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| import gradio as gr | |
| import torch | |
| from PIL import Image | |
| import json | |
| from ultralytics import YOLO | |
| # Images | |
| torch.hub.download_url_to_file( | |
| 'https://i.imgur.com/4GmZXID.jpg', '1.jpg') | |
| torch.hub.download_url_to_file( | |
| 'https://i.imgur.com/ktIGRvs.jpg', '2.jpg') | |
| torch.hub.download_url_to_file( | |
| 'https://i.imgur.com/fSEsXoE.jpg', '3.jpg') | |
| torch.hub.download_url_to_file( | |
| 'https://i.imgur.com/lsVJRzd.jpg', '4.jpg') | |
| torch.hub.download_url_to_file( | |
| 'https://i.imgur.com/1OFmJd1.jpg', '5.jpg') | |
| torch.hub.download_url_to_file( | |
| 'https://i.imgur.com/GhfAWMJ.jpg', '6.jpg') | |
| # Model | |
| # model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # force_reload=True to update | |
| model = torch.hub.load('ultralytics/yolov5', 'custom', path='plate.pt', source="local") | |
| def yolo(im): | |
| model.conf = 0.6 # NMS confidence threshold | |
| # g = (size / max(im.size)) # gain | |
| # im = im.resize((int(x * g) for x in im.size), Image.ANTIALIAS) # resize | |
| results = model(im, size=1280) # inference | |
| results.render() # updates results.imgs with boxes and labels | |
| df = results.pandas().xyxy[0].sort_values('xmin')[['name']].to_json(orient="records") # 可以把[['name']]刪除即可顯示全部 | |
| res = json.loads(df) | |
| return [Image.fromarray(results.ims[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 = "TW_plate_number" | |
| description = "TW_plate_number" | |
| # article = "<p style='text-align: center'>TW_plate_number <a href=\"http://codh.rois.ac.jp/char-shape/\">日本古典籍くずし字データセット</a>.</p>" | |
| examples = [['1.jpg'], ['2.jpg'], ['3.jpg'], ['4.jpg'], ['5.jpg'], ['6.jpg']] | |
| gr.Interface(yolo, inputs, outputs, title=title, description=description, examples=examples, theme="huggingface").launch(enable_queue=True) # cache_examples=True, |