pipeline.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# 1.0 Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "NvJ1Frjt-0Ux"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "xFormers not available\n",
            "xFormers not available\n"
          ]
        }
      ],
      "source": [
        "from ultralytics import YOLO\n",
        "import matplotlib.pyplot as plt\n",
        "from PIL import Image\n",
        "from matplotlib.patches import Polygon\n",
        "import numpy as np\n",
        "import cv2\n",
        "from tqdm import tqdm\n",
        "import itertools\n",
        "from time import time\n",
        "import torch\n",
        "import supervision as sv\n",
        "from depthAnythingModel.depth_anything_v2.dpt import DepthAnythingV2\n",
        "from matplotlib.colors import Normalize\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.linear_model import Ridge\n",
        "from sklearn.metrics import mean_squared_error\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import DataLoader, TensorDataset, random_split\n",
        "from Utils.ObjectDetection import ObjectDetectionUtils\n",
        "from Utils.EstimateHeight import EstimateHeight\n",
        "from collections import defaultdict, deque\n",
        "from Gui.CameraCalibrateApp import CameraCalibrateApp\n",
        "from Utils.DataProcessor import DataProcessor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "VIDEO_PATH = 'walking4.mp4'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "GPU is available. Using GPU.\n"
          ]
        }
      ],
      "source": [
        "# Check if GPU is available\n",
        "if torch.cuda.is_available():\n",
        "    device = torch.device('cuda')\n",
        "    print(\"GPU is available. Using GPU.\")\n",
        "else:\n",
        "    device = torch.device('cpu')\n",
        "    print(\"GPU is not available. Using CPU.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# 2.0 load the models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 2.1 object Detection Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "aO5LwNhl-qfv"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "YOLO(\n",
              "  (model): SegmentationModel(\n",
              "    (model): Sequential(\n",
              "      (0): Conv(\n",
              "        (conv): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "        (act): SiLU(inplace=True)\n",
              "      )\n",
              "      (1): Conv(\n",
              "        (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "        (act): SiLU(inplace=True)\n",
              "      )\n",
              "      (2): RepNCSPELAN4(\n",
              "        (cv1): Conv(\n",
              "          (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "          (act): SiLU(inplace=True)\n",
              "        )\n",
              "        (cv2): Sequential(\n",
              "          (0): RepCSP(\n",
              "            (cv1): Conv(\n",
              "              (conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (cv2): Conv(\n",
              "              (conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (cv3): Conv(\n",
              "              (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (m): Sequential(\n",
              "              (0): RepBottleneck(\n",
              "                (cv1): RepConv(\n",
              "                  (act): SiLU(inplace=True)\n",
              "                  (conv1): Conv(\n",
              "                    (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "                    (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                    (act): Identity()\n",
              "                  )\n",
              "                  (conv2): Conv(\n",
              "                    (conv): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "                    (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                    (act): Identity()\n",
              "                  )\n",
              "                )\n",
              "                (cv2): Conv(\n",
              "                  (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "                  (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                  (act): SiLU(inplace=True)\n",
              "                )\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (1): Conv(\n",
              "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (cv3): Sequential(\n",
              "          (0): RepCSP(\n",
              "            (cv1): Conv(\n",
              "              (conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (cv2): Conv(\n",
              "              (conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (cv3): Conv(\n",
              "              (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (m): Sequential(\n",
              "              (0): RepBottleneck(\n",
              "                (cv1): RepConv(\n",
              "                  (act): SiLU(inplace=True)\n",
              "                  (conv1): Conv(\n",
              "                    (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "                    (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                    (act): Identity()\n",
              "                  )\n",
              "                  (conv2): Conv(\n",
              "                    (conv): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "                    (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                    (act): Identity()\n",
              "                  )\n",
              "                )\n",
              "                (cv2): Conv(\n",
              "                  (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "                  (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                  (act): SiLU(inplace=True)\n",
              "                )\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (1): Conv(\n",
              "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (cv4): Conv(\n",
              "          (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "          (act): SiLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "      (3): ADown(\n",
              "        (cv1): Conv(\n",
              "          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "          (act): SiLU(inplace=True)\n",
              "        )\n",
              "        (cv2): Conv(\n",
              "          (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "          (act): SiLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "      (4): RepNCSPELAN4(\n",
              "        (cv1): Conv(\n",
              "          (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "          (act): SiLU(inplace=True)\n",
              "        )\n",
              "        (cv2): Sequential(\n",
              "          (0): RepCSP(\n",
              "            (cv1): Conv(\n",
              "              (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (cv2): Conv(\n",
              "              (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (cv3): Conv(\n",
              "              (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (m): Sequential(\n",
              "              (0): RepBottleneck(\n",
              "                (cv1): RepConv(\n",
              "                  (act): SiLU(inplace=True)\n",
              "                  (conv1): Conv(\n",
              "                    (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "                    (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                    (act): Identity()\n",
              "                  )\n",
