File size: 1,963 Bytes
23e73f9 | 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 | import os
import glob
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from PIL import Image
from umap import UMAP
sns.set_theme(style="whitegrid")
def load_ood_renders(method_dir):
ood_dir = os.path.join(method_dir, "renders_ood")
if not os.path.exists(ood_dir):
return None
images = sorted(glob.glob(os.path.join(ood_dir, "*.png")))
if not images:
return None
feature_vector = []
for img_path in images:
img = Image.open(img_path).convert("RGB").resize((64, 64))
feature_vector.append(np.array(img).flatten())
return np.concatenate(feature_vector)
def main():
outputs_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "outputs"))
plots_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "plots"))
os.makedirs(plots_dir, exist_ok=True)
features = []
labels = []
for folder in os.listdir(outputs_dir):
method_dir = os.path.join(outputs_dir, folder)
if not os.path.isdir(method_dir) or folder == "pathology_archive":
continue
vec = load_ood_renders(method_dir)
if vec is not None:
features.append(vec)
labels.append(folder.split('_')[0])
if not features:
return
X = np.stack(features)
reducer = UMAP(n_components=2, random_state=42)
embedding = reducer.fit_transform(X)
df = pd.DataFrame({
'UMAP_1': embedding[:, 0],
'UMAP_2': embedding[:, 1],
'Method': labels
})
plt.figure(figsize=(10, 8))
sns.scatterplot(data=df, x='UMAP_1', y='UMAP_2', hue='Method', s=200, palette='tab10', edgecolor='k')
plt.title("Functional Space Convergence (OOD Renders)")
plt.tight_layout()
plt.savefig(os.path.join(plots_dir, "fig3_functional_manifold.png"))
plt.close()
if __name__ == "__main__":
main()
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