,\n",
" 'http://127.0.0.1:7860/',\n",
" 'https://21879.gradio.app')"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import gradio as gr\n",
"import torch\n",
"from PIL import Image\n",
"\n",
"# Images\n",
"#torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg', 'zidane.jpg')\n",
"#torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/bus.jpg', 'bus.jpg')\n",
"\n",
"# Model\n",
"#model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # force_reload=True to update\n",
"\n",
"model = torch.hub.load(cwd+'/yolov5', 'custom', path=cwd+'/saved_model/yolov5s.pt', source='local') # local model\n",
"\n",
"\n",
"\n",
"def yolo(im, size=640):\n",
" g = (size / max(im.size)) # gain\n",
" im = im.resize((int(x * g) for x in im.size), Image.ANTIALIAS) # resize\n",
"\n",
" results = model(im) # inference\n",
" results.render() # updates results.imgs with boxes and labels\n",
" return Image.fromarray(results.imgs[0])\n",
"\n",
"\n",
"inputs = gr.inputs.Image(type='pil', label=\"Original Image\")\n",
"outputs = gr.outputs.Image(type=\"pil\", label=\"Output Image\")\n",
"\n",
"title = \"YOLOv5\"\n",
"description = \"YOLOv5 Gradio demo for object detection. Upload an image or click an example image to use.\"\n",
"article = \"YOLOv5 is a family of compound-scaled object detection models trained on the COCO dataset, and includes \" \\\n",
" \"simple functionality for Test Time Augmentation (TTA), model ensembling, hyperparameter evolution, \" \\\n",
" \"and export to ONNX, CoreML and TFLite. Source code |\" \\\n",
" \"iOS App | PyTorch Hub
\"\n",
"\n",
"examples = [[cwd+'/datasets/zidane.jpg'], [cwd+'/datasets/bus.jpg']]\n",
"gr.Interface(yolo, inputs, outputs, title=title, description=description, article=article, examples=examples, analytics_enabled=False).launch(\n",
" debug=True)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "8kaOPNyz2cEr",
"outputId": "4012dc70-c529-47cb-e8c0-ef053f268629"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"\u001b[31m\u001b[1mrequirements:\u001b[0m PyYAML>=5.3.1 not found and is required by YOLOv5, attempting auto-update...\n",
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Requirement already satisfied: PyYAML>=5.3.1 in /usr/local/lib/python3.7/dist-packages (6.0)\n",
"\n",
"\u001b[31m\u001b[1mrequirements:\u001b[0m 1 package updated per /content/drive/MyDrive/Colab Notebooks/Model Representation/yolov5/requirements.txt\n",
"\u001b[31m\u001b[1mrequirements:\u001b[0m ⚠️ \u001b[1mRestart runtime or rerun command for updates to take effect\u001b[0m\n",
"\n",
"YOLOv5 🚀 2022-6-24 Python-3.7.13 torch-1.12.0+cu113 CPU\n",
"\n",
"Fusing layers... \n",
"Model summary: 213 layers, 7012822 parameters, 0 gradients\n",
"Adding AutoShape... \n",
"/usr/local/lib/python3.7/dist-packages/gradio/inputs.py:257: UserWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
" \"Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\",\n",
"/usr/local/lib/python3.7/dist-packages/gradio/deprecation.py:40: UserWarning: `optional` parameter is deprecated, and it has no effect\n",
" warnings.warn(value)\n",
"/usr/local/lib/python3.7/dist-packages/gradio/outputs.py:43: UserWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n",
" \"Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\",\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Connected (version 2.0, client OpenSSH_7.6p1)\n",
"Authentication (publickey) successful!\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Running on public URL: https://24062.gradio.app\n",
"\n",
"This share link expires in 72 hours. For free permanent hosting, check out Spaces (https://huggingface.co/spaces)\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"text/html": [
""
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Traceback (most recent call last):\n",
" File \"/usr/local/lib/python3.7/dist-packages/gradio/routes.py\", line 256, in run_predict\n",
