""" Copyright 2021, Dana-Farber Cancer Institute and Weill Cornell Medicine License: GNU GPL 2.0 """ import numpy as np import pytest import torch from skimage.draw import ellipse from skimage.measure import label, regionprops from pathml.graph import KNNGraphBuilder, RAGGraphBuilder from pathml.graph.preprocessing import MSTGraphBuilder def make_fake_instance_maps(num, image_size, ellipse_height, ellipse_width): img = np.zeros(image_size) # Draw n ellipses for i in range(num): # Random center for each ellipse center_x = np.random.randint(ellipse_width, image_size[1] - ellipse_width) center_y = np.random.randint(ellipse_height, image_size[0] - ellipse_height) # Coordinates for the ellipse rr, cc = ellipse( center_y, center_x, ellipse_height, ellipse_width, shape=image_size ) # Draw the ellipse img[rr, cc] = 1 label_img = label(img.astype(int)) return label_img @pytest.mark.parametrize("k", [1, 10, 50]) @pytest.mark.parametrize("thresh", [0, 10, 200]) @pytest.mark.parametrize("add_loc_feats", [True, False]) @pytest.mark.parametrize("add_node_labels", [True, False]) @pytest.mark.parametrize("return_networkx", [True, False]) @pytest.mark.parametrize("use_centroids", [True, False]) def test_knn_graph_building( k, thresh, add_loc_feats, add_node_labels, return_networkx, use_centroids ): if not use_centroids: image_size = (1024, 2048) instance_map = make_fake_instance_maps( num=100, image_size=image_size, ellipse_height=10, ellipse_width=8 ) regions = regionprops(instance_map) features = torch.randn(len(regions), 512) num_nodes = len(regions) if add_node_labels: annotation = torch.randn(len(regions), 4) else: annotation = None graph_builder = KNNGraphBuilder( k=k, thresh=thresh, add_loc_feats=add_loc_feats, return_networkx=return_networkx, ) graph = graph_builder.process( instance_map, features, annotation=annotation, target=1 ) elif use_centroids: centroids = torch.randn(100, 2) features = torch.randn(100, 512) if add_node_labels: annotation = torch.randn(100, 4) else: annotation = None num_nodes = 100 graph_builder = KNNGraphBuilder( k=k, thresh=thresh, add_loc_feats=add_loc_feats, return_networkx=return_networkx, ) graph = graph_builder.process_with_centroids( centroids, features, annotation=annotation, image_size=(1000, 1000), target=1, ) if return_networkx: assert len(graph.nodes) == num_nodes if add_loc_feats: assert len(graph.nodes[0]["node_features"]) == 514 else: assert len(graph.nodes[0]["node_features"]) == 512 else: assert graph.node_centroids.shape == (num_nodes, 2) assert graph.edge_index.shape[0] == 2 if add_loc_feats: assert graph.node_features.shape == (num_nodes, 512 + 2) else: assert graph.node_features.shape == (num_nodes, 512) if add_node_labels: assert graph.node_labels.shape == (num_nodes, 4) @pytest.mark.parametrize("kernel_size", [1, 3, 10]) @pytest.mark.parametrize("hops", [1, 2, 5]) @pytest.mark.parametrize("add_loc_feats", [True, False]) @pytest.mark.parametrize("add_node_labels", [True, False]) @pytest.mark.parametrize("return_networkx", [True, False]) def test_rag_graph_building( kernel_size, hops, add_loc_feats, add_node_labels, return_networkx ): image_size = (1024, 2048) instance_map = make_fake_instance_maps( num=100, image_size=image_size, ellipse_height=10, ellipse_width=8 ) regions = regionprops(instance_map) num_nodes = len(regions) features = torch.randn(len(regions), 512) if add_node_labels: annotation = torch.randn(len(regions), 4) else: annotation = None graph_builder = RAGGraphBuilder( kernel_size=kernel_size, hops=hops, add_loc_feats=add_loc_feats, return_networkx=return_networkx, ) graph = graph_builder.process( instance_map, features, annotation=annotation, target=1 ) if return_networkx: assert len(graph.nodes) == num_nodes if add_loc_feats: assert len(graph.nodes[0]["node_features"]) == 514 else: assert len(graph.nodes[0]["node_features"]) == 512 else: assert graph.node_centroids.shape == (num_nodes, 2) assert graph.edge_index.shape[0] == 2 if add_loc_feats: assert graph.node_features.shape == (num_nodes, 512 + 2) else: assert graph.node_features.shape == (num_nodes, 512) if add_node_labels: assert graph.node_labels.shape == (num_nodes, 4) @pytest.mark.parametrize("k", [1, 10, 50]) @pytest.mark.parametrize("thresh", [0, 10, 200]) @pytest.mark.parametrize("add_loc_feats", [True, False]) @pytest.mark.parametrize("add_node_labels", [True, False]) @pytest.mark.parametrize("return_networkx", [True, False]) @pytest.mark.parametrize("use_centroids", [True, False]) def test_mst_graph_building( k, thresh, add_loc_feats, add_node_labels, return_networkx, use_centroids ): if not use_centroids: image_size = (1024, 2048) instance_map = make_fake_instance_maps( num=100, image_size=image_size, ellipse_height=10, ellipse_width=8 ) regions = regionprops(instance_map) features = torch.randn(len(regions), 512) num_nodes = len(regions) if add_node_labels: annotation = torch.randn(len(regions), 4) else: annotation = None graph_builder = MSTGraphBuilder( k=k, thresh=thresh, add_loc_feats=add_loc_feats, return_networkx=return_networkx, ) graph = graph_builder.process( instance_map, features, annotation=annotation, target=1 ) elif use_centroids: centroids = torch.randn(100, 2) features = torch.randn(100, 512) if add_node_labels: annotation = torch.randn(100, 4) else: annotation = None num_nodes = 100 graph_builder = KNNGraphBuilder( k=k, thresh=thresh, add_loc_feats=add_loc_feats, return_networkx=return_networkx, ) graph = graph_builder.process_with_centroids( centroids, features, annotation=annotation, image_size=(1000, 1000), target=1, ) if return_networkx: assert len(graph.nodes) == num_nodes if add_loc_feats: assert len(graph.nodes[0]["node_features"]) == 514 else: assert len(graph.nodes[0]["node_features"]) == 512 else: assert graph.node_centroids.shape == (num_nodes, 2) assert graph.edge_index.shape[0] == 2 if add_loc_feats: assert graph.node_features.shape == (num_nodes, 512 + 2) else: assert graph.node_features.shape == (num_nodes, 512) if add_node_labels: assert graph.node_labels.shape == (num_nodes, 4)