YOLOv8_Object_Counter_OOP.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": "75af878e-c798-46d5-976e-d41eb73a4bb7"
      },
      "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 | 21.78 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"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "hrodd0lKPiDz",
        "outputId": "165180b6-8172-4b7d-cb09-50692a21136d"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/requirements.txt'"
            ],
            "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 imageio\n",
        "import time\n",
        "import matplotlib.pyplot as plt\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": "f1166d58-1669-4d2a-9bb2-958c1da18f28"
      },
      "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": "markdown",
      "source": [
        "## Class definition for `YOLOv8_ObjectDetector` and `YOLOv8_ObjectCounter`"
      ],
      "metadata": {
        "id": "5RZfdp_TwpZJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class YOLOv8_ObjectDetector:\n",
        "    \"\"\"\n",
        "    A class for performing object detection on images and videos using YOLOv8.\n",
        "\n",
        "    Args:\n",
        "    ------------\n",
        "        model_file (str): Path to the YOLOv8 model file or yolo model variant name in ths format: [variant].pt\n",
        "        labels (list[str], optional): A list of class labels for the model. If None, uses the default labels from the model file.\n",
        "        classes (list[str], optional): Alias for labels. Deprecated.\n",
        "        conf (float, optional): Minimum confidence threshold for object detection.\n",
        "        iou (float, optional): Minimum IOU threshold for non-max suppression.\n",
        "\n",
        "    Attributes:\n",
        "    --------------\n",
        "        classes (list[str]): A list of class labels for the model ( a Dict is also acceptable).\n",
        "        conf (float): Minimum confidence threshold for object detection.\n",
        "        iou (float): Minimum IOU threshold for non-max suppression.\n",
        "        model (YOLO): The YOLOv8 model used for object detection.\n",
        "        model_name (str): The name of the YOLOv8 model file (without the .pt extension).\n",
        "\n",
        "    Methods :\n",
        "    -------------\n",
        "        default_display: Returns a default display (ultralytics plot implementation) of the object detection results.\n",
        "        custom_display: Returns a custom display of the object detection results.\n",
        "        predict_video: Predicts objects in a video and saves the results to a file.\n",
        "        predict_img: Predicts objects in an image and returns the detection results.\n",
        "\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, model_file = 'yolov8n.pt', labels= None, classes = None, conf = 0.25, iou = 0.45 ):\n",
        "\n",
        "        self.classes = classes\n",
        "        self.conf = conf\n",
        "        self.iou = iou\n",
        "\n",
        "        self.model = YOLO(model_file)\n",
        "        self.model_name = model_file.split('.')[0]\n",
        "        self.results = None\n",
        "\n",
        "        if labels == None:\n",
        "            self.labels = self.model.names\n",
        "\n",
        "    def predict_img(self, img, verbose=True):\n",
        "        \"\"\"\n",
        "        Runs object detection on a single image.\n",
        "\n",
        "        Parameters\n",
        "        ----------\n",
        "            img (numpy.ndarray): Input image to perform object detection on.\n",
        "            verbose (bool): Whether to print detection details.\n",
        "\n",
        "        Returns:\n",
        "        -----------\n",
        "            'ultralytics.yolo.engine.results.Results': A YOLO results object that contains \n",
        "             details about detection results :\n",
        "                    - Class IDs\n",
        "                    - Bounding Boxes\n",
        "                    - Confidence score\n",
        "                    ...\n",
        "        (pls refer to https://docs.ultralytics.com/reference/results/#results-api-reference for results API reference)\n",
        "\n",
        "        \"\"\"\n",
        "\n",
        "        # Run the model on the input image with the given parameters\n",
        "        results = self.model(img, classes=self.classes, conf=self.conf, iou=self.iou, verbose=verbose)\n",
        "\n",
        "        # Save the original image and the results for further analysis if needed\n",
        "        self.orig_img = img\n",
        "        self.results = results[0]\n",
        "\n",
        "        # Return the detection results\n",
        "        return results[0]\n",
        "\n",
        "\n",
        "\n",
        "    def default_display(self, show_conf=True, line_width=None, font_size=None, \n",
        "                        font='Arial.ttf', pil=False, example='abc'):\n",
        "        \"\"\"\n",
        "        Displays the detected objects on the original input image.\n",
        "\n",
        "        Parameters\n",
        "        ----------\n",
        "        show_conf : bool, optional\n",
