Final_Project.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "InxpsU9DCw4C"
      },
      "source": [
        "# ECSE 415 Final Project: Aman Sidhu (260885556) Partick Ohl (261182154)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gRekjNpFDlwo"
      },
      "source": [
        "# Setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bKqu4Tli8YZQ",
        "outputId": "590cd193-e2e1-4ead-fca0-b4d4cef55642"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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          ]
        }
      ],
      "source": [
        "# Install ultralytics for YOLOv8n\n",
        "%pip install ultralytics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C_tjTc5fFsLM",
        "outputId": "37d7b70b-07c9-4de2-b3cf-496da2b3b9c0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "# Imports\n",
        "from ultralytics import YOLO\n",
        "import cv2\n",
        "import numpy as np\n",
        "import math\n",
        "from ultralytics.solutions import object_counter\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image  as mpimg\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "# Setup\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "base_folder = '/content/drive/My Drive/Colab Notebooks/ECSE415/Assignment6/'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "JlFqG74qEGAt"
      },
      "outputs": [],
      "source": [
        "# Helper Functions for Report\n",
        "def print_img(img, name = \"\", cmap = plt.get_cmap('gray')):\n",
        "    plt.imshow(img, cmap=cmap)\n",
        "    print(name + \" Shape:\", np.shape(img))\n",
        "    plt.xticks([]), plt.yticks([])\n",
        "    plt.show()\n",
        "\n",
        "def plot_confusion_matrix(conf_matrix, title):\n",
        "    class_names = ['Negative', 'Positive']\n",
        "    fig, ax = plt.subplots()\n",
        "    im = ax.imshow(conf_matrix, interpolation='nearest', cmap='viridis')\n",
        "    ax.figure.colorbar(im, ax=ax)\n",
        "    ax.set(xticks=np.arange(conf_matrix.shape[1]),\n",
        "           yticks=np.arange(conf_matrix.shape[0]),\n",
        "           xticklabels=class_names[::-1], yticklabels=class_names[::-1],  # Reversed order\n",
        "           title=\"Confusion Matrix for \" + title,\n",
        "           ylabel='True label',\n",
        "           xlabel='Predicted label')\n",
        "\n",
        "    # Rotate the tick labels and set their alignment.\n",
        "    plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
        "             rotation_mode=\"anchor\")\n",
        "\n",
        "    # Loop over data dimensions and create text annotations.\n",
        "    for i in range(conf_matrix.shape[0]):\n",
        "        for j in range(conf_matrix.shape[1]):\n",
        "            ax.text(j, i, format(conf_matrix[i, j], 'd'),\n",
        "                    ha=\"center\", va=\"center\",\n",
        "                    color=\"white\" if conf_matrix[i, j] > conf_matrix.max() / 2 else \"black\")\n",
        "    fig.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "def calculate_metrics(TP, FP, TN, FN):\n",
        "    recall = TP / (TP + FN)\n",
        "    precision = TP / (TP + FP)\n",
        "    f1_score = 2 * (precision * recall) / (precision + recall)\n",
        "    return recall, precision, f1_score"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JAPvHXGREdzF"
      },
      "source": [
        "# Description of Overall Approach and Assumptions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DnXkSL-fEfjC"
      },
      "source": [
        "Our approach consist of 2 separate tasks:\n",
        "\n",
        "\n",
        "**Identifying/Counting passed cars and people**\n",
        "\n",
        "We keep track of the id's of any cars and people that we passed.\n",
        "\n",
        "We assume that we pass an object according to:\n",
        "\n",
        "*Assumption 1: Any person whose bounding box center is anywhere in the bottom 40% of the screen will be considered as passed, and for cars it is 26.5%.*\n",
        "\n",
        " From our testing, this helped with situations where objects might only be detected in the few frames before they are passed, and reduce instances where occlusion in the environment caused by moving cars and our moving perspective removes the chance of detecting an object. Tighter thresholds would perhaps be more rigorous but greatly reduce the window to detect objects. This threshold also helps reduce the times we recount the same object twice, and count objects near the end of the video that we would not have passed yet. We also assume that any person detected is a pedestrian, which would also include any bikers.\n",
        "\n",
        "**Classifying passed cars as moving/parked**\n",
        "\n",
        "The classification consists of 2 stages:\n",
        "\n",
        "\n",
        "\n",
        "1.   **Initial Guess:**\n",
        "  Based on the observation that moving cars are generally driving in the middle of the road (following a certain path), while parked cars are on the side of the road, we make the assumption that every car (we pass) that passes through a certain region of the screen is a moving car. We thus classify every car that passes through a certain line as moving and every other car as parked. This gives us a usable first guess for the classification.\n",
        "\n",
        "2.   **Refinement Stage:**\n",
        "This stage is based on the flow of the cars. We observed that the flow magnitudes have the general ordering\n",
        "```\n",
        "flow(moving_same_dir) < flow(parked) < flow(moving_opposite_dir)\n",
        "```\n",
        "\n",
        "which follows the idea that the speed of the cars relative to the dashcam (which follow above ordering) are reflected in the flow.\n",
        "\n",
        "We use this observation along with :\n",
        "\n",
        "*Assumption 2: All moving cars that we pass move in the same direction as each other*\n",
        "\n",
        "which is a fair assumption when driving in the city (due to the restricted size of the streets). We then use K-means clustering with K=2 and our previous guess as initial labels in order to identify cars that the previous method may have misclassified. The resulting labels are our final classification.\n",
        "\n",
        "**Note:** We define the flow magnitude of a car as the maximum of all mean flow magnitudes (excluding 0 flows) in the bounding boxes (for every frame) of a car. This is the flow-based feature we found that reflects above ordering the most consistently. When including other features such as flow-angle or the standard deviation in flow-angle, the results got worse.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X8F6546vEow4"
      },
      "source": [
        "# Software Packages Used"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ex33N7_NEqsG"
      },
      "source": [
        "**YOLO (Ultralytics)**\n",
        "You Only Look Once (YOLO), is a popular object detection and image segmentation model, originating from a paper called \"You Only Look Once: Unified, Real-Time Object Detection\" by Joseph Redmon et al. from the University of Washington. The original YOLO implementation uses a single CNN to predict bounding boxes and class probabilities directly from images in one evaluation. It first divides the image into a SxS grid, where it tries to find a suitable bounding box in each cell and the corresponding class confidence. Ultralytics provides an improved version of YOLO capable of providing fast, meaningful results and has a variety of extra features. Some features include detection, segmentation, pose estimation, tracking, and classification. For our purposes, we use a YOLOv8 model, which is pre-trained on the COCO dataset, for object classification, tracking, bounding box generation, and object counting.\n",
        "\n",
        "**OpenCV**\n",
        "Open Source Computer Vision Library (OpenCV) is a very popular computer vision library that provides many essential functionalities such as image processing, and feature extraction. In this project, OpenCV is used for\n",
        "* reading the frames of the video\n",
        "* preprocessing the frames\n",
        "* running calcOpticalFlowFarneback to find the optical flow\n",
        "* K-means Clustering"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nH1AXjofE1G_"
      },
      "source": [
        "# Program Results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iZ2RfIgM84hh"
