File size: 2,782 Bytes
1558db5 | 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 | """Evaluate semantic predictions and render a compact comparison image."""
import json
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import yaml
ROOT = Path(__file__).resolve().parents[1]
def main():
with (ROOT / "conf" / "config.yaml").open(encoding="utf-8") as handle:
config = yaml.safe_load(handle)
input_dir = ROOT / config["paths"]["inference_dir"]
required = [input_dir / "predictions.npy", input_dir / "targets.npy"]
missing = [str(path.relative_to(ROOT)) for path in required if not path.exists()]
if missing:
raise FileNotFoundError(
f"Missing inference outputs: {missing}. Run `python scripts/inference.py` first."
)
predictions = np.load(input_dir / "predictions.npy")
targets = np.load(input_dir / "targets.npy")
classes = config["data"]["num_classes"]
intersections = np.zeros(classes, dtype=np.float64)
unions = np.zeros(classes, dtype=np.float64)
for class_id in range(classes):
predicted = predictions == class_id
expected = targets == class_id
intersections[class_id] = np.logical_and(predicted, expected).sum()
unions[class_id] = np.logical_or(predicted, expected).sum()
per_class_iou = np.divide(intersections, unions, out=np.zeros_like(intersections), where=unions > 0)
metadata_path = input_dir / "metadata.npz"
metadata = np.load(metadata_path) if metadata_path.exists() else None
metrics = {
"pixel_accuracy": float((predictions == targets).mean()),
"mean_iou": float(per_class_iou.mean()),
"per_class_iou": per_class_iou.tolist(),
"samples": int(len(predictions)),
"data_source": str(metadata["data_source"]) if metadata is not None else "unknown",
"protocol": str(metadata["protocol"]) if metadata is not None else "unknown",
}
output_dir = ROOT / config["paths"]["evaluation_dir"]
output_dir.mkdir(parents=True, exist_ok=True)
with (output_dir / "metrics.json").open("w", encoding="utf-8") as handle:
json.dump(metrics, handle, indent=2)
figure, axes = plt.subplots(2, 2, figsize=(7, 7))
for index, axis in enumerate(axes.flat):
sample = index // 2
image = targets[sample] if index % 2 == 0 else predictions[sample]
axis.imshow(image, vmin=0, vmax=classes - 1, cmap="terrain")
axis.set_title(("Target" if index % 2 == 0 else "Prediction") + f" {sample}")
axis.axis("off")
figure.tight_layout()
figure.savefig(output_dir / "comparison.png", dpi=120)
plt.close(figure)
print(f"pixel_accuracy={metrics['pixel_accuracy']:.6f} mean_iou={metrics['mean_iou']:.6f}")
print(f"evaluation={output_dir.relative_to(ROOT)}")
if __name__ == "__main__":
main()
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