087113, 0.112728, 0.11086555, 0.12307407,
|
| 0.13483802, 0.14899566, 0.15559294, 0.15502698, 0.13857946, 0.09519617,
|
| 0.10987266, 0.13104544, 0.13648109] + [0.229, 0.224, 0.225]
|
| self.max_val = 4095.0
|
| self.min_val = 0.0
|
| self.bands = 81
|
|
|
| elif self.dataset_type == 'hsodbit_v2':
|
|
|
| self.mean = [0.00050713, 0.00070553, 0.0011365, 0.00149156, 0.00197441, 0.00223908,
|
| 0.00271003, 0.00345612, 0.00369303, 0.00385219, 0.00435618, 0.00506823,
|
| 0.0070418, 0.00800666, 0.0064554, 0.00628767, 0.00703461, 0.00775983,
|
| 0.00832022, 0.00929337, 0.00898094, 0.00748522, 0.00755848, 0.00718764,
|
| 0.00699057, 0.00689327, 0.00674753, 0.00671624, 0.00751943, 0.00741207,
|
| 0.00676514] + [0.485, 0.456, 0.406]
|
| self.std = [0.00035424, 0.00053509, 0.0009189, 0.00122868, 0.00164547, 0.00186258,
|
| 0.00224559, 0.00286002, 0.0030474, 0.00315195, 0.00353866, 0.00406267,
|
| 0.0055249, 0.00622435, 0.00500298, 0.00480104, 0.0052598, 0.00571489,
|
| 0.00601454, 0.00659049, 0.00634349, 0.00530685, 0.00537778, 0.00513787,
|
| 0.00498475, 0.00488729, 0.00471964, 0.00454973, 0.00490581, 0.0046964,
|
| 0.00421346] + [0.229, 0.224, 0.225]
|
| self.max_val = 1.0
|
| self.min_val = 0.0
|
| self.bands = 31
|
|
|
| elif self.dataset_type == 'vnihdhiatlimafb':
|
|
|
| self.mean = [0.11792768, 0.12286566, 0.13354243, 0.12907951, 0.09845191, 0.13444436,
|
| 0.14597324, 0.14484807, 0.12660842, 0.08799646, 0.11882777, 0.13161479,
|
| 0.10589161, 0.09883164, 0.08022733, 0.09555449, 0.10218862, 0.09988686,
|
| 0.09676628, 0.08561739, 0.1227139, 0.13198491, 0.13013612, 0.12793193,
|
| 0.09031978] + [0.485, 0.456, 0.406]
|
| self.std = [0.06492269, 0.06635557, 0.07162475, 0.07012394, 0.05701815, 0.07323762,
|
| 0.0766644, 0.07810237, 0.07141274, 0.05436147, 0.06862938, 0.07280301,
|
| 0.0626632, 0.05946415, 0.05017714, 0.05620537, 0.0591019, 0.05838145,
|
| 0.05704834, 0.05148091, 0.06747658, 0.07073647, 0.07083215, 0.07006211,
|
| 0.05280771] + [0.229, 0.224, 0.225]
|
| self.max_val = 6.7395835
|
| self.min_val = 0.0
|
| self.bands = 204
|
|
|
| elif self.dataset_type == 'deephsnir':
|
|
|
| self.mean = [0.31596533, 0.31582026, 0.31591017, 0.31598633, 0.31596835, 0.31598215,
|
| 0.31601244, 0.31609212, 0.31615184, 0.31636028, 0.31626798, 0.31632993,
|
| 0.31630893, 0.31629821, 0.31628198, 0.31629123, 0.31628992, 0.31628988,
|
| 0.31629121, 0.31629341, 0.31629719, 0.31630189, 0.31630792, 0.31631442,
|
| 0.31632174, 0.31632916, 0.31633725, 0.31634579, 0.31635505, 0.31636362,
|
| 0.31637288, 0.31638203, 0.31639112, 0.31640022, 0.31640956, 0.31641804,
|
| 0.3164259, 0.31643368, 0.31644078, 0.31644723, 0.31645304, 0.31645748,
|
| 0.31646186, 0.31646554, 0.31646728, 0.3164684, 0.31646862, 0.31646795,
|
| 0.31646639, 0.31646393, 0.31646071, 0.31645649, 0.3164517, 0.31644626,
|
| 0.31643993, 0.3164326, 0.31642488, 0.31641591, 0.316406, 0.31639471,
|
| 0.31638239, 0.3163689, 0.31635382, 0.31633678, 0.3163173, 0.31629476,
|
| 0.31626924, 0.31624086, 0.31621267, 0.31618586, 0.31616282, 0.31614348,
|
| 0.31612933, 0.31611873, 0.31611115, 0.31610538, 0.31610125, 0.3160978,
|
| 0.31609544, 0.31609292, 0.31609089, 0.31608857, 0.31608667, 0.31608452,
|
| 0.31608326, 0.31608272, 0.31608313, 0.31608345, 0.31608475, 0.31608608,
|
| 0.31608797, 0.31609013, 0.31609267, 0.31609534, 0.31609833, 0.31610108,
|
| 0.31610388, 0.3161063, 0.31610849, 0.31611045, 0.3161125, 0.31611421,
|
| 0.31611564, 0.31611635, 0.31611742, 0.31611747, 0.31611709, 0.31611633,
|
| 0.31611496, 0.31611284, 0.3161104, 0.31610654, 0.31610262, 0.31609754,
|
| 0.31609167, 0.31608465, 0.31607718, 0.31606815, 0.3160589, 0.31604863,
|
| 0.3160373, 0.3160252, 0.3160125, 0.31599887, 0.31598522, 0.31597137,
|
| 0.31595758, 0.31594364, 0.31593029, 0.31591749, 0.31590532, 0.31589336,
|
| 0.31588242, 0.31587171, 0.31586104, 0.31584994, 0.31583816, 0.31582486,
|
| 0.3158098, 0.31579286, 0.31577426, 0.31575404, 0.31573181, 0.31570692,
|
| 0.31568126, 0.31565632, 0.31563368, 0.31561237, 0.31559422, 0.31557927,
|
| 0.31556712, 0.31555746, 0.31554977, 0.31554326, 0.31553813, 0.31553397,
|
| 0.31553063, 0.31552774, 0.31552526, 0.31552339, 0.31552228, 0.315521,
|
| 0.31552015, 0.31551944, 0.31551878, 0.31551817, 0.31551761, 0.31551709,
|
| 0.31551661, 0.31551617, 0.31551576, 0.31551538, 0.31551503, 0.31551471,
|
| 0.31551441, 0.31551414, 0.31551389, 0.31551366, 0.31551345, 0.31551326,
|
| 0.31551309, 0.31551294, 0.3155128, 0.31551268, 0.31551257, 0.31551248,
|
| 0.3155124, 0.31551233, 0.31551227, 0.31551222, 0.31551218, 0.31551215,
|
| 0.31551213, 0.31551211, 0.3155121, 0.31551209, 0.31551209, 0.31551209,
|
| 0.3155121, 0.31551211, 0.31551213, 0.31551215, 0.31551218, 0.31551222,
|
| 0.31551227, 0.31551233, 0.3155124, 0.31551248, 0.31551257, 0.31551268,
|
| 0.3155128, 0.31551294, 0.31551309, 0.31551326, 0.31551345, 0.31551366,
|
| 0.31551389, 0.31551414, 0.31551441, 0.31551471, 0.31551503, 0.31551538,
|
| 0.31551576, 0.31551617, 0.31551661, 0.31551709, 0.31551761, 0.31551817,
|
| 0.31551878, 0.31551944, 0.31552015, 0.315521, 0.31552228, 0.31552339,
|
| 0.31552526, 0.31552774, 0.31553063, 0.31553397, 0.31553813, 0.31554326,
|
| 0.31554977, 0.31555746, 0.31556712, 0.31557927, 0.31559422, 0.31561237,
|
| 0.31563368, 0.31565632, 0.31568126, 0.31570692, 0.31573181, 0.31575404,
|
| 0.31577426, 0.31579286, 0.3158098, 0.31582486, 0.31583816, 0.31584994,
|
| 0.31586104, 0.31587171, 0.31588242, 0.31589336, 0.31590532, 0.31591749,
|
| 0.31593029, 0.31594364, 0.31595758, 0.31597137, 0.31598522, 0.31599887,
|
| 0.3160125, 0.3160252, 0.3160373, 0.31604863, 0.3160589, 0.31606815,
|
| 0.31607718, 0.31608465, 0.31609167, 0.31609754, 0.31610262, 0.31610654,
|
| 0.3161104, 0.31611284, 0.31611496, 0.31611633, 0.31611709, 0.31611747,
|
| 0.31611742, 0.31611635, 0.31611564, 0.31611421, 0.3161125, 0.31611045,
|
| 0.31610849, 0.3161063, 0.31610388, 0.31610108, 0.31609833, 0.31609534,
|
| 0.31609267, 0.31609013, 0.31608797, 0.31608608, 0.31608475, 0.31608345,
|
| 0.31608313, 0.31608272, 0.31608326, 0.31608452, 0.31608667, 0.31608857,
|
| 0.31609089, 0.31609292, 0.31609544, 0.3160978, 0.31610125, 0.31610538,
|
| 0.31611115, 0.31611873, 0.31612933, 0.31614348, 0.31616282, 0.31618586,
|
| 0.31621267, 0.31624086, 0.31626924, 0.31629476, 0.3163173, 0.31633678,
|
| 0.31635382, 0.3163689, 0.31638239, 0.31639471, 0.316406, 0.31641591,
|
| 0.31642488, 0.3164326, 0.31643993, 0.31644626, 0.3164517, 0.31645649,
|
| 0.31646071, 0.31646393, 0.31646639, 0.31646795, 0.31646862, 0.3164684,
|
| 0.31646728, 0.31646554, 0.31646186, 0.31645748, 0.31645304, 0.31644723,
|
| 0.31644078, 0.31643368, 0.3164259, 0.31641804, 0.31640956, 0.31640022,
|
| 0.31639112, 0.31638203, 0.31637288, 0.31636362, 0.31635505, 0.31634579,
|
| 0.31633725, 0.31632916, 0.31632174, 0.31631442, 0.31630792, 0.31630189,
|
| 0.31629719, 0.31629341, 0.31629121, 0.31628988, 0.31628992, 0.31629123,
|
| 0.31628198, 0.31629821, 0.31630893, 0.31632993, 0.31626798, 0.31636028,
|
| 0.31615184, 0.31609212, 0.31601244, 0.31598215, 0.31596835, 0.31598633,
|
| 0.31591017, 0.31582026, 0.31596533] + [0.485, 0.456, 0.406]
|
| self.std = [0.00079037, 0.0007425, 0.00065591, 0.00051521, 0.00042861, 0.00035781,
|
| 0.00030852, 0.00027553, 0.00024972, 0.00023215, 0.00021762, 0.00020285,
|
| 0.00019168, 0.00018386, 0.0001759, 0.00017044, 0.00016426, 0.00016082,
|
| 0.00015763, 0.00015393, 0.00015311, 0.00015134, 0.00014883, 0.00014749,
|
| 0.00014741, 0.00014516, 0.00014332, 0.00014341, 0.00014184, 0.00014089,
|
| 0.00014032, 0.00014022, 0.00013943, 0.00014008, 0.00014015, 0.00014017,
|
| 0.00014134, 0.0001425, 0.00014354, 0.00014468, 0.00014716, 0.00015027,
|
| 0.00015342, 0.00015756, 0.00016252, 0.00016839, 0.00017464, 0.00018131,
|
| 0.00018895, 0.00019656, 0.00020385, 0.00021131, 0.00021835, 0.00022541,
|
| 0.00023273, 0.00023976, 0.00024745, 0.00025497, 0.00026362, 0.00027079,
|
| 0.00027759, 0.00028338, 0.00028929, 0.00029449, 0.00029914, 0.00030325,
|
| 0.00030718, 0.00031107, 0.00031513, 0.00031871, 0.00032228, 0.00032645,
|
| 0.00033152, 0.00033614, 0.00034014, 0.00034409, 0.00034907, 0.00035414,
|
| 0.00035885, 0.00036213, 0.00036416, 0.00036582, 0.00036737, 0.00036871,
|
| 0.00037023, 0.00037181, 0.00037464, 0.00037864, 0.00038216, 0.00038387,
|
| 0.00038331, 0.0003815, 0.00037921, 0.00037672, 0.00037407, 0.00037124,
|
| 0.00036876, 0.0003665, 0.00036421, 0.00036161, 0.00035823, 0.00035554,
|
| 0.00035111, 0.00034814, 0.00034807, 0.00035138, 0.00035852, 0.00036978,
|
| 0.00038696, 0.00040979, 0.00043873, 0.00047688, 0.00052208, 0.00057209,
|
| 0.00062645, 0.00067932, 0.00072609, 0.00076801, 0.00080663, 0.0008454,
|
| 0.0008833, 0.0009198, 0.0009528, 0.00098305, 0.00101078, 0.00103879,
|
| 0.0010646, 0.00108751, 0.00110426, 0.00111807, 0.00113049, 0.00114154,
|
| 0.00115239, 0.0011625, 0.00117243, 0.00118141, 0.00118918, 0.00119544,
|
| 0.00120023, 0.00120341, 0.00120584, 0.0012085, 0.00121261, 0.00121765,
|
| 0.0012236, 0.00122975, 0.00123565, 0.00124077, 0.0012446, 0.0012474,
|
| 0.00124991, 0.00125278, 0.00125682, 0.0012612, 0.00126572, 0.00127012,
|
| 0.00127315, 0.00127535, 0.00127643, 0.0012759, 0.0012735, 0.00127003,
|
| 0.00126723, 0.00126586, 0.00126587, 0.0012669, 0.00126801, 0.00126928,
|
| 0.00127057, 0.00127182, 0.0012726, 0.00127326, 0.00127344, 0.00127358,
|
| 0.00127316, 0.00127243, 0.00127118, 0.00127005, 0.00126892, 0.00126813,
|
| 0.00126719, 0.00126615, 0.00126527, 0.00126412, 0.00126246, 0.00126066,
|
| 0.00125855, 0.00125606, 0.00125331, 0.00124968, 0.00124578, 0.00124186,
|
| 0.00123847, 0.00123427, 0.00122877, 0.00122099, 0.00121079, 0.00120015,
|
| 0.00118775, 0.00117478, 0.00116004, 0.00114472, 0.00112692, 0.00110748,
|
| 0.00108788, 0.00106712, 0.00104644, 0.00102789, 0.00101468, 0.00100716,
|
| 0.00100281, 0.00099826, 0.00099611, 0.00099268, 0.00099213, 0.00099252,
|
| 0.00099448, 0.00099648, 0.00099991, 0.00100328, 0.00100812, 0.00101217,
|
| 0.00101756, 0.00102464] + [0.229, 0.224, 0.225]
|
| self.max_val = 1.0
|
| self.min_val = -0.091662824
|
| self.bands = 249
|
|
|
| elif self.dataset_type == 'deephsvis':
|
|
|
| self.mean = [0.31596533] * 249 + [0.485, 0.456, 0.406]
|
