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ndlkotenocr-lite
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import gradio as gr
import numpy as np
from PIL import Image
import os
from rtmdet import RTMDet
from parseq import PARSEQ
from yaml import safe_load



# Model Heading and Description
model_heading = "YOLOv11x くずし字認識サービス（一文字）"
description = """YOLOv11x くずし字認識サービス（一文字） Gradio demo for classification. Upload an image or click an example image to use."""

article = "<p style='text-align: center'>YOLOv11x くずし字認識サービス（一文字） is a classification model trained on the <a href=\"https://lab.hi.u-tokyo.ac.jp/datasets/kuzushiji\">東京大学史料編纂所くずし字データセット</a>.</p>"

image_path = [
    ['samples/default.jpg']
]

# Functions to load models
def get_detector(weights_path, classes_path, device='cpu'):
    assert os.path.isfile(weights_path), f"Weight file not found: {weights_path}"
    assert os.path.isfile(classes_path), f"Classes file not found: {classes_path}"
    return RTMDet(model_path=weights_path,
                  class_mapping_path=classes_path,
                  score_threshold=0.3,
                  conf_thresold=0.3,
                  iou_threshold=0.3,
                  device=device)

def get_recognizer(weights_path, classes_path, device='cpu'):
    assert os.path.isfile(weights_path), f"Weight file not found: {weights_path}"
    assert os.path.isfile(classes_path), f"Classes file not found: {classes_path}"
    
    with open(classes_path, encoding="utf-8") as f:
        charlist = list(safe_load(f)["model"]["charset_train"])
    return PARSEQ(model_path=weights_path, charlist=charlist, device=device)

# YOLO Inference Function
def YOLOv11x_img_inference(image_path: str):
    try:
        # Load the models
        detector = get_detector(
            weights_path="model/rtmdet-s-1280x1280.onnx",
            classes_path="config/ndl.yaml",
            device="cpu"
        )
        recognizer = get_recognizer(
            weights_path="model/parseq-ndl-32x384-tiny-10.onnx",
            classes_path="config/NDLmoji.yaml",
            device="cpu"
        )

        # Load image
        pil_image = Image.open(image_path).convert('RGB')
        npimg = np.array(pil_image)

        # Object detection
        detections = detector.detect(npimg)
        result_json = []

        # Text recognition
        for det in detections:
            xmin, ymin, xmax, ymax = det["box"]
            line_img = npimg[int(ymin):int(ymax), int(xmin):int(xmax)]
            text = recognizer.read(line_img)
            result_json.append({
                "boundingBox": [[xmin, ymin], [xmax, ymin], [xmax, ymax], [xmin, ymax]],
                "text": text,
                "confidence": det["confidence"]
            })

        # Return results in JSON format
        return result_json
    except Exception as e:
        return {"error": str(e)}

# Gradio Inputs and Outputs
inputs_image = gr.Image(type="filepath", label="Input Image")
outputs_image = gr.JSON(label="Output JSON")

# Gradio Interface
demo = gr.Interface(
    fn=YOLOv11x_img_inference,
    inputs=inputs_image,
    outputs=outputs_image,
    title=model_heading,
    description=description,
    examples=image_path,
    article=article,
    cache_examples=False
)

demo.launch(share=False, server_name="0.0.0.0")