roundabout,
created on Sunday, 26 May 2024, 17:44:47 (1716745487),
received on Sunday, 26 May 2024, 17:45:09 (1716745509)
Author identity: vlad <vlad.muntoiu@gmail.com>
7d732a64f3341506d5b8250ef62944640726a73f
COPYING.md
@@ -59,6 +59,8 @@ The images in the repository are licensed depending on their source:
* If the file name has the `dwc` prefix, it comes from https://www.kaggle.com/datasets/arkadiyhacks/drinking-waste-classification and is in the public domain.
* If the file name has the `sushi` prefix, it comes from https://github.com/arthur-cen/smartTrashBin/tree/master/data.
> © 2021 Arthur Cen, [Attribution Share-Alike 4.0](https://creativecommons.org/licenses/by-sa/4.0/).
* If the file name has the `odpadky` prefix, it comes from https://www.kaggle.com/datasets/ackobecko/odpadky.
> © 2023 AdaMb, [Attribution Share-Alike 4.0](https://creativecommons.org/licenses/by-sa/4.0/).
The VIA application shipped in this repository (located at the `/via` path) is licensed under the BSD licence.
> Copyright (c) 2016-2021, Abhishek Dutta, Visual Geometry Group, Oxford University and VIA Contributors.
main.ipynb
@@ -34,66 +34,66 @@
"Defaulting to user installation because normal site-packages is not writeable\n",
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"Installing collected packages: triton, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, networkx, fsspec, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, torch, torchvision\n",
"\u001b[33m WARNING: The scripts convert-caffe2-to-onnx, convert-onnx-to-caffe2 and torchrun are installed in '/home/tudov/.local/bin' which is not on PATH.\n",
" Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\u001b[33m\n",
"\u001b[0mSuccessfully installed fsspec-2024.5.0 networkx-3.3 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.40 nvidia-nvtx-cu12-12.1.105 torch-2.3.0 torchvision-0.18.0 triton-2.3.0\n",
"\u001B[33m WARNING: The scripts convert-caffe2-to-onnx, convert-onnx-to-caffe2 and torchrun are installed in '/home/tudov/.local/bin' which is not on PATH.\n",
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"\u001B[0mSuccessfully installed fsspec-2024.5.0 networkx-3.3 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.40 nvidia-nvtx-cu12-12.1.105 torch-2.3.0 torchvision-0.18.0 triton-2.3.0\n",
"Note: you may need to restart the kernel to use updated packages.\n",
"Defaulting to user installation because normal site-packages is not writeable\n",
"Requirement already satisfied: torch in /home/tudov/.local/lib/python3.10/site-packages (2.3.0)\n",
@@ -124,23 +124,23 @@
"Defaulting to user installation because normal site-packages is not writeable\n",
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" Downloading ultralytics-8.2.22-py3-none-any.whl (778 kB)\n",
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"Collecting opencv-python>=4.6.0\n",
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"\u001B[?25hCollecting numpy>=1.21.2\n",
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"Requirement already satisfied: MarkupSafe>=2.0 in /usr/lib/python3/dist-packages (from jinja2->torch>=1.8.0->ultralytics) (2.0.1)\n",
"Installing collected packages: py-cpuinfo, tzdata, tqdm, numpy, pandas, opencv-python, seaborn, thop, ultralytics\n",
"\u001b[33m WARNING: The script cpuinfo is installed in '/home/tudov/.local/bin' which is not on PATH.\n",
" Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\u001b[33m\n",
"\u001b[0m\u001b[33m WARNING: The script tqdm is installed in '/home/tudov/.local/bin' which is not on PATH.\n",
" Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\u001b[33m\n",
"\u001b[0m\u001b[33m WARNING: The script f2py is installed in '/home/tudov/.local/bin' which is not on PATH.\n",
" Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\u001b[33m\n",
"\u001b[0m\u001b[33m WARNING: The scripts ultralytics and yolo are installed in '/home/tudov/.local/bin' which is not on PATH.\n",
" Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\u001b[33m\n",
"\u001b[0mSuccessfully installed numpy-1.26.4 opencv-python-4.9.0.80 pandas-2.2.2 py-cpuinfo-9.0.0 seaborn-0.13.2 thop-0.1.1.post2209072238 tqdm-4.66.4 tzdata-2024.1 ultralytics-8.2.22\n",
"\u001B[33m WARNING: The script cpuinfo is installed in '/home/tudov/.local/bin' which is not on PATH.\n",
" Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001B[0m\u001B[33m\n",
"\u001B[0m\u001B[33m WARNING: The script tqdm is installed in '/home/tudov/.local/bin' which is not on PATH.\n",
" Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001B[0m\u001B[33m\n",
"\u001B[0m\u001B[33m WARNING: The script f2py is installed in '/home/tudov/.local/bin' which is not on PATH.\n",
" Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001B[0m\u001B[33m\n",
"\u001B[0m\u001B[33m WARNING: The scripts ultralytics and yolo are installed in '/home/tudov/.local/bin' which is not on PATH.\n",
" Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001B[0m\u001B[33m\n",
"\u001B[0mSuccessfully installed numpy-1.26.4 opencv-python-4.9.0.80 pandas-2.2.2 py-cpuinfo-9.0.0 seaborn-0.13.2 thop-0.1.1.post2209072238 tqdm-4.66.4 tzdata-2024.1 ultralytics-8.2.22\n",
"Note: you may need to restart the kernel to use updated packages.\n",
"Defaulting to user installation because normal site-packages is not writeable\n",
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"Successfully installed pillow-10.3.0 pillow_heif-0.16.0\n",
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@@ -726,35 +726,13 @@
},
{
"cell_type": "code",
"execution_count": 4,
"id": "cf66bcb81e1b29f",
"metadata": {
"ExecuteTime": {
"end_time": "2024-05-25T09:10:46.746378Z",
"start_time": "2024-05-25T09:04:33.280298Z"
"end_time": "2024-05-26T16:53:48.532397Z",
"start_time": "2024-05-26T16:50:58.288905Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING ⚠️ 'source' is missing. Using 'source=/home/tudov/.local/lib/python3.10/site-packages/ultralytics/assets'.\n"
]
},
{
"ename": "AttributeError",
"evalue": "'NoneType' object has no attribute '__array_interface__'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[4], line 48\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[38;5;28mcls\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(box\u001b[38;5;241m.\u001b[39mcls[\u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m 47\u001b[0m \u001b[38;5;66;03m# Object label\u001b[39;00m\n\u001b[0;32m---> 48\u001b[0m img_pil \u001b[38;5;241m=\u001b[39m \u001b[43mImage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfromarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 49\u001b[0m draw \u001b[38;5;241m=\u001b[39m ImageDraw\u001b[38;5;241m.\u001b[39mDraw(img_pil)\n\u001b[1;32m 50\u001b[0m draw\u001b[38;5;241m.\u001b[39mtext((x1, y1),\n\u001b[1;32m 51\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconfidence\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m%] \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mclass_mapping[\u001b[38;5;28mcls\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 52\u001b[0m font\u001b[38;5;241m=\u001b[39mfont,\n\u001b[1;32m 53\u001b[0m fill\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m255\u001b[39m, \u001b[38;5;241m255\u001b[39m, \u001b[38;5;241m255\u001b[39m),\n\u001b[1;32m 54\u001b[0m stroke_width\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m 55\u001b[0m stroke_fill\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m))\n",
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/PIL/Image.py:3119\u001b[0m, in \u001b[0;36mfromarray\u001b[0;34m(obj, mode)\u001b[0m\n\u001b[1;32m 3072\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfromarray\u001b[39m(obj, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 3073\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 3074\u001b[0m \u001b[38;5;124;03m Creates an image memory from an object exporting the array interface\u001b[39;00m\n\u001b[1;32m 3075\u001b[0m \u001b[38;5;124;03m (using the buffer protocol)::\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3117\u001b[0m \u001b[38;5;124;03m .. versionadded:: 1.1.6\u001b[39;00m\n\u001b[1;32m 3118\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3119\u001b[0m arr \u001b[38;5;241m=\u001b[39m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__array_interface__\u001b[49m\n\u001b[1;32m 3120\u001b[0m shape \u001b[38;5;241m=\u001b[39m arr[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshape\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 3121\u001b[0m ndim \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(shape)\n",
"\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute '__array_interface__'"
]
}
],
"source": [
"from ultralytics import YOLO\n",
"import os\n",
@@ -821,7 +799,36 @@
"# Stop the webcam\n",
"cap.release()\n",
"cv.destroyAllWindows()"
]
],
"outputs": [
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
"\u001B[0;31mKeyboardInterrupt\u001B[0m Traceback (most recent call last)",
