def test_NewFromDirectory( self ):
""""""
ref_sig_path = 'lymphoma_eosin_channel_MCL_test_img_sj-05-3362-R2_001_E-t6x5_5_4-l.sig'
ref_fv = FeatureVector.NewFromSigFile( pychrm_test_dir + sep + ref_sig_path )
from shutil import copy
tempdir = mkdtemp()
img_filename = "lymphoma_eosin_channel_MCL_test_img_sj-05-3362-R2_001_E-t6x5_5_4-l.tiff"
orig_img_filepath = pychrm_test_dir + sep + img_filename
copy( orig_img_filepath, tempdir )
try:
fs = FeatureSpace.NewFromDirectory( tempdir, quiet=False )
self.assertTrue( compare( fs.data_matrix[0], ref_fv.values ) )
#from numpy.testing import assert_allclose
#assert_allclose( ref_fv.values, fs.data_matrix[0], rtol=1e-05 )
finally:
rmtree( tempdir )
from os import mkdir
toptempdir = mkdtemp()
try:
class_names = []
for letter in 'CBA':
dirname = toptempdir + sep + letter
mkdir( dirname )
copy( orig_img_filepath, dirname )
fs = FeatureSpace.NewFromDirectory( toptempdir, quiet=False, )
self.assertEqual( fs.class_names, ['A', 'B', 'C' ] )
for row_of_features in fs.data_matrix:
self.assertTrue( compare( row_of_features, ref_fv.values ) )
finally:
rmtree( toptempdir )
def test_LargeFeatureSetGrayscale( self ):
"""Large feature set, grayscale image"""
reference_sample = FeatureVector.NewFromSigFile( self.sig_file_path,
image_path=self.test_tif_path )
target_sample = FeatureVector( source_filepath=self.test_tif_path,
long=True).GenerateFeatures( write_to_disk=False )
# This doesn't work since the ranges of features are so wide
# Tried using relative tolerance, but no dice:
# from numpy.testing import assert_allclose
# assert_allclose( reference_sample.values, target_sample.values, rtol=1e-3 )
# Remember we're reading these values in from strings. and the ranges are so wide
# you only have 6 sig figs. Better apples to apples comparison is to
# compare strings.
self.assertTrue( compare( target_sample.values, reference_sample.values ) )
def test_HeatMap_w_FeatureComputationPlan(self):
"""Classification results using SampleImageTiles method and FOF should be the same.
"""
# chris@NIA-LG-01778617 ~/src/wnd-charm/tests/pywndcharm_tests
# $ tiffinfo lymphoma_eosin_channel_MCL_test_img_sj-05-3362-R2_001_E.tif
# TIFF Directory at offset 0x18ea9c (1632924)
# Image Width: 1388 Image Length: 1040
# Bits/Sample: 8
# Compression Scheme: LZW
# Photometric Interpretation: min-is-black
# Samples/Pixel: 1
# Rows/Strip: 5
# Planar Configuration: single image plane
# 5x6 tiling scheme => tile dims 208 x 231.33 each
scan_x = 231
scan_y = 208
#num_features = 200
# Inflate the zipped test fit into a temp file
tempdir = mkdtemp()
try:
import zipfile
reference_sigs = pychrm_test_dir + sep + 'lymphoma_eosin_channel_MCL_test_img_sj-05-3362-R2_001_E_REFERENCE_SIGFILES.zip'
zf = zipfile.ZipFile(reference_sigs, mode='r')
zf.extractall(tempdir)
img_filename = "lymphoma_eosin_channel_MCL_test_img_sj-05-3362-R2_001_E.tif"
orig_img_filepath = pychrm_test_dir + sep + img_filename
from shutil import copy
# copy the tiff to the tempdir so the .sig files end up there too
copy(orig_img_filepath, tempdir)
input_image_path = tempdir + sep + img_filename
# create the tile image iterator
image_iter = SampleImageTiles(input_image_path, scan_x, scan_y,
True)
