YOLOv8_Object_Detection_procedural.ipynb

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            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
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            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
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          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
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            "_view_module_version": "1.2.0",
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "<div align=\"center\">\n",
        "\n",
        "# YOLOv8 simple object detector using a procedural approach\n",
        "\n",
        "</div>\n",
        "\n",
        "The YOLOv8 is a novel an powerful object detection algorithm that combines high accuracy and real-time detection speeds. This Colab notebook demonstrates how to implement a simple object detector using a procedural approach, allowing us to detect objects in both static images and videos.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "__RRUqeCJo-8"
      }
    },
    {
      "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": "68c64dac-7056-4f97-b058-702e37871897"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'Object-tracking-and-counting-using-YOLOV8'...\n",
            "remote: Enumerating objects: 90, done.\u001b[K\n",
            "remote: Counting objects: 100% (15/15), done.\u001b[K\n",
            "remote: Compressing objects: 100% (15/15), done.\u001b[K\n",
            "remote: Total 90 (delta 0), reused 14 (delta 0), pack-reused 75\u001b[K\n",
            "Unpacking objects: 100% (90/90), 244.01 MiB | 8.68 MiB/s, done.\n",
            "Updating files: 100% (49/49), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### importing modules"
      ],
      "metadata": {
        "id": "7ywW_Y4qYmI2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import random\n",
        "import os\n",
        "import cv2\n",
        "import time"
      ],
      "metadata": {
        "id": "FDIvbG71Jkhn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hLCrvX5uxtAt",
        "outputId": "dc24bf7d-6708-499d-d130-fa2a7e46e92c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Ultralytics YOLOv8.0.58 🚀 Python-3.9.16 torch-1.13.1+cu116 CPU\n",
            "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 26.1/107.7 GB disk)\n"
          ]
        }
      ],
      "source": [
        "%pip install ultralytics\n",
        "import ultralytics\n",
        "ultralytics.checks()\n",
        "from ultralytics import YOLO\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### preparing file paths and directories"
      ],
      "metadata": {
        "id": "MVPnI_ZdruJr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "img_results_path = '/content/image_object_detection_results'\n",
        "vid_results_path = '/content/video_object_detection_results'\n",
        "test_imgs_path = '/content/Object-tracking-and-counting-using-YOLOV8/test imgs'\n",
        "test_vids_path = '/content/Object-tracking-and-counting-using-YOLOV8/test vids'\n",
        "\n",
        "if not os.path.isdir(img_results_path):\n",
        "    os.makedirs(img_results_path)\n",
        "\n",
        "if not os.path.isdir(vid_results_path):\n",
        "    os.makedirs(vid_results_path)"
      ],
      "metadata": {
        "id": "Y-xXMgUjiX_0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### importing YOLOv8 object detection variants"
      ],
      "metadata": {
        "id": "MfZjVGJSr6ie"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "yolo_names = ['yolov8n.pt', 'yolov8m.pt', 'yolov8x.pt']\n",
        "\n",
        "# load yolo models and store them in a dict:\n",
        "yolov8_models = {}\n",
        "for yolo_name in yolo_names:\n",
        "    yolov8_models[yolo_name[:-3]] = YOLO(yolo_name)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165,
          "referenced_widgets": [
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        "id": "fZ8xOlMHbWx9",
        "outputId": "82c86b34-c8b2-4147-9949-5908361fb7f0"
      },
      "execution_count": null,
      "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": "523d97f2012a416fb15bab538823d816"
            }
          },
          "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": "2fe6b54712ce4745830849f13f6c8844"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x.pt to yolov8x.pt...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0.00/131M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Implementing object detection"
      ],
      "metadata": {
        "id": "KP059CPfsDsO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### testing object detection on an image"
      ],
      "metadata": {
        "id": "ZB3q4d-DsJe7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for yolo_name , yolo_model in yolov8_models.items():\n",
        "    results = yolo_model('https://ultralytics.com/images/bus.jpg', verbose = True)\n",
        "    results = results [0]\n",
        "    a = results.plot()\n",
        "    cv2.imwrite(yolo_name + '.jpg', a)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 257,
          "referenced_widgets": [
            "11d6e450e5074c14a58fd2c6f18f8326",
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          ]
        },
        "id": "K--75wbEeln_",
        "outputId": "e80cb3dc-1e6d-4123-d7da-bb879bd69158"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "Downloading https://ultralytics.com/images/bus.jpg to bus.jpg...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0.00/476k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "11d6e450e5074c14a58fd2c6f18f8326"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "image 1/1 /content/bus.jpg: 640x480 4 persons, 1 bus, 1 stop sign, 310.4ms\n",
            "Speed: 3.4ms preprocess, 310.4ms inference, 22.7ms postprocess per image at shape (1, 3, 640, 640)\n",
            "\n",
            "Found https://ultralytics.com/images/bus.jpg locally at bus.jpg\n",
            "image 1/1 /content/bus.jpg: 640x480 4 persons, 1 bus, 1175.6ms\n",
