| """Minimal inference example for the Palworld terminal pal classifier. | |
| License: CC BY-NC 4.0. Palworld assets © Pocketpair, Inc. | |
| """ | |
| import json | |
| import numpy as np | |
| import onnxruntime as ort | |
| from PIL import Image | |
| MODEL = "pal_classifier.onnx" | |
| LABELS = "labels.json" | |
| ZH = "zh_names.json" | |
| def classify(cell_image_path, model=MODEL): | |
| sess = ort.InferenceSession(model, providers=["CPUExecutionProvider"]) | |
| labels = json.load(open(LABELS, encoding="utf-8"))["labels"] | |
| zh = json.load(open(ZH, encoding="utf-8")) | |
| img = Image.open(cell_image_path).convert("RGB").resize((96, 96), Image.LANCZOS) | |
| x = np.asarray(img, np.float32) / 255.0 | |
| x = ((x - 0.5) / 0.5).transpose(2, 0, 1)[None] | |
| logits = sess.run(None, {sess.get_inputs()[0].name: x})[0][0] | |
| idx = int(logits.argmax()) | |
| p = np.exp(logits - logits.max()) | |
| p /= p.sum() | |
| return labels[idx], float(p[idx]), zh.get(labels[idx], "") | |
| if __name__ == "__main__": | |
| import sys | |
| path = sys.argv[1] if len(sys.argv) > 1 else "cell.png" | |
| label, conf, name = classify(path) | |
| print(f"{label} ({name}) conf={conf:.3f}") | |