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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from ultralyticsplus import YOLO, render_result"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt to 'yolov8n.pt'...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 6.23M/6.23M [00:00<00:00, 25.2MB/s]\n"
]
},
{
"ename": "FileNotFoundError",
"evalue": "[Errno 2] No such file or directory: 'default.jpg'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[2], line 14\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mos\u001b[39;00m\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m image\u001b[38;5;241m.\u001b[39mstartswith(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttp\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m---> 14\u001b[0m \u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mremove\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 16\u001b[0m \u001b[38;5;66;03m# perform inference\u001b[39;00m\n\u001b[1;32m 17\u001b[0m results \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mpredict(image, conf\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.25\u001b[39m, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
"\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'default.jpg'"
]
}
],
"source": [
"# load model\n",
"model = YOLO('best.pt')\n",
"# model = YOLO(\"nakamura196/yolov8s-layout-detection\")\n",
"\n",
"# set image\n",
"image = \"https://dl.ndl.go.jp/api/iiif/1879314/R0000039/full/640,640/0/default.jpg\"\n",
"\n",
"filename = image.split(\"/\")[-1]\n",
"\n",
"import os\n",
"\n",
"if image.startswith(\"http\"):\n",
" if os.path.exists(filename):\n",
" os.remove(filename)\n",
"\n",
"# perform inference\n",
"results = model.predict(image, conf=0.25, verbose=False)\n",
"\n",
"# observe results\n",
"render = render_result(model=model, image=image, result=results[0])\n",
"render.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.11"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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