| import numpy as np |
| import torch |
| import torch.nn.functional as F |
| import networkx as nx |
| import matplotlib as mpl |
| import matplotlib.pyplot as plt |
| import matplotlib.colors as mcolors |
| from matplotlib.colors import LinearSegmentedColormap |
| from torch_geometric.data import Data |
| from torch_geometric.utils import to_networkx |
| from swomo.data.utils import get_cadis_float_cmap, get_cholecseg8k_float_cmap, get_cataract1k_float_cmap |
|
|
| def visualize_graph_list(data_list, |
| image_shape: tuple, |
| num_classes: int, |
| class_names, |
| node_size=200, |
| save_path: str = None, |
| palette=None): |
| """ |
| Visualizes a list of graphs with nodes colored by their class, keeping colors consistent for class IDs. |
| |
| Args: |
| data_list (list[Data]): A list of PyTorch Geometric Data objects representing the graphs to visualize. |
| image_shape (tuple): The shape of the original image (height, width). |
| num_classes (int): The number of different classes for coloring nodes. |
| class_names (list[str]): A list of class names corresponding to each class. |
| save_path (str, optional): Path to save the visualized graph. If None, the graph is not saved. |
| palette: The palette to use for coloring nodes based on their class. |
| """ |
|
|
| |
| if palette is None: |
| cmap = 'viridis' |
| else: |
| cmap = LinearSegmentedColormap.from_list('CustomCMAP', palette.numpy()[:num_classes - 1]) |
| mask_cmap = plt.cm.get_cmap(cmap, num_classes) |
| class_colors = mcolors.ListedColormap(mask_cmap(np.linspace(0, 1 + 1 / num_classes, num_classes))) |
|
|
| num_graphs = len(data_list) |
| fig, axes = plt.subplots(1, num_graphs, figsize=(5 * num_graphs, 4), frameon=False) |
|
|
| for idx, graph_data in enumerate(data_list): |
|
|
| ax = axes[idx] if num_graphs > 1 else axes |
|
|
| |
| if isinstance(graph_data, Data): |
| graph = to_networkx(graph_data.cpu(), to_undirected=True) |
| |
| for node, data in enumerate(graph_data.x.cpu()): |
| graph.nodes[node]['features'] = data |
| else: |
| graph = graph_data |
|
|
| try: |
| pos = {node: (data['features'][-5] * image_shape[1], (1 - data['features'][-4]) * image_shape[0]) for node, data in |
| graph.nodes(data=True)} |
| except KeyError: |
| pos = {node: (data['x'][-5] * image_shape[1], (1 - data['x'][-4]) * image_shape[0]) for |
| node, data in |
| graph.nodes(data=True)} |
|
|
| |
| for node, (x, y) in pos.items(): |
| features = graph.nodes[node]['features'] |
| class_id = np.argmax(features[:num_classes]) |
| class_name = class_names[class_id] |
| node_color = class_colors(class_id / num_classes) |
| nx.draw_networkx_nodes(graph, pos, nodelist=[node], node_size=node_size, node_color=[node_color], ax=ax) |
| ax.text(x, y, f'{class_name}', fontsize=20, ha='right', va='center') |
|
|
| |
| nx.draw_networkx_edges(graph, pos, edge_color="grey", alpha=0.3, width=1.8, ax=ax) |
|
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| |
| |
|
|
| |
| if save_path is not None: |
| plt.savefig(save_path, format='png') |
| plt.close() |
| plt.show() |
|
|
|
|
| if __name__ == "__main__": |
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| graph1 = torch.load("/path/to/dataset/Cataract-1K/scene_graph/case_2613/0000000040.pt") |
| graph2 = torch.load("/path/to/dataset/Cataract-1K/scene_graph/case_2613/0000000045.pt") |
| graph3 = torch.load("/path/to/dataset/Cataract-1K/scene_graph/case_2613/0000000050.pt") |
| class_names = ["Background", "Cornea", "Pupil", "Lens", "Slit Knife", "Gauge", "Capsulorhexis Cystotome", "Spatula", "Phacoemulsification Tip", "Irrigation-Aspiration", "Lens Injector", "Incision Knife", "Katena Forceps", "Capsulorhexis Forceps"] |
| palette = get_cataract1k_float_cmap() |
| num_classes = 14 |
| |
| save_path = "graph.png" |
| visualize_graph_list(data_list = [graph1, graph2, graph3], |
| image_shape = (128,160), |
| num_classes = num_classes, |
| class_names = class_names, |
| node_size=400, |
| save_path = save_path, |
| palette=palette) |