        "            Whether to show the confidence score of each detected object, by default True.\n",
        "        line_width : int, optional\n",
        "            The thickness of the bounding box line in pixels, by default None.\n",
        "        font_size : int, optional\n",
        "            The font size of the text label for each detected object, by default None.\n",
        "        font : str, optional\n",
        "            The font type of the text label for each detected object, by default 'Arial.ttf'.\n",
        "        pil : bool, optional\n",
        "            Whether to return a PIL Image object, by default False.\n",
        "        example : str, optional\n",
        "            A string to display on the example bounding box, by default 'abc'.\n",
        "\n",
        "        Returns\n",
        "        -------\n",
        "        numpy.ndarray or PIL Image\n",
        "            The original input image with the detected objects displayed as bounding boxes.\n",
        "            If `pil=True`, a PIL Image object is returned instead.\n",
        "\n",
        "        Raises\n",
        "        ------\n",
        "        ValueError\n",
        "            If the input image has not been detected by calling the `predict_img()` method first.\n",
        "        \"\"\"\n",
        "        # Check if the `predict_img()` method has been called before displaying the detected objects\n",
        "        if self.results is None:\n",
        "            raise ValueError('No detected objects to display. Call predict_img() method first.')\n",
        "        \n",
        "        # Call the plot() method of the `self.results` object to display the detected objects on the original image\n",
        "        display_img = self.results.plot(show_conf, line_width, font_size, font, pil, example)\n",
        "        \n",
        "        # Return the displayed image\n",
        "        return display_img\n",
        "\n",
        "        \n",
        "\n",
        "    def custom_display(self, colors, show_cls = True, show_conf = True):\n",
        "        \"\"\"\n",
        "        Custom display method that draws bounding boxes and labels on the original image, \n",
        "        with additional options for showing class and confidence information.\n",
        "\n",
        "        Parameters:\n",
        "        -----------\n",
        "        colors : list\n",
        "            A list of tuples specifying the color of each class.\n",
        "        show_cls : bool, optional\n",
        "            Whether to show class information in the label text. Default is True.\n",
        "        show_conf : bool, optional\n",
        "            Whether to show confidence information in the label text. Default is True.\n",
        "\n",
        "        Returns:\n",
        "        --------\n",
        "        numpy.ndarray\n",
        "            The image with bounding boxes and labels drawn on it.\n",
        "        \"\"\"\n",
        "\n",
        "        img = self.orig_img\n",
        "        # calculate the bounding box thickness based on the image width and height\n",
        "        bbx_thickness = (img.shape[0] + img.shape[1]) // 450\n",
        "\n",
        "        for box in self.results.boxes:\n",
        "            textString = \"\"\n",
        "\n",
        "            # Extract object class and confidence score\n",
        "            score = box.conf.item() * 100\n",
        "            class_id = int(box.cls.item())\n",
        "\n",
        "            x1 , y1 , x2, y2 = np.squeeze(box.xyxy.numpy()).astype(int)\n",
        "\n",
        "            # Print detection info\n",
        "            if show_cls:\n",
        "                textString += f\"{self.labels[class_id]}\"\n",
        "\n",
        "            if show_conf:\n",
        "                textString += f\" {score:,.2f}%\"\n",
        "\n",
        "            # Calculate font scale based on object size\n",
        "            font = cv2.FONT_HERSHEY_COMPLEX\n",
        "            fontScale = (((x2 - x1) / img.shape[0]) + ((y2 - y1) / img.shape[1])) / 2 * 2.5\n",
        "            fontThickness = 1\n",
        "            textSize, baseline = cv2.getTextSize(textString, font, fontScale, fontThickness)\n",
        "\n",
        "            # Draw bounding box, a centroid and label on the image\n",
        "            img = cv2.rectangle(img, (x1,y1), (x2,y2), colors[class_id], bbx_thickness)\n",
        "            center_coordinates = ((x1 + x2)//2, (y1 + y2) // 2)\n",
        "\n",
        "            img =  cv2.circle(img, center_coordinates, 5 , (0,0,255), -1)\n",
        "            \n",
        "             # If there are no details to show on the image\n",
        "            if textString != \"\":\n",
        "                if (y1 < textSize[1]):\n",
        "                    y1 = y1 + textSize[1]\n",
        "                else:\n",
        "                    y1 -= 2\n",
        "                # show the details text in a filled rectangle\n",
        "                img = cv2.rectangle(img, (x1, y1), (x1 + textSize[0] , y1 -  textSize[1]), colors[class_id], cv2.FILLED)\n",
        "                img = cv2.putText(img, textString , \n",