      },
      "outputs": [],
      "source": [
        "# Provides initial analytics of the video\n",
        "class Analyzer:\n",
        "  def __init__(self, video_path):\n",
        "      self.cap = cv2.VideoCapture(video_path)\n",
        "      w, h = (int(self.cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT))\n",
        "      self.frame_shape = (h, w)\n",
        "\n",
        "      self.tracking_model = YOLO('yolov8n.pt')\n",
        "      self.names = self.tracking_model.names\n",
        "\n",
        "      self.passed_cars = set()\n",
        "      self.tracked_flows = {}\n",
        "\n",
        "      self.passed_people = set()\n",
        "\n",
        "      self.bottom_threshold_cars = int(0.735*h)\n",
        "      self.bottom_threshold_person = int(0.6*h)\n",
        "      self.car_confidence_threshold = 0.8\n",
        "      self.person_confidence_threshold = 0.6\n",
        "\n",
        "      self.classes_to_count = [0, 2, 5, 7] # 0: person, 2: car, 5: bus, 7: truck\n",
        "      self.count_names = {i: self.tracking_model.names[i] for i in self.classes_to_count}\n",
        "\n",
        "      self.counter_moving_cars = object_counter.ObjectCounter()\n",
        "      line_points = [(int(w*0.2), int(0.762*h)), (int(w*0.8), int(0.9125*h))]\n",
        "      self.counter_moving_cars.set_args(view_img = True, reg_pts = line_points, classes_names = self.count_names, draw_tracks=True)\n",
        "\n",
        "\n",
        "  def compute_flow(self, img1, img2, flow):\n",
        "    return cv2.calcOpticalFlowFarneback(prev=img1,\n",
        "                                        next=img2,\n",
        "                                        flow=flow,\n",
        "                                        pyr_scale=0.5,\n",
        "                                        levels=3,\n",
        "                                        winsize=15,\n",
        "                                        iterations=3,\n",
        "                                        poly_n=5,\n",
        "                                        poly_sigma=1.2,\n",
        "                                        flags=0 if flow is None else cv2.OPTFLOW_USE_INITIAL_FLOW\n",
        "                                        )\n",
        "\n",
        "\n",
        "  # Returns True if car, optionally adds it to passed_cars\n",
        "  def check_passed_car(self, id, cls, box, conf):\n",
        "    conf = math.ceil(conf*100)/100\n",
        "\n",
        "    is_car = self.names[int(cls)] in ('bus', 'car', 'truck') and conf > self.car_confidence_threshold\n",
        "    if is_car and id:\n",
        "      x_tl, y_tl, x_br, y_bl = box\n",
        "      if (y_tl + abs(y_tl - y_bl)//2) > self.bottom_threshold_cars:\n",
        "        self.passed_cars.add(id)\n",
        "    return is_car\n",
        "\n",
        "\n",
        "  # Adds person to passed_people if we (are going to) pass them\n",
        "  def check_passed_person(self, id, cls, box, conf):\n",
        "    if self.names[int(cls)] == 'person' and conf > self.person_confidence_threshold:\n",
        "            x_tl, y_tl, x_br, y_bl = box\n",
        "            if (y_tl + abs(y_tl - y_bl)//2) > self.bottom_threshold_person:\n",
        "              self.passed_people.add(id)\n",
        "\n",
        "\n",
        "  def analyse_frame_data(self, tracks, flow_mags, flow_angles):\n",
        "      flow_not_zero_mask = np.zeros(self.frame_shape)\n",
        "      flow_not_zero_mask[flow_mags > 0] = 1\n",
        "\n",
        "      boxes = tracks[0].boxes.xyxy.cpu()\n",
        "      clss = tracks[0].boxes.cls.cpu().tolist()\n",
        "      confs = tracks[0].boxes.conf.cpu().tolist()\n",
        "      trk_ids = tracks[0].boxes.id.int().cpu().tolist()\n",
        "\n",
        "      for (box, clss, trk_id, conf) in zip(boxes, clss, trk_ids, confs):\n",
        "          trk_id = int(trk_id)\n",
        "          # Count passed cars\n",
        "          if self.check_passed_car(trk_id, clss, box, conf):\n",
        "            # Compute flow data for every tracked car\n",
        "            # Get tracking box as mask\n",
        "            box_mask = np.zeros(self.frame_shape)\n",
        "            box_mask[int(box[1]):int(box[3]), int(box[0]):int(box[2])] = 1\n",
        "            # Get flow for tracked object\n",
        "            n_flows_in_box = np.sum(box_mask*flow_not_zero_mask)\n",
        "            mean_flow_mag = np.sum(flow_mags * box_mask) / n_flows_in_box\n",
        "            # Update tracked flow features\n",
        "            if trk_id in self.tracked_flows:\n",
        "                self.tracked_flows[trk_id] = max(self.tracked_flows[trk_id], mean_flow_mag)\n",
        "            else:\n",
        "                self.tracked_flows[trk_id] = mean_flow_mag\n",
        "          else:\n",
        "            # Count passed persons\n",
        "            self.check_passed_person(trk_id, clss, box, conf)\n",
        "\n",
        "\n",
        "  def preprocess_frame(self, frame):\n",
        "      frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
        "      frame = cv2.equalizeHist(frame) # To improve robustness against lighting changes\n",
        "      return frame\n",
        "\n",
        "\n",
        "  def analyse(self):\n",
        "    # Get first frame\n",
        "    _, prev_frame = self.cap.read()\n",
        "    prev_processed_frame = self.preprocess_frame(prev_frame)\n",
        "\n",
        "    flow = None\n",
        "    while True:\n",
        "        # Get the next frame\n",
        "        ret, frame = self.cap.read()\n",
        "        if not ret:\n",
        "            break\n",
        "        # Preprocessing\n",
        "        processed_frame = self.preprocess_frame(frame)\n",
        "        # Compute dense optical flow\n",
        "        flow = self.compute_flow(prev_processed_frame, processed_frame, flow)\n",
        "        magnitude, angle = cv2.cartToPolar(flow[:, :, 0], flow[:, :, 1])\n",
        "        # Track objects\n",
        "        results = self.tracking_model.track(frame, classes=self.classes_to_count, persist=True, verbose=False, show=False)\n",
        "        # Get first assumption of driving/parked\n",
        "        self.counter_moving_cars.start_counting(frame, results)\n",
        "        # Get corresponding flows\n",
        "        self.analyse_frame_data(results, magnitude, angle)\n",
        "        # Update previous frame\n",
        "        prev_processed_frame = processed_frame\n",
        "\n",
        "    self.cap.release()\n",
        "\n",
        "  def get_results(self):\n",
        "    return (self.passed_cars, self.counter_moving_cars.count_ids, self.tracked_flows, self.passed_people)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZZ3Z23zMh8G1"
      },
      "outputs": [],
      "source": [
        "def evaluate_results(video_name, analyzer):\n",
        "  passed_cars, moving_ids, tracked_flows, passed_people = analyzer.get_results()\n",
        "\n",
        "  car_ids = np.zeros(len(passed_cars))\n",
        "  car_features = np.zeros((len(passed_cars)))\n",
        "\n",
        "  # Convert flow data to numpy array corresponding to car ids\n",
        "  for (i, c) in enumerate(passed_cars):\n",
        "    max_mean_flow_mag = tracked_flows[c]\n",
        "\n",
        "    car_ids[i] = c\n",
        "    car_features[i] = max_mean_flow_mag\n",
        "  # Normalize\n",
        "  norm = np.linalg.norm(car_features, axis=0, keepdims=True)\n",
        "  norm_car_features = car_features / norm\n",
        "\n",
        "  # Get initial car labels (parked/moving)\n",
        "  labels = np.zeros(norm_car_features.shape[0], dtype=np.int32)\n",
        "  # For all cars that were detected as moving, change the label\n",
        "  for id in moving_ids:\n",
        "    # The id, features and label of a car are located at the same index\n",
        "    car_index = np.where(car_ids == id)[0]\n",
        "    labels[car_index] = 1\n",
        "\n",
        "  # Apply clustering based on flow in order to refine results\n",
        "  criteria = (cv2.TERM_CRITERIA_MAX_ITER+cv2.TERM_CRITERIA_EPS, 20, 0.05)\n",
        "  cv2.kmeans(norm_car_features.astype(np.float32), K=2, bestLabels=labels,\n",
        "                                            criteria=criteria, attempts=1, flags=cv2.KMEANS_USE_INITIAL_LABELS)\n",
        "  parked_car_ids = car_ids[labels == 0]\n",