| self.std = [0.00079012] * 249 + [0.229, 0.224, 0.225]
|
| self.max_val = 1.0
|
| self.min_val = -0.091662824
|
| self.bands = 249
|
|
|
| elif self.dataset_type == 'deephsviscor':
|
|
|
| self.mean = [0.31596533] * 249 + [0.485, 0.456, 0.406]
|
| self.std = [0.00079012] * 249 + [0.229, 0.224, 0.225]
|
| self.max_val = 1.0
|
| self.min_val = -0.091662824
|
| self.bands = 249
|
|
|
| elif self.dataset_type == 'hsodbitv2':
|
|
|
| self.mean = [0.00858756] * 200 + [0.485, 0.456, 0.406]
|
| self.std = [0.00751322] * 200 + [0.229, 0.224, 0.225]
|
| self.max_val = 2.0
|
| self.min_val = 0.0
|
| self.bands = 200
|
|
|
| elif self.dataset_type == 'hotvis':
|
|
|
| self.mean = [0.17354311, 0.16689073, 0.14970031, 0.14982351, 0.17244257, 0.16725978,
|
| 0.17544793, 0.18323516, 0.16343771, 0.18044153, 0.18598581, 0.1752639,
|
| 0.17251257, 0.20304256, 0.19737918, 0.20113443] + [0.485, 0.456, 0.406]
|
| self.std = [0.14717741, 0.14833903, 0.14008888, 0.14012565, 0.14750255, 0.14482089,
|
| 0.1488135, 0.14891803, 0.14469542, 0.14839285, 0.14959444, 0.136904,
|
| 0.14140587, 0.14981111, 0.14334199, 0.15056744] + [0.229, 0.224, 0.225]
|
| self.max_val = 254
|
| self.min_val = 0
|
| self.bands = 16
|
|
|
| elif self.dataset_type == 'hotnir':
|
|
|
| self.mean = [0.17354311] * 128 + [0.485, 0.456, 0.406]
|
| self.std = [0.00202992] * 128 + [0.229, 0.224, 0.225]
|
| self.max_val = 2.742662
|
| self.min_val = -1.432596
|
| self.bands = 128
|
|
|
| elif self.dataset_type == 'hsiroad':
|
|
|
| self.mean = [0.17354311] * 128 + [0.485, 0.456, 0.406]
|
| self.std = [0.00202992] * 128 + [0.229, 0.224, 0.225]
|
| self.max_val = 2.742662
|
| self.min_val = -1.432596
|
| self.bands = 128
|
|
|
| elif self.dataset_type == 'hyperspectralcityv2':
|
|
|
| self.mean = [0.17354311] * 128 + [0.485, 0.456, 0.406]
|
| self.std = [0.00202992] * 128 + [0.229, 0.224, 0.225]
|
| self.max_val = 2.742662
|
| self.min_val = -1.432596
|
| self.bands = 128
|
|
|
| elif self.dataset_type == 'hykov2nir':
|
|
|
| self.mean = [0.1] * 25 + [0.485, 0.456, 0.406]
|
| self.std = [0.05] * 25 + [0.229, 0.224, 0.225]
|
| self.max_val = 1.0
|
| self.min_val = 0.0
|
| self.bands = 25
|
|
|
| elif self.dataset_type == 'hykov2vis':
|
|
|
| self.mean = [0.1] * 15 + [0.485, 0.456, 0.406]
|
| self.std = [0.05] * 15 + [0.229, 0.224, 0.225]
|
| self.max_val = 1.0
|
| self.min_val = 0.0
|
| self.bands = 15
|
|
|
| elif self.dataset_type == 'hyperdrive':
|
|
|
| self.mean = [0.17354311] * 128 + [0.485, 0.456, 0.406]
|
| self.std = [0.00202992] * 128 + [0.229, 0.224, 0.225]
|
| self.max_val = 2.742662
|
| self.min_val = -1.432596
|
| self.bands = 128
|
|
|
| elif self.dataset_type == 'hyperdrivevnir':
|
|
|
| self.mean = [0.17354311] * 128 + [0.485, 0.456, 0.406]
|
| self.std = [0.00202992] * 128 + [0.229, 0.224, 0.225]
|
| self.max_val = 2.742662
|
| self.min_val = -1.432596
|
| self.bands = 128
|
|
|
| elif self.dataset_type == 'hyperdriveswir':
|
|
|
| self.mean = [0.17354311] * 128 + [0.485, 0.456, 0.406]
|
| self.std = [0.00202992] * 128 + [0.229, 0.224, 0.225]
|
| self.max_val = 2.742662
|
| self.min_val = -1.432596
|
| self.bands = 128
|
|
|
| elif self.dataset_type == 'libhsi':
|
|
|
| self.mean = [0.1] * 204 + [0.485, 0.456, 0.406]
|
| self.std = [0.05] * 204 + [0.229, 0.224, 0.225]
|
| self.max_val = 1.0
|
| self.min_val = 0.0
|
| self.bands = 204
|
|
|
| elif self.dataset_type == 'virginia_tech_tree':
|
|
|
| self.mean = [0.1] * 420 + [0.485, 0.456, 0.406]
|
| self.std = [0.05] * 420 + [0.229, 0.224, 0.225]
|
| self.max_val = 65535.0
|
| self.min_val = 0.0
|
| self.bands = 420
|
|
|
| elif self.dataset_type == 'fiftyoutdoor':
|
|
|
| self.mean = [0.5] * 33 + [0.485, 0.456, 0.406]
|
| self.std = [0.5] * 33 + [0.229, 0.224, 0.225]
|
| self.max_val = 1.0
|
| self.min_val = 0.0
|
| self.bands = 33
|
|
|
| elif self.dataset_type == 'aphid':
|
|
|
| self.mean = [0.01749677, 0.01863154, 0.01968502, 0.02096651, 0.0222703, 0.02343416,
|
| 0.02486456, 0.02623226, 0.02711628, 0.02806029, 0.029251, 0.03039208,
|
| 0.03149611, 0.0324034, 0.03310641, 0.03367099, 0.03440539, 0.03559069,
|
| 0.03706571, 0.03864252, 0.04023792, 0.04108739, 0.04091695, 0.04059534,
|
| 0.04176284, 0.04384956, 0.04603461, 0.04808683, 0.04970166, 0.05056294,
|
| 0.05120134, 0.05231753, 0.05404492, 0.05613217, 0.05841183, 0.06040159,
|
| 0.06176928, 0.06352201, 0.06672157, 0.07070624, 0.07452483, 0.07856132,
|
| 0.08336198, 0.08813702, 0.09218899, 0.0957341, 0.09813736, 0.09948482,
|
| 0.10089726, 0.10222418, 0.10359852, 0.10576257, 0.10874553, 0.1115804,
|
| 0.11350075, 0.11437227, 0.11461496, 0.11388246, 0.11216935, 0.11024847,
|
| 0.1088342, 0.1087843, 0.1095063, 0.11013955, 0.11095031, 0.11238496,
|
| 0.11351851, 0.11270449, 0.10897382, 0.10531164, 0.10361046, 0.10310593,
|
| 0.10363231, 0.10527451, 0.107754, 0.11062107, 0.11306273, 0.11433087,
|
| 0.11441181, 0.11306909, 0.110761, 0.10861859, 0.10717231, 0.10603203,
|
| 0.10528735, 0.10519255, 0.10547309, 0.10642145, 0.10789556, 0.10951177,
|
| 0.11057608, 0.11080275, 0.11043563, 0.10899756, 0.10621833, 0.10343582,
|
| 0.10152958, 0.09933766, 0.09696239, 0.09659571, 0.09835973, 0.09998306,
|
| 0.10117153, 0.10197631, 0.10239724, 0.10213523, 0.10149442, 0.10068651,
|
| 0.09948141, 0.09803665, 0.09656651, 0.09445546, 0.09231605, 0.09290426,
|
| 0.09721154, 0.10396054, 0.11291141, 0.12304567, 0.13413498, 0.14725136,
|
| 0.16233688, 0.17846247, 0.19488951, 0.20931178, 0.21713638, 0.21432398,
|
| 0.21035695, 0.21707963, 0.22783414, 0.23649397, 0.24813107, 0.26640504,
|
| 0.29275966, 0.32234601, 0.34856743, 0.37127585, 0.39328289, 0.41307188,
|
| 0.4276742, 0.4356971, 0.43781677, 0.43502193, 0.42373551, 0.39014824,
|
| 0.32058243, 0.25648081, 0.24593042, 0.27940771, 0.32313044, 0.35244421,
|
| 0.3646437, 0.36728434, 0.36622976, 0.36283905, 0.35801778, 0.35183559,
|
| 0.34352493, 0.33310404, 0.32312864, 0.31559416, 0.30912014, 0.30196878,
|
| 0.29375823, 0.28506906, 0.27649879, 0.26673254, 0.25346453, 0.23533093,
|
| 0.21329628, 0.19368162, 0.1830368, 0.17975254, 0.17868787, 0.17938373,
|
| 0.18064577, 0.18095194, 0.18175849, 0.18480291, 0.18925455, 0.19289429,
|
| 0.19460079, 0.19428892, 0.19203493, 0.18722012, 0.17995, 0.1717305,
|
| 0.16454595, 0.15969452, 0.15656967, 0.15329931, 0.14871182, 0.14348243,
|
| 0.13973992, 0.13788484, 0.13644798, 0.13451014, 0.13252546, 0.13048483,
|
| 0.1283227, 0.12643253, 0.124531, 0.12215579, 0.1183463, 0.11226351,
|
| 0.10376037, 0.0947622, 0.08802142, 0.08586727, 0.08575157, 0.0836123,
|
| 0.07931947, 0.07523115, 0.07192349, 0.06935133, 0.06830636, 0.06826021,
|
| 0.06710699, 0.06376308, 0.05800961, 0.05036524, 0.04200856, 0.03558817,
|
| 0.03223782, 0.03177794, 0.03210223, 0.03172927, 0.03087431, 0.03024037,
|
| 0.02975344, 0.02941706, 0.0293257, 0.02927379, 0.0292441, 0.02950881,
|
| 0.03002286, 0.03068394, 0.03161591] + [0.485, 0.456, 0.406]
|
| self.std = [0.01152971, 0.01246639, 0.0133121, 0.01436954, 0.01541135, 0.01638534,
|
| 0.01757471, 0.01858997, 0.01925267, 0.02002466, 0.02094319, 0.02179194,
|
| 0.02270399, 0.02341295, 0.02388981, 0.02430273, 0.02488912, 0.0258316,
|
| 0.02695783, 0.02815591, 0.02925099, 0.02986218, 0.02970572, 0.02945916,
|
| 0.03035832, 0.0318107, 0.03328634, 0.0347459, 0.03576967, 0.03619796,
|
| 0.03647458, 0.03704743, 0.03800565, 0.03917259, 0.04039074, 0.0413286,
|
| 0.04171075, 0.04219915, 0.04369529, 0.04580031, 0.0479429, 0.05019833,
|
| 0.05293343, 0.05563171, 0.05788512, 0.05989067, 0.06113891, 0.06171445,
|
| 0.06234663, 0.06307677, 0.06400757, 0.06546068, 0.06730459, 0.06902729,
|
| 0.07008141, 0.07033944, 0.07022615, 0.06977442, 0.06894731, 0.06814718,
|
| 0.06774428, 0.06810594, 0.06882164, 0.06934666, 0.06990361, 0.07078452,
|
| 0.07139315, 0.07079498, 0.06857792, 0.06630415, 0.06526684, 0.06512386,
|
| 0.06568101, 0.06689893, 0.06856078, 0.07039654, 0.07184651, 0.07240477,
|
| 0.07209843, 0.07100998, 0.06943199, 0.06802907, 0.0671699, 0.06662852,
|
| 0.06625083, 0.0661338, 0.06617793, 0.06658512, 0.06730466, 0.06812465,
|
| 0.06866366, 0.06868333, 0.06821723, 0.06712205, 0.06523205, 0.06327473,
|
| 0.06186961, 0.06043651, 0.05888788, 0.05861247, 0.05975403, 0.06084589,
|
| 0.06163529, 0.0619789, 0.06194087, 0.061432, 0.06063354, 0.05966061,
|
| 0.05842775, 0.05710185, 0.05577594, 0.05394797, 0.05187146, 0.05152278,
|
| 0.0534283, 0.05664466, 0.06106503, 0.06604659, 0.07145322, 0.07784917,
|
| 0.08533052, 0.09329414, 0.1011765, 0.10791616, 0.11120184, 0.10852307,
|
| 0.1045392, 0.10643732, 0.11066276, 0.11409803, 0.11902983, 0.12698301,
|
| 0.13902298, 0.15237588, 0.1633798, 0.17212956, 0.179671, 0.18561263,
|
| 0.18925252, 0.19040748, 0.18937401, 0.18703309, 0.18260644, 0.17033362,
|
| 0.14077065, 0.11084724, 0.1030968, 0.11741595, 0.13745567, 0.15110533,
|
| 0.15682253, 0.15812345, 0.15755359, 0.15585662, 0.15368759, 0.15086846,
|
| 0.14702306, 0.14210789, 0.1372956, 0.13339315, 0.12988061, 0.1262256,
|
| 0.12216039, 0.1178111, 0.11358331, 0.10918471, 0.1034911, 0.09589767,
|
| 0.08665663, 0.07825239, 0.07361674, 0.07224158, 0.07199165, 0.07257136,
|
| 0.07346027, 0.07374678, 0.07415465, 0.07543777, 0.07721565, 0.07861143,
|
| 0.07911456, 0.07863731, 0.07730382, 0.0749192, 0.07162238, 0.06797465,
|
| 0.06475224, 0.06266206, 0.06146712, 0.06030559, 0.05864737, 0.05667419,
|
| 0.05525446, 0.05461379, 0.05417626, 0.05354191, 0.05284226, 0.05207279,
|
| 0.05124803, 0.05051118, 0.04972649, 0.04876982, 0.04732547, 0.04493024,
|
| 0.04143843, 0.03756908, 0.03454615, 0.03337561, 0.03325491, 0.03246053,
|
| 0.03074391, 0.02899512, 0.02752891, 0.02636907, 0.02590251, 0.02585678,
|
| 0.02543508, 0.02426568, 0.02216031, 0.01917821, 0.01588485, 0.01322024,
|
| 0.0117436, 0.01146387, 0.0116006, 0.01157587, 0.01135505, 0.01116603,
|
| 0.01104617, 0.01098228, 0.01100889, 0.01104027, 0.01109781, 0.01125572,
|
| 0.01149373, 0.01179753, 0.01218904] + [0.229, 0.224, 0.225]
|
| self.max_val = 255.0
|
| self.min_val = 0.0
|
| self.bands = 237
|
|
|
| else:
|
| raise ValueError(f"Unsupported dataset type: {self.dataset_type}")
|
|
|
| def _init_wavelength_params(self):
|
| """Initialize wavelength information based on dataset type."""
