class_09_YOLOv8.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QSKZqD1Mmp-C"
   },
   "source": [
    "# YOLOv8 \n",
    "\n",
    "## Using YOLO in Python\n",
    "\n",
    "To use YOLO in Python, we will use the open-source library provided by Ultralytics.\n",
    "\n",
    "* [YOLOv8 GitHub](https://github.com/ultralytics/ultralytics)\n",
    "\n",
    "The code provided in this notebook works equally well when run either locally or from Google CoLab. \n",
    "\n",
    "We begin by obtaining an image to classify.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'ultralytics'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-02a9766c61a0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mdisplay\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclear_output\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0multralytics\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m \u001b[0multralytics\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchecks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'ultralytics'"
     ]
    }
   ],
   "source": [
    "# Pip install method (recommended)\n",
    "\n",
    "!pip install ultralytics==8.0.20\n",
    "\n",
    "from IPython import display\n",
    "display.clear_output()\n",
    "\n",
    "import ultralytics\n",
    "ultralytics.checks()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 337
    },
    "id": "WXEobzvGlFim",
    "outputId": "dbd1cd58-63d1-4abc-bf9c-4ce1499c5bdc"
   },
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'ultralytics'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-f006d0f56252>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0multralytics\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mYOLO\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;31m# Load a model\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mYOLO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"yolov8n.yaml\"\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# build a new model from scratch\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mYOLO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"yolov8n.pt\"\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# load a pretrained model (recommended for training)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'ultralytics'"
     ]
    }
   ],
   "source": [
    "from ultralytics import YOLO\n",
    "\n",
    "# Load a model\n",
    "model = YOLO(\"yolov8n.yaml\")  # build a new model from scratch\n",
    "model = YOLO(\"yolov8n.pt\")  # load a pretrained model (recommended for training)\n",
    "\n",
    "# Use the model\n",
    "model.train(data=\"coco128.yaml\", epochs=3)  # train the model\n",
    "metrics = model.val()  # evaluate model performance on the validation set\n",
    "results = model(\"https://ultralytics.com/images/bus.jpg\")  # predict on an image\n",
    "success = model.export(format=\"onnx\")  # export the model to ONNX format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Image(filename='./images/covidtrain.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ym5_juokofQl"
   },
   "source": [
    "## Installing YOLOv5\n",
    "\n",
    "YOLO is not available directly through either PIP or CONDA. Additionally, YOLO is not installed in Google CoLab by default. Therefore, whether you wish to use YOLO through CoLab or run it locally, you need to go through several steps to install it. This section describes the process of installing YOLO. The same steps apply to either CoLab or a local install. For CoLab, you must repeat these steps each time the system restarts your virtual environment. You must perform these steps only once for your virtual Python environment for a local install. If you are installing locally, install to the same virtual environment you created for this course. The following commands install YOLO directly from its GitHub repository."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VuTjby5MzEre",
    "outputId": "300d24a5-7c7e-44bb-a1c5-2f8d4dbdaec4"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 v6.2-195-gdf80e7c Python-3.7.14 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 38.8/166.8 GB disk)\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "!git clone https://github.com/ultralytics/yolov5 --tag 6.2  # clone\n",
    "!mv /content/6.2 /content/yolov5\n",
    "%pip install -qr /content/yolov5/requirements.txt  # install\n",
    "sys.path.insert(0,'/content/yolov5')\n",
    "\n",
    "import torch\n",
    "import utils\n",
    "display = utils.notebook_init()  # checks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9PSttcoraUlb"
   },
   "source": [
    "Next, we will run YOLO from the command line and classify the previously downloaded kitchen picture.  You can run this classification on any image you choose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 687
    },
    "id": "R8zq_6akz64w",
    "outputId": "eaa075f8-ff1f-4320-db10-5f5f315e4d2e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rm: cannot remove '/content/yolov5/runs/detect/*': No such file or directory\n",
      "mkdir: cannot create directory ‘/content/images’: File exists\n",
      "cp: cannot stat '/content/street/jeff_cook.jpg': No such file or directory\n",
      "\u001b[34m\u001b[1mdetect: \u001b[0mweights=['yolov5s.pt'], source=/content/images/, data=yolov5/data/coco128.yaml, imgsz=[1024, 1024], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=yolov5/runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1\n",
      "YOLOv5 🚀 v6.2-195-gdf80e7c Python-3.7.14 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
      "\n",
      "Downloading https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s.pt to yolov5s.pt...\n",
      "100% 14.1M/14.1M [00:00<00:00, 236MB/s]\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients\n",
