YOLOv8_Object_Counter_OOP_v2.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "<div align=\"center\">\n",
        "\n",
        "# Object Detection, Tracking, and Counting with YOLOv8 and SORT Using OOP Approach\n",
        "\n",
        "</div>\n",
        "\n",
        "Welcome to this Google Colab notebook, which demonstrates how to perform object detection, tracking, and counting on both video files and static images using the YOLOv8 object detection algorithm and the SORT (Simple Online and Realtime Tracking) algorithm for object tracking, all implemented using an Object-Oriented Programming (OOP) approach.\n",
        "\n",
        "In this notebook, we will be using the YOLOv8 algorithm to predict the class probabilities and bounding boxes of multiple objects in an image or video frame. We will also be using the SORT algorithm to track the objects detected by YOLOv8 across multiple frames of a video or a sequence of images. SORT is a simple but effective algorithm that is designed to work in real-time and to handle noisy detections and occlusions.\n",
        "\n",
        "By using an OOP approach, we can organize our code into classes and methods that encapsulate the functionality of different parts of the object detection, tracking, and counting pipeline. "
      ],
      "metadata": {
        "id": "JS7r4B7yuOey"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Importing libraries, modules and files"
      ],
      "metadata": {
        "id": "ZoZrnFsqLDR6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### importing files from my github repo"
      ],
      "metadata": {
        "id": "wwFSZL50YWFh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/mohamedamine99/Object-tracking-and-counting-using-YOLOV8"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q_FEoVtf-igA",
        "outputId": "e9c09f7d-509b-4f94-f12c-f2dd3050b886"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'Object-tracking-and-counting-using-YOLOV8'...\n",
            "remote: Enumerating objects: 182, done.\u001b[K\n",
            "remote: Counting objects: 100% (58/58), done.\u001b[K\n",
            "remote: Compressing objects: 100% (46/46), done.\u001b[K\n",
            "remote: Total 182 (delta 18), reused 49 (delta 11), pack-reused 124\u001b[K\n",
            "Receiving objects: 100% (182/182), 362.29 MiB | 22.42 MiB/s, done.\n",
            "Resolving deltas: 100% (36/36), done.\n",
            "Updating files: 100% (74/74), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### importing modules"
      ],
      "metadata": {
        "id": "7ywW_Y4qYmI2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import shutil\n",
        "import os\n",
        "\n",
        "src_path = \"/content/Object-tracking-and-counting-using-YOLOV8/sort.py\"\n",
        "dest_path = \"/content\"\n",
        "\n",
        "# copy file from source to destination\n",
        "shutil.copy(src_path, dest_path)\n",
        "\n",
        "src_path = \"/content/Object-tracking-and-counting-using-YOLOV8/requirements.txt\"\n",
        "shutil.copy(src_path, dest_path)\n",
        "\n",
        "src_path = \"/content/Object-tracking-and-counting-using-YOLOV8/yolo_detect_and_count.py\"\n",
        "shutil.copy(src_path, dest_path)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "hrodd0lKPiDz",
        "outputId": "843442f3-fc10-4b66-cd4e-5dbf7a3a522b"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/yolo_detect_and_count.py'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -r /content/requirements.txt"
      ],
      "metadata": {
        "id": "wwx247S1H8tn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import random\n",
        "import cv2\n",
        "import sort\n",
        "import time\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "FDIvbG71Jkhn"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hLCrvX5uxtAt",
        "outputId": "19ab8849-9bd0-4ecd-9fc5-ae89142bc50c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Ultralytics YOLOv8.0.59 🚀 Python-3.9.16 torch-1.13.1+cu116 CPU\n",
            "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 26.3/107.7 GB disk)\n"
          ]
        }
      ],
      "source": [
        "%pip install ultralytics\n",
        "import ultralytics\n",
        "ultralytics.checks()\n",
        "from ultralytics import YOLO\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from yolo_detect_and_count import YOLOv8_ObjectDetector, YOLOv8_ObjectCounter"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6SMQO7Wbwcp7",
        "outputId": "fe1a265a-bc95-4aea-bdcb-7e20f7f9010a"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Ultralytics YOLOv8.0.59 🚀 Python-3.9.16 torch-1.13.1+cu116 CPU\n",
            "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 26.3/107.7 GB disk)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Implementing object detection"
      ],
      "metadata": {
        "id": "5RZfdp_TwpZJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### preparing file paths and directories"
      ],
      "metadata": {
        "id": "MVPnI_ZdruJr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "vid_results_path = '/content/video_object_detection_results'\n",
        "test_vids_path = '/content/Object-tracking-and-counting-using-YOLOV8/test vids'\n",
        "\n",
        "\n",