|
| if self.dataset_type == 'harvard':
|
|
|
| self.wavelengths = [420.0, 430.0, 440.0, 450.0, 460.0, 470.0, 480.0, 490.0, 500.0, 510.0,
|
| 520.0, 530.0, 540.0, 550.0, 560.0, 570.0, 580.0, 590.0, 600.0, 610.0,
|
| 620.0, 630.0, 640.0, 650.0, 660.0, 670.0, 680.0, 690.0, 700.0, 710.0, 720.0]
|
| elif self.dataset_type == 'umld2015':
|
| self.wavelengths = [400.0, 410.0, 420.0, 430.0, 440.0, 450.0, 460.0, 470.0, 480.0, 490.0, 500.0, 510.0, 520.0, 530.0, 540.0, 550.0, 560.0, 570.0, 580.0, 590.0, 600.0, 610.0, 620.0, 630.0, 640.0, 650.0, 660.0, 670.0, 680.0, 690.0, 700.0, 710.0, 720.0]
|
| elif self.dataset_type == 'umns2002':
|
|
|
| self.wavelengths = [410.0, 420.0, 430.0, 440.0, 450.0, 460.0, 470.0, 480.0, 490.0, 500.0,
|
| 510.0, 520.0, 530.0, 540.0, 550.0, 560.0, 570.0, 580.0, 590.0, 600.0,
|
| 610.0, 620.0, 630.0, 640.0, 650.0, 660.0, 670.0, 680.0, 690.0, 700.0, 710.0]
|
| elif self.dataset_type == 'umns2004':
|
|
|
| self.wavelengths = [400.0, 410.0, 420.0, 430.0, 440.0, 450.0, 460.0, 470.0, 480.0, 490.0,
|
| 500.0, 510.0, 520.0, 530.0, 540.0, 550.0, 560.0, 570.0, 580.0, 590.0,
|
| 600.0, 610.0, 620.0, 630.0, 640.0, 650.0, 660.0, 670.0, 680.0, 690.0, 700.0, 710.0, 720.0]
|
| elif self.dataset_type == 'umos':
|
|
|
| self.wavelengths = [400.0, 410.0, 420.0, 430.0, 440.0, 450.0, 460.0, 470.0, 480.0, 490.0,
|
| 500.0, 510.0, 520.0, 530.0, 540.0, 550.0, 560.0, 570.0, 580.0, 590.0,
|
| 600.0, 610.0, 620.0, 630.0, 640.0, 650.0, 660.0, 670.0, 680.0, 690.0, 700.0]
|
| elif self.dataset_type == 'umri2015':
|
|
|
| self.wavelengths = [400.0, 410.0, 420.0, 430.0, 440.0, 450.0, 460.0, 470.0, 480.0, 490.0,
|
| 500.0, 510.0, 520.0, 530.0, 540.0, 550.0, 560.0, 570.0, 580.0, 590.0,
|
| 600.0, 610.0, 620.0, 630.0, 640.0, 650.0, 660.0, 670.0, 680.0, 690.0, 700.0, 710.0, 720.0]
|
| elif self.dataset_type == 'umemm':
|
| self.wavelengths = [400.0, 410.0, 420.0, 430.0, 440.0, 450.0, 460.0, 470.0, 480.0, 490.0, 500.0, 510.0, 520.0, 530.0, 540.0, 550.0, 560.0, 570.0, 580.0, 590.0, 600.0, 610.0, 620.0, 630.0, 640.0, 650.0, 660.0, 670.0, 680.0, 690.0, 700.0, 710.0, 720.0]
|
| elif self.dataset_type == 'hyperblood':
|
| self.wavelengths = [ 401.5636, 406.528 , 411.4977, 416.4725, 421.4525, 426.4379,
|
| 431.4284, 436.4241, 441.4251, 446.4313, 451.4427, 456.4594,
|
| 461.4813, 466.5084, 471.5408, 476.5783, 481.6211, 486.6691,
|
| 491.7224, 496.7808, 501.8445, 506.9134, 511.9876, 517.067 ,
|
| 522.1515, 527.2413, 532.3364, 537.4367, 542.5422, 547.6529,
|
| 552.7689, 557.8901, 563.0164, 568.1481, 573.2849, 578.427 ,
|
| 583.5743, 588.7269, 593.8846, 599.0476, 604.2158, 609.3892,
|
| 614.5679, 635.3348, 640.5396, 645.7496, 650.9649, 656.1853,
|
| 661.4111, 666.642 , 671.8782, 677.1195, 682.3661, 687.6179,
|
| 692.875 , 698.1372, 703.4047, 708.6775, 713.9554, 719.2386,
|
| 724.5271, 729.8207, 735.1196, 740.4236, 745.7329, 751.0475,
|
| 756.3672, 761.6923, 767.0225, 772.358 , 777.6987, 783.0445,
|
| 788.3956, 793.752 , 799.1135, 804.4803, 809.8524, 815.2296,
|
| 820.6121, 825.9998, 831.3927, 836.7909, 842.1942, 847.6028,
|
| 853.0167, 858.4358, 863.86 , 869.2896, 874.7243, 880.1642,
|
| 885.6095, 891.0598, 896.5155, 901.9764, 907.4425, 912.9138,
|
| 918.3903, 923.8721, 929.3591, 934.8514, 940.3488, 945.8514,
|
| 951.3594, 956.8725, 962.3909, 967.9144, 973.4432, 978.9773,
|
| 984.5165, 990.061 , 995.6107, 1001.1656, 1006.7258]
|
| elif self.dataset_type == 'hsidrive20':
|
| self.wavelengths = [600.0, 615.625, 631.25, 646.875, 662.5, 678.125, 693.75, 709.375, 725.0, 740.625, 756.25, 771.875, 787.5, 803.125, 818.75, 834.375, 850.0, 865.625, 881.25, 896.875, 912.5, 928.125, 943.75, 959.375, 975.0]
|
| elif self.dataset_type == 'hotrednir':
|
| self.wavelengths = [600.0, 617.857143, 635.714286, 653.571429, 671.428571, 689.285714, 707.142857, 725.0, 742.857143, 760.714286, 778.571429, 796.428571, 814.285714, 832.142857, 850.0]
|
| elif self.dataset_type == 'arad_1k_31':
|
| self.wavelengths = [400.0, 410.0, 420.0, 430.0, 440.0, 450.0, 460.0, 470.0, 480.0, 490.0, 500.0, 510.0, 520.0, 530.0, 540.0, 550.0, 560.0, 570.0, 580.0, 590.0, 600.0, 610.0, 620.0, 630.0, 640.0, 650.0, 660.0, 670.0, 680.0, 690.0, 700.0]
|
| elif self.dataset_type == 'arad_1k_16':
|
| self.wavelengths = [400.0, 440.0, 480.0, 520.0, 560.0, 600.0, 640.0, 680.0, 720.0, 760.0, 800.0, 840.0, 880.0, 920.0, 960.0, 1000.0]
|
| elif self.dataset_type == 'cave':
|
| self.wavelengths = [400.0, 410.0, 420.0, 430.0, 440.0, 450.0, 460.0, 470.0, 480.0, 490.0, 500.0, 510.0, 520.0, 530.0, 540.0, 550.0, 560.0, 570.0, 580.0, 590.0, 600.0, 610.0, 620.0, 630.0, 640.0, 650.0, 660.0, 670.0, 680.0, 690.0, 700.0]
|
| elif self.dataset_type == 'icvl':
|
| self.wavelengths = [400.0, 410.0, 420.0, 430.0, 440.0, 450.0, 460.0, 470.0, 480.0, 490.0, 500.0, 510.0, 520.0, 530.0, 540.0, 550.0, 560.0, 570.0, 580.0, 590.0, 600.0, 610.0, 620.0, 630.0, 640.0, 650.0, 660.0, 670.0, 680.0, 690.0, 700.0]
|
|
|
| elif self.dataset_type == 'hs_sod':
|
|
|
| self.wavelengths = [380.0, 384.25, 388.5, 392.75, 397.0, 401.25, 405.5, 409.75, 414.0, 418.25, 422.5, 426.75, 431.0, 435.25, 439.5, 443.75, 448.0, 452.25, 456.5, 460.75, 465.0, 469.25, 473.5, 477.75, 482.0, 486.25, 490.5, 494.75, 499.0, 503.25, 507.5, 511.75, 516.0, 520.25, 524.5, 528.75, 533.0, 537.25, 541.5, 545.75, 550.0, 554.25, 558.5, 562.75, 567.0, 571.25, 575.5, 579.75, 584.0, 588.25, 592.5, 596.75, 601.0, 605.25, 609.5, 613.75, 618.0, 622.25, 626.5, 630.75, 635.0, 639.25, 643.5, 647.75, 652.0, 656.25, 660.5, 664.75, 669.0, 673.25, 677.5, 681.75, 686.0, 690.25, 694.5, 698.75, 703.0, 707.25, 711.5, 715.75, 720.0]
|
| elif self.dataset_type == 'hsodbit_v2':
|
| self.wavelengths = [401.5, 404.5, 407.5, 410.5, 413.5, 416.5, 419.5, 422.5, 425.5, 428.5, 431.5, 434.5, 437.5, 440.5, 443.5, 446.5, 449.5, 452.5, 455.5, 458.5, 461.5, 464.5, 467.5, 470.5, 473.5, 476.5, 479.5, 482.5, 485.5, 488.5, 491.5, 494.5, 497.5, 500.5, 503.5, 506.5, 509.5, 512.5, 515.5, 518.5, 521.5, 524.5, 527.5, 530.5, 533.5, 536.5, 539.5, 542.5, 545.5, 548.5, 551.5, 554.5, 557.5, 560.5, 563.5, 566.5, 569.5, 572.5, 575.5, 578.5, 581.5, 584.5, 587.5, 590.5, 593.5, 596.5, 599.5, 602.5, 605.5, 608.5, 611.5, 614.5, 617.5, 620.5, 623.5, 626.5, 629.5, 632.5, 635.5, 638.5, 641.5, 644.5, 647.5, 650.5, 653.5, 656.5, 659.5, 662.5, 665.5, 668.5, 671.5, 674.5, 677.5, 680.5, 683.5, 686.5, 689.5, 692.5, 695.5, 698.5, 701.5, 704.5, 707.5, 710.5, 713.5, 716.5, 719.5, 722.5, 725.5, 728.5, 731.5, 734.5, 737.5, 740.5, 743.5, 746.5, 749.5, 752.5, 755.5, 758.5, 761.5, 764.5, 767.5, 770.5, 773.5, 776.5, 779.5, 782.5, 785.5, 788.5, 791.5, 794.5, 797.5, 800.5, 803.5, 806.5, 809.5, 812.5, 815.5, 818.5, 821.5, 824.5, 827.5, 830.5, 833.5, 836.5, 839.5, 842.5, 845.5, 848.5, 851.5, 854.5, 857.5, 860.5, 863.5, 866.5, 869.5, 872.5, 875.5, 878.5, 881.5, 884.5, 887.5, 890.5, 893.5, 896.5, 899.5, 902.5, 905.5, 908.5, 911.5, 914.5, 917.5, 920.5, 923.5, 926.5, 929.5, 932.5, 935.5, 938.5, 941.5, 944.5, 947.5, 950.5, 953.5, 956.5, 959.5, 962.5, 965.5, 968.5, 971.5, 974.5, 977.5, 980.5, 983.5, 986.5, 989.5, 992.5, 995.5, 998.5]
|
| elif self.dataset_type == 'vnihdhiatlimafb':
|
|
|
| self.wavelengths = [397.32, 400.20, 403.09, 405.97, 408.85, 411.74, 414.63, 417.52, 420.40, 423.29, 426.19, 429.08, 431.97, 434.87, 437.76, 440.66, 443.56, 446.45, 449.35, 452.25, 455.16, 458.06, 460.96, 463.87, 466.77, 469.68, 472.59, 475.50, 478.41, 481.32, 484.23, 487.14, 490.06, 492.97, 495.89, 498.80, 501.72, 504.64, 507.56, 510.48, 513.40, 516.33, 519.25, 522.18, 525.10, 528.03, 530.96, 533.89, 536.82, 539.75, 542.68, 545.62, 548.55, 551.49, 554.43, 557.36, 560.30, 563.24, 566.18, 569.12, 572.07, 575.01, 577.96, 580.90, 583.85, 586.80, 589.75, 592.70, 595.65, 598.60, 601.55, 604.51, 607.46, 610.42, 613.38, 616.34, 619.30, 622.26, 625.22, 628.18, 631.15, 634.11, 637.08, 640.04, 643.01, 645.98, 648.95, 651.92, 654.89, 657.87, 660.84, 663.81, 666.79, 669.77, 672.75, 675.73, 678.71, 681.69, 684.67, 687.65, 690.64, 693.62, 696.61, 699.60, 702.58, 705.57, 708.57, 711.56, 714.55, 717.54, 720.54, 723.53, 726.53, 729.53, 732.53, 735.53, 738.53, 741.53, 744.53, 747.54, 750.54, 753.55, 756.56, 759.56, 762.57, 765.58, 768.60, 771.61, 774.62, 777.64, 780.65, 783.67, 786.68, 789.70, 792.72, 795.74, 798.77, 801.79, 804.81, 807.84, 810.86, 813.89, 816.92, 819.95, 822.98, 826.01, 829.04, 832.07, 835.11, 838.14, 841.18, 844.22, 847.25, 850.29, 853.33, 856.37, 859.42, 862.46, 865.50, 868.55, 871.60, 874.64, 877.69, 880.74, 883.79, 886.84, 889.90, 892.95, 896.01, 899.06, 902.12, 905.18, 908.24, 911.30, 914.36, 917.42, 920.48, 923.55, 926.61, 929.68, 932.74, 935.81, 938.88, 941.95, 945.02, 948.10, 951.17, 954.24, 957.32, 960.40, 963.47, 966.55, 969.63, 972.71, 975.79, 978.88, 981.96, 985.05, 988.13, 991.22, 994.31, 997.40, 1000.49, 1003.58]
|
| elif self.dataset_type == 'deephsnir':
|