              "                  (conv2): Conv(\n",
              "                    (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "                    (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                    (act): Identity()\n",
              "                  )\n",
              "                )\n",
              "                (cv2): Conv(\n",
              "                  (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "                  (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                  (act): SiLU(inplace=True)\n",
              "                )\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (1): Conv(\n",
              "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (cv3): Sequential(\n",
              "          (0): RepCSP(\n",
              "            (cv1): Conv(\n",
              "              (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (cv2): Conv(\n",
              "              (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (cv3): Conv(\n",
              "              (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (m): Sequential(\n",
              "              (0): RepBottleneck(\n",
              "                (cv1): RepConv(\n",
              "                  (act): SiLU(inplace=True)\n",
              "                  (conv1): Conv(\n",
              "                    (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "                    (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                    (act): Identity()\n",
              "                  )\n",
              "                  (conv2): Conv(\n",
              "                    (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "                    (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                    (act): Identity()\n",
              "                  )\n",
              "                )\n",
              "                (cv2): Conv(\n",
              "                  (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "                  (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                  (act): SiLU(inplace=True)\n",
              "                )\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (1): Conv(\n",
              "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (cv4): Conv(\n",
              "          (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "          (act): SiLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "      (5): ADown(\n",
              "        (cv1): Conv(\n",
              "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "          (act): SiLU(inplace=True)\n",
              "        )\n",
              "        (cv2): Conv(\n",
              "          (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "          (act): SiLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "      (6): RepNCSPELAN4(\n",
              "        (cv1): Conv(\n",
              "          (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "          (act): SiLU(inplace=True)\n",
              "        )\n",
              "        (cv2): Sequential(\n",
              "          (0): RepCSP(\n",
              "            (cv1): Conv(\n",
              "              (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (cv2): Conv(\n",
              "              (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (cv3): Conv(\n",
              "              (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (m): Sequential(\n",
              "              (0): RepBottleneck(\n",
              "                (cv1): RepConv(\n",
              "                  (act): SiLU(inplace=True)\n",
              "                  (conv1): Conv(\n",
              "                    (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "                    (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                    (act): Identity()\n",
              "                  )\n",
              "                  (conv2): Conv(\n",
              "                    (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "                    (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                    (act): Identity()\n",
              "                  )\n",
              "                )\n",
              "                (cv2): Conv(\n",
              "                  (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "                  (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                  (act): SiLU(inplace=True)\n",
              "                )\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (1): Conv(\n",
              "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (cv3): Sequential(\n",
              "          (0): RepCSP(\n",
              "            (cv1): Conv(\n",
              "              (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (cv2): Conv(\n",
              "              (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (cv3): Conv(\n",
              "              (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (m): Sequential(\n",
              "              (0): RepBottleneck(\n",
              "                (cv1): RepConv(\n",
              "                  (act): SiLU(inplace=True)\n",
              "                  (conv1): Conv(\n",
              "                    (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "                    (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                    (act): Identity()\n",
              "                  )\n",
              "                  (conv2): Conv(\n",
              "                    (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "                    (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                    (act): Identity()\n",
              "                  )\n",
              "                )\n",
              "                (cv2): Conv(\n",
              "                  (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "                  (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                  (act): SiLU(inplace=True)\n",
              "                )\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (1): Conv(\n",
              "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (cv4): Conv(\n",
              "          (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "          (act): SiLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "      (7): ADown(\n",
              "        (cv1): Conv(\n",
              "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "          (act): SiLU(inplace=True)\n",
              "        )\n",
              "        (cv2): Conv(\n",
              "          (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "          (act): SiLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "      (8): RepNCSPELAN4(\n",
              "        (cv1): Conv(\n",
              "          (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "          (act): SiLU(inplace=True)\n",
              "        )\n",
              "        (cv2): Sequential(\n",
              "          (0): RepCSP(\n",
              "            (cv1): Conv(\n",
              "              (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (cv2): Conv(\n",
              "              (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (cv3): Conv(\n",
              "              (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (m): Sequential(\n",
              "              (0): RepBottleneck(\n",
              "                (cv1): RepConv(\n",
              "                  (act): SiLU(inplace=True)\n",