        "if not os.path.isdir(vid_results_path):\n",
        "    os.makedirs(vid_results_path)"
      ],
      "metadata": {
        "id": "Y-xXMgUjiX_0"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Instanciating YOLOv8_ObjectCounter objects "
      ],
      "metadata": {
        "id": "MfZjVGJSr6ie"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "yolo_names = ['yolov8n.pt', 'yolov8m.pt', 'yolov8s.pt',  'yolov8l.pt']\n",
        "colors = []\n",
        "for _ in range(80):\n",
        "    rand_tuple = (random.randint(50, 255), random.randint(50, 255), random.randint(50, 255))\n",
        "    colors.append(rand_tuple)\n"
      ],
      "metadata": {
        "id": "YhaMvVPHAy1r"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "counters = []\n",
        "for yolo_name in yolo_names:\n",
        "    counter = YOLOv8_ObjectCounter(yolo_name, conf = 0.60 )\n",
        "    counters.append(counter)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214,
          "referenced_widgets": [
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        "id": "E7O7yl3A3kxE",
        "outputId": "858d05c9-b1d5-405f-dc6a-a3fc1e1bc438"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt to yolov8n.pt...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0.00/6.23M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0ebf150d76b14ae08d5c09d0d45b8fe5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt to yolov8m.pt...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0.00/49.7M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0ad26eb1abf244e1bd8d0a04e00e241f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s.pt to yolov8s.pt...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0.00/21.5M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fed6c3b7b57a49269eee1840103180d0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l.pt to yolov8l.pt...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0.00/83.7M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "26d4002ed64d4d0c91cd6665123a29fd"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Performing object detection, tracking and counting "
      ],
      "metadata": {
        "id": "fLYwvejewThL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for counter in counters:\n",
        "    counter.predict_video(video_path= '/content/Object-tracking-and-counting-using-YOLOV8/test vids/traffic 2.mp4'\n",
        ", save_dir = vid_results_path, save_format = \"avi\", display = 'custom', colors = colors)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ph04-AKe3z9x",
        "outputId": "ad0d9d68-6719-4cec-90cb-bfb563cb6a07"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "----------------------------\n",
            "DETECTING OBJECTS IN : traffic 2.mp4 : \n",
            "RESOLUTION : 1280x720\n",
            "SAVING TO :/content/video_object_detection_results/yolov8n -- traffic 2.avi\n",
            "Error reading frame\n",
            "26\n",
            "[8, 7, 6, 5, 4, 3, 2, 1, 9, 10, 12, 11, 13, 15, 17, 18, 19, 21, 22, 26, 27, 25, 29, 30, 32, 33]\n",
            "----------------------------\n",
            "DETECTING OBJECTS IN : traffic 2.mp4 : \n",
            "RESOLUTION : 1280x720\n",
            "SAVING TO :/content/video_object_detection_results/yolov8m -- traffic 2.avi\n",
            "Error reading frame\n",
            "25\n",
            "[43, 42, 41, 40, 39, 38, 44, 45, 46, 47, 48, 49, 55, 56, 58, 61, 60, 62, 57, 66, 69, 64, 70, 73, 74]\n",
            "----------------------------\n",
            "DETECTING OBJECTS IN : traffic 2.mp4 : \n",
            "RESOLUTION : 1280x720\n",
            "SAVING TO :/content/video_object_detection_results/yolov8s -- traffic 2.avi\n",
            "Error reading frame\n",
            "20\n",
            "[85, 84, 83, 82, 81, 80, 86, 89, 87, 90, 96, 99, 103, 105, 108, 112, 111, 113, 118, 119]\n",
            "----------------------------\n",
            "DETECTING OBJECTS IN : traffic 2.mp4 : \n",
            "RESOLUTION : 1280x720\n",
            "SAVING TO :/content/video_object_detection_results/yolov8l -- traffic 2.avi\n",
            "Error reading frame\n",
            "29\n",
            "[130, 129, 128, 127, 126, 125, 124, 123, 131, 132, 133, 135, 136, 141, 144, 145, 142, 147, 146, 149, 150, 151, 157, 159, 160, 156, 164, 167, 166]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preparing results for download "
      ],
      "metadata": {
        "id": "2fCGL2kUwh7f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!zip -r vid_results.zip /content/video_object_detection_results"
      ],
      "metadata": {
        "id": "E3GYF0xsxd4h",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dbd7ca79-94f5-4e07-ff5b-4860df198cf5"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "updating: content/video_object_detection_results/ (stored 0%)\n",
            "updating: content/video_object_detection_results/yolov8n -- traffic 2.avi (deflated 0%)\n",
            "updating: content/video_object_detection_results/yolov8m -- traffic 2.avi (deflated 0%)\n",
            "updating: content/video_object_detection_results/yolov8s -- traffic 2.avi (deflated 0%)\n",
            "updating: content/video_object_detection_results/yolov8l -- traffic 2.avi (deflated 0%)\n"
          ]
        }
      ]
    }
  ]
}