        "  moving_car_ids = car_ids[labels == 1]\n",
        "\n",
        "  # Output\n",
        "  print('Results: ', video_name)\n",
        "  print('# Passed Parked Cars: ', parked_car_ids.shape[0])\n",
        "  print('# Passed Moving Cars: ', moving_car_ids.shape[0])\n",
        "  print('# Passed People     : ', len(passed_people))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vz8WrJP7RLtz",
        "outputId": "96a94447-cc43-4a8b-ff10-2c35c3fc5b02"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "WARNING ⚠️ Environment does not support cv2.imshow() or PIL Image.show()\n",
            "\n",
            "Line Counter Initiated.\n"
          ]
        }
      ],
      "source": [
        "analyzer_mcgill = Analyzer(base_folder + 'mcgill_drive.mp4')\n",
        "analyzer_mcgill.analyse()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yg6jKxRBFRFk",
        "outputId": "8a3d6488-7baf-4f73-f1c9-5ea373a7ef03"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "WARNING ⚠️ Environment does not support cv2.imshow() or PIL Image.show()\n",
            "\n",
            "Line Counter Initiated.\n"
          ]
        }
      ],
      "source": [
        "analyzer_st_cath = Analyzer(base_folder + 'st-catherines_drive.mp4')\n",
        "analyzer_st_cath.analyse()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vCpmCHJxFMxV",
        "outputId": "f40b47ff-b4c0-4a00-be13-fc12df5bfecf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Results:  mcgill_drive.mp4\n",
            "# Passed Parked Cars:  10\n",
            "# Passed Moving Cars:  23\n",
            "# Passed People     :  21\n",
            "\n",
            "Results:  st-catherines_drive.mp4\n",
            "# Passed Parked Cars:  50\n",
            "# Passed Moving Cars:  4\n",
            "# Passed People     :  61\n"
          ]
        }
      ],
      "source": [
        "evaluate_results('mcgill_drive.mp4', analyzer_mcgill)\n",
        "print()\n",
        "evaluate_results('st-catherines_drive.mp4', analyzer_st_cath)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "msabMBwdFCyP"
      },
      "source": [
        "**Results:**\n",
        "\n",
        "St. Catherine\n",
        "* Total Number of Passed Moving Cars: 4\n",
        "* Total Number of Passed Parked Cars: 50\n",
        "* Total Number of Pedestrians: 61\n",
        "\n",
        "McGill Drive\n",
        "* Total Number of Passed Moving Cars: 23\n",
        "* Total Number of Passed Parked Cars: 10\n",
        "* Total Number of Pedestrians: 21\n",
        "\n",
        "\n",
        "**Ground Truth:**\n",
        "\n",
        "St. Catherine\n",
        "* Total Number of Passed Moving Cars: 2\n",
        "* Total Number of Passed Parked Cars: 55\n",
        "* Total Number of Pedestrians: 104\n",
        "\n",
        "McGill Drive\n",
        "* Total Number of Passed Moving Cars: 24\n",
        "* Total Number of Passed Parked Cars: 10\n",
        "* Total Number of Pedestrians: 35\n",
        "\n",
        "Since the ground truth results were obtained manually, here is our process for classifying each. Pedestrians are any person either on the sidewalk, beside a car on the road, or crossing the street that is visible from the dashcam. Since bikers could be incorrectly detected as a person thus a false positive, when they are not detected they are true negative examples of pedestrians. While the definition for moving and parked cars are more straightforward, given by their names, stopped cars such as those at intersections waiting for a green light are not. Since they technically are not moving but also still in use, we exclude them from our analysis due to this discrepancy. True negatives for moving cars is the number of cars correctly classified as not moving (parked), and likewise for parked cars it is the amount cars marked as not parked (moving).\n",
        "\n",
        "---------------------------------------\n",
        "\n",
        "Looking at True Positive Counts in each video for all classes relative to ground truth we find:\n",
        "\n",
        "* Pedestrian Detected: (St. Cath) 56.7% and (McGill Drive) 54.3%\n",
        "* Moving Cars Detected: (St. Cath) 50% and (McGill Drive) 50%\n",
        "* Parked Cars Detected: (St. Cath) 91% and (McGill Drive) 10%\n",
        "\n",
        "In terms of program performance, we detected 50-60% of all pedestrians in each video. Any false positives were due to bikers, misclassifications, and repeat counts are kept to a minimum (2 per video). For moving cars, the program on the St. Catherine video had detected 3 false positives cases of moving cars, whereas it counted 10 false positives in McGill Drive. Similarly, almost all parked cars were mostly correctly determined in the St. Catherine video, but 9 detected parked cars in the McGill drive video were false positives causing it to perform very poorly. Overall, the program was effective in determining the correct total number of cars passed, but struggled with detecting 43-45% of all pedestrians. In distinguishing the number of moving and parked cars, our method seems to incorrectly classify these two classes on the busier street scene in the McGill Drive but has better success on the one-way road of St. Catherine."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zkX3jFOJG8SV"
      },
      "source": [
        "# Discussion of Program Performance and Problems"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UnBBOShRHAeU"
      },
      "source": [
        "Based on the above results, using a YOLO back-bone for the object detection seems to provide overall decent results with car, truck, and bus detection. Upon reviewing the annotated videos for each video, many distinct and clear examples of cars are correctly detected in both cases which is reflected in the number of total cars detected. For pedestrians, performance was generally good even in the more complicated St. Catherines video, however the model was not able to detect people consistently in poor lighting conditions. In general, occlusion did prevent some instances of people not being detected. In this case, perhaps a change to a more robust back-bone like Mask R-CNN could have provide better person detection. False positive of pedestrians were also kept low, but one or two bikers, random street objects, and counting an object twice did still occur.\n",
        "\n",
        "Object tracking generally performed very well in both scenes with only a few cases where objects were sometimes classified with a new ID tag despite already being seen before. This was mitigated with decision to only count cars as they pass through the bottom of the screen. However, the low threshold for cars tended to exclude large vehicles like buses and trucks whose centers tended to be higher as they moved out of view.\n",
        "\n",
        "The object_counter object used to get the initial classification of moving/parked cars is able to capture many cars that pass us, but can still be improved. Since moving cars and parked cars can sometime occupy the same region of the screen, we opted to be more conservative with the size of the line to avoid false positive moving cars. This meant that certain vehicles bounding box centers could miss the line and thus be misclassified as a parked car instead. Additionally, stopped cars moving the perpendicular direction at intersections may be incorrectly classified as parked since they appear on similar areas of the screen.\n",
        "However, the refinement process could correct most of these mistakes. The dense flow estimation itself is very computationally intensive, which made testing more difficult. However, since the flow does not uniquely depend on the speed of the cars relative to the dashcam, but may also depend on the specific features observable in the bounding box and the angle at which a car is captured, the method may still be improved.\n",
        "\n",
        "We also experimented with a version of the same program that does not use the flow portion to decide between moving and parked cars, but instead uses only the functionality provided in the Ultralytics library. This method did provide better results by minimizing the number of moving car false positives in the McGill Drive video from 10 to 0, and increasing the number of true positive parked cars from 1 to 10. It achieves similar results on the St. Catherine video, but detect 0 false positve moving cars. This method does improve how the program performs on these two videos, which shows that the potential issue is the optical flow portion which can refined in the future with more experimentation. Furthermore, we still believe that an approach using optical flow or another method to detect motion will be more robust than the streamlined version mentioned here. Below is the streamlined version for reference."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cgxFGQ1aoCj7"