              "                  (conv1): Conv(\n",
              "                    (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "                    (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                    (act): Identity()\n",
              "                  )\n",
              "                  (conv2): Conv(\n",
              "                    (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "                    (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                    (act): Identity()\n",
              "                  )\n",
              "                )\n",
              "                (cv2): Conv(\n",
              "                  (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "                  (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                  (act): SiLU(inplace=True)\n",
              "                )\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (1): Conv(\n",
              "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (cv3): Sequential(\n",
              "          (0): RepCSP(\n",
              "            (cv1): Conv(\n",
              "              (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (cv2): Conv(\n",
              "              (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (cv3): Conv(\n",
              "              (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (m): Sequential(\n",
              "              (0): RepBottleneck(\n",
              "                (cv1): RepConv(\n",
              "                  (act): SiLU(inplace=True)\n",
              "                  (conv1): Conv(\n",
              "                    (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "                    (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                    (act): Identity()\n",
              "                  )\n",
              "                  (conv2): Conv(\n",
              "                    (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "                    (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                    (act): Identity()\n",
              "                  )\n",
              "                )\n",
              "                (cv2): Conv(\n",
              "                  (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "                  (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                  (act): SiLU(inplace=True)\n",
              "                )\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (1): Conv(\n",
              "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (cv4): Conv(\n",
              "          (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "          (act): SiLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "      (9): SPPELAN(\n",
              "        (cv1): Conv(\n",
              "          (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "          (act): SiLU(inplace=True)\n",
              "        )\n",
              "        (cv2): MaxPool2d(kernel_size=5, stride=1, padding=2, dilation=1, ceil_mode=False)\n",
              "        (cv3): MaxPool2d(kernel_size=5, stride=1, padding=2, dilation=1, ceil_mode=False)\n",
              "        (cv4): MaxPool2d(kernel_size=5, stride=1, padding=2, dilation=1, ceil_mode=False)\n",
              "        (cv5): Conv(\n",
              "          (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "          (act): SiLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "      (10): Upsample(scale_factor=2.0, mode='nearest')\n",
              "      (11): Concat()\n",
              "      (12): RepNCSPELAN4(\n",
              "        (cv1): Conv(\n",
              "          (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "          (act): SiLU(inplace=True)\n",
              "        )\n",
              "        (cv2): Sequential(\n",
              "          (0): RepCSP(\n",
              "            (cv1): Conv(\n",
              "              (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (cv2): Conv(\n",
              "              (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (cv3): Conv(\n",
              "              (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (m): Sequential(\n",
              "              (0): RepBottleneck(\n",
              "                (cv1): RepConv(\n",
              "                  (act): SiLU(inplace=True)\n",
              "                  (conv1): Conv(\n",
              "                    (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "                    (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                    (act): Identity()\n",
              "                  )\n",
              "                  (conv2): Conv(\n",
              "                    (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "                    (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                    (act): Identity()\n",
              "                  )\n",
              "                )\n",
              "                (cv2): Conv(\n",
              "                  (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "                  (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                  (act): SiLU(inplace=True)\n",
              "                )\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (1): Conv(\n",
              "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (cv3): Sequential(\n",
              "          (0): RepCSP(\n",
              "            (cv1): Conv(\n",
              "              (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (cv2): Conv(\n",
              "              (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (cv3): Conv(\n",
              "              (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (m): Sequential(\n",
              "              (0): RepBottleneck(\n",
              "                (cv1): RepConv(\n",
              "                  (act): SiLU(inplace=True)\n",
              "                  (conv1): Conv(\n",
              "                    (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "                    (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                    (act): Identity()\n",
              "                  )\n",
              "                  (conv2): Conv(\n",
              "                    (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "                    (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                    (act): Identity()\n",
              "                  )\n",
              "                )\n",
              "                (cv2): Conv(\n",
              "                  (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "                  (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                  (act): SiLU(inplace=True)\n",
              "                )\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (1): Conv(\n",
              "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (cv4): Conv(\n",
              "          (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "          (act): SiLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "      (13): Upsample(scale_factor=2.0, mode='nearest')\n",
              "      (14): Concat()\n",
              "      (15): RepNCSPELAN4(\n",
              "        (cv1): Conv(\n",
              "          (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "          (act): SiLU(inplace=True)\n",
              "        )\n",
              "        (cv2): Sequential(\n",
              "          (0): RepCSP(\n",
              "            (cv1): Conv(\n",