      "image 1/1 /content/images/jeff_cook.jpg: 1024x768 1 person, 1 dog, 2 bottles, 1 microwave, 1 oven, 2 sinks, 21.3ms\n",
      "Speed: 0.8ms pre-process, 21.3ms inference, 41.7ms NMS per image at shape (1, 3, 1024, 1024)\n",
      "Results saved to \u001b[1myolov5/runs/detect/exp\u001b[0m\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "image/jpeg": {
       "width": 300
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prepare directories for YOLO command line\n",
    "!rm -R /content/yolov5/runs/detect/*\n",
    "!mkdir /content/images\n",
    "!cp /content/street/jeff_cook.jpg /content/images\n",
    "\n",
    "# Run YOLO to classify\n",
    "!python /content/yolov5/detect.py --weights yolov5s.pt --img 1024 \\\n",
    "  --conf 0.25 --source /content/images/\n",
    "\n",
    "# Display the images\n",
    "from IPython.display import Image\n",
    "\n",
    "URL = '/content/yolov5/runs/detect/exp/jeff_cook.jpg'\n",
    "Image(filename=URL, width=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MADv9eSnrBed",
    "outputId": "4e0bd648-2f4a-479d-d33b-40273322661c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "benchmarks.py\t detect.py   models\t       runs\t  tutorial.ipynb\n",
      "classify\t export.py   __pycache__       segment\t  utils\n",
      "CONTRIBUTING.md  hubconf.py  README.md\t       setup.cfg  val.py\n",
      "data\t\t LICENSE     requirements.txt  train.py\n"
     ]
    }
   ],
   "source": [
    "!ls /content/yolov5/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "qYOvD3M7ofQl"
   },
   "outputs": [],
   "source": [
    "## Running YOLOv8\n",
    "\n",
    "In addition to the command line execution, we just saw.  The following code adds the downloaded YOLOv5 to Python's environment, allowing **yolov5** to be imported like a regular Python library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 494
    },
    "id": "MY3gVyidmp-K",
    "outputId": "3be3086b-8151-4325-dd7c-cbfdd90cdb1e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/torch/hub.py:267: UserWarning: You are about to download and run code from an untrusted repository. In a future release, this won't be allowed. To add the repository to your trusted list, change the command to {calling_fn}(..., trust_repo=False) and a command prompt will appear asking for an explicit confirmation of trust, or load(..., trust_repo=True), which will assume that the prompt is to be answered with 'yes'. You can also use load(..., trust_repo='check') which will only prompt for confirmation if the repo is not already trusted. This will eventually be the default behaviour\n",
      "  \"You are about to download and run code from an untrusted repository. In a future release, this won't \"\n",
      "Downloading: \"https://github.com/ultralytics/yolov5/zipball/master\" to /root/.cache/torch/hub/master.zip\n",
      "YOLOv5 🚀 v6.2-195-gdf80e7c Python-3.7.14 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients\n",
      "Adding AutoShape... \n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "         xmin        ymin        xmax        ymax  confidence  class  \\\n",
       "0  125.092232  182.010025  219.074036  264.044983    0.928736     16   \n",
       "1   72.338425   36.174423  162.752075  229.957077    0.928245      0   \n",
       "2    0.428009   25.537472   68.613434   89.955139    0.891785     68   \n",
       "3    0.000000   98.033714  103.113159  266.426483    0.739207     69   \n",
       "4  176.110916   76.847527  183.783249  105.030785    0.725925     39   \n",
       "5  189.972397   85.284508  196.409378  105.729591    0.593492     39   \n",
       "6  161.864563  115.693741  237.386475  131.211624    0.571422     71   \n",
       "7  216.053223  137.275635  239.968109  230.737457    0.364453     69   \n",
       "8  181.397934   82.266541  195.568832  105.023056    0.252385     39   \n",
       "\n",
       "        name  \n",
       "0        dog  \n",
       "1     person  \n",
       "2  microwave  \n",
       "3       oven  \n",
       "4     bottle  \n",
       "5     bottle  \n",
       "6       sink  \n",
       "7       oven  \n",
       "8     bottle  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "# Model\n",
    "yolo_model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # or yolov5n - yolov5x6, custom\n",
    "\n",
    "# Inference\n",
    "results = yolo_model(LOCAL_IMG_FILE)\n",
    "\n",
    "# Results\n",
    "df = results.pandas().xyxy[0]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DL173C9_oKEU"
   },
   "source": [
    "It is important to note that the **yolo** class instantiated here is a callable object, which can fill the role of both an object and a function. Acting as a function, *yolo* returns a Pandas dataframe that contains the bounding boxes (xmin/xmax and ymin/ymax), confidence, and name/class of each item detected. \n",
    "\n",
    "Your program should use these values to perform whatever actions you wish due to the input image.  The following code displays the images detected above the threshold.\n",
    "\n",
    "You can obtain the counts of images through the use of a Pandas groupby and pivot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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    },
    "id": "Tg-Y0vTt-220",
    "outputId": "54d217bd-66e0-40ef-cb75-562bde8faa3b"
   },
   "outputs": [
    {
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       "name  image  bottle  dog  microwave  oven  person  sink\n",
       "0         1       3    1          1     2       1     1"
      ]
     },
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   "source": [
    "df2 = df[['name','class']].groupby(by=[\"name\"]).count().reset_index()\n",
    "df2.columns = ['name','count']\n",
    "df2['image'] = 1\n",
    "df2.pivot(index=['image'],columns='name',values='count').reset_index().fillna(0)"
   ]
  }
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