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Flexbrdf/hytools/masks/__init__.py
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Flexbrdf/hytools/masks/__init__.py
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# -*- coding: utf-8 -*-
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"""
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HyTools: Hyperspectral image processing library
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Copyright (C) 2021 University of Wisconsin
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Authors: Adam Chlus, Zhiwei Ye, Philip Townsend.
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This program is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, version 3 of the License.
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This program is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with this program. If not, see <https://www.gnu.org/licenses/>.
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The :mod:`hytools.masks` module include functions image correction.
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"""
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from .masks import *
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from .cloud import *
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from .calc_apply import *
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109
Flexbrdf/hytools/masks/calc_apply.py
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Flexbrdf/hytools/masks/calc_apply.py
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# -*- coding: utf-8 -*-
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"""
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HyTools: Hyperspectral image processing library
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Copyright (C) 2021 University of Wisconsin
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Authors: Adam Chlus, Zhiwei Ye, Philip Townsend.
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This program is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, version 3 of the License.
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This program is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with this program. If not, see <https://www.gnu.org/licenses/>.
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This module contain functions for generating boolean masks specific to apply image corrections and
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models.
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"""
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from scipy.ndimage.morphology import binary_erosion
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import numpy as np
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from .cloud import zhai_cloud
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def ndi(hy_obj,args):
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mask = hy_obj.ndi(args['band_1'],args['band_2'])
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mask = (mask >= float(args['min'])) & (mask <= float(args['max']))
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return mask
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def ancillary(hy_obj,args):
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''' Mask ancillary datasets based off min and max threshold
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'''
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if args['name'] == 'cosine_i':
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mask= hy_obj.cosine_i()
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else:
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mask = hy_obj.get_anc(args['name'])
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mask = (mask >= float(args['min'])) & (mask <= float(args['max']))
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return mask
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def neon_edge(hy_obj,args):
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'''
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Mask artifacts in NEON images around edges.
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'''
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radius =args['radius']
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y_grid, x_grid = np.ogrid[-radius: radius + 1, -radius: radius + 1]
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window = (x_grid**2 + y_grid**2 <= radius**2).astype(np.float32)
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buffer_edge = binary_erosion(hy_obj.mask['no_data'], window).astype(bool)
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return buffer_edge
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def kernel_finite(hy_obj,args):
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'''
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Create NDVI bin class mask
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'''
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k_vol = hy_obj.volume_kernel(hy_obj.brdf['volume'])
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k_geom = hy_obj.geom_kernel(hy_obj.brdf['geometric'],
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b_r=hy_obj.brdf["b/r"],
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h_b =hy_obj.brdf["h/b"])
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mask = np.isfinite(k_vol) & np.isfinite(k_geom)
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return mask
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def cloud(hy_obj,args):
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if args['method'] == 'zhai_2018':
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mask = ~zhai_cloud(hy_obj,args['cloud'],args['shadow'],
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args['T1'], args['t2'], args['t3'],
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args['t4'], args['T7'], args['T8'])
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return mask
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def water(hy_obj,args):
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'''
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Create water mask using NDWI threshold
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'''
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mask = hy_obj.ndi(args['band_1'],args['band_2'])
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mask = mask <= float(args['threshold'])
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mask = binary_erosion(mask)
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return mask
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def external(hy_obj,args):
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'''Load a mask from an external dataset
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'''
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hy_obj.anc_path['external_mask'] = [args['files'][hy_obj.file_name], 0]
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mask = hy_obj.get_anc('external_mask') == args['class']
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return mask
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def band(hy_obj,args):
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'''
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Create mask using band thresholds
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'''
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mask = hy_obj.get_wave(args['band'])
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mask = (mask >= float(args['min'])) & (mask <= float(args['max']))
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return mask
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Flexbrdf/hytools/masks/cloud.py
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Flexbrdf/hytools/masks/cloud.py
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# -*- coding: utf-8 -*-
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'''
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HyTools: Hyperspectral image processing library
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Copyright (C) 2021 University of Wisconsin
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Authors: Adam Chlus, Zhiwei Ye, Philip Townsend.
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This program is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, version 3 of the License.
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This program is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with this program. If not, see <https://www.gnu.org/licenses/>.
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Cloud masks
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'''
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from scipy.ndimage import median_filter
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import numpy as np
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def zhai_cloud(hy_obj,cloud,shadow,T1=0.01,t2=.1,t3=.25,t4=.5,T7= 9,T8= 9):
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'''This function replicates the method of Zhai et al. (2018) for detecting clouds and shadows in
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multispectral and hyperspectral imagery but does not apply shadow spatial refinement.