              "              (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (cv2): Conv(\n",
              "              (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (cv3): Conv(\n",
              "              (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (m): Sequential(\n",
              "              (0): RepBottleneck(\n",
              "                (cv1): RepConv(\n",
              "                  (act): SiLU(inplace=True)\n",
              "                  (conv1): Conv(\n",
              "                    (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "                    (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                    (act): Identity()\n",
              "                  )\n",
              "                  (conv2): Conv(\n",
              "                    (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "                    (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                    (act): Identity()\n",
              "                  )\n",
              "                )\n",
              "                (cv2): Conv(\n",
              "                  (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "                  (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                  (act): SiLU(inplace=True)\n",
              "                )\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (1): Conv(\n",
              "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (cv3): Sequential(\n",
              "          (0): RepCSP(\n",
              "            (cv1): Conv(\n",
              "              (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (cv2): Conv(\n",
              "              (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (cv3): Conv(\n",
              "              (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (m): Sequential(\n",
              "              (0): RepBottleneck(\n",
              "                (cv1): RepConv(\n",
              "                  (act): SiLU(inplace=True)\n",
              "                  (conv1): Conv(\n",
              "                    (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "                    (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                    (act): Identity()\n",
              "                  )\n",
              "                  (conv2): Conv(\n",
              "                    (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "                    (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                    (act): Identity()\n",
              "                  )\n",
              "                )\n",
              "                (cv2): Conv(\n",
              "                  (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "                  (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                  (act): SiLU(inplace=True)\n",
              "                )\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (1): Conv(\n",
              "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (cv4): Conv(\n",
              "          (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "          (act): SiLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "      (16): ADown(\n",
              "        (cv1): Conv(\n",
              "          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "          (act): SiLU(inplace=True)\n",
              "        )\n",
              "        (cv2): Conv(\n",
              "          (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "          (act): SiLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "      (17): Concat()\n",
              "      (18): RepNCSPELAN4(\n",
              "        (cv1): Conv(\n",
              "          (conv): Conv2d(768, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "          (act): SiLU(inplace=True)\n",
              "        )\n",
              "        (cv2): Sequential(\n",
              "          (0): RepCSP(\n",
              "            (cv1): Conv(\n",
              "              (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (cv2): Conv(\n",
              "              (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (cv3): Conv(\n",
              "              (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (m): Sequential(\n",
              "              (0): RepBottleneck(\n",
              "                (cv1): RepConv(\n",
              "                  (act): SiLU(inplace=True)\n",
              "                  (conv1): Conv(\n",
              "                    (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "                    (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                    (act): Identity()\n",
              "                  )\n",
              "                  (conv2): Conv(\n",
              "                    (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "                    (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                    (act): Identity()\n",
              "                  )\n",
              "                )\n",
              "                (cv2): Conv(\n",
              "                  (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "                  (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                  (act): SiLU(inplace=True)\n",
              "                )\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (1): Conv(\n",
              "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (cv3): Sequential(\n",
              "          (0): RepCSP(\n",
              "            (cv1): Conv(\n",
              "              (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (cv2): Conv(\n",
              "              (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (cv3): Conv(\n",
              "              (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (m): Sequential(\n",
              "              (0): RepBottleneck(\n",
              "                (cv1): RepConv(\n",
              "                  (act): SiLU(inplace=True)\n",
              "                  (conv1): Conv(\n",
              "                    (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "                    (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                    (act): Identity()\n",
              "                  )\n",
              "                  (conv2): Conv(\n",
              "                    (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "                    (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                    (act): Identity()\n",
              "                  )\n",
              "                )\n",
              "                (cv2): Conv(\n",
              "                  (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "                  (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                  (act): SiLU(inplace=True)\n",
              "                )\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (1): Conv(\n",
              "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (cv4): Conv(\n",
              "          (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "          (act): SiLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "      (19): ADown(\n",
              "        (cv1): Conv(\n",
              "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "          (act): SiLU(inplace=True)\n",
              "        )\n",
              "        (cv2): Conv(\n",
              "          (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "          (act): SiLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "      (20): Concat()\n",