"Cell \u001B[0;32mIn[20], line 28\u001B[0m\n\u001B[1;32m 25\u001B[0m success, img \u001B[38;5;241m=\u001B[39m cap\u001B[38;5;241m.\u001B[39mread()\n\u001B[1;32m 26\u001B[0m results \u001B[38;5;241m=\u001B[39m model(img, stream\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m, verbose\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m)\n\u001B[0;32m---> 28\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m r \u001B[38;5;129;01min\u001B[39;00m results:\n\u001B[1;32m 29\u001B[0m boxes \u001B[38;5;241m=\u001B[39m r\u001B[38;5;241m.\u001B[39mboxes\n\u001B[1;32m 31\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m box \u001B[38;5;129;01min\u001B[39;00m boxes:\n",
"File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.10/site-packages/torch/utils/_contextlib.py:35\u001B[0m, in \u001B[0;36m_wrap_generator.<locals>.generator_context\u001B[0;34m(*args, **kwargs)\u001B[0m\n\u001B[1;32m 32\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m 33\u001B[0m \u001B[38;5;66;03m# Issuing `None` to a generator fires it up\u001B[39;00m\n\u001B[1;32m 34\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m ctx_factory():\n\u001B[0;32m---> 35\u001B[0m response \u001B[38;5;241m=\u001B[39m \u001B[43mgen\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msend\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m)\u001B[49m\n\u001B[1;32m 37\u001B[0m \u001B[38;5;28;01mwhile\u001B[39;00m \u001B[38;5;28;01mTrue\u001B[39;00m:\n\u001B[1;32m 38\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m 39\u001B[0m \u001B[38;5;66;03m# Forward the response to our caller and get its next request\u001B[39;00m\n",
"File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.10/site-packages/ultralytics/engine/predictor.py:248\u001B[0m, in \u001B[0;36mBasePredictor.stream_inference\u001B[0;34m(self, source, model, *args, **kwargs)\u001B[0m\n\u001B[1;32m 246\u001B[0m \u001B[38;5;66;03m# Inference\u001B[39;00m\n\u001B[1;32m 247\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m profilers[\u001B[38;5;241m1\u001B[39m]:\n\u001B[0;32m--> 248\u001B[0m preds \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43minference\u001B[49m\u001B[43m(\u001B[49m\u001B[43mim\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 249\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39margs\u001B[38;5;241m.\u001B[39membed:\n\u001B[1;32m 250\u001B[0m \u001B[38;5;28;01myield from\u001B[39;00m [preds] \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(preds, torch\u001B[38;5;241m.\u001B[39mTensor) \u001B[38;5;28;01melse\u001B[39;00m preds \u001B[38;5;66;03m# yield embedding tensors\u001B[39;00m\n",
"File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.10/site-packages/ultralytics/engine/predictor.py:142\u001B[0m, in \u001B[0;36mBasePredictor.inference\u001B[0;34m(self, im, *args, **kwargs)\u001B[0m\n\u001B[1;32m 136\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"Runs inference on a given image using the specified model and arguments.\"\"\"\u001B[39;00m\n\u001B[1;32m 137\u001B[0m visualize \u001B[38;5;241m=\u001B[39m (\n\u001B[1;32m 138\u001B[0m increment_path(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msave_dir \u001B[38;5;241m/\u001B[39m Path(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mbatch[\u001B[38;5;241m0\u001B[39m][\u001B[38;5;241m0\u001B[39m])\u001B[38;5;241m.\u001B[39mstem, mkdir\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n\u001B[1;32m 139\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39margs\u001B[38;5;241m.\u001B[39mvisualize \u001B[38;5;129;01mand\u001B[39;00m (\u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msource_type\u001B[38;5;241m.\u001B[39mtensor)\n\u001B[1;32m 140\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;28;01mFalse\u001B[39;00m\n\u001B[1;32m 141\u001B[0m )\n\u001B[0;32m--> 142\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmodel\u001B[49m\u001B[43m(\u001B[49m\u001B[43mim\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43maugment\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43margs\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43maugment\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mvisualize\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mvisualize\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43membed\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43margs\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43membed\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
