File size: 2,368 Bytes
379573c | 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 | import json
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# 1. Load the data
with open('/media/vrt/shared/DATASETS/I-BADAS/results/aupro_isolated_metrics.json', 'r') as f:
data = json.load(f)
# 2. Flatten the data
rows = []
for model, cameras in data.items():
if model == "PadimSmall":
model = "Padim"
for camera, scenes in cameras.items():
for scene, classes in scenes.items():
for cls, metrics in classes.items():
rows.append({
'Model': model,
'Class': cls,
'AUPRO': metrics['Pixel_AUPRO']
})
df = pd.DataFrame(rows)
# 3. Aggregate: Mean performance per Model and Class
df_agg = df.groupby(['Model', 'Class'])['AUPRO'].mean().reset_index()
# 4. Calculate Mean per Class (across all models)
class_means = df_agg.groupby('Class')['AUPRO'].mean()
# 5. Visualization
sns.set_theme(style="whitegrid", font_scale=1.1)
plt.figure(figsize=(14, 5))
# Create barplot
chart = sns.barplot(
data=df_agg,
x='Class',
y='AUPRO',
hue='Model',
palette='viridis'
)
# 6. Add dotted line segments and text labels
classes = df_agg['Class'].unique()
for i, cls in enumerate(classes):
mean_val = class_means[cls]
# Draw horizontal line centered at i with width 0.8
plt.hlines(y=mean_val, xmin=i - 0.4, xmax=i + 0.4,
colors='red', linestyles=':', linewidth=3,
label='Class Mean' if i == 0 else "")
# Add red text below the line
# (Adjust the '0.04' offset if the text overlaps with bars)
plt.text(i + 0.3, mean_val + 0.018, f"{mean_val:.2f}",
color='red', ha='left', va='center',
fontsize=9, fontweight='bold')
# Aesthetics
plt.xticks(rotation=15, ha='right')
plt.xlabel('') # Removed X-axis title
plt.ylabel('Mean Pixel AUPRO Score')
# Place legend inside, top-left corner
plt.legend(
loc='upper left',
ncol=3,
frameon=True,
framealpha=0.2, # 0.0 (transparent) to 1.0 (opaque)
facecolor='white', # Background color of the box
edgecolor='black' # Optional: light gray border, or use 'none' for no border
)
# 7. Save as PDF
plt.tight_layout()
plt.savefig("/media/vrt/shared/DATASETS/I-BADAS/results/performance_aupro_only_new.pdf", bbox_inches='tight')
plt.show()
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