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"""
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)
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