| self.wavelengths = [950.0, 952.988048, 955.976096, 958.964143, 961.952191, 964.940239, 967.928287, 970.916335, 973.904382, 976.89243, 979.880478, 982.868526, 985.856574, 988.844622, 991.832669, 994.820717, 997.808765, 1000.796813, 1003.784861, 1006.772908, 1009.760956, 1012.749004, 1015.737052, 1018.7251, 1021.713147, 1024.701195, 1027.689243, 1030.677291, 1033.665339, 1036.653386, 1039.641434, 1042.629482, 1045.61753, 1048.605578, 1051.593625, 1054.581673, 1057.569721, 1060.557769, 1063.545817, 1066.533865, 1069.521912, 1072.50996, 1075.498008, 1078.486056, 1081.474104, 1084.462151, 1087.450199, 1090.438247, 1093.426295, 1096.414343, 1099.40239, 1102.390438, 1105.378486, 1108.366534, 1111.354582, 1114.342629, 1117.330677, 1120.318725, 1123.306773, 1126.294821, 1129.282869, 1132.270916, 1135.258964, 1138.247012, 1141.23506, 1144.223108, 1147.211155, 1150.199203, 1153.187251, 1156.175299, 1159.163347, 1162.151394, 1165.139442, 1168.12749, 1171.115538, 1174.103586, 1177.091633, 1180.079681, 1183.067729, 1186.055777, 1189.043825, 1192.031873, 1195.01992, 1198.007968, 1200.996016, 1203.984064, 1206.972112, 1209.960159, 1212.948207, 1215.936255, 1218.924303, 1221.912351, 1224.900398, 1227.888446, 1230.876494, 1233.864542, 1236.85259, 1239.840637, 1242.828685, 1245.816733, 1248.804781, 1251.792829, 1254.780876, 1257.768924, 1260.756972, 1263.74502, 1266.733068, 1269.721116, 1272.709163, 1275.697211, 1278.685259, 1281.673307, 1284.661355, 1287.649402, 1290.63745, 1293.625498, 1296.613546, 1299.601594, 1302.589641, 1305.577689, 1308.565737, 1311.553785, 1314.541833, 1317.52988, 1320.517928, 1323.505976, 1326.494024, 1329.482072, 1332.47012, 1335.458167, 1338.446215, 1341.434263, 1344.422311, 1347.410359, 1350.398406, 1353.386454, 1356.374502, 1359.36255, 1362.350598, 1365.338645, 1368.326693, 1371.314741, 1374.302789, 1377.290837, 1380.278884, 1383.266932, 1386.25498, 1389.243028, 1392.231076, 1395.219124, 1398.207171, 1401.195219, 1404.183267, 1407.171315, 1410.159363, 1413.14741, 1416.135458, 1419.123506, 1422.111554, 1425.099602, 1428.087649, 1431.075697, 1434.063745, 1437.051793, 1440.039841, 1443.027888, 1446.015936, 1449.003984, 1451.992032, 1454.98008, 1457.968127, 1460.956175, 1463.944223, 1466.932271, 1469.920319, 1472.908367, 1475.896414, 1478.884462, 1481.87251, 1484.860558, 1487.848606, 1490.836653, 1493.824701, 1496.812749, 1499.800797, 1502.788845, 1505.776892, 1508.76494, 1511.752988, 1514.741036, 1517.729084, 1520.717131, 1523.705179, 1526.693227, 1529.681275, 1532.669323, 1535.657371, 1538.645418, 1541.633466, 1544.621514, 1547.609562, 1550.59761, 1553.585657, 1556.573705, 1559.561753, 1562.549801, 1565.537849, 1568.525896, 1571.513944, 1574.501992, 1577.49004, 1580.478088, 1583.466135, 1586.454183, 1589.442231, 1592.430279, 1595.418327, 1598.406375, 1601.394422, 1604.38247, 1607.370518, 1610.358566, 1613.346614, 1616.334661, 1619.322709, 1622.310757, 1625.298805, 1628.286853, 1631.2749, 1634.262948, 1637.250996, 1640.239044, 1643.227092, 1646.215139, 1649.203187, 1652.191235, 1655.179283, 1658.167331, 1661.155378, 1664.143426, 1667.131474, 1670.119522, 1673.10757, 1676.095618, 1679.083665, 1682.071713, 1685.059761, 1688.047809, 1691.035857, 1694.023904, 1697.011952, 1700.0]
|
| elif self.dataset_type == 'deephsvis':
|
| self.wavelengths = [400.0, 402.690583, 405.381166, 408.071749, 410.762332, 413.452915, 416.143498, 418.834081, 421.524664, 424.215247, 426.90583, 429.596413, 432.286996, 434.977578, 437.668161, 440.358744, 443.049327, 445.73991, 448.430493, 451.121076, 453.811659, 456.502242, 459.192825, 461.883408, 464.573991, 467.264574, 469.955157, 472.64574, 475.336323, 478.026906, 480.717489, 483.408072, 486.098655, 488.789238, 491.479821, 494.170404, 496.860987, 499.55157, 502.242152, 504.932735, 507.623318, 510.313901, 513.004484, 515.695067, 518.38565, 521.076233, 523.766816, 526.457399, 529.147982, 531.838565, 534.529148, 537.219731, 539.910314, 542.600897, 545.29148, 547.982063, 550.672646, 553.363229, 556.053812, 558.744395, 561.434978, 564.125561, 566.816143, 569.506726, 572.197309, 574.887892, 577.578475, 580.269058, 582.959641, 585.650224, 588.340807, 591.03139, 593.721973, 596.412556, 599.103139, 601.793722, 604.484305, 607.174888, 609.865471, 612.556054, 615.246637, 617.93722, 620.627803, 623.318386, 626.008969, 628.699552, 631.390135, 634.080717, 636.7713, 639.461883, 642.152466, 644.843049, 647.533632, 650.224215, 652.914798, 655.605381, 658.295964, 660.986547, 663.67713, 666.367713, 669.058296, 671.748879, 674.439462, 677.130045, 679.820628, 682.511211, 685.201794, 687.892377, 690.58296, 693.273543, 695.964126, 698.654709, 701.345291, 704.035874, 706.726457, 709.41704, 712.107623, 714.798206, 717.488789, 720.179372, 722.869955, 725.560538, 728.251121, 730.941704, 733.632287, 736.32287, 739.013453, 741.704036, 744.394619, 747.085202, 749.775785, 752.466368, 755.156951, 757.847534, 760.538117, 763.2287, 765.919283, 768.609865, 771.300448, 773.991031, 776.681614, 779.372197, 782.06278, 784.753363, 787.443946, 790.134529, 792.825112, 795.515695, 798.206278, 800.896861, 803.587444, 806.278027, 808.96861, 811.659193, 814.349776, 817.040359, 819.730942, 822.421525, 825.112108, 827.802691, 830.493274, 833.183857, 835.874439, 838.565022, 841.255605, 843.946188, 846.636771, 849.327354, 852.017937, 854.70852, 857.399103, 860.089686, 862.780269, 865.470852, 868.161435, 870.852018, 873.542601, 876.233184, 878.923767, 881.61435, 884.304933, 886.995516, 889.686099, 892.376682, 895.067265, 897.757848, 900.44843, 903.139013, 905.829596, 908.520179, 911.210762, 913.901345, 916.591928, 919.282511, 921.973094, 924.663677, 927.35426, 930.044843, 932.735426, 935.426009, 938.116592, 940.807175, 943.497758, 946.188341, 948.878924, 951.569507, 954.26009, 956.950673, 959.641256, 962.331839, 965.022422, 967.713004, 970.403587, 973.09417, 975.784753, 978.475336, 981.165919, 983.856502, 986.547085, 989.237668, 991.928251, 994.618834, 997.309417, 1000.0]
|
| elif self.dataset_type == 'deephsviscor':
|
|
|
| self.wavelengths = [400.0, 402.690583, 405.381166, 408.071749, 410.762332, 413.452915, 416.143498, 418.834081, 421.524664, 424.215247, 426.90583, 429.596413, 432.286996, 434.977578, 437.668161, 440.358744, 443.049327, 445.73991, 448.430493, 451.121076, 453.811659, 456.502242, 459.192825, 461.883408, 464.573991, 467.264574, 469.955157, 472.64574, 475.336323, 478.026906, 480.717489, 483.408072, 486.098655, 488.789238, 491.479821, 494.170404, 496.860987, 499.55157, 502.242152, 504.932735, 507.623318, 510.313901, 513.004484, 515.695067, 518.38565, 521.076233, 523.766816, 526.457399, 529.147982, 531.838565, 534.529148, 537.219731, 539.910314, 542.600897, 545.29148, 547.982063, 550.672646, 553.363229, 556.053812, 558.744395, 561.434978, 564.125561, 566.816143, 569.506726, 572.197309, 574.887892, 577.578475, 580.269058, 582.959641, 585.650224, 588.340807, 591.03139, 593.721973, 596.412556, 599.103139, 601.793722, 604.484305, 607.174888, 609.865471, 612.556054, 615.246637, 617.93722, 620.627803, 623.318386, 626.008969, 628.699552, 631.390135, 634.080717, 636.7713, 639.461883, 642.152466, 644.843049, 647.533632, 650.224215, 652.914798, 655.605381, 658.295964, 660.986547, 663.67713, 666.367713, 669.058296, 671.748879, 674.439462, 677.130045, 679.820628, 682.511211, 685.201794, 687.892377, 690.58296, 693.273543, 695.964126, 698.654709, 701.345291, 704.035874, 706.726457, 709.41704, 712.107623, 714.798206, 717.488789, 720.179372, 722.869955, 725.560538, 728.251121, 730.941704, 733.632287, 736.32287, 739.013453, 741.704036, 744.394619, 747.085202, 749.775785, 752.466368, 755.156951, 757.847534, 760.538117, 763.2287, 765.919283, 768.609865, 771.300448, 773.991031, 776.681614, 779.372197, 782.06278, 784.753363, 787.443946, 790.134529, 792.825112, 795.515695, 798.206278, 800.896861, 803.587444, 806.278027, 808.96861, 811.659193, 814.349776, 817.040359, 819.730942, 822.421525, 825.112108, 827.802691, 830.493274, 833.183857, 835.874439, 838.565022, 841.255605, 843.946188, 846.636771, 849.327354, 852.017937, 854.70852, 857.399103, 860.089686, 862.780269, 865.470852, 868.161435, 870.852018, 873.542601, 876.233184, 878.923767, 881.61435, 884.304933, 886.995516, 889.686099, 892.376682, 895.067265, 897.757848, 900.44843, 903.139013, 905.829596, 908.520179, 911.210762, 913.901345, 916.591928, 919.282511, 921.973094, 924.663677, 927.35426, 930.044843, 932.735426, 935.426009, 938.116592, 940.807175, 943.497758, 946.188341, 948.878924, 951.569507, 954.26009, 956.950673, 959.641256, 962.331839, 965.022422, 967.713004, 970.403587, 973.09417, 975.784753, 978.475336, 981.165919, 983.856502, 986.547085, 989.237668, 991.928251, 994.618834, 997.309417, 1000.0]
|
| elif self.dataset_type == 'hsodbitv2':
|
|
|
| self.wavelengths = [401.5, 404.5, 407.5, 410.5, 413.5, 416.5, 419.5, 422.5, 425.5, 428.5, 431.5, 434.5, 437.5, 440.5, 443.5, 446.5, 449.5, 452.5, 455.5, 458.5, 461.5, 464.5, 467.5, 470.5, 473.5, 476.5, 479.5, 482.5, 485.5, 488.5, 491.5, 494.5, 497.5, 500.5, 503.5, 506.5, 509.5, 512.5, 515.5, 518.5, 521.5, 524.5, 527.5, 530.5, 533.5, 536.5, 539.5, 542.5, 545.5, 548.5, 551.5, 554.5, 557.5, 560.5, 563.5, 566.5, 569.5, 572.5, 575.5, 578.5, 581.5, 584.5, 587.5, 590.5, 593.5, 596.5, 599.5, 602.5, 605.5, 608.5, 611.5, 614.5, 617.5, 620.5, 623.5, 626.5, 629.5, 632.5, 635.5, 638.5, 641.5, 644.5, 647.5, 650.5, 653.5, 656.5, 659.5, 662.5, 665.5, 668.5, 671.5, 674.5, 677.5, 680.5, 683.5, 686.5, 689.5, 692.5, 695.5, 698.5, 701.5, 704.5, 707.5, 710.5, 713.5, 716.5, 719.5, 722.5, 725.5, 728.5, 731.5, 734.5, 737.5, 740.5, 743.5, 746.5, 749.5, 752.5, 755.5, 758.5, 761.5, 764.5, 767.5, 770.5, 773.5, 776.5, 779.5, 782.5, 785.5, 788.5, 791.5, 794.5, 797.5, 800.5, 803.5, 806.5, 809.5, 812.5, 815.5, 818.5, 821.5, 824.5, 827.5, 830.5, 833.5, 836.5, 839.5, 842.5, 845.5, 848.5, 851.5, 854.5, 857.5, 860.5, 863.5, 866.5, 869.5, 872.5, 875.5, 878.5, 881.5, 884.5, 887.5, 890.5, 893.5, 896.5, 899.5, 902.5, 905.5, 908.5, 911.5, 914.5, 917.5, 920.5, 923.5, 926.5, 929.5, 932.5, 935.5, 938.5, 941.5, 944.5, 947.5, 950.5, 953.5, 956.5, 959.5, 962.5, 965.5, 968.5, 971.5, 974.5, 977.5, 980.5, 983.5, 986.5, 989.5, 992.5, 995.5, 998.5]
|
| elif self.dataset_type == 'hotvis':
|
|
|
| self.wavelengths = [463.0, 472.0, 481.0, 490.0, 499.0, 508.0, 517.0, 526.0, 535.0, 544.0, 553.0, 562.0, 571.0, 580.0, 589.0, 598.0]
|
| elif self.dataset_type == 'hotnir':
|
|
|
| self.wavelengths = [668.0, 680.0, 692.0, 704.0, 716.0, 728.0, 740.0, 752.0, 764.0, 776.0, 788.0, 800.0, 812.0, 824.0, 836.0, 848.0, 860.0, 872.0, 884.0, 896.0, 908.0, 920.0, 932.0, 944.0, 956.0]
|
| elif self.dataset_type == 'hsiroad':
|
|
|
| self.wavelengths = [600.0, 615.0, 630.0, 645.0, 660.0, 675.0, 690.0, 705.0, 720.0, 735.0, 750.0, 765.0, 780.0, 795.0, 810.0, 825.0, 840.0, 855.0, 870.0, 885.0, 900.0, 915.0, 930.0, 945.0, 960.0]
|
| elif self.dataset_type == 'hyperspectralcityv2':
|
|
|