        "                    (x1, y1), font, \n",
        "                    fontScale,  (0, 0, 0), fontThickness, cv2.LINE_AA)\n",
        "\n",
        "        return img\n",
        "\n",
        "\n",
        "    def predict_video(self, video_path, save_dir, save_format=\"avi\", display='custom', verbose=True, **display_args):\n",
        "        \"\"\"Runs object detection on each frame of a video and saves the output to a new video file.\n",
        "\n",
        "        Args:\n",
        "        ----------\n",
        "            video_path (str): The path to the input video file.\n",
        "            save_dir (str): The path to the directory where the output video file will be saved.\n",
        "            save_format (str, optional): The format for the output video file. Defaults to \"avi\".\n",
        "            display (str, optional): The type of display for the detection results. Defaults to 'custom'.\n",
        "            verbose (bool, optional): Whether to print information about the video file and output file. Defaults to True.\n",
        "            **display_args: Additional arguments to be passed to the display function.\n",
        "\n",
        "        Returns:\n",
        "        ------------\n",
        "            None\n",
        "        \"\"\"\n",
        "        # Open the input video file\n",
        "        cap = cv2.VideoCapture(video_path)\n",
        "\n",
        "        # Get the name of the input video file\n",
        "        vid_name = os.path.basename(video_path)\n",
        "\n",
        "        # Get the dimensions of each frame in the input video file\n",
        "        width = int(cap.get(3))  # get `width`\n",
        "        height = int(cap.get(4))  # get `height`\n",
        "\n",
        "        # Create the directory for the output video file if it does not already exist\n",
        "        if not os.path.isdir(save_dir):\n",
        "            os.makedirs(save_dir)\n",
        "\n",
        "        # Set the name and path for the output video file\n",
        "        save_name = self.model_name + ' -- ' + vid_name.split('.')[0] + '.' + save_format\n",
        "        save_file = os.path.join(save_dir, save_name)\n",
        "\n",
        "        # Print information about the input and output video files if verbose is True\n",
        "        if verbose:\n",
        "            print(\"----------------------------\")\n",
        "            print(f\"DETECTING OBJECTS IN : {vid_name} : \")\n",
        "            print(f\"RESOLUTION : {width}x{height}\")\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 input video file was opened correctly\n",
        "        if not cap.isOpened():\n",
        "            print(\"Error opening video stream or file\")\n",
        "\n",
        "        # Read each frame of the input video file\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 = self.predict_img(frame, verbose=False)\n",
        "            if results is None:\n",
        "                print('***********************************************')\n",
        "            fps = 1 / (time.time() - beg)\n",
        "\n",
        "            # Display the detection results\n",
        "            if display == 'default':\n",
        "                frame = self.default_display(**display_args)\n",
        "            elif display == 'custom':\n",
        "                frame == self.custom_display(**display_args)\n",
        "\n",
        "            # Display the FPS on the 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",
        "            # Write the frame to the output video file\n",
        "            out.write(frame)\n",
        "\n",
        "            # Exit the loop if the 'q' button is pressed\n",
        "            if cv2.waitKey(1) & 0xFF == ord('q'):\n",
        "                break\n",
        "\n",
        "        # After the loop release the cap and video writer\n",
        "        cap.release()\n",
        "        out.release()\n",
        "\n",
        "    \n"
      ],
      "metadata": {
        "id": "FxwvNbWImzEG"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class YOLOv8_ObjectCounter(YOLOv8_ObjectDetector):\n",
        "    \"\"\"\n",
        "    A class for counting objects in images or videos using the YOLOv8 Object Detection model.\n",
        "\n",
        "    Attributes:\n",
        "    -----------\n",
        "    model_file : str\n",
        "        The filename of the YOLOv8 object detection model.\n",
        "    labels : list or None\n",
        "        The list of labels for the object detection model. If None, the labels will be loaded from the model file.\n",
        "    classes : list or None\n",
        "        The list of classes to detect. If None, all classes will be detected.\n",
        "    conf : float\n",
        "        The confidence threshold for object detection.\n",
        "    iou : float\n",
        "        The Intersection over Union (IoU) threshold for object detection.\n",
        "    track_max_age : int\n",
        "        The maximum age (in frames) of a track before it is deleted.\n",
        "    track_min_hits : int\n",
        "        The minimum number of hits required for a track to be considered valid.\n",