print "Number of samples = " + str(image_iter.samples)
base, ext = splitext(input_image_path)
# Just grab the first tile:
import pdb
pdb.set_trace()
tile_cropped_px_plane = image_iter.sample()
kwargs = {}
kwargs['name'] = input_image_path
kwargs['source_filepath'] = tile_cropped_px_plane
#kwargs[ 'feature_names' ] = fw.feature_names
#kwargs[ 'feature_computation_plan' ] = comp_plan
kwargs['long'] = True
kwargs['tile_num_cols'] = image_iter.tiles_x
kwargs['tile_num_rows'] = image_iter.tiles_y
kwargs['tiling_scheme'] = '{0}x{1}'.format(image_iter.tiles_x,
image_iter.tiles_y)
kwargs['tile_col_index'] = image_iter.current_col
kwargs['tile_row_index'] = image_iter.current_row
kwargs['sample_group_id'] = 0
top_left_tile_feats = FeatureVector(**kwargs).GenerateFeatures(
quiet=False, write_to_disk=False)
top_left_tile_reference_feats = FeatureVector.NewFromSigFile(
tempdir + sep + 'sj-05-3362-R2_001_E-t5x6_0_0-l.sig')
# Remember we're reading these values in from strings. and the ranges are so wide
# you only have 6 sig figs. Better apples to apples comparison is to
# compare strings.
self.assertTrue(
compare(top_left_tile_feats.values,
top_left_tile_reference_feats.values))
# Setting feature_names initiates the feature reduce from
# the larger set of features that comes back from computation
#kwargs[ 'feature_names' ] = fw.feature_names
# if these are set, then the code will try to take a ROI of a ROI:
#kwargs[ 'x' ] = image_iter.current_x
#kwargs[ 'y' ] = image_iter.current_y
#kwargs[ 'w' ] = image_iter.tile_width
#kwargs[ 'h' ] = image_iter.tile_height
finally:
rmtree(tempdir)
def test_FeatureComputationFromROI(self):
"""Specify bounding box to FeatureVector, calc features, then compare
with C++ implementation-calculated feats."""
# orig image lymphoma_eosin_channel_MCL_test_img_sj-05-3362-R2_001_E.tif
# has size=1388x1040
# WND-CHARM command line specifies via -tCxR param
# where C is columns and R is rows, ergo 5 rows, 6 cols = -t6x5
# tile dims => w=1388/6 cols = 231.33px wide, h=1040/5 rows = 208 px tall
ROI_width = 231
ROI_height = 208
# Inflate the zipped test fit into a temp file
tempdir = mkdtemp()
try:
import zipfile
reference_sigs = pychrm_test_dir + sep + 'lymphoma_eosin_channel_MCL_test_img_sj-05-3362-R2_001_E_t6x5_REFERENCE_SIGFILES.zip'
zf = zipfile.ZipFile(reference_sigs, mode='r')
zf.extractall(tempdir)
img_filename = "lymphoma_eosin_channel_MCL_test_img_sj-05-3362-R2_001_E.tif"
orig_img_filepath = pychrm_test_dir + sep + img_filename
from shutil import copy
# copy the tiff to the tempdir so the .sig files end up there too
copy(orig_img_filepath, tempdir)
input_image_path = tempdir + sep + img_filename
kwargs = {}
kwargs['name'] = img_filename
kwargs['source_filepath'] = input_image_path
#kwargs[ 'feature_names' ] = fw.feature_names
#kwargs[ 'feature_computation_plan' ] = comp_plan
kwargs['long'] = True
kwargs['x'] = 0
kwargs['y'] = 0
kwargs['w'] = ROI_width
kwargs['h'] = ROI_height
kwargs['sample_group_id'] = 0
top_left_tile_feats = FeatureVector(**kwargs).GenerateFeatures(
quiet=False, write_to_disk=False)
top_left_tile_reference_feats = FeatureVector.NewFromSigFile(
tempdir + sep +
'lymphoma_eosin_channel_MCL_test_img_sj-05-3362-R2_001_E-t6x5_0_0-l.sig'
)