| self.wavelengths = [450.0, 453.937008, 457.874016, 461.811024, 465.748031, 469.685039, 473.622047, 477.559055, 481.496063, 485.433071, 489.370079, 493.307087, 497.244094, 501.181102, 505.11811, 509.055118, 512.992126, 516.929134, 520.866142, 524.80315, 528.740157, 532.677165, 536.614173, 540.551181, 544.488189, 548.425197, 552.362205, 556.299213, 560.23622, 564.173228, 568.110236, 572.047244, 575.984252, 579.92126, 583.858268, 587.795276, 591.732283, 595.669291, 599.606299, 603.543307, 607.480315, 611.417323, 615.354331, 619.291339, 623.228346, 627.165354, 631.102362, 635.03937, 638.976378, 642.913386, 646.850394, 650.787402, 654.724409, 658.661417, 662.598425, 666.535433, 670.472441, 674.409449, 678.346457, 682.283465, 686.220472, 690.15748, 694.094488, 698.031496, 701.968504, 705.905512, 709.84252, 713.779528, 717.716535, 721.653543, 725.590551, 729.527559, 733.464567, 737.401575, 741.338583, 745.275591, 749.212598, 753.149606, 757.086614, 761.023622, 764.96063, 768.897638, 772.834646, 776.771654, 780.708661, 784.645669, 788.582677, 792.519685, 796.456693, 800.393701, 804.330709, 808.267717, 812.204724, 816.141732, 820.07874, 824.015748, 827.952756, 831.889764, 835.826772, 839.76378, 843.700787, 847.637795, 851.574803, 855.511811, 859.448819, 863.385827, 867.322835, 871.259843, 875.19685, 879.133858, 883.070866, 887.007874, 890.944882, 894.88189, 898.818898, 902.755906, 906.692913, 910.629921, 914.566929, 918.503937, 922.440945, 926.377953, 930.314961, 934.251969, 938.188976, 942.125984, 946.062992, 950.0]
|
| elif self.dataset_type == 'hyperdrive':
|
|
|
| self.wavelengths = [660.0, 670.434783, 680.869565, 691.304348, 701.73913, 712.173913, 722.608696, 733.043478, 743.478261, 753.913043, 764.347826, 774.782609, 785.217391, 795.652174, 806.086957, 816.521739, 826.956522, 837.391304, 847.826087, 858.26087, 868.695652, 879.130435, 889.565217, 900.0] + [1100.0, 1175.0, 1250.0, 1325.0, 1400.0, 1475.0, 1550.0, 1625.0, 1700.0]
|
| elif self.dataset_type == 'hyperdrivevnir':
|
|
|
| self.wavelengths = [660.0, 670.434783, 680.869565, 691.304348, 701.73913, 712.173913, 722.608696, 733.043478, 743.478261, 753.913043, 764.347826, 774.782609, 785.217391, 795.652174, 806.086957, 816.521739, 826.956522, 837.391304, 847.826087, 858.26087, 868.695652, 879.130435, 889.565217, 900.0]
|
| elif self.dataset_type == 'hyperdriveswir':
|
|
|
| self.wavelengths = [1100.0, 1175.0, 1250.0, 1325.0, 1400.0, 1475.0, 1550.0, 1625.0, 1700.0]
|
| elif self.dataset_type == 'hykov2nir':
|
|
|
| self.wavelengths = [ 673.37, 674.88, 689.24, 714.08, 728.15, 740.42, 754.96, 767.18, 780.18, 791.21, 803.8, 822.46, 834.33, 844.29, 855.44, 865.25, 875.2, 884.62, 893.66, 909.95, 917.28, 924.59, 930.97, 939.62, 944.82]
|
| elif self.dataset_type == 'hykov2vis':
|
|
|
| self.wavelengths = [ 468.2, 478.34, 490.83, 503.56, 514.11, 526.92, 541.8, 555.12, 569.3, 578.54, 592.71, 600.25, 611.57, 622.49, 642.55]
|
| elif self.dataset_type == 'libhsi':
|
|
|
| self.wavelengths = [397.32,400.20,403.09,405.97,408.85,411.74,414.63,417.52,420.40,423.29,426.19,429.08,431.97,434.87,437.76,440.66,443.56,446.45,449.35,452.25,455.16,458.06,460.96,463.87,466.77,469.68,472.59,475.50,478.41,481.32,484.23,487.14,490.06,492.97,495.89,498.80,501.72,504.64,507.56,510.48,513.40,516.33,519.25,522.18,525.10,528.03,530.96,533.89,536.82,539.75,542.68,545.62,548.55,551.49,554.43,557.36,560.30,563.24,566.18,569.12,572.07,575.01,577.96,580.90,583.85,586.80,589.75,592.70,595.65,598.60,601.55,604.51,607.46,610.42,613.38,616.34,619.30,622.26,625.22,628.18,631.15,634.11,637.08,640.04,643.01,645.98,648.95,651.92,654.89,657.87,660.84,663.81,666.79,669.77,672.75,675.73,678.71,681.69,684.67,687.65,690.64,693.62,696.61,699.60,702.58,705.57,708.57,711.56,714.55,717.54,720.54,723.53,726.53,729.53,732.53,735.53,738.53,741.53,744.53,747.54,750.54,753.55,756.56,759.56,762.57,765.58,768.60,771.61,774.62,777.64,780.65,783.67,786.68,789.70,792.72,795.74,798.77,801.79,804.81,807.84,810.86,813.89,816.92,819.95,822.98,826.01,829.04,832.07,835.11,838.14,841.18,844.22,847.25,850.29,853.33,856.37,859.42,862.46,865.50,868.55,871.60,874.64,877.69,880.74,883.79,886.84,889.90,892.95,896.01,899.06,902.12,905.18,908.24,911.30,914.36,917.42,920.48,923.55,926.61,929.68,932.74,935.81,938.88,941.95,945.02,948.10,951.17,954.24,957.32,960.40,963.47,966.55,969.63,972.71,975.79,978.88,981.96,985.05,988.13,991.22,994.31,997.40,1000.49,1003.58]
|
| elif self.dataset_type == 'virginiatree':
|
|
|
| self.wavelengths = [394.7, 396.09, 397.48, 398.87, 400.26, 401.66, 403.05, 404.44, 405.83, 407.22, 408.62, 410.01, 411.4, 412.8, 414.19, 415.58, 416.98, 418.37, 419.77, 421.17, 422.56, 423.96, 425.35, 426.75, 428.15, 429.55, 430.94, 432.34, 433.74, 435.14, 436.54, 437.94, 439.34, 440.74, 442.14, 443.54, 444.94, 446.34, 447.75, 449.15, 450.55, 451.95, 453.36, 454.76, 456.17, 457.57, 458.97, 460.38, 461.78, 463.19, 464.6, 466, 467.41, 468.82, 470.22, 471.63, 473.04, 474.45, 475.86, 477.26, 478.67, 480.08, 481.49, 482.9, 484.31, 485.72, 487.14, 488.55, 489.96, 491.37, 492.78, 494.2, 495.61, 497.02, 498.44, 499.85, 501.27, 502.68, 504.1, 505.51, 506.93, 508.34, 509.76, 511.18, 512.59, 514.01, 515.43, 516.85, 518.27, 519.68, 521.1, 522.52, 523.94, 525.36, 526.78, 528.2, 529.62, 531.05, 532.47, 533.89, 535.31, 536.74, 538.16, 539.58, 541.01, 542.43, 543.85, 545.28, 546.7, 548.13, 549.55, 550.98, 552.41, 553.83, 555.26, 556.69, 558.12, 559.54, 560.97, 562.4, 563.83, 565.26, 566.69, 568.12, 569.55, 570.98, 572.41, 573.84, 575.27, 576.71, 578.14, 579.57, 581, 582.44, 583.87, 585.3, 586.74, 588.17, 589.61, 591.04, 592.48, 593.91, 595.35, 596.79, 598.22, 599.66, 601.1, 602.54, 603.97, 605.41, 606.85, 608.29, 609.73, 611.17, 612.61, 614.05, 615.49, 616.93, 618.37, 619.82, 621.26, 622.7, 624.14, 625.59, 627.03, 628.47, 629.92, 631.36, 632.81, 634.25, 635.7, 637.14, 638.59, 640.04, 641.48, 642.93, 644.38, 645.83, 647.27, 648.72, 650.17, 651.62, 653.07, 654.52, 655.97, 657.42, 658.87, 660.32, 661.77, 663.22, 664.68, 666.13, 667.58, 669.03, 670.49, 671.94, 673.4, 674.85, 676.3, 677.76, 679.21, 680.67, 682.13, 683.58, 685.04, 686.5, 687.95, 689.41, 690.87, 692.33, 693.79, 695.24, 696.7, 698.16, 699.62, 701.08, 702.54, 704.01, 705.47, 706.93, 708.39, 709.85, 711.32, 712.78, 714.24, 715.7, 717.17, 718.63, 720.1, 721.56, 723.03, 724.49, 725.96, 727.42, 728.89, 730.36, 731.83, 733.29, 734.76, 736.23, 737.7, 739.17, 740.64, 742.1, 743.57, 745.04, 746.52, 747.99, 749.46, 750.93, 752.4, 753.87, 755.35, 756.82, 758.29, 759.76, 761.24, 762.71, 764.19, 765.66, 767.14, 768.61, 770.09, 771.56, 773.04, 774.52, 775.99, 777.47, 778.95, 780.43, 781.91, 783.38, 784.86, 786.34, 787.82, 789.3, 790.78, 792.26, 793.74, 795.23, 796.71, 798.19, 799.67, 801.15, 802.64, 804.12, 805.6, 807.09, 808.57, 810.06, 811.54, 813.03, 814.51, 816, 817.48, 818.97, 820.46, 821.95, 823.43, 824.92, 826.41, 827.9, 829.39, 830.88, 832.37, 833.86, 835.35, 836.84, 838.33, 839.82, 841.31, 842.8, 844.29, 845.79, 847.28, 848.77, 850.27, 851.76, 853.25, 854.75, 856.24, 857.74, 859.23, 860.73, 862.23, 863.72, 865.22, 866.72, 868.21, 869.71, 871.21, 872.71, 874.21, 875.71, 877.21, 878.71, 880.21, 881.71, 883.21, 884.71, 886.21, 887.71, 889.21, 890.72, 892.22, 893.72, 895.22, 896.73, 898.23, 899.74, 901.24, 902.75, 904.25, 905.76, 907.26, 908.77, 910.28, 911.78, 913.29, 914.8, 916.31, 917.82, 919.32, 920.83, 922.34, 923.85, 925.36, 926.87, 928.38, 929.89, 931.4, 932.92, 934.43, 935.94, 937.45, 938.97, 940.48, 941.99, 943.51, 945.02, 946.54, 948.05, 949.57, 951.08, 952.6, 954.11, 955.63, 957.15, 958.66, 960.18, 961.7, 963.22, 964.74, 966.26, 967.77, 969.29, 970.81, 972.33, 973.85, 975.38, 976.9, 978.42, 979.94, 981.46, 982.98, 984.51, 986.03, 987.55, 989.08, 990.6, 992.13, 993.65, 995.18, 996.7, 998.23, 999.75, 1001.28, 1002.81, 1004.33, 1005.86 ]
|
| elif self.dataset_type == 'fiftyoutdoor':
|
|
|
| self.wavelengths = [400.0, 410.0, 420.0, 430.0, 440.0, 450.0, 460.0, 470.0, 480.0, 490.0, 500.0, 510.0, 520.0, 530.0, 540.0, 550.0, 560.0, 570.0, 580.0, 590.0, 600.0, 610.0, 620.0, 630.0, 640.0, 650.0, 660.0, 670.0, 680.0, 690.0, 700.0, 710.0, 720.0]
|
| elif self.dataset_type == 'aphid':
|
|
|
| self.wavelengths = [436.0, 438.0, 440.0, 442.0, 445.0, 447.0, 449.0, 451.0, 454.0, 456.0, 458.0, 460.0, 463.0, 465.0, 467.0, 469.0, 472.0, 474.0, 476.0, 478.0, 481.0, 483.0, 485.0, 487.0, 490.0, 492.0, 494.0, 496.0, 499.0, 501.0, 503.0, 505.0, 508.0, 510.0, 512.0, 514.0, 516.0, 519.0, 521.0, 523.0, 525.0, 528.0, 530.0, 532.0, 534.0, 537.0, 539.0, 541.0, 543.0, 546.0, 548.0, 550.0, 552.0, 555.0, 557.0, 559.0, 561.0, 564.0, 566.0, 568.0, 570.0, 573.0, 575.0, 577.0, 579.0, 582.0, 584.0, 586.0, 588.0, 591.0, 593.0, 595.0, 597.0, 600.0, 602.0, 604.0, 606.0, 609.0, 611.0, 613.0, 615.0, 617.0, 620.0, 622.0, 624.0, 626.0, 629.0, 631.0, 633.0, 635.0, 638.0, 640.0, 642.0, 644.0, 647.0, 649.0, 651.0, 653.0, 656.0, 658.0, 660.0, 662.0, 665.0, 667.0, 669.0, 671.0, 674.0, 676.0, 678.0, 680.0, 683.0, 685.0, 687.0, 689.0, 692.0, 694.0, 696.0, 698.0, 701.0, 703.0, 705.0, 707.0, 709.0, 712.0, 714.0, 716.0, 718.0, 721.0, 723.0, 725.0, 727.0, 730.0, 732.0, 734.0, 736.0, 739.0, 741.0, 743.0, 745.0, 748.0, 750.0, 752.0, 754.0, 757.0, 759.0, 761.0, 763.0, 766.0, 768.0, 770.0, 772.0, 775.0, 777.0, 779.0, 781.0, 784.0, 786.0, 788.0, 790.0, 793.0, 795.0, 797.0, 799.0, 801.0, 804.0, 806.0, 808.0, 810.0, 813.0, 815.0, 817.0, 819.0, 822.0, 824.0, 826.0, 828.0, 831.0, 833.0, 835.0, 837.0, 840.0, 842.0, 844.0, 846.0, 849.0, 851.0, 853.0, 855.0, 858.0, 860.0, 862.0, 864.0, 867.0, 869.0, 871.0, 873.0, 876.0, 878.0, 880.0, 882.0, 885.0, 887.0, 889.0, 891.0, 894.0, 896.0, 898.0, 900.0, 902.0, 905.0, 907.0, 909.0, 911.0, 914.0, 916.0, 918.0, 920.0, 923.0, 925.0, 927.0, 929.0, 932.0, 934.0, 936.0, 938.0, 941.0, 943.0, 945.0, 947.0, 950.0, 952.0, 954.0, 956.0, 959.0, 961.0, 963.0, 965.0]
|
| else:
|
|
|
| self.wavelengths = [400.0, 410.0, 420.0, 430.0, 440.0, 450.0, 460.0, 470.0, 480.0, 490.0,
|
| 500.0, 510.0, 520.0, 530.0, 540.0, 550.0, 560.0, 570.0, 580.0, 590.0,
|
| 600.0, 610.0, 620.0, 630.0, 640.0, 650.0, 660.0, 670.0, 680.0, 690.0, 700.0]
|
|
|
| def transform(self, results: dict) -> dict:
|
| """Transform function to load hyperspectral image.