" fn_index, raw_input, username, session_state\n",
" File \"/usr/local/lib/python3.7/dist-packages/gradio/blocks.py\", line 599, in process_api\n",
" predictions, duration = await self.call_function(fn_index, processed_input)\n",
" File \"/usr/local/lib/python3.7/dist-packages/gradio/blocks.py\", line 515, in call_function\n",
" block_fn.fn, *processed_input, limiter=self.limiter\n",
" File \"/usr/local/lib/python3.7/dist-packages/anyio/to_thread.py\", line 32, in run_sync\n",
" func, *args, cancellable=cancellable, limiter=limiter\n",
" File \"/usr/local/lib/python3.7/dist-packages/anyio/_backends/_asyncio.py\", line 937, in run_sync_in_worker_thread\n",
" return await future\n",
" File \"/usr/local/lib/python3.7/dist-packages/anyio/_backends/_asyncio.py\", line 867, in run\n",
" result = context.run(func, *args)\n",
" File \"/usr/local/lib/python3.7/dist-packages/gradio/interface.py\", line 487, in \n",
" if len(self.output_components) == 1\n",
" File \"/usr/local/lib/python3.7/dist-packages/gradio/interface.py\", line 666, in run_prediction\n",
" prediction = self.fn(*processed_input)\n",
" File \"\", line 23, in yolo\n",
" g = (size / max(im.size)) # gain\n",
"AttributeError: 'NoneType' object has no attribute 'size'\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Keyboard interruption in main thread... closing server.\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(,\n",
" 'http://127.0.0.1:7860/',\n",
" 'https://24062.gradio.app')"
]
},
"metadata": {},
"execution_count": 7
}
],
"source": [
"import gradio as gr\n",
"import torch\n",
"from PIL import Image\n",
"\n",
"\n",
"import os\n",
"cwd = os.getcwd()\n",
"\n",
"# !pip install -qr https://raw.githubusercontent.com/ultralytics/yolov5/master/requirements.txt gradio\n",
"\n",
"\n",
"\n",
"# Images\n",
"#torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg', 'zidane.jpg')\n",
"#torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/bus.jpg', 'bus.jpg')\n",
"\n",
"# Model\n",
"#model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # force_reload=True to update\n",
"model = torch.hub.load(cwd+'/yolov5', 'custom', path=cwd+'/saved_model/s1000_best.pt', source='local') # local model\n",
"\n",
"\n",
"def yolo(im, size=640):\n",
" g = (size / max(im.size)) # gain\n",
" im = im.resize((int(x * g) for x in im.size), Image.ANTIALIAS) # resize\n",
"\n",
" results = model(im) # inference\n",
" results.render() # updates results.imgs with boxes and labels\n",
" return Image.fromarray(results.imgs[0])\n",
"\n",
"\n",
"inputs = gr.inputs.Image(type='pil', label=\"Original Image\")\n",
"outputs = gr.outputs.Image(type=\"pil\", label=\"Output Image\")\n",
"\n",
"title = \"S1000 Detection\"\n",
"description = \"YOLOv5 Gradio demo for object detection. Upload an image or click an example image to use.\"\n",
"article = \"YOLOv5 is a family of compound-scaled object detection models trained on the COCO dataset, and includes \" \\\n",
" \"simple functionality for Test Time Augmentation (TTA), model ensembling, hyperparameter evolution, \" \\\n",
" \"and export to ONNX, CoreML and TFLite. Source code |\" \\\n",
" \"iOS App | PyTorch Hub
\"\n",
"\n",
"path_folder = cwd+'/datasets/s1000/'\n",
"examples = [[path_folder+'s1000_50_metre.jpg'], [path_folder+'s1000_100_metre.jpg'],[path_folder+'s1000_150_metre.jpg'],[path_folder+'s1000_200_metre.jpg'],[path_folder+'s1000_250_metre.jpg']]\n",
"gr.Interface(yolo, inputs, outputs, title=title, description=description, article=article, examples=examples, analytics_enabled=False).launch(\n",
" debug=True)\n",
"\n",
"\n",
"'''\n",
"\n",
"\n",
"git init\n",
"git config user.name bulentsofttech\n",
"git config user.email bulent.softtech@gmail.com\n",
"git add *\n",
"git commit -m \"WriteCommit\"\n",
"git push origin master\n",
"\n",
"\n",
"'''"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "Gradio.ipynb",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}