            "Speed: 1.0ms preprocess, 1175.6ms inference, 1.1ms postprocess per image at shape (1, 3, 640, 640)\n",
            "\n",
            "Found https://ultralytics.com/images/bus.jpg locally at bus.jpg\n",
            "image 1/1 /content/bus.jpg: 640x480 5 persons, 1 bicycle, 1 bus, 4443.8ms\n",
            "Speed: 0.9ms preprocess, 4443.8ms inference, 2.1ms postprocess per image at shape (1, 3, 640, 640)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Performing object detection on multiple static images in a directory using different YOLOv8 variants"
      ],
      "metadata": {
        "id": "-JYrK_jtsSdr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for yolo_name , yolo_model in yolov8_models.items():\n",
        "    print(f\"Model : {yolo_name}\")\n",
        "    for img_name in os.listdir(test_imgs_path):\n",
        "        print(f\"Target image : {img_name}\")\n",
        "        print(img_name.split('.')[0])\n",
        "        print(\"-------------------------\")\n",
        "        img = cv2.imread(os.path.join(test_imgs_path, img_name))\n",
        "        results = yolo_model(img, verbose = True)\n",
        "        results = results [0]\n",
        "        result_img = results.plot()\n",
        "\n",
        "        save_dir = os.path.join(img_results_path, yolo_name)\n",
        "        if not os.path.isdir(save_dir):\n",
        "            os.makedirs(save_dir)\n",
        "\n",
        "        save_name = yolo_name + ' -- ' + img_name.split('.')[0] + '.jpg'\n",
        "        save_file = os.path.join(img_results_path, save_dir, save_name) \n",
        "        print(f\"Saving results to : {save_file}\")\n",
        "        cv2.imwrite(save_file, result_img)"
      ],
      "metadata": {
        "id": "Hn6Zjv37oVnP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Performing object detection on multiple video files from a directory using different YOLOv8 variants"
      ],
      "metadata": {
        "id": "Y67Znx-Lswai"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for yolo_name , yolo_model in yolov8_models.items():\n",
        "    print(\".......................................\")\n",
        "    print(f\"Model : {yolo_name}\")\n",
        "    print(\".......................................\")\n",
        "\n",
        "    for vid_name in os.listdir(test_vids_path):\n",
        "        video_path = os.path.join(test_vids_path, vid_name)\n",
        "      \n",
        "       # Open input video file\n",
        "        cap = cv2.VideoCapture(video_path)\n",
        "        print(\"----------------------------\")\n",
        "\n",
        "        # Get video name \n",
        "        print(f\"Detecting object in {vid_name} : \")\n",
        "\n",
        "        # Get frame dimensions and print information about input video file\n",
        "        width  = int(cap.get(3) )  # get `width` \n",
        "        height = int(cap.get(4) )  # get `height` \n",
        "        print(f\"resolution : {width}x{height}\")\n",
        "        #print(video_path)\n",
        "\n",
        "        save_dir = os.path.join(vid_results_path, yolo_name)\n",
        "        if not os.path.isdir(save_dir):\n",
        "            os.makedirs(save_dir)\n",
        "\n",
        "        save_name = yolo_name + ' -- ' + vid_name.split('.')[0] + '.avi'\n",
        "        save_file = os.path.join(vid_results_path, save_dir, save_name)\n",
        "        print('SAVING TO :' + save_file)\n",
        "\n",
        "        # define an output VideoWriter  object\n",
        "        out = cv2.VideoWriter(save_file,\n",
        "                            cv2.VideoWriter_fourcc(*\"MJPG\"),\n",
        "                            30,(width,height))\n",
        "\n",
        "        # Check if the video is opened correctly\n",
        "        if not cap.isOpened():\n",
        "            print(\"Error opening video stream or file\")\n",
        "\n",
        "        # Read the video frames\n",
        "        while cap.isOpened():\n",
        "            ret, frame = cap.read()\n",
        "\n",
        "            # If the frame was not read successfully, break the loop\n",
        "            if not ret:\n",
        "                print(\"Error reading frame\")\n",
        "                break\n",
        "\n",
        "            # Run object detection on the frame and calculate FPS\n",
        "            beg = time.time()\n",
        "            results = yolo_model(frame, verbose = False)\n",
        "            fps = 1 / (time.time() - beg)\n",
        "            results = results [0]\n",
        "            frame = results.plot()\n",
        "            # Display FPS on frame\n",
        "            frame = cv2.putText(frame,f\"FPS : {fps:,.2f}\" , \n",
        "                                (5,15), cv2.FONT_HERSHEY_COMPLEX, \n",
        "                            0.5,  (0,0,255), 1, cv2.LINE_AA)\n",
        "            \n",
        "\n",
        "            # append frame to the video file\n",
        "            out.write(frame)\n",
        "            \n",
        "            # the 'q' button is set as the\n",
        "            # quitting button you may use any\n",
        "            # desired button of your choice\n",
        "\n",
        "            if cv2.waitKey(1) & 0xFF == ord('q'):\n",
        "                break\n",
        "\n",
        "        # After the loop release the cap \n",
        "        cap.release()\n",
        "        out.release()"
      ],
      "metadata": {
        "id": "LtkkF8UD0gWn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preparing results for download"
      ],
      "metadata": {
        "id": "PjGqwIzMs2E7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!zip -r img_results.zip /content/image_object_detection_results"
      ],
      "metadata": {
        "id": "NssC0XolnGk_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!zip -r vid_results.zip /content/video_object_detection_results"
      ],
      "metadata": {
        "id": "E3GYF0xsxd4h"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}