t81_558_class_06_5_yolo.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XKzF6dMaiLyP"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_06_5_yolo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YDTXd8-Lmp8Q"
      },
      "source": [
        "# T81-558: Applications of Deep Neural Networks\n",
        "**Module 6: Convolutional Neural Networks (CNN) for Computer Vision**\n",
        "* Instructor: [Jeff Heaton](https://sites.wustl.edu/jeffheaton/), McKelvey School of Engineering, [Washington University in St. Louis](https://engineering.wustl.edu/Programs/Pages/default.aspx)\n",
        "* For more information visit the [class website](https://sites.wustl.edu/jeffheaton/t81-558/)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ncNrAEpzmp8S"
      },
      "source": [
        "# Module 6 Material\n",
        "\n",
        "* Part 6.1: Image Processing in Python [[Video]](https://www.youtube.com/watch?v=V-IUrfTJMm4&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_06_1_python_images.ipynb)\n",
        "* Part 6.2: Using Convolutional Neural Networks [[Video]](https://www.youtube.com/watch?v=nU_T2PPigUQ&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_06_2_cnn.ipynb)\n",
        "* Part 6.3: Using Pretrained Neural Networks with Keras [[Video]](https://www.youtube.com/watch?v=TXqI9fp0imI&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_06_3_resnet.ipynb)\n",
        "* Part 6.4: Looking at Keras Generators and Image Augmentation [[Video]](https://www.youtube.com/watch?v=epfpxiXRL3U&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_06_4_keras_images.ipynb)\n",
        "* **Part 6.5: Recognizing Multiple Images with YOLOv5** [[Video]](https://www.youtube.com/watch?v=zwEmzElquHw&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_06_5_yolo.ipynb)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z0stsqSVoKD0"
      },
      "source": [
        "# Google CoLab Instructions\n",
        "\n",
        "The following code ensures that Google CoLab is running the correct version of TensorFlow.\n",
        "  Running the following code will map your GDrive to ```/content/drive```."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fU9UhAxTmp8S",
        "outputId": "afccb25f-6fdd-4b62-fc9b-fa32e3ee1aad"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Note: using Google CoLab\n",
            "Colab only includes TensorFlow 2.x; %tensorflow_version has no effect.\n"
          ]
        }
      ],
      "source": [
        "try:\n",
        "    from google.colab import drive\n",
        "    COLAB = True\n",
        "    print(\"Note: using Google CoLab\")\n",
        "    %tensorflow_version 2.x\n",
        "except:\n",
        "    print(\"Note: not using Google CoLab\")\n",
        "    COLAB = False"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QSKZqD1Mmp-C"
      },
      "source": [
        "# Part 6.5: Recognizing Multiple Images with YOLO5\n",
        "\n",
        "Programmers typically design convolutional neural networks to classify a single item centered in an image. However, as humans, we can recognize many items in our field of view in real-time. It is advantageous to recognize multiple items in a single image. One of the most advanced means of doing this is YOLOv5. You Only Look Once (YOLO) was introduced by Joseph Redmon, who supported YOLO up through V3. [[Cite:redmon2016you]](https://arxiv.org/abs/1506.02640) The fact that YOLO must only look once speaks to the efficiency of the algorithm. In this context, to \"look\" means to perform one scan over the image. It is also possible to run YOLO on live video streams.\n",
        "\n",
        " \n",
        "Joseph Redmon left computer vision to pursue other interests. The current version, YOLOv5 is supported by the startup company [Ultralytics](https://ultralytics.com/), who released the open-source library that we use in this class.[[Cite:zhu2021tph]](https://arxiv.org/abs/2108.11539)\n",
        "\n",
        "Researchers have trained YOLO on a variety of different computer image datasets. The version of YOLO weights used in this course is from the dataset Common Objects in Context (COCO). [[Cite: lin2014microsoft]](https://arxiv.org/abs/1405.0312) This dataset contains images labeled into 80 different classes. COCO is the source of the file coco.txt used in this module.\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",
        "* [YOLOv5 GitHub](https://github.com/ultralytics/yolov5)\n",
        "\n",
        "The code provided in this notebook works equally well when run either locally or from Google CoLab. It is easier to run YOLOv5 from CoLab, which is recommended for this course.\n",
        "\n",
        "We begin by obtaining an image to classify.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "WXEobzvGlFim",
        "outputId": "dbd1cd58-63d1-4abc-bf9c-4ce1499c5bdc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "import urllib.request\n",
        "import shutil\n",
        "from IPython.display import Image\n",
        "!mkdir /content/images/\n",
        "\n",
        "URL = \"https://github.com/jeffheaton/t81_558_deep_learning\"\n",
        "URL += \"/raw/master/photos/jeff_cook.jpg\"\n",
        "LOCAL_IMG_FILE = \"/content/images/jeff_cook.jpg\"\n",
        "\n",
        "with urllib.request.urlopen(URL) as response, \\\n",
        "  open(LOCAL_IMG_FILE, 'wb') as out_file:\n",
        "    shutil.copyfileobj(response, out_file)\n",
        "\n",
        "Image(filename=LOCAL_IMG_FILE)"
      ]
    },
    {
      "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": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "YOLOv5 🚀 v6.2-195-gdf80e7c Python-3.7.14 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "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": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "image/jpeg": {
              "width": 300
            }
          },
          "execution_count": 4
        }
      ],
      "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",
      "source": [
