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| import gradio as gr | |
| import torch | |
| from PIL import Image | |
| import json | |
| # Images | |
| torch.hub.download_url_to_file( | |
| 'https://iiif.dl.itc.u-tokyo.ac.jp/iiif/genji/TIFF/A00_6587/01/01_0004.tif/full/1024,/0/default.jpg', '『源氏物語』(東京大学総合図書館所蔵).jpg') | |
| torch.hub.download_url_to_file( | |
| 'https://rmda.kulib.kyoto-u.ac.jp/iiif/RB00007030/01/RB00007030_00003_0.ptif/full/1024,/0/default.jpg', '『源氏物語』(京都大学所蔵).jpg') | |
| torch.hub.download_url_to_file( | |
| 'https://kotenseki.nijl.ac.jp/api/iiif/100312034/v4/HRSM/HRSM-00396/HRSM-00396-00012.tif/full/1024,/0/default.jpg', '『平家物語』(国文学研究資料館提供).jpg') | |
| # Model | |
| # model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # force_reload=True to update | |
| model = torch.hub.load('ultralytics/yolov5', 'custom', | |
| path='best.pt', source="local") | |
| def yolo(im, size=1024): | |
| g = (size / max(im.size)) # gain | |
| im = im.resize((int(x * g) for x in im.size), resample=Image.Resampling.LANCZOS) # resize | |
| results = model(im) # inference | |
| results.render() # updates results.imgs with boxes and labels | |
| df = results.pandas().xyxy[0].to_json(orient="records") | |
| res = json.loads(df) | |
| return [ | |
| Image.fromarray(results.imgs[0]), | |
| res | |
| ] | |
| inputs = gr.inputs.Image(type='pil', label="Original Image") | |
| outputs = [ | |
| gr.outputs.Image(type="pil", label="Output Image"), | |
| gr.outputs.JSON(label="Output JSON") | |
| ] | |
| title = "YOLOv5 NDL-DocL Datasets" | |
| description = "YOLOv5 NDL-DocL Datasets Gradio demo for object detection. Upload an image or click an example image to use." | |
| article = "<p style='text-align: center'>YOLOv5 NDL-DocL Datasets is an object detection model trained on the <a href=\"https://github.com/ndl-lab/layout-dataset\">NDL-DocL Datasets</a>.</p>" | |
| examples = [['『源氏物語』(東京大学総合図書館所蔵).jpg'], ['『源氏物語』(京都大学所蔵).jpg'], ['『平家物語』(国文学研究資料館提供).jpg']] | |
| gr.Interface(yolo, inputs, outputs, title=title, description=description, article=article, | |
| examples=examples, theme="huggingface").launch(enable_queue=True) # cache_examples=True, | |