|
|
|
| Args:
|
| results (dict): Result dict containing img_path.
|
|
|
| Returns:
|
| dict: The dict contains loaded image and meta information.
|
| """
|
| _, logger = _get_rank_logger(
|
| 'pipeline',
|
| logger_cache=self.rank_logger_cache,
|
| enable=self.enable_rank_logging,
|
| )
|
| dataset_name = results.get('dataset_name', results.get('dataset', 'unknown'))
|
| img_path = results['img_path']
|
| if logger is not None:
|
| logger.info(f"Loading : {img_path}")
|
|
|
| if self.dataset_type == 'harvard':
|
| img = self._load_harvard_image(img_path)
|
| elif self.dataset_type == 'umld2015':
|
| img = self._load_umld2015_image(img_path)
|
| elif self.dataset_type == 'umns2002':
|
| img = self._load_umns2002_image(img_path)
|
| elif self.dataset_type == 'umns2004':
|
| img = self._load_umns2004_image(img_path)
|
| elif self.dataset_type == 'umos':
|
| img = self._load_umos_image(img_path)
|
| elif self.dataset_type == 'umri2015':
|
| img = self._load_umri2015_image(img_path)
|
| elif self.dataset_type == 'umemm':
|
| img = self._load_umemm_image(img_path)
|
| elif self.dataset_type == 'hyperblood':
|
| img = self._load_hyperblood_image(img_path)
|
| elif self.dataset_type == 'hsidrive20':
|
| img = self._load_hsidrive20_image(img_path)
|
| elif self.dataset_type == 'hotrednir':
|
| img = self._load_hotrednir_image(img_path)
|
| elif self.dataset_type == 'arad_1k_31':
|
| img = self._load_arad_1k_31_image(img_path)
|
| elif self.dataset_type == 'arad_1k_16':
|
| img = self._load_arad_1k_16_image(img_path)
|
| elif self.dataset_type == 'cave':
|
| img = self._load_cave_image(img_path)
|
| elif self.dataset_type == 'icvl':
|
| img = self._load_icvl_image(img_path)
|
| elif self.dataset_type == 'hs_sod':
|
| img = self._load_hs_sod_image(img_path)
|
| elif self.dataset_type == 'vnihdhiatlimafb':
|
| img = self._load_vnihdhiatlimafb_image(img_path)
|
| elif self.dataset_type == 'deephsnir':
|
| img = self._load_deephsnir_image(img_path)
|
| elif self.dataset_type == 'deephsvis':
|
| img = self._load_deephsvis_image(img_path)
|
| elif self.dataset_type == 'deephsviscor':
|
| img = self._load_deephsviscor_image(img_path)
|
| elif self.dataset_type == 'hsodbitv2':
|
| img = self._load_hsodbit_v2_image(img_path)
|
| elif self.dataset_type == 'hotvis':
|
| img = self._load_hotvis_image(img_path)
|
| elif self.dataset_type == 'hotnir':
|
| img = self._load_hotnir_image(img_path)
|
| elif self.dataset_type == 'hsiroad':
|
| img = self._load_hsiroad_image(img_path)
|
| elif self.dataset_type == 'hyperspectralcityv2':
|
| img = self._load_hyperspectralcityv2_image(img_path)
|
| elif self.dataset_type == 'hykov2nir':
|
| img = self._load_hykov2nir_image(img_path)
|
| elif self.dataset_type == 'hykov2vis':
|
| img = self._load_hykov2vis_image(img_path)
|
| elif self.dataset_type == 'hyperdrive':
|
| img = self._load_hyperdrive_image(img_path)
|
| elif self.dataset_type == 'hyperdrivevnir':
|
| img = self._load_hyperdrivevnir_image(img_path)
|
| elif self.dataset_type == 'hyperdriveswir':
|
| img = self._load_hyperdriveswir_image(img_path)
|
| elif self.dataset_type == 'libhsi':
|
| img = self._load_libhsi_image(img_path)
|
| elif self.dataset_type == 'virginia_tech_tree':
|
| img = self._load_virginiatree_image(img_path)
|
| elif self.dataset_type == 'fiftyoutdoor':
|
| img = self._load_fiftyoutdoor_image(img_path)
|
| elif self.dataset_type == 'aphid':
|
| img = self._load_aphid_image(img_path)
|
| else:
|
| raise ValueError(f"Unsupported dataset type: {self.dataset_type}")
|
|
|
|
|
| if self.to_float32:
|
| img = img.astype(np.float32)
|
|
|
| results['img'] = img
|
| results['img_shape'] = img.shape[:2]
|
| results['ori_shape'] = img.shape[:2]
|
| results['wavelengths'] = self.wavelengths
|
|
|
| if logger is not None:
|
| logger.info(f"Finished: {img_path}")
|
|
|
| return results
|
|
|
| def _load_harvard_image(self, img_path):
|
| """Load Harvard dataset image (.mat file)."""
|
| img_cube = loadmat(img_path)
|
| img_hsi = img_cube['ref'][:].astype(np.float32)
|
|
|
| if self.append_rgb:
|
|
|
| img_rgb = img_hsi[:, :, [22, 13, 5]]
|
| img = np.concatenate((img_hsi, img_rgb), axis=2)
|
| else:
|
| img = img_hsi
|
|
|
| return img
|
|
|
| def _load_umld2015_image(self, img_path):
|
| """Load UMDL2015 dataset image (.mat file)."""
|
| img_cube = loadmat(img_path)
|
| img_hsi = img_cube[list(img_cube.keys())[-1]][:].astype(np.float32)
|
|
|
| if self.append_rgb:
|
|
|
| img_rgb = img_hsi[:, :, [24, 15, 7]]
|
| img = np.concatenate((img_hsi, img_rgb), axis=2)
|
| else:
|
| img = img_hsi
|
|
|
| return img
|
|
|
| def _load_umns2002_image(self, img_path):
|
| """Load UMNS2002 dataset image (.mat file)."""
|
| img_cube = loadmat(img_path)
|
| img_hsi = img_cube[list(img_cube.keys())[-1]][:].astype(np.float32)
|
|
|
| if self.append_rgb:
|
|
|
| img_rgb = img_hsi[:,:,[23,14,6]]
|
| img = np.concatenate((img_hsi, img_rgb), axis=2)
|
| else:
|
| img = img_hsi
|
|
|
| return img
|
|
|
| def _load_umns2004_image(self, img_path):
|
| """Load UMNS2004 dataset image (.mat file)."""
|
| img_cube = loadmat(img_path)
|
| img_hsi = img_cube[list(img_cube.keys())[-1]][:].astype(np.float32)
|
|
|
| if self.append_rgb:
|
|
|
| img_rgb = img_hsi[:, :, [24, 15, 7]]
|
| img = np.concatenate((img_hsi, img_rgb), axis=2)
|
| else:
|
| img = img_hsi
|
|
|
| return img
|
|
|
| def _load_umos_image(self, img_path):
|
| """Load UMOS dataset image (.mat file)."""
|
| img_cube = loadmat(img_path)
|
| img_hsi = img_cube[list(img_cube.keys())[-1]][:].astype(np.float32)
|
|
|
| if self.append_rgb:
|
|
|
| img_rgb = img_hsi[:,:,[24,15,7]]
|
| img = np.concatenate((img_hsi, img_rgb), axis=2)
|
| else:
|
| img = img_hsi
|
|
|
| return img
|
|
|
| def _load_umri2015_image(self, img_path):
|
| """Load UMRI2015 dataset image (.mat file)."""
|
| img_cube = loadmat(img_path)
|
| img_hsi = img_cube[list(img_cube.keys())[-1]][:].astype(np.float32)
|
|
|
| if self.append_rgb:
|
|
|
| img_rgb = img_hsi[:, :, [24, 15, 7]]
|
| img = np.concatenate((img_hsi, img_rgb), axis=2)
|
| else:
|
| img = img_hsi
|
|
|
| return img
|
|
|
| def _load_umemm_image(self, img_path):
|
| """Load UMEMM dataset image (.mat file)."""
|
| img_cube = loadmat(img_path)
|
| img_hsi = img_cube[list(img_cube.keys())[-1]][:].astype(np.float32)
|
|
|
| if self.append_rgb:
|
|
|
| img_rgb = img_hsi[:,:,[23,14,6]]
|
| img = np.concatenate((img_hsi, img_rgb), axis=2)
|
| else:
|
| img = img_hsi
|
|
|
| return img
|
|
|
| def _load_hyperblood_image(self, img_path):
|
|
|
|
|
|
|
|
|
| img_cube,wav = ds_load.get_data(img_path)
|
|
|
| img_rgb = ds_load.get_rgb(img_cube, wav)
|
| img_hsi = img_cube
|
|
|
| if self.append_rgb:
|
| img_rgb = ds_load.get_rgb(img_cube, wav)
|
| img = np.concatenate((img_hsi, img_rgb), axis=2)
|
| else:
|
| img = img_hsi
|
|
|
|
|
|
|
|
|
| return img
|
|
|
| def _load_hsidrive20_image(self, img_path):
|
| """Load HSI-Drive20 dataset image (.npy file)."""
|
| img_cube = np.load(img_path)
|
| img_hsi = img_cube.astype(np.float32)
|
|
|
| if self.append_rgb:
|
|
|
| rgb_path = img_path.replace('_TC.npy', '_pseudocolor.png')
|
| rgb_path = rgb_path.replace('_MF', '')
|
| rgb_path = rgb_path.replace('/images/', '/RGB/')
|
| rgb_path = rgb_path.replace('/test/', '/')
|
| rgb_path = rgb_path.replace('/training/', '/')
|
| rgb_path = rgb_path.replace('/validation/', '/')
|
|
|
| if os.path.exists(rgb_path):
|
| img_rgb = Image.open(rgb_path)
|
| img_rgb = np.asanyarray(img_rgb).astype(np.float32) / 255
|
| img = np.concatenate((img_hsi, img_rgb), axis=2)
|
| else:
|
|
|
| if img_hsi.shape[2] >= 25:
|
| img_rgb = img_hsi[:, :, [20, 15, 10]]
|
| img = np.concatenate((img_hsi, img_rgb), axis=2)
|
| else:
|
| img = img_hsi
|
| else:
|
| img = img_hsi
|
|
|
| return img
|
|
|
| def _load_hotrednir_image(self, img_path):
|
| """Load HOTRedNIR dataset image (.png file)."""
