def test_OpenImage(self):
"""Testing ROI open functionality"""
tempdir = mkdtemp()
# crop the test image (size=1388x1040) to be bottom right tile of the test image
# tiled with 6 cols and 5 rows => ROI= 231x208+1155+832
orig_big = join(
pychrm_test_dir,
'lymphoma_eosin_channel_MCL_test_img_sj-05-3362-R2_001_E.tif')
orig_cropped = join(
pychrm_test_dir,
'lymphoma_eosin_channel_MCL_test_img_sj-05-3362-R2_001_E-t6x5_5_4-l.tiff'
)
test_cropped_path = join(tempdir, 'TEST_OpenImageROI.tif')
x = 1155
y = 832
w = 231
h = 208
from wndcharm import rect
bb = rect()
bb.x = x
bb.y = y
bb.w = w
bb.h = h
# setting mean = 0 is flag to not use mean in ImageMatrix::OpenImage()
downsample = 0
#bb = None
mean = 0.0
stddev = 0.0
try:
cropped_on_load_im = PyImageMatrix()
if 1 != cropped_on_load_im.OpenImage(orig_big, downsample, bb,
mean, stddev):
self.fail('Could not build an ImageMatrix from ' + orig_big)
cropped_on_load_im.SaveTiff(test_cropped_path)
orig_pixels = plt.imread(orig_cropped)
cropped_pixels = plt.imread(test_cropped_path)
assert_equal(orig_pixels, cropped_pixels)
finally:
rmtree(tempdir)
def test_OpenImage(self):
"""Testing ROI open functionality"""
tempdir = mkdtemp()
# crop the test image (size=1388x1040) to be bottom right tile of the test image
# tiled with 6 cols and 5 rows => ROI= 231x208+1155+832
orig_big = join(
pychrm_test_dir,
'lymphoma_eosin_channel_MCL_test_img_sj-05-3362-R2_001_E.tif')
orig_cropped = join(
pychrm_test_dir,
'lymphoma_eosin_channel_MCL_test_img_sj-05-3362-R2_001_E-t6x5_5_4-l.tiff'
)
test_cropped_path = join(tempdir, 'TEST_OpenImageROI.tif')
x = 1155
y = 832
w = 231
h = 208
from wndcharm import rect
bb = rect()
bb.x = x
bb.y = y
bb.w = w
bb.h = h
# setting mean = 0 is flag to not use mean in ImageMatrix::OpenImage()
downsample = 0
#bb = None
mean = 0.0
stddev = 0.0
try:
cropped_on_load_im = PyImageMatrix()
if 1 != cropped_on_load_im.OpenImage(orig_big, downsample, bb,
mean, stddev):
self.fail('Could not build an ImageMatrix from ' + orig_big)
cropped_on_load_im.SaveTiff(test_cropped_path)
orig_pixels = plt.imread(orig_cropped)
cropped_pixels = plt.imread(test_cropped_path)
assert_equal(orig_pixels, cropped_pixels)
finally:
rmtree(tempdir)
def GenerateFeatures( self, write_to_disk=True, quiet=True ):
"""@brief Loads precalculated features, or calculates new ones, based on which instance
attributes have been set, and what their values are.
write_to_disk (bool) - save features to text file which by convention has extension ".sig"
Returns self for convenience."""
# 0: What features does the user want?
# 1: are there features already calculated somewhere?
# 2: if so are they current/complete/expected/correct?
# 3: if not, what's left to calculate?
# 4: Calculate the rest
# 5: Reduce the features down to what the user asked for
if self.values is not None and len( self.values ) != 0:
return self
# Make sure Feature Vector version string is correct, etc:
#self.Update()
partial_load = False
try:
self.LoadSigFile( quiet=quiet )
# FIXME: Here's where you'd calculate a small subset of features
# and see if they match what was loaded from file. The file could be corrupted
# incomplete, or calculated with different options, e.g., -S1441
return self
except IOError:
# File doesn't exist
pass
except WrongFeatureSetVersionError:
# File has different feature version than desired
pass
except IncompleteFeatureSetError:
# LoadSigFile should create a FeatureComputationPlan
if not quiet:
print 'Loaded {0} features from disk for sample "{1}"'.format(
len( self.temp_names ), self.name )
partial_load = True
pass
# All hope is lost, calculate features.
# Use user-assigned feature computation plan, if provided:
if self.feature_computation_plan != None:
comp_plan = self.feature_computation_plan
# I Commented the following out because the computation plan may only reflect
# the subset of features that haven't been calculated yet:
# comp_plan.feature_vec_type seems to only contain the minor version
# i.e., number after the '.'. Assume major version is the latest.
#self.feature_set_version = '{0}.{1}'.format(
# feature_vector_major_version, comp_plan.feature_vec_type )
else:
major, minor = self.feature_set_version.split('.')
