YOLOv8_Object_Counter_OOP.ipynb

{
  "cells": [
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "JS7r4B7yuOey"
      },
      "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. "
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "ZoZrnFsqLDR6"
      },
      "source": [
        "## Importing libraries, modules and files"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "7ywW_Y4qYmI2"
      },
      "source": [
        "### importing modules"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "hrodd0lKPiDz",
        "outputId": "165180b6-8172-4b7d-cb09-50692a21136d"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/content/requirements.txt'"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wwx247S1H8tn"
      },
      "outputs": [],
      "source": [
        "!pip install -r /content/requirements.txt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "FDIvbG71Jkhn"
      },
      "outputs": [],
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hLCrvX5uxtAt",
        "outputId": "f1166d58-1669-4d2a-9bb2-958c1da18f28"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "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"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "5RZfdp_TwpZJ"
      },
      "source": [
        "## Class definition for `YOLOv8_ObjectDetector` and `YOLOv8_ObjectCounter`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "FxwvNbWImzEG"
      },
      "outputs": [],
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "qI-I-BBS81Fa"
      },
      "outputs": [],
      "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"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "MVPnI_ZdruJr"
      },
      "source": [
        "### preparing file paths and directories"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "Y-xXMgUjiX_0"
      },
      "outputs": [],
      "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)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "MfZjVGJSr6ie"
      },
      "source": [
        "### Instanciating YOLOv8_ObjectCounter objects "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "YhaMvVPHAy1r"
      },
      "outputs": [],
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214,
          "referenced_widgets": [
            "74c63422783a4b9393937c147ab52312",
            "bdf76418bd444bda805e6bc72c20b41d",
            "c23dbe0f8ae842ac86431dcf6df4e30e",
            "589f98818ece45c08e8b08aba2e10a52",
            "cdd6765e1e8c4d9290b925723668ce4d",
            "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",
            "6137f253f1a948aa988e0f7610d4c6f1",
            "c21f8ec0668041008a7ee7b4243147d9",
            "d8c7c0efc98f435ab4d6472f0b503d22",
            "e01152f2c2c5454d8e03da07a2de2b0b",
            "8b38405fd384473cabaabec6d7aebfcf"
          ]
        },
        "id": "E7O7yl3A3kxE",
        "outputId": "3ca4fbb5-3211-40c7-90cb-779a6bb055d8"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt to yolov8n.pt...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "74c63422783a4b9393937c147ab52312",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0.00/6.23M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt to yolov8m.pt...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d63e0f6aab9c4f39a30077a535aa2fdb",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0.00/49.7M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s.pt to yolov8s.pt...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f13a88dff2da4a009813c56a05da6c47",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0.00/21.5M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l.pt to yolov8l.pt...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d60b6159cd94445193dcd3c710944f0b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0.00/83.7M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "counters = []\n",
        "for yolo_name in yolo_names:\n",
        "    counter = YOLOv8_ObjectCounter(yolo_name, conf = 0.60 )\n",
        "    counters.append(counter)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "fLYwvejewThL"
      },
      "source": [
        "### Performing object detection, tracking and counting "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ph04-AKe3z9x",
        "outputId": "af509b99-84b9-47e2-9241-22f6d256c592"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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"
          ]
        }
      ],
      "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)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "2fCGL2kUwh7f"
      },
      "source": [
        "## Preparing results for download "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E3GYF0xsxd4h",
        "outputId": "69aba09c-2538-4981-e016-8abca982341b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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"
          ]
        }
      ],
      "source": [
        "!zip -r vid_results.zip /content/video_object_detection_results"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "0c6b9533bf5940c88d407d6ab4c2d3ac": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "0e9f5322c2534d9fb0e62a3d4064b9dc": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "19b7ecc1a0394234bae9059916ba5f2c": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_ae75f9cc781541d6864e4c534f0f934d",
            "placeholder": "​",
            "style": "IPY_MODEL_c203344233064ec2a22bb8ca52475952",
            "value": "100%"
          }
        },
        "275c1550ed0c4ebfb10623e0cb831f40": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "283fcfb3503f4ac6af092bcbc65539eb": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_9d203e13b0c340cb83d10cd019b24834",
            "placeholder": "​",
            "style": "IPY_MODEL_8b6fed3895aa41cdafa60d9247406960",
            "value": "100%"
          }
        },
        "296e08bae8b14d03b6ba6aecbf54c0e1": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_3af23971bbbb47788282c06fa9567059",
            "max": 22573363,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_8f779fc5df5949c1b78adf7b80af68cb",
            "value": 22573363
          }
        },
