| '''
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| ************************************************************************
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| Copyright 2020 Institute of Theoretical and Applied Informatics,
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| Polish Academy of Sciences https://www.iitis.pl
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|
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| Licensed under the Apache License, Version 2.0 (the "License");
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| you may not use this file except in compliance with the License.
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| You may obtain a copy of the License at
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|
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| http://www.apache.org/licenses/LICENSE-2.0
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|
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| Unless required by applicable law or agreed to in writing, software
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| distributed under the License is distributed on an "AS IS" BASIS,
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| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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| See the License for the specific language governing permissions and
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| limitations under the License.
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| ************************************************************************
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|
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| HSI blood classification dataset by M. Romaszewski, P.Glomb, M. Cholewa, A. Sochan
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| Institute of Theoretical and Applied Informatics, Polish Academy of Sciences (ITAI PAS) https://www.iitis.pl
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| Dataset DOI: 10.5281/zenodo.3984905
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| HyperBlood API
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| Basic loader for dataset files
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| Warning:
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| * By default, data is cleared by removing noisy bands and broken line in the image.
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| * Note that the 'F(2k)' image was captured with different camera. Its bands were interpolated
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| to match remaining images. However, due to spectral range differences between cameras, it has
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| less bands. After cleaning (default) all images have the same matching 113 bands.
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| NOISY_BANDS_INDICES = np.array([0,1,2,3,4,48,49,50,121,122,123,124,125,126,127])
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|
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| @author: mromaszewski@iitis.pl
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| '''
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| import warnings
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| warnings.filterwarnings("ignore")
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| import unittest
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| import spectral.io.envi as envi
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| import numpy as np
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| import matplotlib.pyplot as plt
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| from osgeo import gdal
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| gdal.PushErrorHandler('CPLQuietErrorHandler')
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| IMAGES = ['A(1)','B(1)','C(1)','D(1)','E(1)','E(7)','E(21)','F(1)','F(1a)','F(1s)','F(2)','F(2k)','F(7)','F(21)']
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| def get_data(name,remove_bands=True,clean=True):
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| """
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| Returns HSI data from a datacube
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| Parameters:
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| ---------------------
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| name: name
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| remove_bands: if True, noisy bands are removed (leaving 113 bands)
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| clean: if True, remove damaged line
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| Returns:
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| -----------------------
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| data, wavelenghts as numpy arrays (float32)
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| """
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| hsimage = gdal.Open(name)
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| hsimage = hsimage.ReadAsArray()
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| wavs = np.asarray([376.8200 , 381.7583 , 386.7018 , 391.6505 , 396.6044 , 401.5636 , 406.5280 , 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 , 517.0670 , 522.1515 , 527.2413 , 532.3364 , 537.4367 , 542.5422 , 547.6529 , 552.7689 , 557.8901 , 563.0164 , 568.1481 , 573.2849 , 578.4270 , 583.5743 , 588.7269 , 593.8846 , 599.0476 , 604.2158 , 609.3892 , 614.5679 , 619.7518 , 624.9409 , 630.1353 , 635.3348 , 640.5396 , 645.7496 , 650.9649 , 656.1853 , 661.4111 , 666.6420 , 671.8782 , 677.1195 , 682.3661 , 687.6179 , 692.8750 , 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 , 772.3580 , 777.6987 , 783.0445 , 788.3956 , 793.7520 , 799.1135 , 804.4803 , 809.8524 , 815.2296 , 820.6121 , 825.9998 , 831.3927 , 836.7909 , 842.1942 , 847.6028 , 853.0167 , 858.4358 , 863.8600 , 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 , 990.0610 , 995.6107 , 1001.1656 , 1006.7258 , 1012.2913 , 1017.8618 , 1023.4377 , 1029.0188 , 1034.6050 , 1040.1965 , 1045.7932])
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| data = np.asarray(hsimage[:,:,:],dtype=np.float32).transpose(1,2,0)
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| fname = name.split('/')[-1].replace('.tif','')
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| if clean and fname!='F_2k':
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| data = np.delete(data,445,0)
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| if not remove_bands:
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| return data,wavs
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| return data[:,:,get_good_indices(fname)],wavs[get_good_indices(fname)]
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| def get_anno(name,remove_uncertain_blood=True,clean=True):
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| """
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| Returns annotation (GT) for data files as 2D int numpy array
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| Classes:
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| 0 - background
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| 1 - blood
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| 2 - ketchup
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| 3 - artificial blood
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| 4 - beetroot juice
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| 5 - poster paint
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| 6 - tomato concentrate
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| 7 - acrtylic paint
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| 8 - uncertain blood
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| Parameters:
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| ---------------------
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| name: name
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| clean: if True, remove damaged line
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| remove_uncertain_blood: if True, removes class 8
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|
