def setUpClass(self):
self.current_path = os.path.dirname(__file__)
self.data_path = os.path.join(self.current_path, '..', 'data')
download_test_data(self.data_path)
self.image_path = os.path.join(self.data_path, 'images')
self.image_name = '16B0001851_Block_Region_3.jpg'
self.out_path = os.path.join(self.data_path, 'tissue_mask_test')
if os.path.exists(self.out_path) and os.path.isdir(self.out_path):
shutil.rmtree(self.out_path)
os.makedirs(self.out_path)
def setUpClass(self):
self.current_path = os.path.dirname(__file__)
self.data_path = os.path.join(self.current_path, '..', 'data')
download_test_data(self.data_path)
self.graph_path = os.path.join(self.data_path, 'tissue_graphs')
self.graph_name = '283_dcis_4.bin'
self.out_path = os.path.join(self.data_path, 'graph_lrp_test')
if os.path.exists(self.out_path) and os.path.isdir(self.out_path):
shutil.rmtree(self.out_path)
os.makedirs(self.out_path)
def setUpClass(self):
self.current_path = os.path.dirname(__file__)
self.data_path = os.path.join(self.current_path, '..', 'data')
download_test_data(self.data_path)
self.model_fname = os.path.join(self.data_path, 'models',
'tg_model.pt')
self.graph_path = os.path.join(self.data_path, 'tissue_graphs')
self.checkpoint_path = os.path.join(self.data_path, 'checkpoints')
self.graph_name = '283_dcis_4.bin'
os.makedirs(self.checkpoint_path, exist_ok=True)
def setUpClass(self):
self.current_path = os.path.dirname(__file__)
self.data_path = os.path.join(self.current_path, '..', 'data')
download_test_data(self.data_path)
self.image_path = os.path.join(self.data_path, 'images')
self.image_name = '283_dcis_4.png'
self.out_path = os.path.join(self.data_path, 'nuclei_extraction_test')
if os.path.exists(self.out_path) and os.path.isdir(self.out_path):
shutil.rmtree(self.out_path)
os.makedirs(self.out_path)
def setUpClass(self):
self.current_path = os.path.dirname(__file__)
self.data_path = os.path.join(self.current_path, '..', 'data')
download_test_data(self.data_path)
self.image_path = os.path.join(self.data_path, 'images')
self.image_name = '283_dcis_4.png'
self.nuclei_map_path = os.path.join(self.data_path, 'nuclei_maps')
self.nuclei_map_name = '283_dcis_4.h5'
self.annotation_name = '283_dcis_4_annotation.png'
self.out_path = os.path.join(self.data_path, 'graph_builder_test')
if os.path.exists(self.out_path) and os.path.isdir(self.out_path):
shutil.rmtree(self.out_path)
os.makedirs(self.out_path)
def setUpClass(self):
self.current_path = os.path.dirname(__file__)
self.data_path = os.path.join(self.current_path, "..", "data")
download_test_data(self.data_path)
self.image_path = os.path.join(self.data_path, "images")
self.image_name = "283_dcis_4.png"
self.cell_graph_path = os.path.join(self.data_path, "cell_graphs")
self.graph_name = "283_dcis_4.bin"
self.tissue_graph_path = os.path.join(self.data_path, "tissue_graphs")
self.tissue_graph_name = "283_dcis_4.bin"
self.out_path = os.path.join(self.data_path, "visualization_test")
if os.path.exists(self.out_path) and os.path.isdir(self.out_path):
shutil.rmtree(self.out_path)
os.makedirs(self.out_path)
graph = graph[0]
graph = set_graph_on_cuda(graph) if IS_CUDA else graph
# b. load corresponding image
image_path = [
x for x in image_fnames if graph_name in x.replace('.png', '.bin')
][0]
_, image_name = os.path.split(image_path)
image = np.array(Image.open(image_path))
# c. run explainer
importance_scores, _ = explainer.process(graph)
# d. visualize and save the output
node_attrs = {"color": importance_scores}
canvas = visualizer.process(image, graph, node_attributes=node_attrs)
canvas.save(os.path.join('output', 'explainer', image_name))
if __name__ == "__main__":
# 1. download pre-computed images/cell_graph
download_test_data('output')
# 2. create output directories
os.makedirs(os.path.join('output', 'explainer'), exist_ok=True)
# 3. generate tissue graphs
explain_cell_graphs(cell_graph_path=os.path.join('output', 'cell_graphs'),
image_path=os.path.join('output', 'images'))