File size: 5,944 Bytes
2508db9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 | 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.
"""
# Create a color map for the nodes based on their class. Default to 'viridis' if no cmap provided.
if palette is None:
cmap = 'viridis'
else:
cmap = LinearSegmentedColormap.from_list('CustomCMAP', palette.numpy()[:num_classes - 1]) # w/o ignore
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
# Convert to NetworkX graph if necessary
if isinstance(graph_data, Data):
graph = to_networkx(graph_data.cpu(), to_undirected=True)
# Manually transfer node features from PyG Data to NetworkX
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)}
# Add labels to the nodes (e.g., class name and node ID)
for node, (x, y) in pos.items():
features = graph.nodes[node]['features']
class_id = np.argmax(features[:num_classes]) # Extract class ID from one-hot vector # w/o ignore
class_name = class_names[class_id] # Get class name based on class ID
node_color = class_colors(class_id / num_classes) # Map class ID to color
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')
# Draw edges
nx.draw_networkx_edges(graph, pos, edge_color="grey", alpha=0.3, width=1.8, ax=ax)
# ax.set_aspect(1)
# ax.set_title(f"Graph {idx + 1}")
# plt.tight_layout()
if save_path is not None:
plt.savefig(save_path, format='png')
plt.close()
plt.show()
if __name__ == "__main__":
# graph1 = torch.load("/path/to/dataset/Cholec-80/scene_graph/video01/0000003904.pt")
# graph2 = torch.load("/path/to/dataset/Cholec-80/scene_graph/video01/0000003906.pt")
# graph3 = torch.load("/path/to/dataset/Cholec-80/scene_graph/video01/0000003907.pt")
# class_names = ["Black Background", "Abdominal Wall", "Liver", "Gastrointestinal Tract", "Fat", "Grasper", "Connective Tissue", "Blood", "Cystic Dust", "L-hook Electrocautery", "Gallblader", "Hepatic Vein", "Liver Ligament"]
# palette = get_cholecseg8k_float_cmap()
# num_classes = 13
# graph1 = torch.load("/path/to/dataset/Cataracts-50/scene_graph/train47/0000000696.pt")
# graph2 = torch.load("/path/to/dataset/Cataracts-50/scene_graph/train47/0000000704.pt")
# graph3 = torch.load("/path/to/dataset/Cataracts-50/scene_graph/train47/0000000711.pt")
# class_names = ["Pupil", "Surg. Tape", "Hand", "Eye Retr.", "Iris", "Skin", "Cornea", "Cannula", "Cap. Cystotome", "Tissue Forceps", "Primary Knife", "Ph. Handpiece", "Lens Injector", "I/A Handpiece", "Secondary Knife", "Micromanipulator", "Cap. Forceps", "Ignore"]
# palette = get_cadis_float_cmap()
# num_classes = 18
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) |