      },
      "source": [
        "# Appendix"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ve34c7n4oCj7"
      },
      "source": [
        "## Streamlined Version (Without Optical Flow)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TYhh2r_goCj7"
      },
      "source": [
        "This approach makes extensive use of the YOLO model features and functions from Ultralytics to perform key computer vision techniques needed for this problem: object detection/classification, multi-object tracking, and bounding box generation. Specifically, we use the pre-trained YOLOv8 model which is capable of these tasks, while providing fast and accurate results. Futhermore, as we are trying to detect number of passed cars and people in the scene, we limit the object detection to only focus on 'car', 'bus', 'truck', and 'person' objects.\n",
        "\n",
        "Counting unique objects first involves creating a python set variable, an array which excludes repetitions, for both cars and pedestrians, respectively. In both sets, we include the tracking id of each passed pedestrian, and car even if it is parked or moving. This arises our first assumption: any person whose bounding box center is anywhere in the bottom 40% of the screen will be considered as passed, and for cars it is 26.5%. From our testing, this helped with situations where objects might only be detected in the few frames before they are passed, and reduce instances where occlusion in the environment caused by moving cars and our moving perspective removes the chance of detecting an object. Tighter thresholds would perhaps be more rigorous but greatly reduce the window to detect objects. This threshold also helps reduce the times we recount the same object twice, and count objects near the end of the video that we would not have passed yet. We also assume that any person detected is a pedestrian, which would also include any bikers. To distinguish between moving and parked cars, we use object_counter object from Ultralytics to count the tracking IDs of cars whose bounding box centers pass through a certain region of the screen, specifically a line. This approach tries to exploit how moving cars that we pass generally follow a path across the screen. This line is angled and positioned near the very bottom of the screen to ensure that only passed, moving cars are counted. This separate set of tracking IDs is then removed from the set of all car IDs to provide the final number of parked and moving cars. Lastly, a confidence threshold of 80% and 60% is used for cars and person objects, respectively, to ensure false positives of wrong objects is kept low."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TNfH7jgOoCj7",
        "outputId": "87ec0d29-fae0-4716-fa1d-3194cf3ce22c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8n.pt to 'yolov8n.pt'...\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 6.23M/6.23M [00:00<00:00, 56.8MB/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "WARNING ⚠️ Environment does not support cv2.imshow() or PIL Image.show()\n",
            "\n",
            "Line Counter Initiated.\n",
            "\u001b[31m\u001b[1mrequirements:\u001b[0m Ultralytics requirement ['lapx>=0.5.2'] not found, attempting AutoUpdate...\n",
            "Collecting lapx>=0.5.2\n",
            "  Downloading lapx-0.5.7-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.7 MB)\n",
            "     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.7/1.7 MB 12.6 MB/s eta 0:00:00\n",
            "Requirement already satisfied: Cython>=0.29.32 in /usr/local/lib/python3.10/dist-packages (from lapx>=0.5.2) (3.0.10)\n",
            "Requirement already satisfied: numpy>=1.21.6 in /usr/local/lib/python3.10/dist-packages (from lapx>=0.5.2) (1.25.2)\n",
            "Installing collected packages: lapx\n",
            "Successfully installed lapx-0.5.7\n",
            "\n",
            "\u001b[31m\u001b[1mrequirements:\u001b[0m AutoUpdate success ✅ 6.3s, installed 1 package: ['lapx>=0.5.2']\n",
            "\u001b[31m\u001b[1mrequirements:\u001b[0m ⚠️ \u001b[1mRestart runtime or rerun command for updates to take effect\u001b[0m\n",
            "\n",
            "Video frame is empty or video processing has been successfully completed.\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Video: st-catherines_drive.mp4\n",
            "Total Number of Cars: 54\n",
            "Total Number of Parked Cars: 53\n",
            "Total Number of Moving Cars: 1\n",
            "Total Number of Pedestrians: 61\n",
            "WARNING ⚠️ Environment does not support cv2.imshow() or PIL Image.show()\n",
            "\n",
            "Line Counter Initiated.\n",
            "Video frame is empty or video processing has been successfully completed.\n",
            "----------------------------------------------------------------------------------------------------\n",
            "Video: mcgill_drive.mp4\n",
            "Total Number of Cars: 33\n",
            "Total Number of Parked Cars: 18\n",
            "Total Number of Moving Cars: 15\n",
            "Total Number of Pedestrians: 21\n"
          ]
        }
      ],
      "source": [
        "path = '/content/drive/MyDrive/Olympus in the Sky/McGill/2024 - Winter/ECSE 415/Final Project/'\n",
        "\n",
        "video_1 = 'st-catherines_drive.mp4'\n",
        "video_2 = 'mcgill_drive.mp4'\n",
        "\n",
        "model = YOLO(\"yolov8n.pt\")\n",
        "# Yolo, Object_Counter, threshold Arguments\n",
        "classes_to_count            = [0, 2, 5, 7] # 0: person, 2: car, 5: bus, 7: truck\n",
        "car_confidence_threshold    = 0.8\n",
        "person_confidence_threshold = 0.6\n",
        "count_names                 = {i: model.names[i] for i in classes_to_count}\n",
        "\n",
        "for vid_num, video in enumerate((video_1, video_2)):\n",
        "  cap = cv2.VideoCapture(path + video)\n",
        "  assert cap.isOpened(), \"Error reading video file\"\n",
        "  w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))\n",
        "\n",
        "  bottom_threshold_cars       = int(0.735*h)\n",
        "  bottom_threshold_person     = int(0.6*h)\n",
        "\n",
        "  # Init Object Counters\n",
        "  # Tracks the motion of cars that pass through the bottom of the screen\n",
        "  counter_cars = object_counter.ObjectCounter()\n",
        "  line_points = [(int(w*0.2), int(0.762*h)), (int(w*0.8), int(0.9125*h))]\n",
        "  counter_cars.set_args(view_img = True, reg_pts = line_points, classes_names = count_names, draw_tracks=True)\n",
        "\n",
        "  # Video writer\n",
        "  video_writer = cv2.VideoWriter(\"video_{}.avi\".format(vid_num), cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))\n",
        "\n",
        "  unique_cars   = set()\n",
        "  unique_people = set()\n",
        "\n",
        "  while cap.isOpened():\n",
        "      success, im0 = cap.read()\n",
        "      if not success:\n",
        "          print(\"Video frame is empty or video processing has been successfully completed.\")\n",
        "          break\n",
        "\n",
        "      # Tracking\n",
        "      tracks = model.track(im0, persist=True, show=False, classes=classes_to_count, verbose = False)\n",
        "\n",
        "      # Process Bounding Boxes\n",
        "      for r in tracks:\n",
        "        for box in r.boxes:\n",
        "          conf = math.ceil(box.conf[0]*100)/100\n",
        "\n",
        "          # Count all cars and people that pass near the bottom 40% of the screen\n",
        "          if r.names[int(box.cls[0])] in ('bus', 'car', 'truck') and conf > car_confidence_threshold:\n",
        "            x_tl, y_tl, x_br, y_bl = box.xyxy[0]\n",
        "            if (y_tl + abs(y_tl - y_bl)//2) > bottom_threshold_cars:\n",
        "              unique_cars.add(int(box.id))\n",
        "\n",
        "          if r.names[int(box.cls[0])] == 'person' and conf > person_confidence_threshold:\n",
        "            x_tl, y_tl, x_br, y_bl = box.xyxy[0]\n",
        "            if (y_tl + abs(y_tl - y_bl)//2) > bottom_threshold_person:\n",
        "              unique_people.add(int(box.id))\n",
