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def main(args):
args = utils.splitArgs(args)
args.macenko.I_0 = numpy.array(args.macenko.I_0)
utils.create_dask_client(args.dask)
sample = utils.sample_pixels(args.sample)
stain_matrix = rgb_separate_stains_macenko_pca(sample.T, **vars(args.macenko))
with open(args.returnParameterFile, 'w') as f:
for i, stain in enumerate(stain_matrix.T):
f.write('stainColor_{} = {}\n'.format(i+1, ','.join(map(str, stain))))
def main(args):
print('\n>> CLI Parameters ...\n')
print(args)
#
# Initiate Dask client
#
print('\n>> Creating Dask client ...\n')
c = cli_utils.create_dask_client(args)
print(c)
#
# read model file
#
print('\n>> Loading classification model ...\n')
clf_model = joblib.load(args.inputModelFile)
#
# read feature file
#
print('\n>> Loading nuclei feature file ...\n')
ddf = read_feature_file(args)
def main(args):
utils.create_dask_client(args)
ts = large_image.getTileSource(args.inputImageFile)
make_label_image = getattr(args, 'outputLabelImage', None) is not None
region = utils.get_region_dict(
args.region,
*(args.maxRegionSize, ts) if make_label_image else ()
).get('region')
ppc_params = ppc.Parameters(
**{k: getattr(args, k) for k in ppc.Parameters._fields}
)
results = ppc.count_slide(
args.inputImageFile, ppc_params, region,
args.tile_grouping, make_label_image,
)
if make_label_image:
stats, label_image = results
# Colorize label image. Colors from the "coolwarm" color map
print('\n>> Reading VGG pre-trained model ... \n')
model = applications.VGG16(include_top=True, weights='imagenet')
model = Model(inputs=model.input, outputs=model.get_layer('fc1').output)
print('\n>> Load PCA fitted model ... \n')
pca = joblib.load(args.inputPCAModel)
#
# Initiate Dask client
#
print('\n>> Creating Dask client ...\n')
start_time = time.time()
c = cli_utils.create_dask_client(args)
print(c)
dask_setup_time = time.time() - start_time
print('Dask setup time = {}'.format(
cli_utils.disp_time_hms(dask_setup_time)))
slide_superpixels = []
slide_x_centroids = []
slide_y_centroids = []
slide_names = []
slide_spixel_index = []
first_spixel_index = np.zeros((n_slides, args.columnSize), dtype=np.int32)
slide_wsi_mean = np.zeros((n_slides, args.channelSize), dtype=np.float32)
slide_wsi_std = np.zeros((n_slides, args.channelSize), dtype=np.float32)