        "!ls /content/yolov5/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MADv9eSnrBed",
        "outputId": "4e0bd648-2f4a-479d-d33b-40273322661c"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qYOvD3M7ofQl"
      },
      "source": [
        "## Running YOLOv5\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": [
        {
          "output_type": "stream",
          "name": "stderr",
          "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"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "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  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9da1ef20-7f7c-4bbe-ae47-674b868b221a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>xmin</th>\n",
              "      <th>ymin</th>\n",
              "      <th>xmax</th>\n",
              "      <th>ymax</th>\n",
              "      <th>confidence</th>\n",
              "      <th>class</th>\n",
              "      <th>name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>125.092232</td>\n",
              "      <td>182.010025</td>\n",
              "      <td>219.074036</td>\n",
              "      <td>264.044983</td>\n",
              "      <td>0.928736</td>\n",
              "      <td>16</td>\n",
              "      <td>dog</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>72.338425</td>\n",
              "      <td>36.174423</td>\n",
              "      <td>162.752075</td>\n",
              "      <td>229.957077</td>\n",
              "      <td>0.928245</td>\n",
              "      <td>0</td>\n",
              "      <td>person</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.428009</td>\n",
              "      <td>25.537472</td>\n",
              "      <td>68.613434</td>\n",
              "      <td>89.955139</td>\n",
              "      <td>0.891785</td>\n",
              "      <td>68</td>\n",
              "      <td>microwave</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>98.033714</td>\n",
              "      <td>103.113159</td>\n",
              "      <td>266.426483</td>\n",
              "      <td>0.739207</td>\n",
              "      <td>69</td>\n",
              "      <td>oven</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>176.110916</td>\n",
              "      <td>76.847527</td>\n",
              "      <td>183.783249</td>\n",
              "      <td>105.030785</td>\n",
              "      <td>0.725925</td>\n",
              "      <td>39</td>\n",
              "      <td>bottle</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>189.972397</td>\n",
              "      <td>85.284508</td>\n",
              "      <td>196.409378</td>\n",
              "      <td>105.729591</td>\n",
              "      <td>0.593492</td>\n",
              "      <td>39</td>\n",
              "      <td>bottle</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>161.864563</td>\n",
              "      <td>115.693741</td>\n",
              "      <td>237.386475</td>\n",
              "      <td>131.211624</td>\n",
              "      <td>0.571422</td>\n",
              "      <td>71</td>\n",
              "      <td>sink</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>216.053223</td>\n",
              "      <td>137.275635</td>\n",
              "      <td>239.968109</td>\n",
              "      <td>230.737457</td>\n",
              "      <td>0.364453</td>\n",
              "      <td>69</td>\n",
              "      <td>oven</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>181.397934</td>\n",
              "      <td>82.266541</td>\n",
              "      <td>195.568832</td>\n",
              "      <td>105.023056</td>\n",
              "      <td>0.252385</td>\n",
              "      <td>39</td>\n",
              "      <td>bottle</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9da1ef20-7f7c-4bbe-ae47-674b868b221a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "          document.querySelector('#df-9da1ef20-7f7c-4bbe-ae47-674b868b221a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9da1ef20-7f7c-4bbe-ae47-674b868b221a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "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",
      "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)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "Tg-Y0vTt-220",
        "outputId": "54d217bd-66e0-40ef-cb75-562bde8faa3b"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "name  image  bottle  dog  microwave  oven  person  sink\n",
              "0         1       3    1          1     2       1     1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a46c1180-cd8f-4546-8510-c11b38b88b88\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>name</th>\n",
              "      <th>image</th>\n",
              "      <th>bottle</th>\n",
              "      <th>dog</th>\n",
              "      <th>microwave</th>\n",
              "      <th>oven</th>\n",
              "      <th>person</th>\n",
              "      <th>sink</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a46c1180-cd8f-4546-8510-c11b38b88b88 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a46c1180-cd8f-4546-8510-c11b38b88b88');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0eBtaFbimp-M"
      },
      "source": [
        "# Module 6 Assignment\n",
        "\n",
        "You can find the first assignment here: [assignment 6](https://github.com/jeffheaton/t81_558_deep_learning/blob/master/assignments/assignment_yourname_class6.ipynb)"
      ]
    }
  ],
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    "accelerator": "GPU",
    "anaconda-cloud": {},
    "colab": {
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      "provenance": []
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      "display_name": "Python 3.9 (tensorflow)",
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      "name": "tensorflow"
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      "nbconvert_exporter": "python",
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