File size: 2,462 Bytes
7c34b66 | 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 | 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']
# Transpose for easier reading
feature_df = df[['image'] + feature_cols].set_index('image').T
f.write(feature_df.to_markdown())
f.write("\n")
if __name__ == "__main__":
debug_flowers()
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