"File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.10/site-packages/torch/nn/modules/module.py:1532\u001B[0m, in \u001B[0;36mModule._wrapped_call_impl\u001B[0;34m(self, *args, **kwargs)\u001B[0m\n\u001B[1;32m 1530\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_compiled_call_impl(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs) \u001B[38;5;66;03m# type: ignore[misc]\u001B[39;00m\n\u001B[1;32m 1531\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m-> 1532\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_call_impl\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
"File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.10/site-packages/torch/nn/modules/module.py:1541\u001B[0m, in \u001B[0;36mModule._call_impl\u001B[0;34m(self, *args, **kwargs)\u001B[0m\n\u001B[1;32m 1536\u001B[0m \u001B[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001B[39;00m\n\u001B[1;32m 1537\u001B[0m \u001B[38;5;66;03m# this function, and just call forward.\u001B[39;00m\n\u001B[1;32m 1538\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m (\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_backward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_forward_pre_hooks\n\u001B[1;32m 1539\u001B[0m \u001B[38;5;129;01mor\u001B[39;00m _global_backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_backward_hooks\n\u001B[1;32m 1540\u001B[0m \u001B[38;5;129;01mor\u001B[39;00m _global_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_forward_pre_hooks):\n\u001B[0;32m-> 1541\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mforward_call\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 1543\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m 1544\u001B[0m result \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n",
"File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.10/site-packages/ultralytics/nn/autobackend.py:453\u001B[0m, in \u001B[0;36mAutoBackend.forward\u001B[0;34m(self, im, augment, visualize, embed)\u001B[0m\n\u001B[1;32m 451\u001B[0m \u001B[38;5;66;03m# PyTorch\u001B[39;00m\n\u001B[1;32m 452\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mpt \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mnn_module:\n\u001B[0;32m--> 453\u001B[0m y \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmodel\u001B[49m\u001B[43m(\u001B[49m\u001B[43mim\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43maugment\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43maugment\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mvisualize\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mvisualize\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43membed\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43membed\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 455\u001B[0m \u001B[38;5;66;03m# TorchScript\u001B[39;00m\n\u001B[1;32m 456\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mjit:\n",
"File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.10/site-packages/torch/nn/modules/module.py:1532\u001B[0m, in \u001B[0;36mModule._wrapped_call_impl\u001B[0;34m(self, *args, **kwargs)\u001B[0m\n\u001B[1;32m 1530\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_compiled_call_impl(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs) \u001B[38;5;66;03m# type: ignore[misc]\u001B[39;00m\n\u001B[1;32m 1531\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m-> 1532\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_call_impl\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
"File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.10/site-packages/torch/nn/modules/module.py:1541\u001B[0m, in \u001B[0;36mModule._call_impl\u001B[0;34m(self, *args, **kwargs)\u001B[0m\n\u001B[1;32m 1536\u001B[0m \u001B[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001B[39;00m\n\u001B[1;32m 1537\u001B[0m \u001B[38;5;66;03m# this function, and just call forward.\u001B[39;00m\n\u001B[1;32m 1538\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m (\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_backward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_forward_pre_hooks\n\u001B[1;32m 1539\u001B[0m \u001B[38;5;129;01mor\u001B[39;00m _global_backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_backward_hooks\n\u001B[1;32m 1540\u001B[0m \u001B[38;5;129;01mor\u001B[39;00m _global_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_forward_pre_hooks):\n\u001B[0;32m-> 1541\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mforward_call\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 1543\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m 1544\u001B[0m result \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n",
