| import os |
| import sys |
| import json |
| import pandas as pd |
| import cv2 |
|
|
| sys.path.append(os.path.join(os.path.dirname(__file__), '..')) |
| from predict import run_prediction |
|
|
| def debug_flowers(): |
| test_cases = [ |
| "manual_test/failure_cases/real/real 1.jpeg", |
| "manual_test/failure_cases/real/real 2.jpeg", |
| "manual_test/failure_cases/screen/fake 1.jpeg", |
| "manual_test/failure_cases/screen/fake 2.jpeg" |
| ] |
| |
| results = [] |
| |
| for case in test_cases: |
| path = os.path.join(os.path.dirname(__file__), '..', case) |
| if not os.path.exists(path): |
| print(f"Skipping {path} - not found.") |
| continue |
| |
| img = cv2.imread(path) |
| if img is None: |
| continue |
| |
| res = run_prediction(img, no_rules=False) |
| |
| row = { |
| 'image': os.path.basename(case), |
| 'true_label': 'real' if 'real' in case else 'screen', |
| 'raw_score': res['raw_model_score'], |
| 'rule_boost': res['rule_boost_total'], |
| 'final_score': res['final_score'], |
| 'predicted_label': res['predicted_label'], |
| 'boosts': json.dumps(res.get('individual_rule_boosts', {})), |
| } |
| |
| for k, v in res.get('features', {}).items(): |
| row[k] = v |
| |
| results.append(row) |
| |
| if not results: |
| print("No test cases found.") |
| return |
| |
| df = pd.DataFrame(results) |
| |
| os.makedirs(os.path.join(os.path.dirname(__file__), '..', 'reports'), exist_ok=True) |
| df.to_csv(os.path.join(os.path.dirname(__file__), '..', 'reports', 'flower_failure_debug.csv'), index=False) |
| |
| with open(os.path.join(os.path.dirname(__file__), '..', 'reports', 'flower_failure_summary.md'), 'w') as f: |
| f.write("# Flower Failure Debug Summary\n\n") |
| |
| summary_cols = ['image', 'true_label', 'raw_score', 'rule_boost', 'final_score', 'boosts'] |
| f.write("## Prediction Summary\n") |
| f.write(df[summary_cols].to_markdown(index=False)) |
| f.write("\n\n") |
| |
| f.write("## Feature Values\n") |
| feature_cols = [c for c in df.columns if c not in summary_cols and c != 'predicted_label'] |
| |
| feature_df = df[['image'] + feature_cols].set_index('image').T |
| f.write(feature_df.to_markdown()) |
| f.write("\n") |
|
|
| if __name__ == "__main__": |
| debug_flowers() |
|
|
