| """Create compact score indexes and verify completed TruFor inference outputs.""" |
|
|
| import csv |
| import json |
| from pathlib import Path |
|
|
| import numpy as np |
|
|
|
|
| DATASETS = { |
| "IMD2020": (Path("IMD2020_output"), 2423), |
| "CASIA": (Path("CASIA_output"), 11996), |
| "CocoGlide": (Path("CocoGlide_output"), 1024), |
| } |
|
|
|
|
| def scalar(value): |
| return float(np.asarray(value).reshape(-1)[0]) |
|
|
|
|
| def pair(value): |
| values = np.asarray(value).reshape(-1).tolist() |
| return int(values[0]), int(values[1]) |
|
|
|
|
| def summarize(name, output_dir, expected): |
| files = sorted(output_dir.rglob("*.npz")) |
| if len(files) != expected: |
| raise RuntimeError(f"{name}: expected {expected} files, found {len(files)}") |
|
|
| rows = [] |
| scores = [] |
| scores_by_class = {} |
| resized = 0 |
| for path in files: |
| with np.load(path, allow_pickle=False) as data: |
| score = scalar(data["score"]) |
| height, width = pair(data["imgsize"]) |
| if "processed_imgsize" in data.files: |
| processed_height, processed_width = pair(data["processed_imgsize"]) |
| else: |
| processed_height, processed_width = height, width |
| was_resized = (height, width) != (processed_height, processed_width) |
| resized += int(was_resized) |
| scores.append(score) |
| relative_path = path.relative_to(output_dir).as_posix() |
| row = { |
| "path": relative_path, |
| "score": f"{score:.9f}", |
| "height": height, |
| "width": width, |
| "processed_height": processed_height, |
| "processed_width": processed_width, |
| "resized": str(was_resized).lower(), |
| } |
| if name == "IMD2020": |
| image_class = "authentic" if "_orig." in path.name else "tampered" |
| row = {"path": relative_path, "class": image_class, **row} |
| scores_by_class.setdefault(image_class, []).append(score) |
| rows.append(row) |
|
|
| csv_path = output_dir / "scores.csv" |
| with open(csv_path, "w", newline="") as stream: |
| writer = csv.DictWriter(stream, fieldnames=rows[0].keys()) |
| writer.writeheader() |
| writer.writerows(rows) |
|
|
| score_array = np.asarray(scores, dtype=np.float64) |
| summary = { |
| "dataset": name, |
| "files": len(files), |
| "resized_over_1024": resized, |
| "score_min": float(score_array.min()), |
| "score_max": float(score_array.max()), |
| "score_mean": float(score_array.mean()), |
| "score_median": float(np.median(score_array)), |
| } |
| if scores_by_class: |
| summary["classes"] = { |
| image_class: { |
| "files": len(class_scores), |
| "score_mean": float(np.mean(class_scores)), |
| "score_median": float(np.median(class_scores)), |
| } |
| for image_class, class_scores in sorted(scores_by_class.items()) |
| } |
| with open(output_dir / "summary.json", "w") as stream: |
| json.dump(summary, stream, indent=2) |
| stream.write("\n") |
| print(json.dumps(summary, sort_keys=True), flush=True) |
| return summary |
|
|
|
|
| def main(): |
| summaries = [summarize(name, path, expected) for name, (path, expected) in DATASETS.items()] |
| with open("inference_summary.json", "w") as stream: |
| json.dump(summaries, stream, indent=2) |
| stream.write("\n") |
| print("SUMMARY_COMPLETE", flush=True) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