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| Returns:
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| -----------------------
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| annotation as numpy 2D array
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| """
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| name = convert_name(name)
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| filename = "{}anno/{}".format(PATH_DATA,name)
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| anno = np.load(filename+'.npz')['gt']
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| if clean and name!='F_2k':
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| anno = np.delete(anno,445,0)
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| if remove_uncertain_blood:
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| anno[anno>7]=0
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| else:
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| anno[anno>8]=0
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| return anno
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| def get_good_indices(name=None):
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| """
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| Returns indices of bands which are not noisy
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| Parameters:
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| ---------------------
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| name: name
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| Returns:
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| -----------------------
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| numpy array of good indices
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| """
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| name = convert_name(name)
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| if name!='F_2k':
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| indices = np.arange(128)
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| indices = indices[5:-7]
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| else:
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| indices = np.arange(116)
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| indices=np.delete(indices,[43,44,45])
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| return indices
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| def convert_name(name):
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| """
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| Ensures that the name is in the filename format
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| Parameters:
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| ---------------------
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| name: name
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|
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| Returns:
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| -----------------------
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| cleaned name
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| """
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| name = name.replace('(','_')
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| name = name.replace(')','')
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| return name
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| def get_rgb(data,wavelengths,gamma=0.7,vnir_bands=[600, 550, 450]):
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| """
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| Treturns an (over)simplified RGB visualization of HSI data
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| Parameters:
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| ---------------------
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| data: data cube as nparray
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| annotation: wavelengths - band wavelenghts
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| gamma: gamma correction value
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| vnir_bands: bands used for RGB
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| Returns:
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| -----------------------
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| rgb image as numpy array
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| """
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| assert data.shape[2]==len(wavelengths)
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| max_data = np.max(data)
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| rgb_i = [np.argmin(np.abs(wavelengths - b)) for b in vnir_bands]
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| ret = data[:,:,rgb_i].copy()/max_data
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| if gamma!=1.0:
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| for i in range(3):
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| ret[:,:,i]=np.power(ret[:,:,i],gamma)
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| return ret
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|
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| class LoadTest(unittest.TestCase):
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| def test_load(self):
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| """
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| test image loading
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| """
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| for name in IMAGES:
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| data,wavelengths = get_data(name,remove_bands=True)
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| anno = get_anno(name)
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| self.assertEqual(data.shape[2],113)
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| self.assertEqual(data.shape[2],wavelengths.shape[0])
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| rgb = get_rgb(data,wavelengths)
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| plt.subplot(1,2,1)
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| plt.imshow(rgb,interpolation='nearest')
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| plt.subplot(1,2,2)
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| plt.imshow(anno,interpolation='nearest')
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| plt.show()
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| plt.close()
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| def dis_test_indices(self):
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| '''
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| Ensure F_2k is loaded correctly
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| '''
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| _,wavs = get_data('F_2k',remove_bands=False)
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| assert 619.7518 in wavs
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| _,wavs = get_data('F_2k',remove_bands=True)
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| assert 619.7518 not in wavs
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| _,wavs2 = get_data('F_1',remove_bands=True)
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| assert np.sum(wavs-wavs2)==0
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| data,wavelengths = get_data('F_1',remove_bands=False)
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| self.assertEqual(data.shape[2],128)
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| self.assertEqual(data.shape[2],wavelengths.shape[0])
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| data,wavelengths = get_data('F_2k',remove_bands=False)
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| self.assertEqual(data.shape[2],116)
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| self.assertEqual(data.shape[2],wavelengths.shape[0])
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| anno = get_anno('F_1')
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| if __name__ == '__main__':
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| unittest.main()
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|