"File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.10/site-packages/ultralytics/nn/tasks.py:89\u001B[0m, in \u001B[0;36mBaseModel.forward\u001B[0;34m(self, x, *args, **kwargs)\u001B[0m\n\u001B[1;32m 87\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(x, \u001B[38;5;28mdict\u001B[39m): \u001B[38;5;66;03m# for cases of training and validating while training.\u001B[39;00m\n\u001B[1;32m 88\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mloss(x, \u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m---> 89\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpredict\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
"File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.10/site-packages/ultralytics/nn/tasks.py:107\u001B[0m, in \u001B[0;36mBaseModel.predict\u001B[0;34m(self, x, profile, visualize, augment, embed)\u001B[0m\n\u001B[1;32m 105\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m augment:\n\u001B[1;32m 106\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_predict_augment(x)\n\u001B[0;32m--> 107\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_predict_once\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mprofile\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mvisualize\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43membed\u001B[49m\u001B[43m)\u001B[49m\n",
"File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.10/site-packages/ultralytics/nn/tasks.py:128\u001B[0m, in \u001B[0;36mBaseModel._predict_once\u001B[0;34m(self, x, profile, visualize, embed)\u001B[0m\n\u001B[1;32m 126\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m profile:\n\u001B[1;32m 127\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_profile_one_layer(m, x, dt)\n\u001B[0;32m--> 128\u001B[0m x \u001B[38;5;241m=\u001B[39m \u001B[43mm\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[43m)\u001B[49m \u001B[38;5;66;03m# run\u001B[39;00m\n\u001B[1;32m 129\u001B[0m y\u001B[38;5;241m.\u001B[39mappend(x \u001B[38;5;28;01mif\u001B[39;00m m\u001B[38;5;241m.\u001B[39mi \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msave \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m) \u001B[38;5;66;03m# save output\u001B[39;00m\n\u001B[1;32m 130\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m visualize:\n",
"File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.10/site-packages/torch/nn/modules/module.py:1532\u001B[0m, in \u001B[0;36mModule._wrapped_call_impl\u001B[0;34m(self, *args, **kwargs)\u001B[0m\n\u001B[1;32m 1530\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_compiled_call_impl(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs) \u001B[38;5;66;03m# type: ignore[misc]\u001B[39;00m\n\u001B[1;32m 1531\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m-> 1532\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_call_impl\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
"File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.10/site-packages/torch/nn/modules/module.py:1541\u001B[0m, in \u001B[0;36mModule._call_impl\u001B[0;34m(self, *args, **kwargs)\u001B[0m\n\u001B[1;32m 1536\u001B[0m \u001B[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001B[39;00m\n\u001B[1;32m 1537\u001B[0m \u001B[38;5;66;03m# this function, and just call forward.\u001B[39;00m\n\u001B[1;32m 1538\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m (\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_backward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_forward_pre_hooks\n\u001B[1;32m 1539\u001B[0m \u001B[38;5;129;01mor\u001B[39;00m _global_backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_backward_hooks\n\u001B[1;32m 1540\u001B[0m \u001B[38;5;129;01mor\u001B[39;00m _global_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_forward_pre_hooks):\n\u001B[0;32m-> 1541\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mforward_call\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 1543\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m 1544\u001B[0m result \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n",