        "    track_iou_threshold : float\n",
        "        The IoU threshold for matching detections to existing tracks.\n",
        "\n",
        "    Methods:\n",
        "    --------\n",
        "    predict_img(img, verbose=True)\n",
        "        Predicts objects in a single image and counts them.\n",
        "    predict_video(video_path, save_dir, save_format=\"avi\", display='custom', verbose=True, **display_args)\n",
        "        Predicts objects in a video and counts them.\n",
        "\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(self, model_file = 'yolov8n.pt', labels= None, classes = None, conf = 0.25, iou = 0.45, \n",
        "                 track_max_age = 45, track_min_hits= 15, track_iou_threshold = 0.3 ):\n",
        "\n",
        "        super().__init__(model_file , labels, classes, conf, iou)\n",
        "\n",
        "        self.track_max_age = track_max_age\n",
        "        self.track_min_hits = track_min_hits\n",
        "        self.track_iou_threshold = track_iou_threshold\n",
        "\n",
        "\n",
        "        \n",
        "\n",
        "    def predict_video(self, video_path, save_dir, save_format = \"avi\", \n",
        "                      display = 'custom', verbose = True, **display_args):\n",
        "        \n",
        "        \"\"\"\n",
        "    Runs object detection on a video file and saves the output as a new video file.\n",
        "    \n",
        "    Args:\n",
        "        video_path (str): Path to the input video file.\n",
        "        save_dir (str): Path to the directory where the output video file will be saved.\n",
        "        save_format (str, optional): Format of the output video file. Defaults to \"avi\".\n",
        "        display (str, optional): Type of display to use for object detection results. Options are \"default\" or \"custom\". \n",
        "                                Defaults to \"custom\".\n",
        "        verbose (bool, optional): If True, prints information about the input and output video files. Defaults to True.\n",
        "        **display_args (dict, optional): Additional arguments to pass to the display function. \n",
        "\n",
        "    Returns:\n",
        "        None\n",
        "        \"\"\"\n",
        "        cap = cv2.VideoCapture(video_path)\n",
        "        # Get video name \n",
        "        vid_name = os.path.basename(video_path)\n",
        "\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",
        "\n",
        "        if not os.path.isdir(save_dir):\n",
        "            os.makedirs(save_dir)\n",
        "\n",
        "        save_name = self.model_name + ' -- ' + vid_name.split('.')[0] + '.' + save_format\n",
        "        save_file = os.path.join(save_dir, save_name)\n",
        "\n",
        "        if verbose:\n",
        "            print(\"----------------------------\")\n",
        "            print(f\"DETECTING OBJECTS IN : {vid_name} : \")\n",
        "            print(f\"RESOLUTION : {width}x{height}\")\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",
        "        # Initialize object tracker\n",
        "        tracker = sort.Sort(max_age = self.track_max_age, min_hits= self.track_min_hits , \n",
        "                            iou_threshold = self.track_iou_threshold)\n",
        "        \n",
        "        # Initialize variables for object counting\n",
        "        totalCount = []\n",
        "        currentArray = np.empty((0, 5))\n",
        "\n",
        "\n",
        "        # Read the video frames\n",
        "        while cap.isOpened():\n",
        "\n",
        "            detections = np.empty((0, 5))\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 = self.predict_img(frame, verbose = False)\n",
        "            if results == None:\n",
        "                print('***********************************************')\n",
        "            fps = 1 / (time.time() - beg)\n",
        "            for box in results.boxes:\n",
        "                score = box.conf.item() * 100\n",
        "                class_id = int(box.cls.item())\n",
        "\n",
        "                x1 , y1 , x2, y2 = np.squeeze(box.xyxy.numpy()).astype(int)\n",
        "\n",
        "                currentArray = np.array([x1, y1, x2, y2, score])\n",
        "                detections = np.vstack((detections, currentArray))\n",
        "\n",
        "            # Update object tracker \n",
        "            resultsTracker = tracker.update(detections)\n",
        "            for result in resultsTracker:\n",
        "                #print(type(result))\n",
        "\n",
        "                # Get the tracker results\n",
        "                x1, y1, x2, y2, id = result\n",
        "                x1, y1, x2, y2, id = int(x1), int(y1), int(x2), int(y2), int(id)\n",
        "                #print(result)\n",
        "\n",
        "                # Display current objects IDs\n",
        "                w, h = x2 - x1, y2 - y1\n",
        "                cx, cy = x1 + w // 2, y1 + h // 2\n",
        "                id_txt = f\"ID: {str(id)}\"\n",
        "                cv2.putText(frame, id_txt, (cx, cy), 4, 0.5, (0, 0, 255), 1)\n",
        "\n",