        "364379aa31b8436995eff048997e1889": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "3af23971bbbb47788282c06fa9567059": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "3b75806d3422402e88fe4a6a34ac0c13": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "440d6d451ea149c0a88032241b62e89e": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_5a631dc99881412c9248457c56339e51",
            "placeholder": "​",
            "style": "IPY_MODEL_94ff81cffe4d437e99cb324f03434222",
            "value": " 49.7M/49.7M [00:00&lt;00:00, 144MB/s]"
          }
        },
        "51a52a8f1f9a437091a15d8e4698ba0a": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_57786ef5757b43a08728e78292f3f364",
            "placeholder": "​",
            "style": "IPY_MODEL_6137f253f1a948aa988e0f7610d4c6f1",
            "value": "100%"
          }
        },
        "57786ef5757b43a08728e78292f3f364": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "589f98818ece45c08e8b08aba2e10a52": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_364379aa31b8436995eff048997e1889",
            "placeholder": "​",
            "style": "IPY_MODEL_f3cb7f2d8cab439aaf1be6627d896861",
            "value": " 6.23M/6.23M [00:00&lt;00:00, 93.1MB/s]"
          }
        },
        "5a631dc99881412c9248457c56339e51": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "6137f253f1a948aa988e0f7610d4c6f1": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "740c00fec1534ba39dae12b4c21ba4bc": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_e01152f2c2c5454d8e03da07a2de2b0b",
            "placeholder": "​",
            "style": "IPY_MODEL_8b38405fd384473cabaabec6d7aebfcf",
            "value": " 83.7M/83.7M [00:00&lt;00:00, 117MB/s]"
          }
        },
        "74c63422783a4b9393937c147ab52312": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_bdf76418bd444bda805e6bc72c20b41d",
              "IPY_MODEL_c23dbe0f8ae842ac86431dcf6df4e30e",
              "IPY_MODEL_589f98818ece45c08e8b08aba2e10a52"
            ],
            "layout": "IPY_MODEL_cdd6765e1e8c4d9290b925723668ce4d"
          }
        },
        "8b38405fd384473cabaabec6d7aebfcf": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "8b6fed3895aa41cdafa60d9247406960": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "8f779fc5df5949c1b78adf7b80af68cb": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "94ff81cffe4d437e99cb324f03434222": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "9d203e13b0c340cb83d10cd019b24834": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "9fe5f9418c6b4ba59f9cfa672418bdc1": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "a06e3aeca38a4a40a38c4100c3b99a29": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_b861e108e0fb44f299d6ee4bf4f31716",
            "max": 52117635,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_cfb4327e8b524deab10fdf36251279d4",
            "value": 52117635
          }
        },
        "acc1ea899bc747478bd68d716511bfcc": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_0e9f5322c2534d9fb0e62a3d4064b9dc",
            "placeholder": "​",
            "style": "IPY_MODEL_3b75806d3422402e88fe4a6a34ac0c13",
            "value": " 21.5M/21.5M [00:00&lt;00:00, 131MB/s]"
          }
        },
        "ae75f9cc781541d6864e4c534f0f934d": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "b0e0fc63c332428686df86928b393d4d": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "b7d130944252418cb01436e472e99045": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "b861e108e0fb44f299d6ee4bf4f31716": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "bdf76418bd444bda805e6bc72c20b41d": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_fd8b2392e0724dbca38117ea7e3d8092",
            "placeholder": "​",
            "style": "IPY_MODEL_b7d130944252418cb01436e472e99045",
            "value": "100%"
          }
        },
        "c203344233064ec2a22bb8ca52475952": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "c21f8ec0668041008a7ee7b4243147d9": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "c23dbe0f8ae842ac86431dcf6df4e30e": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_9fe5f9418c6b4ba59f9cfa672418bdc1",
            "max": 6534387,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_275c1550ed0c4ebfb10623e0cb831f40",
            "value": 6534387
          }
        },
        "cdd6765e1e8c4d9290b925723668ce4d": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "cfb4327e8b524deab10fdf36251279d4": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "cff8848a7ca448eab31e86871e0d965a": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "d60b6159cd94445193dcd3c710944f0b": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_51a52a8f1f9a437091a15d8e4698ba0a",
              "IPY_MODEL_fdd0228e512f4cd1a2437ccb832e784c",
              "IPY_MODEL_740c00fec1534ba39dae12b4c21ba4bc"
            ],
            "layout": "IPY_MODEL_0c6b9533bf5940c88d407d6ab4c2d3ac"
          }
        },
        "d63e0f6aab9c4f39a30077a535aa2fdb": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_283fcfb3503f4ac6af092bcbc65539eb",
              "IPY_MODEL_a06e3aeca38a4a40a38c4100c3b99a29",
              "IPY_MODEL_440d6d451ea149c0a88032241b62e89e"
            ],
            "layout": "IPY_MODEL_cff8848a7ca448eab31e86871e0d965a"
          }
        },
        "d8c7c0efc98f435ab4d6472f0b503d22": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "e01152f2c2c5454d8e03da07a2de2b0b": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "f13a88dff2da4a009813c56a05da6c47": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_19b7ecc1a0394234bae9059916ba5f2c",
              "IPY_MODEL_296e08bae8b14d03b6ba6aecbf54c0e1",
              "IPY_MODEL_acc1ea899bc747478bd68d716511bfcc"
            ],
            "layout": "IPY_MODEL_b0e0fc63c332428686df86928b393d4d"
          }
        },
        "f3cb7f2d8cab439aaf1be6627d896861": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "fd8b2392e0724dbca38117ea7e3d8092": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "fdd0228e512f4cd1a2437ccb832e784c": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_c21f8ec0668041008a7ee7b4243147d9",
            "max": 87769683,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_d8c7c0efc98f435ab4d6472f0b503d22",
            "value": 87769683
          }
        }
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}