        "\n",
        "      im0 = counter_cars.start_counting(im0, tracks)\n",
        "      video_writer.write(im0)\n",
        "\n",
        "  cap.release()\n",
        "  video_writer.release()\n",
        "  cv2.destroyAllWindows()\n",
        "\n",
        "  print('-'*100)\n",
        "  print('Video: ' + video)\n",
        "  all_cars = len(unique_cars)\n",
        "  print('Total Number of Cars: {}'.format(all_cars)) # all cars\n",
        "\n",
        "  for moving_car in counter_cars.count_ids:\n",
        "    unique_cars.discard(moving_car)\n",
        "\n",
        "  parked_cars = len(unique_cars)\n",
        "  print('Total Number of Parked Cars: {}'.format(parked_cars)) # all cars - marked outbound cars\n",
        "  print('Total Number of Moving Cars: {}'.format(len(counter_cars.count_ids))) # number of moving cars\n",
        "  print('Total Number of Pedestrians: {}'.format(len(unique_people))) # number of passed cars"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ThBiJljkoCj8",
        "outputId": "6d6428a4-c900-409d-ecef-4edc14ad4e60"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Recall: 0.5\n",
            "Precision: 1.0\n",
            "F1 Score: 0.6666666666666666\n",
            "\n",
            "----------------------------------------------------------------------------------------------------\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Recall: 0.9636363636363636\n",
            "Precision: 1.0\n",
            "F1 Score: 0.9814814814814815\n",
            "\n",
            "----------------------------------------------------------------------------------------------------\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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gb2+PdevWYc2aNfjqq68yvZYZ1Go1Zs+ejeLFi0OhUCB//vxwcnLCpUuXJH3IFSpUSNLgyF9//RV58+bFhQsXMHfuXDg7O2d733fdu3cPxYsX1yRBGUqXLq1Z/zZPT09Jx69WrRr27duH/fv34/jx43j27BlWrVoFa2trPH36FLGxsViyZIkmwchYMpKpjIF69+7dQ7FixTINaHv3/REZGQkg/Y/6u8dctmwZkpOTNT+b6dOn459//oGbmxuqVq2KcePGSU6as/t63L9/X5MI29nZwcnJCXXr1gWATO8VKyurTAnau7+Tffr0QYkSJRAQEIDChQvjhx9+0Lqc8mOEEFm2t2/fHkeOHMHLly+xd+9edOjQAefPn0fTpk2RlJSU7eNnkPr5BaR3H/j5+cHW1haOjo5wcnLCf/7zHwCZX6sPkfJ5IITgYMkcwDEPRkSpVMLV1RX//POPpP2y+4tgbm6eZfv7Plyycw6VSqX12NraGocPH0Z4eDh27NiB3bt3Y926dahfvz727t373hik0uW5ZFAoFGjVqhVWrlyJ27dvY9y4ce/ddsqUKRg9ejR++OEHTJw4EXnz5oWZmRkGDRqU7QoLgGx9c33b+fPnNX9UL1++rFU10DepsebPnx9+fn5Zrst4jTp27IigoKAst6lQoYKk82Ucc8aMGahYsWKW22SMW2jTpo1m3om9e/dixowZmDZtGjZv3oyAgIBsnS87r4dKpULDhg3x4sULjBgxAqVKlYKtrS0ePnyILl26ZHqvZOf3wdnZGRcuXMCePXuwa9cu7Nq1CyEhIejcuTNWrlz53v0yxpp8LKFWKpVo2LAhGjZsCEtLS6xcuRInT57UJDzZJfXz69atW2jQoAFKlSqFWbNmwc3NDXK5HDt37sTs2bMl/V5J+TyIjY1F/vz5s31syhqTByPz7bffYsmSJYiIiICvr+8Ht3V3d4darUZkZKTm2yOQPjAtNjYW7u7uORZXnjx5EBsbm6k9q7KgmZkZGjRogAYNGmDWrFmYMmUKRo0ahfDw8Cz/uGTEef369Uzrrl27hvz588PW1lb3J5GFDh064I8//oCZmRnatWv33u02btyIevXqYfny5Vrt734Q5eQ3mtevX6Nr164oU6YMatSogenTp6Nly5b46quvPul47u7uuHTpEtRqtVb14dq1a5r1+uLk5AR7e3uoVKr3Jhhvx/nPP/9k+ob47vujaNGiANL/aH3smABQsGBB9OnTB3369MGTJ09QuXJlTJ48WZM85MTP7vLly7hx4wZWrlyJzp07a9qldjm8Sy6Xo2nTpmjatCnUajX69OmDxYsXY/To0e+tlhUpUgTW1ta4c+dOts9TpUoVrFy5EtHR0QCkvyZSPr+2bduG5ORkbN26VatyEB4enmnbnPq9evjwIVJSUrQ+L+nTsNvCyAwfPhy2trbo3r07YmJiMq2/desWfvvtNwDpZXcAmWbFmzVrFgCgSZMmORZX0aJFERcXh0uXLmnaoqOjM13R8eLFi0z7ZnwrTE5OzvLYBQsWRMWKFbFy5UqtBOWff/7B3r17Nc9TH+rVq4eJEyfi999/h4uLy3u3Mzc3z/QtZsOGDXj48KFWW0aSk1WiJdWIESNw//59rFy5ErNmzYKHhweCgoLe+zp+zDfffIPHjx9rjUBPS0vDvHnzYGdnJ/mbphTm5uZo3bo1Nm3alOU304z5RTLifPToETZu3KhpS0xMxJIlS7T28fHxQdGiRfHrr78iISHhvcdUqVSZSuDOzs5wdXXVei1tbW0llcqzkvEN+O33ihBC8zv7KTLGLmQwMzPTVGk+9F6wtLRElSpVMk3dnJiYiIiIiCz32bVrF4D/dRF96P387NkzXLt2DYmJiZo2KZ9fWb1WcXFxCAkJybSfra1tjvxOnT17FsCHrxih7GHlwcgULVoUoaGhaNu2LUqXLo3OnTujXLlySElJwfHjxzWX1gGAt7c3goKCsGTJEsTGxqJu3bo4deoUVq5ciRYtWqBevXo5Fle7du0wYsQItGzZEgMGDEBiYiIWLlyIEiVKaA0YnDBhAg4fPowmTZrA3d0dT548wYIFC1C4cGHUqlXrvcefMWMGAgIC4Ovri27dumku1XRwcPhgd4KuzMzM8Msvv3x0u2+//RYTJkxA165dUaNGDVy+fBlr1qyBl5eX1nZFixaFo6MjFi1aBHt7e9ja2qJatWqSxw8cOHAACxYswNixYzWXjoaEhODrr7/G6NGjMX36dEnHA9IHlS1evBhdunTB2bNn4eHhgY0bN+LYsWOYM2eOpIFun2Lq1KkIDw9HtWrV0KNHD5QpUwYvXrzAuXPnsH//fk3i2aNHD/z+++/o3Lkzzp49i4IFC2L16tWwsbHROp6ZmRmWLVuGgIAAlC1bFl27dkWhQoXw8OFDhIeHQ6lUYtu2bXj16hUKFy6M7777Dt7e3rCzs8P+/ftx+vRpzJw5U3M8Hx8frFu3DkOGDMFXX30FOzs7rcmVsqNUqVIoWrQohg0bhocPH0KpVGLTpk06jSvq3r07Xrx4gfr166Nw4cK4d+8e5s2bh4oVK370G3Tz5s0xatQoxMfHa8YkJCYmokaNGqhevToaN24MNzc3xMbGYsuWLThy5AhatGihuYzxQ+/n33//HePHj0d4eLjmvixSPr8aNWqkqaj8+OOPSEhIwNKlS+Hs7KypfGTw8fHBwoULMWnSJBQrVgzOzs6oX7++5Ndy3759KFKkCC/TzAkGucaDPurGjRuiR48ewsPDQ8jlcmFvby9q1qwp5s2bpzUBVGpqqhg/frzw9PQUlpaWws3N7YOTRL3r3UsE33epphDpkz+VK1dOyOVyUbJkSfHf//4306WaYWFhonnz5sLV1VXI5XLh6uoq2rdvL27cuJHpHO9e/rV//35Rs2ZNYW1tLZRKpWjatOl7J4l691LQjEu2MiZDep+3L9V8n/ddqjl06FBRsGBBYW1tLWrWrCkiIiKyvMTy77//FmXKlBEWFhZZThKVlbePEx8fL9zd3UXlypVFamqq1naDBw8WZmZmIiIi4oPP4X0/75iYGNG1a1eRP39+IZfLRfny5TP9HD70HpB6vqzO37dvX+Hm5iYsLS2Fi4uLaNCggViyZInWdvfu3RPNmjUTNjY2In/+/GLgwIHvnSTq/PnzolWrViJfvnxCoVAId3d30aZNGxEWFiaESJ/6+6effhLe3t7C3t5e2NraCm9v70yTESUkJIgOHToIR0fHLCeJ2rBhQ6bnk9WlmleuXBF+fn7Czs5O5M+fX/To0UNcvHgx03v+fe/Fd3+nNm7cKBo1aqSZWKtIkSLixx9/FNHR0dl6vS0sLMTq1as1bampqWLp0qWiRYsWwt3dXSgUCmFjYyMqVaokZsyYkWma9Pe9nzPizGq68Ox+fm3dulVUqFBBWFlZCQ8PDzFt2jTxxx9/ZPpdfvz4sWjSpImwt7fPcpKod6enzurnolKpRMGCBcUvv/zy0deNPk4mhIQRZkRE9EXp1q0bbty4gSNHjhg6FIPasmULOnTogFu3bmlNm02fhskDEZEJu3//PkqUKIGwsLBMd9bMTXx9fVG7du1P6vKjzJg8EBERkSS82oKIiIgkYfJAREREkjB5ICIiIkmYPBAREZEknCTKCKnVajx69Aj29va8gQsRUQ4SQuDVq1dwdXXNdJM4fUhKSkJKSorOx5HL5bCyssqBiHIGkwcj9OjRI7i5uRk6DCIikxUVFYXChQvr9RxJSUnwdLfD4yeqj2/8ES4uLrhz547RJBBMHoxQxjTB9855QGnHniUyfq19PnwTJCJjkSZScThhvd6nYweAlJQUPH6iwr2zHlDaf/pnefwrNdx97iIlJYXJA71fRleF0s5Mpzcc0ediIZMbOgQiST5nl7CdvQx29p9+PjWMr/uayQMREZEeqYQaKh2mY1QJdc4Fk0OYPBAREemRGgJqfHr2oMu++sKaOBEREUnCygMREZEeqaGGLh0Puu2tH0weiIiI9EglBFQ63INSl331hckDERGRHnHMAxERERmtcePGQSaTaS2lSpXSrE9KSkLfvn2RL18+2NnZoXXr1oiJiZF8HiYPREREeqSGgEqHRWrloWzZsoiOjtYsR48e1awbPHgwtm3bhg0bNuDQoUN49OgRWrVqJfk5sduCiIhIjz53t4WFhQVcXFwytcfFxWH58uUIDQ1F/fr1AQAhISEoXbo0Tpw4gerVq2f7HKw8EBER6VHGgEldFikiIyPh6uoKLy8vBAYG4v79+wCAs2fPIjU1FX5+fpptS5UqhSJFiiAiIkLSOVh5ICIi+gLEx8drPVYoFFAoFFpt1apVw4oVK1CyZElER0dj/PjxqF27Nv755x88fvwYcrkcjo6OWvsUKFAAjx8/lhQLkwciIiI9Uv//osv+ADLdbXns2LEYN26cVltAQIDm/xUqVEC1atXg7u6O9evXw9raWocotDF5ICIi0qOMgY+67A+k30ZcqVRq2t+tOmTF0dERJUqUwM2bN9GwYUOkpKQgNjZWq/oQExOT5RiJD+GYByIiIj1SCd0XAFAqlVpLdpKHhIQE3Lp1CwULFoSPjw8sLS0RFhamWX/9+nXcv38fvr6+kp4TKw9EREQmYtiwYWjatCnc3d3x6NEjjB07Fubm5mjfvj0cHBzQrVs3DBkyBHnz5oVSqUT//v3h6+sr6UoLgMkDERGRXuXUmIfsePDgAdq3b4/nz5/DyckJtWrVwokTJ+Dk5AQAmD17NszMzNC6dWskJyfD398fCxYskBwTkwciIiI9UkMGFWQ67Z9da9eu/eB6KysrzJ8/H/Pnz//keACOeSAiIiKJWHkgIiLSI7VIX3TZ39gweSAiItIjlY7dFrrsqy9MHoiIiPTIFJMHjnkgIiIiSVh5ICIi0iO1kEEtdLjaQod99YXJAxERkR6ZYrcFkwciIiI9UsEMKh1GCahyMJacwjEPREREJAkrD0RERHokdBzzIDjmgYiIKHfhmAciIiKSRCXMoBI6jHkwwhkmOeaBiIiIJGHlgYiISI/UkEGtw3d1NYyv9MDkgYiISI845oGIiIgk0X3Mg/FVHjjmgYiIiCRh5YGIiEiP0sc86HBvC3ZbEBER5S5qHaen5oBJIiKiXIZjHoiIiCjXY+WBiIhIj9Qw4zwPRERElH0qIYNKh5tb6bKvvjB5ICIi0iOVjgMmVUZYeeCYByIiIpKElQciIiI9UgszqHW42kJthFdbMHkgIiLSI1PstmDyQEREpEdq6DboUZ1zoeQYjnkgIiIiSVh5ICIi0iPd53kwvu/5TB6IiIj0SPfpqZk8EBER5SqmeFdN40tniIiIyKix8kBERKRH7LYgIiIiSXSf54HJAxERUa6iFjKodZnnwQhvjGV86QwREREZNVYeiIiI9EitY7cF53kgIiLKZXS/MZbxJQ/GFxEREREZNVYeiIiI9EgFGVQ6TPSky776wuSBiIhIj0yx24LJAxERkR6poFv1QJVzoeQY40tniIiIyKix8kBERKRH7LYgIiIiSXhvCyIiIpJE6HhLbmGEV1sYXzpDRERERo2VByIiIj1itwURERFJYop31WTyQEREpEcqHW+Mpcu++mJ8EREREZFRY+WBiIhIj9htQURERJKoYQa1DoV+XfbVFyYPREREeqQSMqh0qB7osq++GF86Q0RERDli6tSpkMlkGDRokKYtKSkJffv2Rb58+WBnZ4fWrVsjJiZG0nGZPBAREelRxpgHXZZPcfr0aSxevBgVKlTQah88eDC2bduGDRs24NChQ3j06BFatWol6dhMHoiIiPRI/P+NsT51EZ8wSVRCQgICAwOxdOlS5MmTR9MeFxeH5cuXY9asWahfvz58fHwQEhKC48eP48SJE9k+PpMHIiIiPVJBpvMiVd++fdGkSRP4+flptZ89exapqala7aVKlUKRIkUQERGR7eNzwCQREdEXID4+XuuxQqGAQqHItN3atWtx7tw5nD59OtO6x48fQy6Xw9HRUau9QIECePz4cbZjYfJAJkVm1x8yuwFabSLtFsSzxoDMIX2dohZg7gqoXwBJ+yESZgMiwUARE2m7nXwJT1Lv4bU6FmYyCziaO6OEogpszR0MHRp9IrXQba4GtUj/183NTat97NixGDdunFZbVFQUBg4ciH379sHKyuqTz/kxuTZ5OHjwIOrVq4eXL19mysDe5uHhgUGDBmmNVCXjJlJvQLwMeqtBlf6vuTNgXgDi1TQg7SZg7gqZcgJk5s4Qsf0NEyzRO16mPYabvBQczPNDQCAy+SzOJu5BDbuWsJBZGjo8+gQZYxd02R9ITwyUSqWmPauqw9mzZ/HkyRNUrlxZ06ZSqXD48GH8/vvv2LNnD1JSUhAbG6v1ty8mJgYuLi7Zjsnoxzx06dIFMpkMMpkMcrkcxYoVw4QJE5CWlqbTcWvUqIHo6Gg4OKRn8ytWrMgyiTh9+jR69uyp07noc1MB6mf/W8TL9Oa0SIjYfkDyAUB1H0g5AfFqFqCoD8DcoBETZfCxbYRC8uKwM88De/O8KGdVG0niNeJVzw0dGn0iNWQ6LwCgVCq1lqyShwYNGuDy5cu4cOGCZqlSpQoCAwM1/7e0tERYWJhmn+vXr+P+/fvw9fXN9nP6IioPjRs3RkhICJKTk7Fz50707dsXlpaWGDly5CcfUy6XZyvLcnJy+uRzkIGYu0PmdBQQKUDqeYhXvwLq6Ky3NbP//y4L1WcNkSi70pACALCUZf5DQfQue3t7lCtXTqvN1tYW+fLl07R369YNQ4YMQd68eaFUKtG/f3/4+vqievXq2T