File size: 3,451 Bytes
ae2060e c04f846 ae2060e c04f846 ae2060e c04f846 ae2060e c04f846 ae2060e c04f846 ae2060e | 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 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 | """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()
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