"File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.10/site-packages/ultralytics/nn/modules/head.py:47\u001B[0m, in \u001B[0;36mDetect.forward\u001B[0;34m(self, x)\u001B[0m\n\u001B[1;32m 45\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"Concatenates and returns predicted bounding boxes and class probabilities.\"\"\"\u001B[39;00m\n\u001B[1;32m 46\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m i \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mnl):\n\u001B[0;32m---> 47\u001B[0m x[i] \u001B[38;5;241m=\u001B[39m torch\u001B[38;5;241m.\u001B[39mcat((\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcv2\u001B[49m\u001B[43m[\u001B[49m\u001B[43mi\u001B[49m\u001B[43m]\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[43m[\u001B[49m\u001B[43mi\u001B[49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m, \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcv3[i](x[i])), \u001B[38;5;241m1\u001B[39m)\n\u001B[1;32m 48\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mtraining: \u001B[38;5;66;03m# Training path\u001B[39;00m\n\u001B[1;32m 49\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m x\n",
"File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.10/site-packages/torch/nn/modules/module.py:1528\u001B[0m, in \u001B[0;36mModule._wrapped_call_impl\u001B[0;34m(self, *args, **kwargs)\u001B[0m\n\u001B[1;32m 1525\u001B[0m tracing_state\u001B[38;5;241m.\u001B[39mpop_scope()\n\u001B[1;32m 1526\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m result\n\u001B[0;32m-> 1528\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m_wrapped_call_impl\u001B[39m(\u001B[38;5;28mself\u001B[39m, \u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[1;32m 1529\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_compiled_call_impl \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m 1530\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_compiled_call_impl(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs) \u001B[38;5;66;03m# type: ignore[misc]\u001B[39;00m\n",
"\u001B[0;31mKeyboardInterrupt\u001B[0m: "
]
}
],
"execution_count": 20
},
{
"cell_type": "markdown",
@@ -833,36 +840,18 @@
},
{
"cell_type": "code",
"execution_count": 3,
"id": "5c3a7e358cf3efb5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"0: 640x640 1 Plastic, 71.6ms\n",
"Speed: 5.6ms preprocess, 71.6ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n"
]
},
{
"ename": "NameError",
"evalue": "name 'class_mapping' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[3], line 52\u001b[0m\n\u001b[1;32m 48\u001b[0m img_pil \u001b[38;5;241m=\u001b[39m Image\u001b[38;5;241m.\u001b[39mfromarray(img)\n\u001b[1;32m 49\u001b[0m draw \u001b[38;5;241m=\u001b[39m ImageDraw\u001b[38;5;241m.\u001b[39mDraw(img_pil)\n\u001b[1;32m 51\u001b[0m draw\u001b[38;5;241m.\u001b[39mtext((x1, y1),\n\u001b[0;32m---> 52\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconfidence\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m%] \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[43mclass_mapping\u001b[49m[\u001b[38;5;28mcls\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 53\u001b[0m font\u001b[38;5;241m=\u001b[39mfont,\n\u001b[1;32m 54\u001b[0m fill\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m255\u001b[39m, \u001b[38;5;241m255\u001b[39m, \u001b[38;5;241m255\u001b[39m),\n\u001b[1;32m 55\u001b[0m stroke_width\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m 56\u001b[0m stroke_fill\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m))\n\u001b[1;32m 57\u001b[0m img \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(img_pil)\n\u001b[1;32m 59\u001b[0m cv\u001b[38;5;241m.\u001b[39mimwrite(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(output_dir, os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mbasename(image_path\u001b[38;5;241m.\u001b[39mpath)), img)\n",
"\u001b[0;31mNameError\u001b[0m: name 'class_mapping' is not defined"
]
"metadata": {
"ExecuteTime": {
"end_time": "2024-05-26T15:28:54.658369Z",
"start_time": "2024-05-26T15:28:49.740170Z"
}
],
},
"source": [
"from ultralytics import YOLO\n",
"import os\n",
"\n",
"model = YOLO(\"runs/detect/train23/weights/last.pt\")\n",
"model = YOLO(\"runs/detect/train26/weights/last.pt\")\n",
"\n",
"import cv2 as cv\n",
"import numpy as np\n",
@@ -875,7 +864,7 @@
"import numpy as np\n",
"import math\n",
"\n",
"font = ImageFont.truetype(\"/usr/share/fonts/truetype/dejavu/DejaVuSansMono.ttf\", 20)\n",
"font = ImageFont.truetype(\"~/.local/share/fonts/Roboto-Regular.ttf\", 40)\n",
"\n",
"image_dir = \"./test_data/\"\n",
"output_dir = \"./output/\"\n",
@@ -921,7 +910,120 @@
