def test_GetPreprocessedFullPixelPlane( self ):
"""Exercise pixel intensity mean/std shift, and downsample"""
debug = False
if debug:
from wndcharm import package_versions
print package_versions
print '==========================='
img = wndchrm_test_dir + sep + "t1_s01_c05_ij.tif"
# px plane min max mean std
# np orig: 411.00 1465.00 912.19 129.31
samp = FeatureVector( source_filepath=img )
orig = samp.GetOriginalPixelPlane( cache=True )
orig_np = orig.as_ndarray()
target_mean = 1000
target_std = 100
if debug:
frmt = "{}:\t{:0.2f}\t{:0.2f}\t{:0.2f}\t{:0.2f}"
print "target mean: {}, target std: {}".format( target_mean, target_std )
print "px plane\tmin\tmax\tmean\tstd"
d = "np orig", orig_np.min(), orig_np.max(), orig_np.mean(), orig_np.std(),
print frmt.format( *d )
# Mean shift only
samp.pixel_intensity_mean = target_mean
mean_shifted = samp.GetPreprocessedFullPixelPlane( cache=False )
mean_shifted_np = mean_shifted.as_ndarray()
if debug:
d = ( "mean ", mean_shifted_np.min(), mean_shifted_np.max(), mean_shifted_np.mean(), mean_shifted_np.std() )
print frmt.format( *d )
self.assertEqual( target_mean, int( mean_shifted_np.mean() ) )
# Mean and std shift
samp.pixel_intensity_stdev = target_std
mean_std_shifted = samp.GetPreprocessedFullPixelPlane( cache=False )
mean_std_shifted_np = mean_std_shifted.as_ndarray()
if debug:
d = ( "mean&std", mean_std_shifted_np.min(), mean_std_shifted_np.max(),
mean_std_shifted_np.mean(), mean_std_shifted_np.std() )
print frmt.format( *d )
self.assertEqual( target_mean, int( round( mean_std_shifted_np.mean() ) ) )
self.assertEqual( target_std, int( round( mean_std_shifted_np.std() ) ) )
# downsample
samp.pixel_intensity_mean = None
samp.pixel_intensity_std = None
w = orig.width
h = orig.height
downsample = 25
samp.downsample = downsample
target_w = int( w * float( downsample ) / 100 )
target_h = int( h * float( downsample ) / 100 )
ds = samp.GetPreprocessedFullPixelPlane( cache=False )
ds_np = ds.as_ndarray()
new_h, new_w = ds_np.shape
if debug:
print "orig dimensions: {}x{}".format( w, h )
print "downsample {}%: {}x{}".format( downsample, new_w, new_h )
self.assertEqual( target_w, new_w )
self.assertEqual( target_h, new_h )