if minor == '0':
comp_plan = GenerateFeatureComputationPlan( self.feature_names )
elif minor == '1':
comp_plan = wndcharm.StdFeatureComputationPlans.getFeatureSet()
elif minor == '2':
comp_plan = wndcharm.StdFeatureComputationPlans.getFeatureSetLong()
elif minor == '3':
comp_plan = wndcharm.StdFeatureComputationPlans.getFeatureSetColor()
elif minor == '4':
comp_plan = wndcharm.StdFeatureComputationPlans.getFeatureSetColorLong()
else:
raise ValueError( "Not sure which features you want." )
self.feature_computation_plan = comp_plan
# Here are the ImageMatrix API calls:
# void normalize(double min, double max, long range, double mean, double stddev);
# int OpenImage(char *image_file_name, int downsample, rect *bounding_rect, double mean, double stddev);
# void Rotate (const ImageMatrix &matrix_IN, double angle);
if self.rot is not None:
raise NotImplementedError( "FIXME: Implement rotations." )
if self.x is not None and self.y is not None and self.w is not None and self.h is not None:
bb = wndcharm.rect()
bb.x = self.x
bb.y = self.y
bb.w = self.w
bb.h = self.h
else:
bb = None
if self.pixel_intensity_mean:
mean = self.pixel_intensity_mean
# stddev arg only used in ImageMatrix::OpenImage() if mean is set
stddev = self.pixel_intensity_stddev
else:
# setting mean = 0 is flag to not use mean in ImageMatrix::OpenImage()
mean = 0
stddev = 0
from .PyImageMatrix import PyImageMatrix
if isinstance( self.source_filepath, str ):
the_tiff = PyImageMatrix()
if 1 != the_tiff.OpenImage( self.source_filepath, self.downsample, bb, mean, stddev ):
raise ValueError( 'Could not build an ImageMatrix from {0}, check the path.'.\
format( self.source_filepath ) )
elif isinstance( self.source_filepath, wndcharm.ImageMatrix ):
if self.downsample or mean:
raise NotImplementedError( 'still need to implement modifying open pixel plane with downsample, mean or stddev' )
if not bb:
the_tiff = self.source_filepath
else:
# API calls for copying desired pixels into empty ImageMatrix instance:
# the_tiff is garbage collected on return
the_tiff = PyImageMatrix()
# bb only used when calling OpenImage
# ImageMatrix::submatrix() has a funky signature:
# void ImageMatrix::submatrix (const ImageMatrix &matrix, const unsigned int x1, const unsigned int y1, const unsigned int x2, const unsigned int y2);
# where x2 and y2 are INCLUSIVE, i.e., must subtract 1 from both
x1 = self.x
y1 = self.y
x2 = x1 + self.w - 1
y2 = y1 + self.h - 1
if 1 != the_tiff.submatrix( self.source_filepath, x1, y1, x2, y2 ):
raise ValueError( 'Could not crop bounding box ({0},{1}),({2},{3}) from image "{4}"'.\
format( x1, y1, x2, y2, self.source_filepath.source ) )
else:
raise ValueError("image parameter 'image_path_or_mat' is not a string or a wndcharm.ImageMatrix")
# pre-allocate space where the features will be stored (C++ std::vector<double>)
tmp_vec = wndcharm.DoubleVector( comp_plan.n_features )
# Get an executor for this plan and run it
plan_exec = wndcharm.FeatureComputationPlanExecutor( comp_plan )
plan_exec.run( the_tiff, tmp_vec, 0 )
# get the feature names from the plan
comp_names = [ comp_plan.getFeatureNameByIndex(i) for i in xrange( comp_plan.n_features ) ]
# convert std::vector<double> to native python list of floats
comp_vals = list( tmp_vec )
# Feature Reduction/Reorder step:
# Feature computation may give more features than are asked for by user, or out of order.
if self.feature_names:
if self.feature_names != comp_names:
if partial_load:
# If we're here, we've already loaded some but not all of the features
# we need. Take what we've already loaded and slap it at the end
# of what was calculated. Doesn't matter if some of the features are
# redundant, because the .index() method returns the first item it finds.
# FIXME: if there is overlap between what was loaded and what was
# calculated, check to see that they match.
comp_names.extend( self.temp_names )
comp_vals.extend( self.temp_values )
del self.temp_names
del self.temp_values
self.values = np.array( [ comp_vals[ comp_names.index( name ) ] for name in self.feature_names ] )
else:
self.feature_names = comp_names
self.values = comp_vals
if not quiet:
if len( comp_vals ) != len( self ):
print "CALCULATED {0} TOTAL FEATURES, REDUCED TO: {1}".format(
len( comp_vals ), self )
else:
print "CALCULATED: " + str( self )
# FIXME: maybe write to disk BEFORE feature reduce? Provide flag to let user decide?
if write_to_disk:
self.ToSigFile( quiet=quiet )
# Feature names need to be modified for their sampling options.
# Base case is that channel goes in the innermost parentheses, but really it's not
# just channel, but all sampling options.
# For now, let the FeatureSpace constructor code handle the modification of feature names
# for its own self.feature_names
return self