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Suggested values for coefficients and params:
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T1 : 0.01, 0.1, 1, 10, 100
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t2 : 1/10, 1/9, 1/8, 1/7, 1/6, 1/5, 1/4, 1/3, 1/2
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t3 : 1/4, 1/3, 1/2, 2/3, 3/4
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t4 : 1/2, 2/3, 3/4, 4/5, 5/6
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T7 : 3, 5, 7, 9, 11
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T8 : 3, 5, 7, 9, 11
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Zhai, H., Zhang, H., Zhang, L., & Li, P. (2018).
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Cloud/shadow detection based on spectral indices for multi/hyperspectral optical remote sensing imagery.
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ISPRS journal of photogrammetry and remote sensing, 144, 235-253.
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https://doi.org/10.1016/j.isprsjprs.2018.07.006
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Args:
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hy_obj : HyTools data container object:
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cloud (bool): Detect clouds.
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shadow (bool): Detect clouds.
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T1 (float): Threshold T1.
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t2 (float): Adjusting coefficient t2.
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t3 (float): Adjusting coefficient t3.
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t4 (float): Adjusting coefficient t4.
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T7 (float): Parameter T7.
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T8 (float): Parameter T8.
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Returns:
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mask (nd.array): Boolean array where detected clouds and/or shadows = True.
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'''
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blue= hy_obj.get_wave(440)
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green= hy_obj.get_wave(550)
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red= hy_obj.get_wave(660)
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nir = hy_obj.get_wave(850)
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#If SWIR not available
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if hy_obj.wavelengths.max() < 1570:
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# Zhai et al. 2018 Eq. 1a,b
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CI_1 = (3*nir)/(blue+green+red)
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CI_2 = (blue+green+red+nir)/4
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# Zhai et al. 2018 Eq. 3
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CSI = nir
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else:
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swir1 = hy_obj.get_wave(1570)
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swir2= hy_obj.get_wave(2110)
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# Zhai et al. 2018 Eq. 1a,b
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CI_1 = (nir+ 2*swir1)/(blue+green+red)
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CI_2 = (blue+green+red+nir+swir1+swir2)/6
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# Zhai et al. 2018 Eq. 3
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CSI = (nir + swir1)/2
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# Zhai et al. 2018 Eq.5
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T2 = np.mean(CI_2[hy_obj.mask['no_data']]) + t2*(np.max(CI_2[hy_obj.mask['no_data']])-np.mean(CI_2[hy_obj.mask['no_data']]))
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# Zhai et al. 2018 Eq.6
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T3 = np.min(CSI[hy_obj.mask['no_data']]) + t3*(np.mean(CSI[hy_obj.mask['no_data']])-np.min(CSI[hy_obj.mask['no_data']]))
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# Zhai et al. 2018 Eq.7
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T4 = np.min(blue[hy_obj.mask['no_data']]) + t4*(np.mean(blue[hy_obj.mask['no_data']])-np.min(blue[hy_obj.mask['no_data']]))
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mask = np.zeros((hy_obj.lines,hy_obj.columns)).astype(bool)
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if cloud:
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clouds = (np.abs(CI_1) < T1) | (CI_2 > T2)
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clouds = median_filter(clouds, T7)
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mask[clouds] = True
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if shadow:
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shadows = (CSI<T3) & (blue<T4)
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shadows = median_filter(shadows,T8)
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mask[shadows] = True
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return mask
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42
Flexbrdf/hytools/masks/masks.py
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42
Flexbrdf/hytools/masks/masks.py
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# -*- coding: utf-8 -*-
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'''
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HyTools: Hyperspectral image processing library
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Copyright (C) 2021 University of Wisconsin
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Authors: Adam Chlus, Zhiwei Ye, Philip Townsend.
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This program is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, version 3 of the License.
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This program is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with this program. If not, see <https://www.gnu.org/licenses/>.
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'''
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from .calc_apply import *
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from .cloud import *
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mask_dict = {'ndi' : ndi,
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'neon_edge' : neon_edge,
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'kernel_finite': kernel_finite,
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'ancillary': ancillary,
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'cloud': cloud,
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'water': water,
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'band': band,
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'external' : external}
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def mask_create(hy_obj,masks):
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''' Combine a series of boolean masks using an
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and operator
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'''
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mask = np.copy(hy_obj.mask['no_data'])
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for mask_name,args in masks:
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mask &= mask_dict[mask_name](hy_obj,args)
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return mask
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