6P0VcegPTSjIuLC9zd3dG7d2/4+flh69atePnyJTp37ow8efLAxsYGAQEBiIyM1Ox37949NG3aFHny5IGtrS3Kli2LnTt3AkjvtpDJZIiNjcXBgwfRtWtXxMXFaaocGf1IHh4emDNnDgCgQ4cOaNu2rVZsqampyJ8/P1atWgUAUKvVCA4OhqenJ6ytreHt7Y2NGzfq/0UiAIBIuQgRNwLiZTeI+DGAeWHI8v0JyGwzbyzLA5ldXyBx7ecPlCgbhBC4lnQSjubOsDfP8/EdyChlzDCpy5KTZs+ejW+//RatW7dGnTp14OLigs2bN0s6xhdReXiXtbU1nj9/ji5duiAyMhJbt26FUqnEiBEj8M033+DKlSuwtLRE3759kZKSgsOHD8PW1hZXrlyBnZ1dpuPVqFEDc+bMwZgxY3D9+nUAyHK7wMBAfP/990hISNCs37NnDxITE9GyZUsAQHBwMP773/9i0aJFKF68OA4fPoyOHTvCyckJdevWzfL5JCcnIzk5WfP43RG1JEHK4bceXId4eREyp0OAVQDw5q0kTmYHWZ6lQNpNiIR5nz1Mouy4mhSBBFUsqtp+Y+hQSAc5NebhUx08eFDrsZWVFebPn4/58+d/8jG/qORBCIGwsDDs2bMHAQEB2LJlC44dO4YaNWoAANasWQM3Nzds2bIF33//Pe7fv4/WrVujfPnyAAAvL68sjyuXy+Hg4ACZTPbBrgx/f3/Y2trir7/+QqdOnQAAoaGhaNasGezt7ZGcnIwpU6Zg//79mr4jLy8vHD16FIsXL35v8hAcHIzx48d/8utCHyBeAao7kJm7Q2S0yWwhy7McEAkQL/sA0G38DJE+XH0TgadpUfjK9htYmWVROaMvhho63lXzE+Z50Lcvotti+/btsLOzg5WVFQICAtC2bVt06dIFFhYWqFatmma7fPnyoWTJkrh69SoAYMCAAZg0aRJq1qyJsWPH4tKlSzrFYWFhgTZt2mDNmjUAgNevX+Pvv/9GYGAgAODmzZtITExEw4YNYWdnp1lWrVqFW7duvfe4I0eORFxcnGaJiorSKU56i8wGMC8CoX76/4/tIMsTAiAV4mUv4P/7k4mMhRACV99E4EnafVSxaQwbM3tDh0SUyRdReahXrx4WLlwIuVwOV1dXWFhYYOvWrR/dr3v37vD398eOHTuwd+9eBAcHY+bMmejf/9MvywsMDETdunXx5MkT7Nu3D9bW1mjcuDGA9OlAAWDHjh0oVKiQ1n5ZjYp9e92H1lP2yexHQCSFA+qHgJkzZHYDAaiBN9v/lzjIrCBihwFmdgD+v3tK/SJ9OyIDu5p0Ao9Tb6OiTQNYyCyRrE4EAFjI5DCXfREf2fQO8dYVE5+6v7H5It6Jtra2KFasmFZb6dKlkZaWhpMnT2q6LZ4/f47r16+jTJkymu3c3NzQq1cv9OrVCyNHjsTSpUuzTB7kcjlUqo+PuK9Rowbc3Nywbt067Nq1C99//z0sLdOvvS5TpgwUCgXu37//3i4K0jMzF8gcZwFmedITgpQzEM+/B8QLQF4VMnlFAIDMKUxrN/XTrwHVw88fL9E7HqReAwCcSdyl1V7WqhYKyYsbIiTSkS43t8rY39h8EclDVooXL47mzZujR48eWLx4Mezt7fHzzz+jUKFCaN68OQBg0KBBCAgIQIkSJfDy5UuEh4ejdOnSWR7Pw8MDCQkJCAsLg7e3N2xsbGBjY5Plth06dMCiRYtw48YNhIeHa9rt7e0xbNgwDB48GGq1GrVq1UJcXByOHTsGpVKJoKCgLI9HOUfEDX7/ypRTUD/mhy8Zt0bKroYOgXKYoQdM6oPxRSRBSEgIfHx88O2338LX1xdCCOzcuVNTCVCpVOjbty9Kly6Nxo0bo0SJEliwYEGWx6pRowZ69eqFtm3bwsnJCdOnT3/veQMDA3HlyhUUKlQINWvW1Fo3ceJEjB49GsHBwZrz7tixA56enjn3xImIiAxIJoQQH9+MPqf4+Hg4ODjg5Q0vKO2/6PyOcomAkrUNHQJRtqSJFBx4tQZxcXFaUz3rQ8ZnefO9P8DSVv7Jx0l9nYK/G/3xWWLOri+224KIiOhLoNZxwKQxXqrJ5IGIiEiPTHHAJGviREREJAkrD0RERHpkipUHJg9ERER6ZIrJA7stiIiISBJWHoiIiPTIFCsPTB6IiIj0SEC3yy2NcTImJg9ERER6ZIqVB455ICIiIklYeSAiItIjU6w8MHkgIiLSIyYPREREJIkpJg8c80BERESSsPJARESkR0LIIHSoHuiyr74weSAiItIj3pKbiIiIJOGYByIiIsr1WHkgIiLSI455ICIiIklMsduCyQMREZEemWLlgWMeiIiISBJWHoiIiPRI6NhtYYyVByYPREREeiQACKHb/saGyQMREZEeqSGDzMQmieKYByIiIpKElQciIiI9MsWrLZg8EBER6ZFayCDjPA9ERESUXULoOGDSCEdMcswDERERScLKAxERkR5xzAMRERFJwuSBiIiIJDHFAZMc80BERESSsPJARESkR6Z4tQWTByIiIj1KTx50GfOQg8HkECYPREREemSKAyY55oGIiIgkYeWBiIhIjwR0u622EfZaMHkgIiLSJ1PstmDyQEREpE8mWHrgmAciIiKShJUHIiIifdKx2wLstiAiIspdTHGSKHZbEBERkSSsPBAREekRr7YgIiIiaYRMt3ELX2rysHXr1mwfsFmzZp8cDBERkakxxTEP2UoeWrRoka2DyWQyqFQqXeIhIiKiT7Rw4UIsXLgQd+/eBQCULVsWY8aMQUBAAAAgKSkJQ4cOxdq1a5GcnAx/f38sWLAABQoUkHSebA2YVKvV2VqYOBAREb1D5MCSTYULF8bUqVNx9uxZnDlzBvXr10fz5s3x77//AgAGDx6Mbdu2YcOGDTh06BAePXqEVq1aSX5KOo15SEpKgpWVlS6HICIiMmmfc8Bk06ZNtR5PnjwZCxcuxIkTJ1C4cGEsX74coaGhqF+/PgAgJCQEpUuXxokTJ1C9evVsn0fypZoqlQoTJ05EoUKFYGdnh9u3bwMARo8ejeXLl0s9HBERken7DFWHd6lUKqxduxavX7+Gr68vzp49i9TUVPj5+Wm2KVWqFIoUKYKIiAhJx5acPEyePBkrVqzA9OnTIZfLNe3lypXDsmXLpB6OiIiIsiE+Pl5rSU5OznK7y5cvw87ODgqFAr169cJff/2FMmXK4PHjx5DL5XB0dNTavkCBAnj8+LGkWCQnD6tWrcKSJUsQGBgIc3NzTbu3tzeuXbsm9XBEREQmLaPbQpcFANzc3ODg4KBZgoODszxfyZIlceHCBZw8eRK9e/dGUFAQrly5kqPPSfKYh4cPH6JYsWKZ2tVqNVJTU3MkKCIiIpORQ3fVjIqKglKp1DQrFIosN5fL5Zq/0z4+Pjh9+jR+++03tG3bFikpKYiNjdWqPsTExMDFxUVSSJIrD2XKlMGRI0cytW/cuBGVKlWSejgiIiITJ8uBBVAqlVrL+5KHd6nVaiQnJ8PHxweWlpYICwvTrLt+/Tru378PX19fSc9IcuVhzJgxCAoKwsOHD6FWq7F582Zcv34dq1atwvbt26UejoiIiHLIyJEjERAQgCJFiuDVq1cIDQ3FwYMHsWfPHjg4OKBbt24YMmQI8ubNC6VSif79+8PX11fSlRbAJyQPzZs3x7Zt2zBhwgTY2tpizJgxqFy5MrZt24aGDRtKPRwREZFpy6Fui+x48uQJOnfujOjoaDg4OKBChQrYs2eP5u/z7NmzYWZmhtatW2tNEiXVJ83zULt2bezbt+9TdiUiIspdPmPy8LEpE6ysrDB//nzMnz9fh4B0mCTqzJkzuHr1KoD0cRA+Pj46BUJERGSScuuNsd724MEDtG/fHseOHdOM1oyNjUWNGjWwdu1aFC5cOKdjJCIiIiMi+WqL7t27IzU1FVevXsWLFy/w4sULXL16FWq1Gt27d9dHjERERF+sjLtq6rIYG8mVh0OHDuH48eMoWbKkpq1kyZKYN28eateunaPBERERffE+45iHz0Vy8uDm5pblZFAqlQqurq45EhQREZHJMMExD5K7LWbMmIH+/fvjzJkzmrYzZ85g4MCB+PXXX3M0OCIiIjI+2ao85MmTBzLZ/zKf169fo1q1arCwSN89LS0NFhYW+OGHH9CiRQu9BEpERPQlkon0RZf9jU22koc5c+boOQwiIiITlVvHPAQFBek7DiIiItNkgmMePnmSKABISkpCSkqKVtvbd/wiIiIi0yN5wOTr16/Rr18/ODs7w9bWFnny5NFaiIiI6C0iBxYjIzl5GD58OA4cOICFCxdCoVBg2bJlGD9+PFxdXbFq1Sp9xEhERPTlMsHkQXK3xbZt27Bq1Sp8/fXX6Nq1K2rXro1ixYrB3d0da9asQWBgoD7iJCIi+jKZ4IBJyZWHFy9ewMvLC0D6+IYXL14AAGrVqoXDhw/nbHRERERkdCQnD15eXrhz5w4AoFSpUli/fj2A9IpExo2yiIiI6P9lXG2hy2JkJCcPXbt2xcWLFwEAP//8M+bPnw8rKysMHjwYP/30U44HSERE9CXLmCRKl8XYSB7zMHjwYM3//fz8cO3aNZw9exbFihVDhQoVcjQ4IiKiL54JjnnQaZ4HAHB3d4e7u3tOxEJERERfgGwlD3Pnzs32AQcMGPDJwRAREZHxy1byMHv27GwdTCaTMXkgIiJ6iww63hgrxyLJOdlKHjKurqDPa2OCI2xgbugwiD5K/eqVoUMgyha1SP38JzXBe1tIvtqCiIiIcjedB0wSERHRB/BqCyIiIpLEBJMHdlsQERGRJKw8EBER6ZGus0Qa4wyTn1R5OHLkCDp27AhfX188fPgQALB69WocPXo0R4MjIiL64pngLbklJw+bNm2Cv78/rK2tcf78eSQnJwMA4uLiMGXKlBwPkIiI6IvG5AGYNGkSFi1ahKVLl8LS0lLTXrNmTZw7dy5HgyMiIiLjI3nMw/Xr11GnTp1M7Q4ODoiNjc2JmIiIiEwGxzwAcHFxwc2bNzO1Hz16FF5eXjkSFBERkcnImGFSl8XISE4eevTogYEDB+LkyZOQyWR49OgR1qxZg2HDhqF37976iJGIiOjLZYJjHiR3W/z8889Qq9Vo0KABEhMTUadOHSgUCgwbNgz9+/fXR4xERERkRCQnDzKZDKNGjcJPP/2EmzdvIiEhAWXKlIGdnZ0+4iMiIvqimeKYh0+eJEoul6NMmTI5GQsREZHpMcHpqSUnD/Xq1YNM9v7BGwcOHNApICIiIpOiY+XBJJKHihUraj1OTU3FhQsX8M8//yAoKCin4iIiIiIjJTl5mD17dpbt48aNQ0JCgs4BERERmRQT7LbIsbtqduzYEX/88UdOHY6IiMg08FLN94uIiICVlVVOHY6IiMgk8GoLAK1atdJ6LIRAdHQ0zpw5g9GjR+dYYERERGScJCcPDg4OWo/NzMxQsmRJTJgwAY0aNcqxwIiIiMg4SUoeVCoVunbtivLlyyNPnjz6iomIiMh05PYBk+bm5mjUqBHvnklERJRNGWMedFmMjeSrLcqVK4fbt2/rIxYiIiL6AkhOHiZNmoRhw4Zh+/btiI6ORnx8vNZCRERE7zChyzQBCWMeJkyYgKFDh+Kbb74BADRr1kxrmmohBGQyGVQqVc5HSURE9KUywTEP2U4exo8fj169eiE8PFyf8RAREZmUXD3PgxDp0detW1dvwRAREZHxk3Sp5ofupklERERZyM3dFgBQokSJjyYQL1680CkgIiIiU5Kruy2A9HEP784wSURERB+Q2ysP7dq1g7Ozs75iISIioi9AtpMHjncgIiL6BLm58pBxtQURERFlX64e86BWq/UZBxERkWkywcqD5OmpiYiIyDgFBwfjq6++gr29PZydndGiRQtcv35da5ukpCT07dsX+fLlg52dHVq3bo2YmBhJ52HyQEREpE+63NdCYtXi0KFD6Nu3L06cOIF9+/YhNTUVjRo1wuvXrzXbDB48GNu2bcOGDRtw6NAhPHr0CK1atZL0lCRdbUFERETSfM4xD7t379Z6vGLFCjg7O+Ps2bOoU6cO4uLisHz5coSGhqJ+/foAgJCQEJQuXRonTpxA9erVs3UeVh6IiIj0KYcqD+/exTo5Ofmjp46LiwMA5M2bFwBw9uxZpKamws/PT7NNqVKlUKRIEURERGT7KTF5ICIi+gK4ubnBwcFBswQHB39we7VajUGDBqFmzZooV64cAODx48eQy+VwdHTU2rZAgQJ4/PhxtmNhtwUREZEe5VS3RVRUFJRKpaZdoVB8cL++ffvin3/+wdGjRz/95O/B5IGIiEifcuhSTaVSqZU8fEi/fv2wfft2HD58GIULF9a0u7i4ICUlBbGxsVrVh5iYGLi4uGQ7JHZbEBERmQghBPr164e//voLBw4cgKenp9Z6Hx8fWFpaIiwsTNN2/fp13L9/H76+vtk+DysPRERE+vQZJ4nq27cvQkND8ffff8Pe3l4zjsHBwQHW1tZwcHBAt27dMGTIEOTNmxdKpRL9+/eHr69vtq+0AJg8EBER6ZXs/xdd9s+uhQsXAgC+/vprrfaQkBB06dIFADB79myYmZmhdevWSE5Ohr+/PxYsWCApJiYPRERE+vQZKw/ZuQ+VlZUV5s+fj/nz539ySBzzQERERJKw8kBERKRHufqumkRERPQJTPCumkweiIiI9M0IEwBdcMwDERERScLKAxERkR5xzAMRERFJwzEPREREJIUpVh445oGIiIgkYeWBiIhIn9htQURERFKYYrcFkwciIiJ9MsHKA8c8EBERkSSsPBAREemTCVYemDwQERHpEcc8EBERkTQmWHngmAciIiKShJUHIiIiPZIJAZn49PKBLvvqC5MHIiIifTLBbgsmD0RERHpkigMmOeaBiIiIJGHlgYiISJ/YbUFERERSmGK3BZMHIiIifTLBygPHPBAREZEkrDwQERHpEbstiIiISBoT7LZg8kBERKRnxlg90AXHPBAREZEkrDwQERHpkxDpiy77GxkmD0RERHrEAZNEREQkjQkOmOSYByIiIpKElQciIiI9kqnTF132NzZMHj7Cw8MDgwYNwqBBgwwdCn2CMnk6o2L+frj28k+cezYbANCg0EIUsPHR2i4ybjNOP5lqiBCJMokSN3EPN5CCJNjBASVRCQ6yvIYOiz4Vuy1yVpcuXSCTyTB1qvaH9pYtWyCTyT5rLCtWrICjo2Om9tOnT6Nnz56fNRbKGXkVpVHMoRVeJkdmWncz7i9svh2gWc4/m2eACIkyeyyicAOX4IUyqAo/2MMR53EEKSLJ0KERaRh8zIOVlRWmTZuGly9fGjqULDk5OcHGxsbQYZBEFjJr1HCZiJMxk5Giis+0Pk2dhCTVc82Spn5tgCiJMruPGygET7jKPGAnU6IUKsMc5niEu4YOjT5RxtUWuizGxuDJg5+fH1xcXBAcHPzebY4ePYratWvD2toabm5uGDBgAF6//t+HfXR0NJo0aQJra2t4enoiNDQUHh4emDNnjmabWbNmoXz58rC1tYWbmxv69OmDhIQEAMDBgwfRtWtXxMXFQSaTQSaTYdy4cQCgdZwOHTqgbdu2WrGlpqYif/78WLVqFQBArVYjODgYnp6esLa2hre3NzZu3JgDrxRJUcV5OB69PoaYN6ezXO9h3xitvPbimyJ/wjtfH5jLFJ85QqLM1EKNV4hFXjhr2mQyGfKiAGLx3ICRkU4y5nnQZTEyBk8ezM3NMWXKFMybNw8PHjzItP7WrVto3LgxWrdujUuXLmHdunU4evQo+vXrp9mmc+fOePToEQ4ePIhNmzZhyZIlePLkidZxzMzMMHfuXPz7779YuXIlDhw4gOHDhwMAatSogTlz5kCpVCI6OhrR0dEYNmxYplgCAwOxbds2TdIBAHv27EFiYiJatmwJAAgODsaqVauwaNEi/Pvvvxg8eDA6duyIQ4cO5cjrRR/nbtcQeRUlceH5/CzX3321BxExYxH2oDeuvFwBT/sA1HCZ8JmjJMosFckQEJDDSqtdDgVSwG6LL5UpVh6MYsBky5YtUbFiRYwdOxbLly/XWhccHIzAwEDNgMXixYtj7ty5qFu3LhYuXIi7d+9i//79OH36NKpUqQIAWLZsGYoXL651nLcHPHp4eGDSpEno1asXFixYALlcDgcHB8hkMri4uLw3Tn9/f9ja2uKvv/5Cp06dAAChoaFo1qwZ7O3tkZycjClTpmD//v3w9fUFAHh5eeHo0aNYvHgx6tatm+Vxk5OTkZycrHkcH5+5zE7ZY2PhjMpOQxD+sD/UIiXLbW7Fb9H8Py7lFt6kPUeDwgtgZ1kICakPP1OkRERfLqNIHgBg2rRpqF+/fqZv/BcvXsSlS5ewZs0aTZsQAmq1Gnfu3MGNGzdgYWGBypUra9YXK1YMefLk0TrO/v37ERwcjGvXriE+Ph5paWlISkpCYmJitsc0WFhYoE2bNlizZg06deqE169f4++//8batWsBADdv3kRiYiIaNmyotV9KSgoqVar03uMGBwdj/Pjx2YqBPiyvojSsLfKhcZFVmjYzmQWcrSuhhOP3WHezFgS