        "                # if we haven't seen aprticular object ID before, register it in a list \n",
        "                if totalCount.count(id) == 0:\n",
        "                    totalCount.append(id)\n",
        "\n",
        "            # Display detection results\n",
        "            if display == 'default':\n",
        "                frame = self.default_display(**display_args)\n",
        "            \n",
        "            elif display == 'custom':\n",
        "                frame == self.custom_display(**display_args)\n",
        "\n",
        "            # Display FPS on frame\n",
        "            frame = cv2.putText(frame,f\"FPS : {fps:,.2f}\" , \n",
        "                                (5,55), cv2.FONT_HERSHEY_COMPLEX, \n",
        "                            0.5,  (0,255,255), 1, cv2.LINE_AA)\n",
        "            \n",
        "            # Display Counting results\n",
        "            count_txt = f\"TOTAL COUNT : {len(totalCount)}\"\n",
        "            frame = cv2.putText(frame, count_txt, (5,45), cv2.FONT_HERSHEY_COMPLEX, 2, (0, 0, 255), 2)\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()\n",
        "        print(len(totalCount))\n",
        "        print(totalCount)\n",
        "    \n"
      ],
      "metadata": {
        "id": "qI-I-BBS81Fa"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "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": 8,
      "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": 9,
      "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": [
            "74c63422783a4b9393937c147ab52312",
            "bdf76418bd444bda805e6bc72c20b41d",
            "c23dbe0f8ae842ac86431dcf6df4e30e",
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            "fd8b2392e0724dbca38117ea7e3d8092",
            "b7d130944252418cb01436e472e99045",
            "9fe5f9418c6b4ba59f9cfa672418bdc1",
            "275c1550ed0c4ebfb10623e0cb831f40",
            "364379aa31b8436995eff048997e1889",
            "f3cb7f2d8cab439aaf1be6627d896861",
            "d63e0f6aab9c4f39a30077a535aa2fdb",
            "283fcfb3503f4ac6af092bcbc65539eb",
            "a06e3aeca38a4a40a38c4100c3b99a29",
            "440d6d451ea149c0a88032241b62e89e",
            "cff8848a7ca448eab31e86871e0d965a",
            "9d203e13b0c340cb83d10cd019b24834",
            "8b6fed3895aa41cdafa60d9247406960",
            "b861e108e0fb44f299d6ee4bf4f31716",
            "cfb4327e8b524deab10fdf36251279d4",
            "5a631dc99881412c9248457c56339e51",
            "94ff81cffe4d437e99cb324f03434222",
            "f13a88dff2da4a009813c56a05da6c47",
            "19b7ecc1a0394234bae9059916ba5f2c",
            "296e08bae8b14d03b6ba6aecbf54c0e1",
            "acc1ea899bc747478bd68d716511bfcc",
            "b0e0fc63c332428686df86928b393d4d",
            "ae75f9cc781541d6864e4c534f0f934d",
            "c203344233064ec2a22bb8ca52475952",
            "3af23971bbbb47788282c06fa9567059",
            "8f779fc5df5949c1b78adf7b80af68cb",
            "0e9f5322c2534d9fb0e62a3d4064b9dc",
            "3b75806d3422402e88fe4a6a34ac0c13",
            "d60b6159cd94445193dcd3c710944f0b",
            "51a52a8f1f9a437091a15d8e4698ba0a",
            "fdd0228e512f4cd1a2437ccb832e784c",
            "740c00fec1534ba39dae12b4c21ba4bc",
            "0c6b9533bf5940c88d407d6ab4c2d3ac",
            "57786ef5757b43a08728e78292f3f364",
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            "c21f8ec0668041008a7ee7b4243147d9",
            "d8c7c0efc98f435ab4d6472f0b503d22",
            "e01152f2c2c5454d8e03da07a2de2b0b",
            "8b38405fd384473cabaabec6d7aebfcf"
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        },
        "id": "E7O7yl3A3kxE",
        "outputId": "3ca4fbb5-3211-40c7-90cb-779a6bb055d8"
      },
      "execution_count": 10,
      "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": "74c63422783a4b9393937c147ab52312"
            }
          },
          "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",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d63e0f6aab9c4f39a30077a535aa2fdb"
            }
          },
          "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",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f13a88dff2da4a009813c56a05da6c47"
            }
          },
          "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": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d60b6159cd94445193dcd3c710944f0b"
            }
          },
          "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": "af509b99-84b9-47e2-9241-22f6d256c592"
      },
      "execution_count": 11,
      "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": "69aba09c-2538-4981-e016-8abca982341b"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  adding: content/video_object_detection_results/ (stored 0%)\n",
            "  adding: content/video_object_detection_results/yolov8n -- traffic 2.avi (deflated 0%)\n",
            "  adding: content/video_object_detection_results/yolov8m -- traffic 2.avi (deflated 0%)\n",
            "  adding: content/video_object_detection_results/yolov8s -- traffic 2.avi (deflated 0%)\n",
            "  adding: content/video_object_detection_results/yolov8l -- traffic 2.avi (deflated 0%)\n"
          ]
        }
      ]
    }
  ]
}