|
|
|
| img_cube = get_image_loader(img_file=img_path, cellSize=4)[:, :, :-1]
|
| img_hsi = img_cube.astype(np.float32)
|
|
|
| if self.append_rgb:
|
| rgb_path = img_path.replace('/HSI-RedNIR/', '/HSI-RedNIR-FalseColor/').replace(".png", ".jpg")
|
| rgb_cube = get_image_loader(rgb_path, cellSize=-1)
|
| img_rgb = np.array(rgb_cube).astype(np.float32) / 255
|
| img = np.concatenate((img_hsi, img_rgb), axis=2)
|
| else:
|
| img = img_hsi
|
|
|
| return img
|
|
|
| def _load_arad_1k_31_image(self, img_path):
|
| """Load ARAD_1K_31 dataset image (.mat file)."""
|
|
|
|
|
|
|
| img_cube = h5py.File(img_path, 'r')
|
| img_hsi = img_cube['cube'][:].transpose(2, 1, 0).astype(np.float32)
|
|
|
| if self.append_rgb:
|
| rgb_cube_path = img_path.replace('mats', 'rgb')
|
| rgb_cube_path = rgb_cube_path.replace('.mat', '.jpg')
|
| rgb_cube = Image.open(rgb_cube_path)
|
| img_rgb = np.array(rgb_cube).astype(np.float32)/255
|
| img = np.concatenate((img_hsi, img_rgb), axis=2)
|
| else:
|
| img = img_hsi
|
|
|
|
|
|
|
|
|
| return img
|
|
|
| def _load_arad_1k_16_image(self, img_path):
|
| """Load ARAD_1K_16 dataset image (.mat file)."""
|
|
|
|
|
|
|
| img_cube = h5py.File(img_path, 'r')
|
| img_hsi = img_cube['cube'][:].transpose(2, 1, 0).astype(np.float32)
|
|
|
| if self.append_rgb:
|
| img = np.concatenate((img_hsi, img_hsi[:,:,[7,4,1]]), axis=2)
|
|
|
|
|
|
|
|
|
| return img
|
|
|
| def _load_cave_image(self, img_path):
|
| """Load CAVE dataset image (.mat file)."""
|
| img_cube = loadmat(img_path)
|
| img_hsi = img_cube['DataCube'][:].astype(np.float32)
|
|
|
| if self.append_rgb:
|
| img_rgb = img_hsi[:, :, [24, 15, 7]]
|
| img = np.concatenate((img_hsi, img_rgb), axis=2)
|
| else:
|
| img = img_hsi
|
|
|
| return img
|
|
|
| def _load_icvl_image(self, img_path):
|
| """Load ICVL dataset image (.h5 file)."""
|
| img_cube = h5py.File(img_path, 'r')
|
| img_hsi = img_cube['rad'][:].astype(np.float32)
|
| img_rgb = img_cube['rgb'][:].astype(np.float32)
|
|
|
| if self.append_rgb:
|
| img_hsi = np.transpose(img_hsi, (2, 1, 0))
|
| img_hsi = np.rot90(img_hsi)
|
| img_rgb = np.transpose(img_rgb, (2, 1, 0))
|
| rgb_height, rgb_width = img_rgb.shape[:2]
|
| img_hsi = cv2.resize(img_hsi, (rgb_width, rgb_height))
|
| img = np.concatenate((img_hsi, img_rgb), axis=2)
|
| else:
|
| img = img_hsi
|
| def _load_hs_sod_image(self, img_path):
|
| """Load HSSOD dataset image (.h5 file)."""
|
| try:
|
| img_cube = h5py.File(img_path, 'r')
|
| img_hsi = img_cube['hypercube'][:].astype(np.float32)
|
| img_hsi = np.transpose(img_hsi, (2, 1, 0))
|
| except Exception:
|
| img_cube = loadmat(img_path)
|
| img_hsi = img_cube['hypercube'].astype(np.float32)
|
|
|
| if self.append_rgb:
|
| rgb_cube_path = img_path.replace('hyperspectral', 'color')
|
| rgb_cube_path = rgb_cube_path.replace('.mat', '.jpg')
|
| rgb_cube = Image.open(rgb_cube_path)
|
| img_rgb = np.array(rgb_cube).astype(np.float32) / 255
|
| img = np.concatenate((img_hsi, img_rgb), axis=2)
|
| else:
|
| img = img_hsi
|
|
|
| return img
|
|
|
| def _load_hsodbit_v2_image(self, img_path):
|
| """Load HSODBIT-V2 dataset image (.mat file)."""
|
|
|
| try:
|
| img_cube = h5py.File(img_path)
|
| img_hsi = img_cube['dataset'][:].transpose(2,1,0)
|
| except Exception:
|
| img_cube = loadmat(img_path)
|
| img_hsi = img_cube['dataset'].astype(np.float32)
|
|
|
| if self.append_rgb:
|
| rgb_path = img_path.replace("/hyperspectral/","/color/")
|
| rgb_path = rgb_path.replace(".h5",".jpg")
|
| rgb_cube = Image.open(rgb_path)
|
| img_rgb = np.array(rgb_cube).astype(np.float32)/255
|
| img = np.concatenate((img_hsi, img_rgb), axis=2)
|
| else:
|
| img = img_hsi
|
|
|
|
|
|
|
|
|
| img = img.transpose(1, 0, 2)
|
| return img
|
|
|
| def _load_vnihdhiatlimafb_image(self, img_path):
|
| """Load VNIHDHIATLIMAFB dataset image (.hdr file)."""
|
|
|
| img_cube = envi.open(img_path).asarray()
|
| img_hsi = img_cube
|
|
|
| if self.append_rgb:
|
| img_rgb = img_hsi[:,:,[90, 40, 28]]
|
| img = np.concatenate((img_hsi, img_rgb), axis=2)
|
| else:
|
| img = img_hsi
|
|
|
|
|
|
|
|
|
| img = img.transpose(1, 0, 2)
|
| return img
|
|
|
| def _load_deephsnir_image(self, img_path):
|
| """Load DeepHSNIR dataset image (.bin file)."""
|
|
|
| img_cube = envi.open(img_path.replace('.bin','.hdr'), img_path).asarray()
|
| img_hsi = img_cube
|
|
|
| if self.append_rgb:
|
| img = np.concatenate((img_hsi, img_hsi[:,:,[206,124,42]]), axis=2)
|
|
|
|
|
|
|
|
|
| return img
|
|
|
| def _load_deephsvis_image(self, img_path):
|
| """Load DeepHSVIS dataset image (.bin file)."""
|
|
|
| img_cube = envi.open(img_path.replace('.bin','.hdr'), img_path).asarray()
|
| img_hsi = img_cube
|
|
|
| if self.append_rgb:
|
| img = np.concatenate((img_hsi, img_hsi[:,:,[74,56,19]]), axis=2)
|
|
|
|
|
|
|
|
|
| return img
|
|
|
| def _load_deephsviscor_image(self, img_path):
|
| """Load DeepHSVISCOR dataset image (.bin file)."""
|
|
|
| img_cube = envi.open(img_path.replace('.bin','.hdr'), img_path).asarray()
|
| img_hsi = img_cube
|
|
|
| if self.append_rgb:
|
| img = np.concatenate((img_hsi, img_hsi[:,:,[100,75,24]]), axis=2)
|
|
|
|
|
|
|
|
|
| return img
|
|
|
| def _load_hotvis_image(self, img_path):
|
| """Load HOTVIS dataset image (.png file)."""
|
|
|
| img_cube = get_image_loader(img_file = img_path, cellSize=4)
|
| img_hsi = img_cube
|
|
|
| if self.append_rgb:
|
| rgb_path = img_path.replace('/HSI-VIS/', '/HSI-VIS-FalseColor/').replace(".png",".jpg")
|
| rgb_cube = get_image_loader(rgb_path, cellSize=-1)
|
| img_rgb = np.array(rgb_cube).astype(np.float32)/255
|
| img = np.concatenate((img_hsi, img_rgb), axis=2)
|
| else:
|
| img = img_hsi
|
|
|
|
|
|
|
|
|
| return img
|
|
|
| def _load_hotnir_image(self, img_path):
|
| """Load HOTNIR dataset image (.png file)."""
|
|
|
| img_cube = get_image_loader(img_file = img_path, cellSize=5)
|
| img_hsi = img_cube
|
|
|
| if self.append_rgb:
|
| rgb_path = img_path.replace('/HSI-NIR/', '/HSI-NIR-FalseColor/').replace(".png",".jpg")
|
| rgb_cube = get_image_loader(rgb_path, cellSize=-1)
|
| img_rgb = np.array(rgb_cube).astype(np.float32)/255
|
| img = np.concatenate((img_hsi, img_rgb), axis=2)
|
| else:
|
| img = img_hsi
|
|
|
|
|
|
|
|
|
| return img
|
|
|
| def _load_hsiroad_image(self, img_path):
|
| """Load HSIROAD dataset image (.tif file)."""
|
|
|
| img_hsi = tifffile.imread(img_path).astype(np.float32) / 255
|
| if self.append_rgb:
|
|
|
|
|
| img_rgb = img_hsi[[21,13,5],...]
|
| img = np.concatenate((img_hsi, img_rgb), axis=0)
|
| else:
|
| img = img_hsi
|
| img = img.transpose(1,2,0)
|
|
|
|
|
|
|
|
|
| return img
|
|
|
| def _load_hyperspectralcityv2_image(self, img_path):
|
| """Load hyperspectralcityv2 dataset image (.hsd file)."""
|
|
|
| data= read_HSD(img_path)
|
|
|
|
|
|
|
|
|
|
|
| if self.append_rgb:
|
|
|
|
|
| if data.shape[2] >= 3:
|
| img_rgb = data[:, :,[51, 15, 6]]
|
| else:
|
|
|
| img_rgb = np.repeat(data[:, :, :3], 3, axis=2)
|
| img = np.concatenate((data, img_rgb), axis=2)
|
| else:
|
| img = data
|
|
|
| return img
|
|
|
| def _load_hykov2nir_image(self, img_path):
|
|
|
| img_cube = loadmat(img_path)
|
|
|
|
|
| img_hsi = img_cube['data']
|
|
|
|
|
|
|
|
|
| if self.append_rgb:
|
|
|
|
|
| if img_hsi.shape[2] >= 3:
|
| img_rgb = img_hsi[:, :, [20, 12, 4]]
|
| else:
|
| img_rgb = np.repeat(img_hsi[:, :, :3], 3, axis=2)
|
| img = np.concatenate((img_hsi, img_rgb), axis=2)
|
| else:
|
| img = img_hsi
|
|
|
| return img
|
|
|
|
|
| def _load_hykov2vis_image(self, img_path):
|
| """Load hykov2vis dataset image (.mat file)."""
|
|
|
| img_cube = loadmat(img_path)
|
|
|
|
|
| img_hsi = img_cube['data']
|
|
|
|
|
|
|
| if self.append_rgb:
|
|
|
|
|
| if img_hsi.shape[2] >= 3:
|
| img_rgb = img_hsi[:, :, [14, 5, 0]]
|
| else:
|
| img_rgb = np.repeat(img_hsi[:, :, :3], 3, axis=2)
|
| img = np.concatenate((img_hsi, img_rgb), axis=2)
|
| else:
|
| img = img_hsi
|
|
|
| return img
|
|
|
| def _load_hyperdrive_image(self, img_path):
|
| """Load Hyperdrive dataset image (.npz file)."""
|
| img_hsi = np.load(img_path)['cube.npy']
|
|
|
| if self.append_rgb:
|
|
|
| if img_hsi.shape[2] >= 3:
|
| rgb_path = img_path.replace('.npz', '.png').replace('/HSI_REGISTERED/', '/RGB_REGISTERED/')
|
| img_rgb = np.asarray(Image.open(rgb_path)).astype(np.float32)/255
|
|
|
|
|
|
|
| else:
|
| img_rgb = np.repeat(img_hsi[:, :, :3], 3, axis=2)
|
| img = np.concatenate((img_hsi, img_rgb), axis=2)
|
| else:
|
| img = img_hsi
|
|
|
| return img
|
|
|
| def _load_hyperdrivevnir_image(self, img_path):
|
| """
|
| Load VNIR hyperspectral image from file (first 24 bands only).
|
| """
|
| img_hsi = np.load(img_path)['cube.npy']
|
|
|
|
|
| img_hsi = img_hsi[:, :, :24]
|
|
|
| if self.append_rgb:
|
|
|
| if img_hsi.shape[2] >= 3:
|
| img_rgb = img_hsi[:, :, [20, 12, 4]]
|
| else:
|
| img_rgb = np.repeat(img_hsi[:, :, :3], 3, axis=2)
|
| img = np.concatenate((img_hsi, img_rgb), axis=2)
|
| else:
|
| img = img_hsi
|
|
|
| return img
|
|
|
| def _load_hyperdriveswir_image(self, img_path):
|
| """
|
| Load SWIR hyperspectral image from file (last 9 bands only).
|
| """
|
| img_hsi = np.load(img_path)['cube.npy']
|
|
|
|
|
| img_hsi = img_hsi[:, :, 24:33]
|
|
|
| if self.append_rgb:
|
|
|
| if img_hsi.shape[2] >= 3:
|
| img_rgb = img_hsi[:, :, [7,4,1]]
|
| else:
|
| img_rgb = np.repeat(img_hsi[:, :, :3], 3, axis=2)
|
| img = np.concatenate((img_hsi, img_rgb), axis=2)
|
| else:
|
| img = img_hsi
|
|
|
| return img
|
|
|
|
|
| def _load_libhsi_image(self, img_path):
|
| """Load LIBHSI dataset image (.hdr file)."""
