feat: initial commit

.gitignore

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+.venv
+.DS_Store
+__pycache__
+src/*
+!src/app.py

.gitmodules

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+[submodule "ndlkotenocr-lite"]
+	path = ndlkotenocr-lite
+	url = https://github.com/ndl-lab/ndlkotenocr-lite.git

README.md

@@ -5,7 +5,7 @@ colorFrom: red
 colorTo: blue
 sdk: gradio
 sdk_version: 5.7.1
-app_file: app.py
+app_file: src/app.py
 pinned: false
 ---
 

install.sh

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+#!/bin/bash
+# サブモジュールを初期化して更新
+git submodule update --init --recursive
+cp -rp ndlkotenocr-lite/src .

ndlkotenocr-lite

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+Subproject commit 268e3534d87cff119af67e6451082d6ab1de1e5e

reuirements.txt

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+# git+https://github.com/ndl-lab/ndlkotenocr-lite
+dill
+flet
+lxml
+networkx
+numpy
+onnxruntime
+pillow
+ordered-set
+protobuf
+pyparsing
+PyYAML
+tqdm
+gradio

src/app.py

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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 = [
+    ['../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)