0r3t6lvQPAMDe0o3JAxmUJRSQQZapypCC5EzVCPqCmODVFkaTPNSpUwf+/v4YOXIkunTpomlPSEjAjz/+iAEDBmTap0iRIrhx48ZHj3337l18++236N27NyZPnoy8efPi6NGj6NatG1JSUiQNiAwMDETdunXx5MkT7Nu3D9bW1mjcuLEmVgDYsWMHChUqpLWfQvH+PvWRI0diyJAhmsfx8fFwc3PLdkz0P48TT2PHvXZabdULjEF8yl1cebkqU+IAAHkUJQAAb9KefZYYid7HTGYGe+GIF3gCZ6R/hggh8AJP4IaiBo6OPhWnp9azqVOnomLFiihZsqSmrXLlyrhy5QqKFSuW5T4lS5ZEWloazp8/Dx+f9Gv3b968qXX1xtmzZ6FWqzFz5kyYmaUP81i/fr3WceRyOVQq1UdjrFGjBtzc3LBu3Trs2rUL33//PSwtLQEAZcqUgUKhwP3799/bRZEVhULxweSCsi9NJCIu5bZ2m/oNklVxiEu5DTvLQvCw98fD18eRooqDo6IYKucfjJjEc4hNuWmgqIn+pwhK4ApOQynywAF5cR+RUCENBeFh6NDoU/HGWPpVvnx5BAYGYu7cuZq2ESNGoHr16ujXrx+6d+8OW1tbXLlyBfv27cPvv/+OUqVKwc/PDz179sTChQthaWmJoUOHwtraWjNXRLFixZCamop58+ahadOmOHbsGBYtWqR1bg8PDyQkJCAsLAze3t6wsbF5b0WiQ4cOWLRoEW7cuIHw8HBNu729PYYNG4bBgwdDrVajVq1aiIuLw7Fjx6BUKhEUFKSHV42kUItUFLCuipKO7WEhs0JiWgyiEsLxz8s/DB0aEQDAReaGVJGM27iCZCTBHg6ohFpQyNhtQcbDqJIHAJgwYQLWrVuneVyhQgUcOnQIo0aNQu3atSGEQNGiRbUumVy1ahW6deuGOnXqaC77/Pfff2Fllf7L5u3tjVmzZmHatGkYOXIk6tSpg+DgYHTu3FlzjBo1aqBXr15o27Ytnj9/jrFjx2ou13xXYGAgJk+eDHd3d9SsWVNr3cSJE+Hk5ITg4GDcvn0bjo6OqFy5Mv7zn//k4KtEUoQ97K35f2LaE4Q97GXAaIg+zk1WDG7IutpKXx5T7LaQCWGE9RAdPXjwAG5ubti/fz8aNGhg6HAki4+Ph4ODA5aeqwwbO3NDh0P0USEl3Q0dAlG2pIlUHMTfiIuLg1Kp1Ou5Mj7LfRtPgIXlp1eO0lKTELF7zGeJObuMrvLwKQ4cOICEhASUL18e0dHRGD58ODw8PFCnTh1Dh0ZERLmcKVYeTCJ5SE1NxX/+8x/cvn0b9vb2qFGjBtasWaMZyEhEREQ5xySSB39/f/j7+xs6DCIioszUIn3RZX8jYxLJAxERkdHiJFFEREQkhQw6jnnIsUhyjsFvjEVERERfFlYeiIiI9IkzTBIREZEUvFSTiIiIpDHBAZMc80BERGRCDh8+jKZNm8LV1RUymQxbtmzRWi+EwJgxY1CwYEFYW1vDz88PkZGRks7B5IGIiEiPZELovEjx+vVreHt7Y/78+Vmunz59OubOnYtFixbh5MmTsLW1hb+/P5KSkrJ9DnZbEBER6ZP6/xdd9pcgICAAAQEBWa4TQmDOnDn45Zdf0Lx5cwDpN5csUKAAtmzZgnbt2mXrHKw8EBER6VFOVR7i4+O1luTkZMmx3LlzB48fP4afn5+mzcHBAdWqVUNERES2j8PkgYiI6Avg5uYGBwcHzRIcHCz5GI8fPwYAFChQQKu9QIECmnXZwW4LIiIifcqhqy2ioqK0bsmtUCh0CksXrDwQERHpU8YkUbosAJRKpdbyKcmDi4sLACAmJkarPSYmRrMuO5g8EBER6VHGJFG6LDnF09MTLi4uCAsL07TFx8fj5MmT8PX1zfZx2G1BRERkQhISEnDz5k3N4zt37uDChQvImzcvihQpgkGDBmHSpEkoXrw4PD09MXr0aLi6uqJFixbZPgeTByIiIn36zPe2OHPmDOrVq6d5PGTIEABAUFAQVqxYgeHDh+P169fo2bMnYmNjUatWLezevRtWVlbZPgeTByIiIj2SqdMXXfaX4uuvv4b4QMIhk8kwYcIETJgw4ZNjYvJARESkTyZ4V00OmCQiIiJJWHkgIiLSJxO8qyaTByIiIj36lJtbvbu/sWHyQEREpE8c80BERES5HSsPRERE+iSg2y25ja/wwOSBiIhIn0xxzAO7LYiIiEgSVh6IiIj0SUDHAZM5FkmOYfJARESkTyZ4tQWTByIiIn1SA5DpuL+R4ZgHIiIikoSVByIiIj0yxastmDwQERHpE8c8EBERkSQmmDxwzAMRERFJwsoDERGRPplg5YHJAxERkT6Z4KWaTB6IiIj0yBSvtuCYByIiIpKElQciIiJ94pgHIiIikkQtAJkOCYCayQMREVHuYoKVB455ICIiIklYeSAiItIrHSsPML7KA5MHIiIifTLBbgsmD0RERPqkFtCpemCEAyY55oGIiIgkYeWBiIhIn4Q6fdFlfyPD5IGIiEifOOaBiIiIJOGYByIiIsrtWHkgIiLSJ3ZbEBERkSQCOiYPORZJjmHyQEREpE8mWHngmAciIiKShJUHIiIifVKrAegwV4Oa8zwQERHlLibYbcHkgYiISJ9MMHngmAciIiKShJUHIiIifTLBGSaZPBAREemREGoIHW5upcu++sLkgYiISJ+E0K16wDEPRERE9KVj5YGIiEifhI5jHoyw8sDkgYiISJ/UakCmw7gFIxzzwG4LIiIikoSVByIiIn1itwURERFJIdRqCB26LXipJhERUW5jgpUHjnkgIiIiSVh5ICIi0ie1AGSmVXlg8kBERKRPQgDQ5VJNJg9ERES5ilALCB0qD8IIkweOeSAiIjIx8+fPh4eHB6ysrFCtWjWcOnUqR4/P5IGIiEifhFr3RYJ169ZhyJAhGDt2LM6dOwdvb2/4+/vjyZMnOfaUmDwQERHpkVALnRcpZs2ahR49eqBr164oU6YMFi1aBBsbG/zxxx859pyYPBAREenTZ6w8pKSk4OzZs/Dz89O0mZmZwc/PDxERETn2lDhg0ghlDI55k6AycCRE2ZMmUg0dAlG2pCH9vfo5ByGmIVWnOaIyYo6Pj9dqVygUUCgUWm3Pnj2DSqVCgQIFtNoLFCiAa9eufXoQ72DyYIRevXoFABhQ56KBIyHKrnOGDoBIklevXsHBwUGv55DL5XBxccHRxzt1PpadnR3c3Ny02saOHYtx48bpfOxPweTBCLm6uiIqKgr29vaQyWSGDsdkxMfHw83NDVFRUVAqlYYOh+iD+H7VDyEEXr16BVdXV72fy8rKCnfu3EFKSorOxxJCZPp78G7VAQDy588Pc3NzxMTEaLXHxMTAxcVF5zgyMHkwQmZmZihcuLChwzBZSqWSH8b0xeD7Nefpu+LwNisrK1hZWX2288nlcvj4+CAsLAwtWrQAAKjVaoSFhaFfv345dh4mD0RERCZkyJAhCAoKQpUqVVC1alXMmTMHr1+/RteuXXPsHEweiIiITEjbtm3x9OlTjBkzBo8fP0bFihWxe/fuTIModcHkgXINhUKBsWPHZtlPSGRs+H4lXfTr1y9HuyneJRPGOGk2ERERGS1OEkVERESSMHkgIiIiSZg8EBERkSRMHoiIiEgSJg9EREQkCZMHIiIikoTJAxGREVGrs3/7ZSJD4SRRZPIybiiTmJiI1NRUrXnts7rZDJGhqNVqmJmlf6cLDw9HVFQUXFxc4OnpieLFixs4OqL/4SRRZNIykoNt27Zh6dKluHz5Mvz8/FCpUiX06dPH0OERZWnEiBFYt24dXFxcYGZmhqSkJEybNg0NGzY0dGhEANhtQSZOJpNh+/btaNu2LWrWrInff/8dycnJGDFiBI4cOWLo8IgyCQkJwapVqxAaGooTJ06gWbNmuHLlCl6/fm3o0Ig0WHkgkyWEwKtXr9CxY0fUqlULw4cPR1xcHEqVKoW2bdtizpw5hg6RSCOjSjZgwACYmZlhzpw52LJlCzp37oxff/0VPXv2RGJiIp4/fw43NzdDh0u5HCsPZLJkMhmsrKwQExODqlWrIioqCmXLlkXTpk01icO2bdtw9uxZwwZKudbb393S0tI0/y9RogT27t2LTp06YcaMGejZsydUKhU2btyIXbt2ITk52RDhEmkweSCTdPv2bbx48QJJSUmws7PDsWPHUK9ePQQEBGDx4sUAgEePHmHDhg2IjIwEC3BkCBmDdRcvXozDhw8DAJycnDBkyBC0bNkSc+fOxY8//ggAePXqFVavXo3o6GjeaZMMjt0WZFLUajXu3r2LqlWrYufOnahatSoWL16M3r17o169eggLC9NsO2rUKGzatAm7d++Gh4eH4YKmXK9ChQpwc3PDjh07AACdOnXC1q1bcejQIeTPnx9paWno1asXXrx4gePHj8PCghfKkWExeSCT1LBhQ1haWuKvv/6CQqHA6NGjMXnyZAwePBhmZmZ4+fIlNmzYgEOHDqFixYqGDpdyqYxLM8PDwzFgwABMnToVTZo0wd27d9GnTx+cOHECVlZWKFy4MCwtLXHw4EFYWlpCpVLB3Nzc0OFTLsb0lb5ob18XDwApKSmQy+Xo1asXZs6ciQsXLqBatWoYPXo03N3dsX79eqSmpqJkyZKIiIhAmTJlDBg95Tbvvl8z/l+iRAnkyZMHBw4cQJMmTeDh4YGdO3di9+7dSEpKQp48eVC7dm2YmZkhLS2NlQcyOFYe6IsUHR2NggULah5HRUWhcOHCmj7khIQEVK9eHV999RVCQkI02yUmJsLGxgapqamwtLT87HFT7rRy5Uo0adIE+fPnBwCsW7cOsbGxmvEMALB+/XoEBQXh0KFDqFq1apbHeTf5IDIUvgvpizN+/HiMHz9eM+L8n3/+QatWreDn54d///0XT58+hZ2dHX799VeEh4dj3759mn2tra0BgN/c6LNZuXIl1qxZg7x58wIAnjx5grVr1+I///kPGjZsiJUrV+LFixdo06YNAgICsHnzZqSkpGQ5TTUTBzIWfCfSF6dmzZro378/FAoFkpOT4enpieHDh8PS0hJNmjRBnz59sGPHDpQvXx6lSpXC+fPnAaR/a8uoTHBKavpcgoKCsGvXLpiZmeHIkSOws7PDpk2bcP78edjb22Pp0qWoXr069u7di7x582Lv3r149eoVEwUyauy2oC/Ku3P/L126FGPGjEGpUqUAAGvWrMGxY8ewZMkS9OvXD4cPH8b169dx48YNFCpUyJChUy6UnJysuazy1KlTqFWrFkaNGoXu3bujUKFCSEtLw7Vr17BgwQIcPnwYBQoUQHh4OKZPn45hw4YZOHqi92Ptloze2wmDSqWCmZkZVCoVLCwssGvXLlhbW6Nv376oXLkyAgMDERgYiPbt22PZsmVIS0vDmzdvtCbgIfoc0tLSNInD0aNHUatWLYwZMwbLly+HhYUFgoKCULhwYZQrV06TPFy+fBkKhQKDBg0ybPBEH8HKA30Rbt26hcTERJQvXx6bNm3C0aNHMXv2bOzduxc9evRA/fr1MWjQIHh7e2v2efXqFV6+fAkhBNzd3Q0YPeU2u3fvxrhx43DixAkMGTIEYWFhOHLkCJRKJSZNmoTFixejV69e+OGHH7QG/r6NV1WQMeM7k4zemzdvMH78eGzcuBETJ07ETz/9hBUrVgAAGjVqhCVLlqBnz54AgCFDhqB8+fIAADs7O9jb2xsqbMqlMsbWxMXFoWjRonjx4gXOnj0LpVIJAPjll18AAIsWLQIAdOvWDS4uLpmOw8SBjJogMlJ///235v/3798XlStXFhYWFmLSpElCCCGSk5OFSqUSQgixe/duUaRIEdGtWzdx7tw5g8RL9LZOnToJmUwmqlSpomlLSkrS/H/SpEnC3d1dDB8+XDx79swQIRJ9Mg7nJaN05swZdO3aFQ8ePACQXkUwNzdHyZIlsXDhQly8eBFyuRwqlQpqtRr+/v5YunQp1q1bh6VLlyIlJcXAz4ByK7VajbS0NHz77bdYtGgRUlNTUadOHajVaigUCiQmJgJInx69c+fOuHbtmuYyTqIvBcc8kFFKS0vDq1evkCdPHly7dg2lSpXCixcv8OzZMwwfPhynTp3Czp07UbFiRa0Jn06cOIF8+fKhePHiBn4GlJu8b/ImtVqN3bt3Y/jw4ciXLx8OHTqkWbdr1y4EBARobsWd8S/Rl4DJAxm1R48ewc3NDX369MG8efMAABcuXMD48eNx6tQp7Nq1CxUqVMC0adPw9OlTzJgxgx/A9Fm9nTiEhobiypUrMDc3R9OmTVGlShUkJycjPDwcP/30E2xtbbFkyRIMGzYMKpUK+/fvZ+JAXyQmD2R03v0gXb58OQYOHIgff/wRM2fOBABcvHgREydOxJYtW/Dtt99i27ZtOHv2LG9yRQYzYsQIrF27FmXLloWNjQ327NmDv/76C35+fkhJScHx48cxbNgwPH36FO7u7ggLC4OlpSUTB/oiMXkgo5LxQXrq1Cm8ePECtWvXhq2tLdasWYNu3bqhT58+mDVrFgDg4cOH2LRpE27duoXevXtrJooi+tyWLFmCSZMmYfPmzahSpQr+/PNPBAYGwtzcHOvXr0fLli0hhEBqair+/fdfeHt78yZX9EXju5aMRkbisHnzZvTo0QNDhw5FyZIl4enpiTZt2kAIgW7dugEAZs2ahUKFCmHAgAG8WRAZVHx8PG7duoWxY8eiSpUq2L59u+aurleuXEG7du2wdetW+Pv7Qy6Xo1KlSgDSuzuYONCXipUHMiphYWFo1aoVZs6ciU6dOmlm6MtILFatWoXevXujU6dOmuvkiT6nrJLVS5cuwc7ODiqVCk2aNEH//v3Rv39/bN++Hc2aNQOQPp163bp1DREyUY5j2ktGJWMMQ/fu3ZGYmIjTp09j1apVUCgUaN++PTp37oyUlBT88ssvmDBhApydnQ0dMuUiQgitwZE2NjZo3rw5KlSoACD9/ZsvXz4EBgYCABwdHdGzZ0/4+PigZs2aBoubKKcxeSCj8PagsZcvX2L79u1Yv349nj59iujoaBQsWBD9+vXDzp07ERQUhDZt2mhm7CP6HN6uONy9e1czm6mdnR38/PwAAImJiTh58iTu3r0LtVqN6dOnw8nJCT169ADAKafJdLCjmAzm7R6zjMThm2++QUxMDLp27QqVSoU+ffrgwoULaNu2LSwsLGBlZQVLS0smDvTZZSQOw4cPx4QJE1CgQAFERERg+PDh2Lt3L4QQaNWqFVq1aoUqVaqgRo0auHPnjqZ7TQjBxIFMBt/JZBAZlYaIiAhcuHABUVFRaNOmDQICAuDr64vo6GiULl1ak2BcvXoVlpaWSE1NhbW1tYGjp9xq8eLFWLZsGfbv3w8nJyekpqaiadOmGDNmDMzMzODn54fQ0FDs3bsXqampaNasGczNzVlxIJPDAZNkMBs3bsQPP/wAb29vPHnyBE+ePEGfPn3Qo0cPeHh4AEifpnr9+vVYvHgxDh8+rHXXTKLPbdCgQbh58ya2b9+u6caIiYlBjRo14ODggKlTp6Jhw4Za8zaoVCqYm5sbMGqinMduC9I7tVqdqe3GjRsYNGgQfvvtN4SFheH69ev45ZdfsGPHDoSEhCAhIQGRkZGYNGkSjhw5giNHjjBxIINRqVQAgKSkJMTHxwNI78ZISkpCgQIFMGPGDFy6dAmzZ8/G8ePHAfyvW46JA5kiVh5IrzK+nT18+BBHjx6FWq1G+fLlYW9vj7p162LLli3w9vbWfFObMWMGpk6dihMnTqB48eK4ceMGlEpllrcsJtKX980dcuTIEdStWxezZ8/GwIEDNe2bN2/G5s2bcebMGRQvXhzbtm37nOESfXbshCO9yfgAvnTpElq2bAkrKytcv34dxYsXR7169WBpaQm5XA6ZTIbExETY2Njgp59+wrx587B161YMHToUJUqUMPTToFzm7cRh3bp1iIyMxJs3b9CyZUvUrl0bU6dOxfDhw/HmzRt07NgRAPDHH3/A398fgwYNQtWqVREREQFfX19DPg0ivWK3BenF24mDr68vvvvuO+zbtw9///03ihQpgjNnzuDNmzfo0KEDAMDGxgZA+mx9+fPnh6urqyHDp1wsI3H46aefMGLECJw9exb37t1D1apVsXnzZnTr1g2zZ8/G5MmT4evri+rVqyMqKgo9e/aEXC6Hp6cnnJycDPwsiPSLlQfSCzMzM0RFRaFBgwZo0qQJpk2bBgBwdXVFVFQURowYgZCQEAQHB8Pb2xtLliyBSqXC7t278eDBA1SvXt3Az4Byo4yrIjZv3ozQ0FBs2bIFX331FXbs2IHQ0FAkJycjX7586NOnDxo3bowrV67A0tISfn5+MDc3R2hoKJRKJRwcHAz9VIj0iskD6Y1KpYKnpyeSk5Nx9OhR1KpVCwBQtGhRWFtbw9PTE0uXLsWwYcPQrFkz2NnZQaFQYPfu3fD09DRw9JSb7Nu3D35+fprLKR88eAB/f3989dVX2LhxI7p27YpFixahffv2iIuLQ2xsLLy8vODl5QUAuHbtGn799Vds3rwZ4eHhrDyQyWO3BemNh4cH1qxZg5SUFEycOBFXr15FQkICAgMD0alTJ1SqVAkVK1bE/v37sXv3buzatQsHDx5E5cqVDR065SIvXrxAz549UaZMGc0VEgkJCXj+/LnmcuLp06ejZ8+eAIC///4bwcHBSEhIAACkpqbiwYMHkMvlvJyYcg1ebUF6FxkZiYEDByIxMRGXLl1CUFAQZs+eDSD9g9fS0tLAEVJuJoRAREQEfvzxR1hYWODcuXO4fPkyOnXqhBs3bmDKlCkYPHgwgPSkol27dvDy8sJvv/2muUpIpVIhNTUVVlZWhnwqRJ8NKw+kd8WLF8dvv/0Gc3NzKJVKtGzZUrOOs+6RoclkMlSvXh1LlizBmzdv4OvriwoVKqBNmzZwcHDAmzdv8O+//yIiIgLff/89Hj58iFmzZkEmk2nN5cDEgXITVh7os7l58yb69+8PIQRGjx7NuwySwZw6dQrPnz9HQECAZpBkWloazp07h3bt2qFQoUI4cuQIRo8ejR07duDChQuoVq0a7O3tsWPHDlhaWnLmSMrVmDzQZxUZGYkhQ4bg2bNnmD17Nq+qoM8uPDwcDRo0AABUq1YNpUqVQvPmzVG5cmUUKVIEp0+fRs+ePWFra4ujR48iNTUVx48fh6enJwoXLgwzMzPeq4JyPSYP9Nldu3YNo0ePxsyZM1GkSBFDh0O5zK1bt9CpUyekpqYif/78KFGiBFatWoV8+fKhXLlyqFevHhwdHfHLL7+gVKlS2Lt3r9a9Kt43+yRRbsLkgQwiJSUFcrnc0GFQLhUZGYkRI0YgJSUFkydPRpEiRXDu3DnMmzcPL1++xKlTp+Dk5IQHDx5gwIABmDNnjqFDJjIqTB6IKFe6ceMGBgwYALVajfHjx2umk1apVNi5cydu376NiIgIrF69mlcEEb2DyQMR5VqRkZHo378/AOA///kP6tSpk+V2vKSYSBuTByLK1SIjIzFgwAAAwC+//MKrgIiygaN+iChXK168OObOnQtzc3MMGjQIly5dMnRIREaPyQMR5XrFixfHjBkzUKdOHZQrV87Q4RAZPXZbEBG9g5djEn0YkwciIiKShKk1ERERScLkgYiIiCRh8kBERESSMHkgIiIiSZg8EBERkSRMHoiIiEgSJg9EuUCXLl3QokULzeOvv/4agwYN+uxxHDx4EDKZDLGxse/dRiaTYcuWLdk+5rhx41CxYkWd4rp79y5kMhkuXLig03GIcgsmD0QG0qVLF8hkMshkMsjlchQrVgwTJkxAWlqa3s+9efNmTJw4MVvbZucPPhHlLhaGDoAoN2vcuDFCQkKQnJyMnTt3om/fvrC0tMTIkSMzbZuSkgK5XJ4j582bN2+OHIeIcidWHogMSKFQwMXFBe7u7ujduzf8/PywdetWAP/rapg8eTJcXV1RsmRJAEBUVBTatGkDR0dH5M2bF82bN8fdu3c1x1SpVBgyZAgcHR2RL18+DB8+HO9OJPtut0VycjJGjBgBNzc3KBQKFCtWDMuXL8fdu3dRr149AECePHkgk8nQpUsXAOlTOAcHB8PT0xPW1tbw9vbGxo0btc6zc+dOlChRAtbW1qhXr55WnNk1YsQIlChRAjY2NvDy8sLo0aORmpqaabvFixfDzc0NNjY2aNOmDeLi4rTWL1u2DKVLl4aVlRVKlSqFBQsWSI6FiNIxeSAyItbW1khJSdE8DgsLw/Xr17Fv3z5s374dqamp8Pf3h729PY4cOYJjx47Bzs4OjRs31uw3c+ZMrFixAn/88QeOHj2KFy9e4K+//vrgeTt37ow///wTc+fOxdWrV7F48WLY2dnBzc0NmzZtAgBcv34d0dHR+O233wAAwcHBWLVqFRYtWoR///0XgwcPRseOHXHo0CEA6UlOq1at0LRpU1y4cAHdu3fHzz//LPk1sbe3x4oVK3DlyhX89ttvWLp0KWbPnq21zc2bN7F+/Xps27YNu3fvxvnz59GnTx/N+jVr1mDMmDGYPHkyrl69iilTpmD06NFYuXKl5HiICIAgIoMICgoSzZs3F0IIoVarxb59+4RCoRDDhg3TrC9QoIBITk7W7LN69WpRsmRJoVarNW3JycnC2tpa7NmzRwghRMGCBcX06dM161NTU0XhwoU15xJCiLp164qBAwcKIYS4fv26ACD27duXZZzh4eECgHj58qWmLSkpSdjY2Ijjx49rbdutWzfRvn17IYQQI0eOFGXKlNFaP2LEiEzHehcA8ddff713/YwZM4SPj4/m8dixY4W5ubl48OCBpm3Xrl3CzMxMREdHCyGEKFq0qAgNDdU6zsSJE4Wvr68QQog7d+4IAOL8+fPvPS8R/Q/HPBAZ0Pbt22FnZ4fU1FSo1Wp06NAB48aN06wvX7681jiHixcv4ubNm7C3t9c6TlJSEm7duoW4uDhER0ejWrVqmnUWFhaoUqVKpq6LDBcuXIC5uTnq1q2b7bhv3ryJxMRENGzYUKs9JSUFlSpVAgBcvXpVKw4A8PX1zfY5Mqxbtw5z587FrVu3kJCQgLS0NCiVSq1tihQpgkKFCmmdR61W4/r167C3t8etW7fQrVs39OjRQ7NNWloaHBwcJMdDRBwwSWRQ9erVw8KFCyGXy+Hq6goLC+1fSVtbW63HCQkJ8PHxwZo1azIdy8nJ6ZNisLa2lrxPQkICAGDHjh1af7SB9HEcOSUiIgKBgYEYP348/P394eDggLVr12LmzJmSY126dGmmZMbc3DzHYiXKTZg8EBmQra0tihUrlu3tK1eujHXr1sHZ2TnTt+8MBQsWxMmTJ1GnTh0A6d+wz549i8qVK2e5ffny5aFWq3Ho0CH4+fllWp9R+VCpVJq2MmXKQKFQ4P79+++tWJQuXVoz+DPDiRMnPv4k33L8+HG4u7tj1KhRmrZ79+5l2u7+/ft49OgRXF1dNecxMzNDyZIlUaBAAbi6uuL27dsIDAyUdH4iyhoHTBJ9QQIDA5E/f340b94cR44cwZ07d3Dw4EEMGDAADx48AAAMHDgQU6dOxZYtW3Dt2jX06dPng3M0eHh4ICgoCD/88AO2bNmiOeb69esBAO7u7pDJZNi+fTuePn2KhIQE2NvbY9iwYRg8eDBWrlyJW7du4dy5c5g3b55mEGKvXr0QGRmJn376CdevX0doaChWrFgh6fkWL14c9+/fx9q1a3Hr1i3MnTs3y8GfVlZWCAoKwsWLF3HkyBEMGDAAbdq0gYuLCwBg/PjxCA4Oxty5c3Hjxg1cvnwZISEhmDVrlqR4iCgdkweiL4iNjQ0OHz6MIkWKoFWrVihdujS6deuGpKQkTSVi6NCh6NSpE4KCguDr6wt7e3u0bNnyg8dduHAhvvvuO/Tp0welSpVCjx498Pr1awBAoUKFMH78ePz8888oUKAA+vXrBwCYOHEiRo8ejeDgYJQuXRqNGzfGjh074OnpCSB9HMKmTZuwZcsWeHt7Y9GiRZgyZYqk59usWTMMHjwY/fr1Q8WKFXH8+HGMHj0603bFihVDq1at8M0336BRo0aoUKGC1qWY3bt3x7JlyxASEoLy5cujbt26WLFihSZWIpJGJt43ioqIiIgoC6w8EBERkSRMHoiIiEgSJg9EREQkCZMHIiIikoTJAxEREUnC5IGIiIgkYfJAREREkjB5ICIiIkmYPBAREZEkTB6IiIhIEiYPREREJAmTByIiIpLk/wBoj6RkWSxiQwAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Recall: 0.5360824742268041\n",
            "Precision: 0.9629629629629629\n",
            "F1 Score: 0.6887417218543046\n"
          ]
        }
      ],
      "source": [
        "# Moving Cars\n",
        "TP = 1\n",
        "FP = 0\n",
        "TN = 59\n",
        "FN = 1\n",
        "\n",
        "res_st_cath = []\n",
        "conf_matrix = np.array([[TP, FP], [FN, TN]])\n",
        "recall, precision, f1_score = calculate_metrics(TP, FP, TN, FN)\n",
        "res_st_cath.append([recall, precision, f1_score])\n",
        "\n",
        "plot_confusion_matrix(conf_matrix, 'Moving Cars (St. Cath)')\n",
        "print(\"Recall:\", recall)\n",
        "print(\"Precision:\", precision)\n",
        "print(\"F1 Score:\", f1_score)\n",
        "\n",
        "print('\\n' + '-'*100 + '\\n')\n",
        "\n",
        "# Parked Cars\n",
        "TP = 53\n",
        "FP = 0\n",
        "TN = 2\n",
        "FN = 2\n",
        "\n",
        "conf_matrix = np.array([[TP, FP], [FN, TN]])\n",
        "recall, precision, f1_score = calculate_metrics(TP, FP, TN, FN)\n",
        "res_st_cath.append([recall, precision, f1_score])\n",
        "\n",
        "plot_confusion_matrix(conf_matrix, 'Parked Cars (St.Cath)')\n",
        "print(\"Recall:\", recall)\n",
        "print(\"Precision:\", precision)\n",
        "print(\"F1 Score:\", f1_score)\n",
        "\n",
        "print('\\n' + '-'*100 + '\\n')\n",
        "\n",
        "# Pedestrians\n",
        "TP = 52\n",
        "FP = 2\n",
        "TN = 0\n",
        "FN = 45\n",
        "\n",
        "conf_matrix = np.array([[TP, FP], [FN, TN]])\n",
        "recall, precision, f1_score = calculate_metrics(TP, FP, TN, FN)\n",
        "res_st_cath.append([recall, precision, f1_score])\n",
        "\n",
        "plot_confusion_matrix(conf_matrix, 'Pedestrians (St.Cath)')\n",
        "print(\"Recall:\", recall)\n",
        "print(\"Precision:\", precision)\n",
        "print(\"F1 Score:\", f1_score)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "OJdggLpcoCj8",
        "outputId": "7d02b7b2-d5d4-4e82-bc14-779f29e7fa8a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Recall: 0.625\n",
            "Precision: 1.0\n",
            "F1 Score: 0.7692307692307693\n",
            "\n",
            "----------------------------------------------------------------------------------------------------\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Recall: 1.0\n",
            "Precision: 0.5555555555555556\n",
            "F1 Score: 0.7142857142857143\n",
            "\n",
            "----------------------------------------------------------------------------------------------------\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Recall: 0.5428571428571428\n",
            "Precision: 0.9047619047619048\n",
            "F1 Score: 0.6785714285714285\n"
          ]
        }
      ],
      "source": [
        "# Moving Cars\n",
        "TP = 15\n",
        "FP = 0\n",
        "TN = 10\n",
        "FN = 9\n",
        "\n",
        "res_mcgill_drive = []\n",
        "conf_matrix = np.array([[TP, FP], [FN, TN]])\n",
        "recall, precision, f1_score = calculate_metrics(TP, FP, TN, FN)\n",
        "res_mcgill_drive.append([recall, precision, f1_score])\n",
        "\n",
        "plot_confusion_matrix(conf_matrix, 'Moving Cars (McGill Drive)')\n",
        "print(\"Recall:\", recall)\n",
        "print(\"Precision:\", precision)\n",
        "print(\"F1 Score:\", f1_score)\n",
        "\n",
        "print('\\n' + '-'*100 + '\\n')\n",
        "\n",
        "# Parked Cars\n",
        "TP = 10\n",
        "FP = 8\n",
        "TN = 15\n",
        "FN = 0\n",
        "\n",
        "conf_matrix = np.array([[TP, FP], [FN, TN]])\n",
        "recall, precision, f1_score = calculate_metrics(TP, FP, TN, FN)\n",
        "res_mcgill_drive.append([recall, precision, f1_score])\n",
        "\n",
        "plot_confusion_matrix(conf_matrix, 'Parked Cars (McGill Drive)')\n",
        "print(\"Recall:\", recall)\n",
        "print(\"Precision:\", precision)\n",
        "print(\"F1 Score:\", f1_score)\n",
        "\n",
        "print('\\n' + '-'*100 + '\\n')\n",
        "\n",
        "# Pedestrians\n",
        "TP = 19\n",
        "FP = 2\n",
        "TN = 5\n",
        "FN = 16\n",
        "\n",
        "conf_matrix = np.array([[TP, FP], [FN, TN]])\n",
        "recall, precision, f1_score = calculate_metrics(TP, FP, TN, FN)\n",
        "res_mcgill_drive.append([recall, precision, f1_score])\n",
        "\n",
        "plot_confusion_matrix(conf_matrix, 'Pedestrians (McGill Drive)')\n",
        "print(\"Recall:\", recall)\n",
        "print(\"Precision:\", precision)\n",
        "print(\"F1 Score:\", f1_score)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [
        "gRekjNpFDlwo",
        "nH1AXjofE1G_"
      ],
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}