|
| img_hsi = np.rot90(envi.open(img_path).asarray().astype(np.float32), -1,axes=(0,1))
|
|
|
|
|
| if self.append_rgb:
|
|
|
| rgb_path = img_path.replace('.hdr', '.png').replace('reflectance_cubes', 'rgb')
|
|
|
|
|
| img_rgb_pil = Image.open(rgb_path)
|
| if img_rgb_pil.mode != 'RGB':
|
| img_rgb_pil = img_rgb_pil.convert('RGB')
|
|
|
|
|
| img_rgb = np.array(img_rgb_pil).astype(np.float32) / 255.0
|
|
|
|
|
| hsi_h, hsi_w = img_hsi.shape[:2]
|
| rgb_h, rgb_w = img_rgb.shape[:2]
|
|
|
| if hsi_h != rgb_h or hsi_w != rgb_w:
|
|
|
| img_rgb_pil_resized = img_rgb_pil.resize((hsi_w, hsi_h), Image.LANCZOS)
|
| img_rgb = np.array(img_rgb_pil_resized).astype(np.float32) / 255.0
|
|
|
| img = np.concatenate((img_hsi, img_rgb), axis=2)
|
| else:
|
| img = img_hsi
|
|
|
| return img
|
|
|
| def _load_virginiatree_image(self, img_path):
|
| """Load VirginiaTechTrees dataset image (.hdr file)."""
|
|
|
| img_hsi = envi.open(img_path).asarray().astype(np.float32)
|
|
|
|
|
| img_hsi = img_hsi / 4096
|
|
|
| if self.append_rgb:
|
|
|
|
|
|
|
| red_band = 163
|
| green_band = 93
|
| blue_band = 50
|
|
|
| img_rgb = img_hsi[:, :, [red_band, green_band, blue_band]]
|
|
|
|
|
| img = np.concatenate((img_hsi, img_rgb), axis=2)
|
| else:
|
| img = img_hsi
|
|
|
| img = img.transpose(1, 0, 2)
|
| return img
|
|
|
| def _load_fiftyoutdoor_image(self, img_path):
|
| """Load FiftyOutdoor dataset image (.mat file)."""
|
|
|
| data = loadmat(img_path)
|
| img_hsi = data['hsi'].astype(np.float32)
|
|
|
|
|
| if img_hsi.max() > 1.0:
|
| img_hsi = img_hsi / img_hsi.max()
|
|
|
| if self.append_rgb:
|
|
|
|
|
|
|
|
|
|
|
| img_rgb = img_hsi[:, :, [22, 12, 6]]
|
|
|
|
|
| img = np.concatenate((img_hsi, img_rgb), axis=2)
|
| else:
|
| img = img_hsi
|
|
|
| return img
|
|
|
| def _load_aphid_image(self, img_path):
|
| """Load APHID dataset image (.npy file)."""
|
|
|
|
|
|
|
| img_cube = np.load(img_path).transpose(1,2,0)
|
| img_hsi = img_cube
|
|
|
| if self.append_rgb:
|
| img_rgb = img_hsi[:,:,[84, 40, 13]]
|
| img = np.concatenate((img_hsi, img_rgb), axis=2)
|
| else:
|
| img = img_hsi
|
|
|
|
|
|
|
|
|
| return img
|
|
|
| def _normalize_image(self, img):
|
| """Normalize image using dataset-specific parameters."""
|
|
|
| img = (img - self.min_val) / (self.max_val - self.min_val)
|
| return img
|
|
|
|
|
|
|
| harvard_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='harvard', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| umld2015_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='umld2015', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| umemm_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='umemm', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| umns2002_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='umns2002', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| umns2004_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='umns2004', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| umos_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='umos', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| umri2015_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='umri2015', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| hyperblood_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='hyperblood', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| hsidrive20_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='hsidrive20', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| hotrednir_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='hotrednir', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| arad_1k_31_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='arad_1k_31', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| arad_1k_16_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='arad_1k_16', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| cave_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='cave', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| icvl_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='icvl', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| hs_sod_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='hs_sod', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| hsodbit_v2_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='hsodbit_v2', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
|
|
|
|
| harvard_test_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='harvard', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| umld2015_test_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='umld2015', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| umemm_test_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='umemm', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| umns2002_test_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='umns2002', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| umns2004_test_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='umns2004', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| umos_test_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='umos', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| umri2015_test_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='umri2015', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| hyperblood_test_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='hyperblood', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| hsidrive20_test_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='hsidrive20', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| hotrednir_test_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='hotrednir', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| arad_1k_31_test_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='arad_1k_31', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| arad_1k_16_test_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='arad_1k_16', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| cave_test_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='cave', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| icvl_test_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='icvl', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| hs_sod_test_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='hs_sod', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| hsodbit_v2_test_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='hsodbit_v2', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
|
|
|
|
| vnihdhiatlimafb_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='vnihdhiatlimafb', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| vnihdhiatlimafb_test_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='vnihdhiatlimafb', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| deephsnir_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='deephsnir', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| deephsnir_test_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='deephsnir', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| deephsvis_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='deephsvis', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| deephsvis_test_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='deephsvis', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| deephsviscor_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='deephsviscor', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| deephsviscor_test_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='deephsviscor', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| hotvis_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='hotvis', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| hotvis_test_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='hotvis', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| hotnir_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='hotnir', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| hotnir_test_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='hotnir', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| hsiroad_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='hsiroad', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| hsiroad_test_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='hsiroad', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| hyperspectralcityv2_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='hyperspectralcityv2', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| hyperspectralcityv2_test_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='hyperspectralcityv2', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| hykov2nir_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='hykov2nir', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| hykov2nir_test_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='hykov2nir', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| hykov2vis_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='hykov2vis', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| hykov2vis_test_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='hykov2vis', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| hyperdrive_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='hyperdrive', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| hyperdrive_test_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='hyperdrive', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| hyperdrivevnir_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='hyperdrivevnir', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| hyperdrivevnir_test_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='hyperdrivevnir', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| hyperdriveswir_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='hyperdriveswir', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| hyperdriveswir_test_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='hyperdriveswir', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| libhsi_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='libhsi', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| libhsi_test_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='libhsi', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| virginiatree_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='virginiatree', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| virginiatree_test_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='virginiatree', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| fiftyoutdoor_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='fiftyoutdoor', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| fiftyoutdoor_test_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='fiftyoutdoor', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
|
|
| aphid_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='aphid', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
| aphid_test_pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type='aphid', to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
|
|
| def create_universal_train_pipeline(dataset_type, crop_size=(512, 512)):
|
| """Create a universal training pipeline for all hyperspectral datasets.
|
|
|
| Args:
|
| dataset_type (str): Type of dataset
|
| crop_size (tuple): Crop size for RandomCrop
|
|
|
| Returns:
|
| list: Training pipeline configuration
|
| """
|
| pipeline = [
|
| dict(type='LoadHyperspectralImage', dataset_type=dataset_type, to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
|
|
|
|
| dict(type='RandomFlip', prob=.5),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
|
| 'scale_factor', 'flip', 'flip_direction', 'wavelengths')),
|
| ]
|
| return pipeline
|
|
|
|
|
|
|
| def create_universal_test_pipeline(dataset_type):
|
| """Create a universal test pipeline for all hyperspectral datasets.
|
|
|
| Args:
|
| dataset_type (str): Type of dataset
|
|
|
| Returns:
|
| list: Test pipeline configuration
|
| """
|
| return [
|
| dict(type='LoadHyperspectralImage', dataset_type=dataset_type, to_float32=True, append_rgb=True),
|
| dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
|
| dict(type='PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
|
| 'scale_factor', 'flip', 'flip_direction', 'wavelengths'))
|
| ]
|
|
|
|
|
|
|
| harvard_universal_train_pipeline = create_universal_train_pipeline('harvard')
|
| harvard_universal_test_pipeline = create_universal_test_pipeline('harvard')
|
|
|
| umld2015_universal_train_pipeline = create_universal_train_pipeline('umld2015')
|
| umld2015_universal_test_pipeline = create_universal_test_pipeline('umld2015')
|
|
|
| hsodbitv2_universal_train_pipeline = create_universal_train_pipeline('hsodbitv2')
|
| hsodbitv2_universal_test_pipeline = create_universal_test_pipeline('hsodbitv2')
|
|
|
| vnihdhiatlimafb_universal_train_pipeline = create_universal_train_pipeline('vnihdhiatlimafb')
|
| vnihdhiatlimafb_universal_test_pipeline = create_universal_test_pipeline('vnihdhiatlimafb')
|
|
|
| virginia_tech_tree_universal_train_pipeline = create_universal_train_pipeline('virginia_tech_tree')
|
| virginia_tech_tree_universal_test_pipeline = create_universal_test_pipeline('virginia_tech_tree')
|
|
|
|
|
| arad_1k_31_universal_train_pipeline = create_universal_train_pipeline('arad_1k_31')
|
| arad_1k_31_universal_test_pipeline = create_universal_test_pipeline('arad_1k_31')
|
|
|
| hs_sod_universal_train_pipeline = create_universal_train_pipeline('hs_sod')
|
| hs_sod_universal_test_pipeline = create_universal_test_pipeline('hs_sod')
|
|
|
| cave_universal_train_pipeline = create_universal_train_pipeline('cave')
|
| cave_universal_test_pipeline = create_universal_test_pipeline('cave')
|
|
|
| icvl_universal_train_pipeline = create_universal_train_pipeline('icvl')
|
| icvl_universal_test_pipeline = create_universal_test_pipeline('icvl')
|
|
|
|
|
| umld2015_universal_train_pipeline = create_universal_train_pipeline('umld2015')
|
| umld2015_universal_test_pipeline = create_universal_test_pipeline('umld2015')
|
|
|
| umns2002_universal_train_pipeline = create_universal_train_pipeline('umns2002')
|
| umns2002_universal_test_pipeline = create_universal_test_pipeline('umns2002')
|
|
|
| umns2004_universal_train_pipeline = create_universal_train_pipeline('umns2004')
|
| umns2004_universal_test_pipeline = create_universal_test_pipeline('umns2004')
|
|
|
| umos_universal_train_pipeline = create_universal_train_pipeline('umos')
|
| umos_universal_test_pipeline = create_universal_test_pipeline('umos')
|
|
|
| umri2015_universal_train_pipeline = create_universal_train_pipeline('umri2015')
|
| umri2015_universal_test_pipeline = create_universal_test_pipeline('umri2015')
|
|
|
| umemm_universal_train_pipeline = create_universal_train_pipeline('umemm')
|
| umemm_universal_test_pipeline = create_universal_test_pipeline('umemm')
|
|
|
| hyperblood_universal_train_pipeline = create_universal_train_pipeline('hyperblood')
|
| hyperblood_universal_test_pipeline = create_universal_test_pipeline('hyperblood')
|
|
|
| hsidrive20_universal_train_pipeline = create_universal_train_pipeline('hsidrive20')
|
| hsidrive20_universal_test_pipeline = create_universal_test_pipeline('hsidrive20')
|
|
|
| hotrednir_universal_train_pipeline = create_universal_train_pipeline('hotrednir')
|
| hotrednir_universal_test_pipeline = create_universal_test_pipeline('hotrednir')
|
|
|
| arad_1k_16_universal_train_pipeline = create_universal_train_pipeline('arad_1k_16')
|
| arad_1k_16_universal_test_pipeline = create_universal_test_pipeline('arad_1k_16')
|
|
|
| hotvis_universal_train_pipeline = create_universal_train_pipeline('hotvis')
|
| hotvis_universal_test_pipeline = create_universal_test_pipeline('hotvis')
|
|
|
| hotnir_universal_train_pipeline = create_universal_train_pipeline('hotnir')
|
| hotnir_universal_test_pipeline = create_universal_test_pipeline('hotnir')
|
|
|
| libhsi_universal_train_pipeline = create_universal_train_pipeline('libhsi')
|
| libhsi_universal_test_pipeline = create_universal_test_pipeline('libhsi')
|
|
|
| fiftyoutdoor_universal_train_pipeline = create_universal_train_pipeline('fiftyoutdoor')
|
| fiftyoutdoor_universal_test_pipeline = create_universal_test_pipeline('fiftyoutdoor')
|
|
|
| aphid_universal_train_pipeline = create_universal_train_pipeline('aphid')
|
| aphid_universal_test_pipeline = create_universal_test_pipeline('aphid')
|
|
|
| hyperspectralcityv2_universal_train_pipeline = create_universal_train_pipeline('hyperspectralcityv2')
|
| hyperspectralcityv2_universal_test_pipeline = create_universal_test_pipeline('hyperspectralcityv2')
|
|
|
| hykov2nir_universal_train_pipeline = create_universal_train_pipeline('hykov2nir')
|
| hykov2nir_universal_test_pipeline = create_universal_test_pipeline('hykov2nir')
|
|
|
| hykov2vis_universal_train_pipeline = create_universal_train_pipeline('hykov2vis')
|
| hykov2vis_universal_test_pipeline = create_universal_test_pipeline('hykov2vis')
|
|
|
| deephsnir_universal_train_pipeline = create_universal_train_pipeline('deephsnir')
|
| deephsnir_universal_test_pipeline = create_universal_test_pipeline('deephsnir')
|
|
|
| deephsvis_universal_train_pipeline = create_universal_train_pipeline('deephsvis')
|
| deephsvis_universal_test_pipeline = create_universal_test_pipeline('deephsvis')
|
|
|
| deephsviscor_universal_train_pipeline = create_universal_train_pipeline('deephsviscor')
|
| deephsviscor_universal_test_pipeline = create_universal_test_pipeline('deephsviscor')
|
|
|
| hsiroad_universal_train_pipeline = create_universal_train_pipeline('hsiroad')
|
| hsiroad_universal_test_pipeline = create_universal_test_pipeline('hsiroad')
|
|
|
| hyperdrivevnir_universal_train_pipeline = create_universal_train_pipeline('hyperdrivevnir')
|
| hyperdrivevnir_universal_test_pipeline = create_universal_test_pipeline('hyperdrivevnir')
|
|
|
| hyperdriveswir_universal_train_pipeline = create_universal_train_pipeline('hyperdriveswir')
|
| hyperdriveswir_universal_test_pipeline = create_universal_test_pipeline('hyperdriveswir')
|
|
|
| hyperdrive_universal_train_pipeline = create_universal_train_pipeline('hyperdrive')
|
| hyperdrive_universal_test_pipeline = create_universal_test_pipeline('hyperdrive')
|
|
|
