feat: initial commit

.gitattributes

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.gitignore

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+.DS_Store
+yolov5s.pt
+__pycache__
+.venv
+flagged

app.py

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+import gradio as gr
+from PIL import Image, ImageDraw
+import yolov5
+import json
+
+model = yolov5.load("./best.pt")
+
+def yolo(im):
+
+    results = model(im)  # inference
+
+    df = results.pandas().xyxy[0].to_json(orient="records")
+
+    res = json.loads(df)
+
+    draw = ImageDraw.Draw(im)
+
+    for bb in res:
+        xmin = bb['xmin']
+        ymin = bb['ymin']
+        xmax = bb['xmax']
+        ymax = bb['ymax']
+        draw.rectangle([xmin, ymin, xmax, ymax], outline="red", width=3)
+
+    return [
+        res,
+        im,
+    ]
+
+
+inputs = gr.Image(type='pil', label="Original Image")
+outputs = [
+    gr.JSON(label="Output JSON"),
+    gr.Image(type='pil', label="Output Image with Boxes"),
+]
+
+title = "YOLOv5 Face"
+description = "YOLOv5 Face Gradio demo for object detection. Upload an image or click an example image to use."
+article = "<p style='text-align: center'>YOLOv5 Face is an object detection model trained on the <a href=\"https://doi.org/10.20676/00000353\">顔コレデータセット</a>.</p>"
+
+examples = [
+    ['『源氏百人一首』(大阪公立大学中百舌鳥図書館 国文学研究資料館).jpg'],
+    ['『源氏物語』(国文学研究資料館).jpg'],
+    ['『百鬼夜行図』（東京大学）.jpg']
+]
+demo = gr.Interface(yolo, inputs, outputs, title=title, description=description, article=article, examples=examples)
+
+demo.css = """
+.json-holder {
+    height: 300px;
+    overflow: auto;
+}
+"""
+
+demo.launch(share=False)

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9eda13a77a3cd6b939ab060a61c62748dfd6b57000df843d053cd669d6f432a8
+size 691395070

requirements.txt

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+yolov5
+gradio