# Remember we're reading these values in from strings. and the ranges are so wide
# you only have 6 sig figs. Better apples to apples comparison is to
# compare strings.
self.assertEqual(top_left_tile_feats.feature_names,
top_left_tile_reference_feats.feature_names)
self.assertTrue(
compare(top_left_tile_feats.values,
top_left_tile_reference_feats.values))
kwargs['x'] = 1155
kwargs['y'] = 832
bot_right_tile_feats = FeatureVector(**kwargs).GenerateFeatures(
quiet=False, write_to_disk=False)
bot_right_tile_reference_feats = FeatureVector.NewFromSigFile(
tempdir + sep +
'lymphoma_eosin_channel_MCL_test_img_sj-05-3362-R2_001_E-t6x5_5_4-l.sig'
)
self.assertEqual(bot_right_tile_feats.feature_names,
bot_right_tile_reference_feats.feature_names)
self.assertTrue(
compare(bot_right_tile_feats.values,
bot_right_tile_reference_feats.values))
finally:
rmtree(tempdir)
def test_HeatMap_w_FeatureComputationPlan( self ):
"""Classification results using SampleImageTiles method and FOF
should be the same."""
# chris@NIA-LG-01778617 ~/src/wnd-charm/tests/pywndcharm_tests
# $ tiffinfo lymphoma_eosin_channel_MCL_test_img_sj-05-3362-R2_001_E.tif
# TIFF Directory at offset 0x18ea9c (1632924)
# Image Width: 1388 Image Length: 1040
# Bits/Sample: 8
# Compression Scheme: LZW
# Photometric Interpretation: min-is-black
# Samples/Pixel: 1
# Rows/Strip: 5
# Planar Configuration: single image plane
# WND-CHARM command line specifies via -tCxR param
# where C is columns and R is rows, ergo 5 rows, 6 cols = -t6x5
# tile dims => w=1388/6 cols = 231.33px wide, h=1040/5 rows = 208 px tall
#scan_x = 231
#scan_y = 208
#num_features = 200
# Inflate the zipped test fit into a temp file
sourcedir = mkdtemp()
targetdir = mkdtemp()
try:
import zipfile
reference_sigs = pychrm_test_dir + sep + 'lymphoma_eosin_channel_MCL_test_img_sj-05-3362-R2_001_E_t6x5_REFERENCE_SIGFILES.zip'
zf = zipfile.ZipFile( reference_sigs, mode='r' )
zf.extractall( targetdir )
img_filename = "lymphoma_eosin_channel_MCL_test_img_sj-05-3362-R2_001_E.tif"
orig_img_filepath = pychrm_test_dir + sep + img_filename
from shutil import copy
# copy the tiff to the tempdir so the .sig files end up there too
copy( orig_img_filepath, sourcedir )
input_image_path = sourcedir + sep + img_filename
# Create sliding window that emulates 6x5 tiling:
kwargs = {}
kwargs[ 'source_filepath' ] = input_image_path
kwargs[ 'tile_num_cols' ] = 6
kwargs[ 'tile_num_rows' ] = 5
kwargs[ 'long' ] = True
window = SlidingWindow( **kwargs )
print "Number of samples = " + str( window.num_positions )
base, ext = splitext( input_image_path )
ref_file = 'lymphoma_eosin_channel_MCL_test_img_sj-05-3362-R2_001_E-t6x5_{}_{}-l.sig'
# top left:
for test_feats in window.sample():
test_feats.GenerateFeatures( quiet=False, write_to_disk=False, cache=True )
reference_feats = FeatureVector.NewFromSigFile( targetdir + sep + ref_file.format(0,0) )
self.assertTrue( compare( test_feats.values, reference_feats.values ) )
break
# below top left:
#window.GenerateFeatures( quiet=False, write_to_disk=False, cache=True )
#reference_feats = FeatureVector.NewFromSigFile( targetdir + sep + ref_file.format(0,1) )
#self.assertTrue( compare( window.values, reference_feats.values ) )
# Setting feature_names initiates the feature reduce from
# the larger set of features that comes back from computation
#kwargs[ 'feature_names' ] = fw.feature_names
finally:
rmtree( sourcedir )
rmtree( targetdir )