211 lines
7.4 KiB
Python
211 lines
7.4 KiB
Python
import json
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import os
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import warnings
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import sys
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import ray
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import numpy as np
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import time
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import hytools as ht
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from hytools.io.envi import *
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from hytools.topo import calc_topo_coeffs
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from hytools.brdf import calc_brdf_coeffs
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from hytools.masks import mask_create
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from hytools.misc import update_topo_group
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import logging
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logging.basicConfig(format='%(asctime)s %(message)s',level=logging.INFO)
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warnings.filterwarnings("ignore")
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np.seterr(divide='ignore', invalid='ignore')
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def main():
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time_start = time.perf_counter()
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config_file = sys.argv[1]
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with open(config_file, 'r') as outfile:
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config_dict = json.load(outfile)
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images= config_dict["input_files"]
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if ray.is_initialized():
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ray.shutdown()
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logging.info("Using %s CPUs." % config_dict['num_cpus'])
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ray.init(num_cpus = config_dict['num_cpus'])
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HyTools = ray.remote(ht.HyTools)
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if "subgrouped" in config_dict["topo"]:
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if config_dict["topo"]["subgrouped"]:
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subgroup_list, group_tag_list = update_topo_group(config_dict["topo"]["subgroup"])
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actor_subgroup = []
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for file_name_list in subgroup_list:
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actor_subgroup+=[[HyTools.remote() for image in file_name_list]]
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actors = []
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for actor_list in actor_subgroup:
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actors+= actor_list
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else:
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actors = [HyTools.remote() for image in images]
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actor_subgroup = None
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group_tag_list=None
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else:
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actors = [HyTools.remote() for image in images]
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actor_subgroup = None
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group_tag_list=None
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if config_dict['file_type'] == 'envi' or config_dict['file_type'] == 'ncav' or config_dict['file_type'] == 'emit':
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anc_files = config_dict["anc_files"]
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_ = ray.get([a.read_file.remote(image,config_dict['file_type'],
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anc_files[image]) for a,image in zip(actors,images)])
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elif config_dict['file_type'] == 'neon':
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_ = ray.get([a.read_file.remote(image,config_dict['file_type']) for a,image in zip(actors,images)])
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#Here is where the outlier detection should probably happen.
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_ = ray.get([a.create_bad_bands.remote(config_dict['bad_bands']) for a in actors])
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for correction in config_dict["corrections"]:
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if correction =='topo':
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time_topo_start = time.perf_counter()
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calc_topo_coeffs(actors,config_dict['topo'],actor_group_list=actor_subgroup,group_tag_list=group_tag_list)
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time_topo_end = time.perf_counter()
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logging.info("TOPO Time: {} sec.".format(time_topo_end - time_topo_start))
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elif correction == 'brdf':
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time_brdf_start = time.perf_counter()
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calc_brdf_coeffs(actors,config_dict)
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time_brdf_end = time.perf_counter()
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logging.info("BRDF Time: {} sec.".format(time_brdf_end - time_brdf_start))
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if config_dict['export']['coeffs'] and len(config_dict["corrections"]) > 0:
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logging.info("Exporting correction coefficients.")
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_ = ray.get([a.do.remote(export_coeffs,config_dict['export']) for a in actors])
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time_export_start = time.perf_counter()
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if config_dict['export']['image']:
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logging.info("Exporting corrected images.")
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_ = ray.get([a.do.remote(apply_corrections,config_dict) for a in actors])
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time_export_end = time.perf_counter()
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logging.info("Export Time: {} sec.".format(time_export_end - time_export_start))
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ray.shutdown()
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time_end = time.perf_counter()
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logging.info("Total Time: {} sec.".format(time_end - time_start))
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def export_coeffs(hy_obj,export_dict):
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'''Export correction coefficients to file.
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'''
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for correction in hy_obj.corrections:
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if correction=='unsmooth':
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continue
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coeff_file = export_dict['output_dir']
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coeff_file += os.path.splitext(os.path.basename(hy_obj.file_name))[0]
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coeff_file += "_%s_coeffs_%s.json" % (correction,export_dict["suffix"])
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with open(coeff_file, 'w') as outfile:
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if correction == 'topo':
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corr_dict = hy_obj.topo
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else:
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corr_dict = hy_obj.brdf
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json.dump(corr_dict,outfile)
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def apply_corrections(hy_obj,config_dict):
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'''Apply correction to image and export
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to file.
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'''
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header_dict = hy_obj.get_header()
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header_dict['data ignore value'] = hy_obj.no_data
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header_dict['data type'] = 4
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output_name = config_dict['export']['output_dir']
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output_name += os.path.splitext(os.path.basename(hy_obj.file_name))[0]
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output_name += "_%s" % config_dict['export']["suffix"]
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#Export all wavelengths
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if len(config_dict['export']['subset_waves']) == 0:
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if config_dict["resample"] == True:
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hy_obj.resampler = config_dict['resampler']
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waves= hy_obj.resampler['out_waves']
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else:
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waves = hy_obj.wavelengths
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header_dict['bands'] = len(waves)
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header_dict['wavelength'] = waves
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header_dict['fwhm'] = hy_obj.fwhm
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writer = WriteENVI(output_name,header_dict)
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iterator = hy_obj.iterate(by = 'line', corrections = hy_obj.corrections,
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resample = config_dict['resample'])
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while not iterator.complete:
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line = iterator.read_next()
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writer.write_line(line,iterator.current_line)
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writer.close()
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#Export subset of wavelengths
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else:
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waves = config_dict['export']['subset_waves']
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bands = [hy_obj.wave_to_band(x) for x in waves]
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waves = [round(hy_obj.wavelengths[x],2) for x in bands]
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header_dict['bands'] = len(bands)
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header_dict['wavelength'] = waves
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header_dict['fwhm'] = [hy_obj.fwhm[x] for x in bands]
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writer = WriteENVI(output_name,header_dict)
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for b,band_num in enumerate(bands):
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#print('image_correct.py Ln 123',hy_obj.corrections)
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band = hy_obj.get_band(band_num,
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corrections = hy_obj.corrections)
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writer.write_band(band, b)
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writer.close()
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#Export masks
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# does not work for precomputed json coeffs
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if (config_dict['export']['masks']) and (len(config_dict["corrections"]) > 0):
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masks = []
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mask_names = []
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for correction in config_dict["corrections"]:
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if correction=='unsmooth':
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continue
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#for mask_type in config_dict[correction]['calc_mask']:
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for mask_type in config_dict[correction]['apply_mask']:
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mask_names.append(correction + '_' + mask_type[0])
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masks.append(mask_create(hy_obj, [mask_type]))
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header_dict['data type'] = 1
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header_dict['bands'] = len(masks)
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header_dict['band names'] = mask_names
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header_dict['samples'] = hy_obj.columns
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header_dict['lines'] = hy_obj.lines
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header_dict['wavelength'] = []
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header_dict['fwhm'] = []
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header_dict['wavelength units'] = ''
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header_dict['data ignore value'] = 255
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output_name = config_dict['export']['output_dir']
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output_name += os.path.splitext(os.path.basename(hy_obj.file_name))[0]
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output_name += "_%s_mask" % config_dict['export']["suffix"]
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writer = WriteENVI(output_name,header_dict)
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for band_num,mask in enumerate(masks):
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mask =mask.astype(int)
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mask[~hy_obj.mask['no_data']] = 255
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writer.write_band(mask,band_num)
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del masks
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if __name__== "__main__":
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main()
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