" #cv.imshow(\"Photo\", img)\n",
" #cv.waitKey(27) # escape\n",
" #cv.destroyAllWindows()"
]
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"0: 640x640 1 Glass, 21.5ms\n",
"Speed: 1.8ms preprocess, 21.5ms inference, 0.4ms postprocess per image at shape (1, 3, 640, 640)\n",
"\n",
"0: 640x640 1 Plastic, 40.2ms\n",
"Speed: 1.2ms preprocess, 40.2ms inference, 0.5ms postprocess per image at shape (1, 3, 640, 640)\n",
"\n",
"0: 640x640 (no detections), 27.3ms\n",
"Speed: 2.9ms preprocess, 27.3ms inference, 0.3ms postprocess per image at shape (1, 3, 640, 640)\n",
"\n",
"0: 640x640 1 Paper, 65.2ms\n",
"Speed: 3.7ms preprocess, 65.2ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/vlad/.local/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3432: RuntimeWarning:\n",
"\n",
"Mean of empty slice.\n",
"\n",
"/home/vlad/.local/lib/python3.10/site-packages/numpy/core/_methods.py:190: RuntimeWarning:\n",
"\n",
"invalid value encountered in double_scalars\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"0: 640x640 (no detections), 37.9ms\n",
"Speed: 4.0ms preprocess, 37.9ms inference, 0.3ms postprocess per image at shape (1, 3, 640, 640)\n",
"\n",
"0: 640x640 (no detections), 31.0ms\n",
"Speed: 12.4ms preprocess, 31.0ms inference, 0.3ms postprocess per image at shape (1, 3, 640, 640)\n",
"\n",
"0: 640x640 (no detections), 21.6ms\n",
"Speed: 1.7ms preprocess, 21.6ms inference, 0.3ms postprocess per image at shape (1, 3, 640, 640)\n",
"\n",
"0: 480x640 1 Plastic, 22.5ms\n",
"Speed: 1.2ms preprocess, 22.5ms inference, 0.4ms postprocess per image at shape (1, 3, 480, 640)\n",
"\n",
"0: 640x640 4 Plastics, 21.2ms\n",
"Speed: 1.7ms preprocess, 21.2ms inference, 0.4ms postprocess per image at shape (1, 3, 640, 640)\n",
"\n",
"0: 640x640 3 Metals, 23.6ms\n",
"Speed: 1.8ms preprocess, 23.6ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
"\n",
"0: 640x640 (no detections), 21.8ms\n",
"Speed: 1.4ms preprocess, 21.8ms inference, 0.3ms postprocess per image at shape (1, 3, 640, 640)\n",
"\n",
"0: 640x640 1 Plastic, 1 Glass, 22.5ms\n",
"Speed: 2.1ms preprocess, 22.5ms inference, 0.5ms postprocess per image at shape (1, 3, 640, 640)\n",
"\n",
"0: 640x640 1 Glass, 24.5ms\n",
"Speed: 1.7ms preprocess, 24.5ms inference, 0.5ms postprocess per image at shape (1, 3, 640, 640)\n",
"\n",
"0: 640x640 1 Plastic, 20.8ms\n",
"Speed: 1.2ms preprocess, 20.8ms inference, 0.4ms postprocess per image at shape (1, 3, 640, 640)\n",
"\n",
"0: 640x640 1 Paper, 20.3ms\n",
"Speed: 1.7ms preprocess, 20.3ms inference, 0.5ms postprocess per image at shape (1, 3, 640, 640)\n",
"\n",
"0: 640x640 1 Glass, 20.2ms\n",
"Speed: 1.8ms preprocess, 20.2ms inference, 0.4ms postprocess per image at shape (1, 3, 640, 640)\n",
"\n",
"0: 640x640 2 Metals, 19.5ms\n",
"Speed: 1.3ms preprocess, 19.5ms inference, 0.4ms postprocess per image at shape (1, 3, 640, 640)\n",
"\n",
"0: 640x640 1 Paper, 21.3ms\n",
"Speed: 1.7ms preprocess, 21.3ms inference, 0.5ms postprocess per image at shape (1, 3, 640, 640)\n",
"\n",
"0: 640x640 (no detections), 19.5ms\n",
"Speed: 1.4ms preprocess, 19.5ms inference, 0.2ms postprocess per image at shape (1, 3, 640, 640)\n",
"\n",
"0: 640x640 1 Plastic, 30.9ms\n",
"Speed: 2.9ms preprocess, 30.9ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
"\n",
"0: 640x640 3 Papers, 89.0ms\n",
"Speed: 61.3ms preprocess, 89.0ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
"\n",
"0: 640x640 (no detections), 26.7ms\n",
"Speed: 1.5ms preprocess, 26.7ms inference, 0.3ms postprocess per image at shape (1, 3, 640, 640)\n",
"\n",
"0: 640x640 1 Glass, 27.0ms\n",
"Speed: 1.7ms preprocess, 27.0ms inference, 0.7ms postprocess per image at shape (1, 3, 640, 640)\n",
"\n",
"0: 640x640 1 Plastic, 28.0ms\n",
"Speed: 1.7ms preprocess, 28.0ms inference, 0.8ms postprocess per image at shape (1, 3, 640, 640)\n",
"\n",
"0: 640x640 1 Plastic, 18.8ms\n",
"Speed: 1.3ms preprocess, 18.8ms inference, 0.4ms postprocess per image at shape (1, 3, 640, 640)\n",
"\n",
"0: 640x640 1 Plastic, 19.1ms\n",
"Speed: 1.3ms preprocess, 19.1ms inference, 0.4ms postprocess per image at shape (1, 3, 640, 640)\n",
"\n",
"0: 640x640 (no detections), 20.3ms\n",
"Speed: 1.5ms preprocess, 20.3ms inference, 0.3ms postprocess per image at shape (1, 3, 640, 640)\n",
"\n",
"0: 640x640 1 Plastic, 20.5ms\n",
"Speed: 1.9ms preprocess, 20.5ms inference, 0.4ms postprocess per image at shape (1, 3, 640, 640)\n"
]
}
],
"execution_count": 19
},
{
"cell_type": "code",