              "      (21): RepNCSPELAN4(\n",
              "        (cv1): Conv(\n",
              "          (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "          (act): SiLU(inplace=True)\n",
              "        )\n",
              "        (cv2): Sequential(\n",
              "          (0): RepCSP(\n",
              "            (cv1): Conv(\n",
              "              (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (cv2): Conv(\n",
              "              (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (cv3): Conv(\n",
              "              (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (m): Sequential(\n",
              "              (0): RepBottleneck(\n",
              "                (cv1): RepConv(\n",
              "                  (act): SiLU(inplace=True)\n",
              "                  (conv1): Conv(\n",
              "                    (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "                    (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                    (act): Identity()\n",
              "                  )\n",
              "                  (conv2): Conv(\n",
              "                    (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "                    (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                    (act): Identity()\n",
              "                  )\n",
              "                )\n",
              "                (cv2): Conv(\n",
              "                  (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "                  (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                  (act): SiLU(inplace=True)\n",
              "                )\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (1): Conv(\n",
              "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (cv3): Sequential(\n",
              "          (0): RepCSP(\n",
              "            (cv1): Conv(\n",
              "              (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (cv2): Conv(\n",
              "              (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (cv3): Conv(\n",
              "              (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (m): Sequential(\n",
              "              (0): RepBottleneck(\n",
              "                (cv1): RepConv(\n",
              "                  (act): SiLU(inplace=True)\n",
              "                  (conv1): Conv(\n",
              "                    (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "                    (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                    (act): Identity()\n",
              "                  )\n",
              "                  (conv2): Conv(\n",
              "                    (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "                    (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                    (act): Identity()\n",
              "                  )\n",
              "                )\n",
              "                (cv2): Conv(\n",
              "                  (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "                  (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "                  (act): SiLU(inplace=True)\n",
              "                )\n",
              "              )\n",
              "            )\n",
              "          )\n",
              "          (1): Conv(\n",
              "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (cv4): Conv(\n",
              "          (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "          (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "          (act): SiLU(inplace=True)\n",
              "        )\n",
              "      )\n",
              "      (22): Segment(\n",
              "        (cv2): ModuleList(\n",
              "          (0): Sequential(\n",
              "            (0): Conv(\n",
              "              (conv): Conv2d(256, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (1): Conv(\n",
              "              (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (2): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "          )\n",
              "          (1-2): 2 x Sequential(\n",
              "            (0): Conv(\n",
              "              (conv): Conv2d(512, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (1): Conv(\n",
              "              (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (2): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))\n",
              "          )\n",
              "        )\n",
              "        (cv3): ModuleList(\n",
              "          (0): Sequential(\n",
              "            (0): Conv(\n",
              "              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (1): Conv(\n",
              "              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (2): Conv2d(256, 80, kernel_size=(1, 1), stride=(1, 1))\n",
              "          )\n",
              "          (1-2): 2 x Sequential(\n",
              "            (0): Conv(\n",
              "              (conv): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (1): Conv(\n",
              "              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (2): Conv2d(256, 80, kernel_size=(1, 1), stride=(1, 1))\n",
              "          )\n",
              "        )\n",
              "        (dfl): DFL(\n",
              "          (conv): Conv2d(16, 1, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "        )\n",
              "        (proto): Proto(\n",
              "          (cv1): Conv(\n",
              "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (upsample): ConvTranspose2d(256, 256, kernel_size=(2, 2), stride=(2, 2))\n",
              "          (cv2): Conv(\n",
              "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "          (cv3): Conv(\n",
              "            (conv): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
              "            (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "            (act): SiLU(inplace=True)\n",
              "          )\n",
              "        )\n",
              "        (cv4): ModuleList(\n",
              "          (0): Sequential(\n",
              "            (0): Conv(\n",
              "              (conv): Conv2d(256, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (1): Conv(\n",
              "              (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (2): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1))\n",
              "          )\n",
              "          (1-2): 2 x Sequential(\n",
              "            (0): Conv(\n",
              "              (conv): Conv2d(512, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (1): Conv(\n",
              "              (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
              "              (act): SiLU(inplace=True)\n",
              "            )\n",
              "            (2): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1))\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              ")"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Build a YOLOv9c model from pretrained weight\n",
        "yolo_model = YOLO(\"pretrained_weights/yolov9c-seg.pt\")\n",
        "tracker = sv.ByteTrack()\n",
        "\n",
        "yolo_model.to(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [],
      "source": [
        "MyDetector =  ObjectDetectionUtils(yolo_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 2.2 Depth estimation model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [],
      "source": [
        "def load_depth_model(device, encoder='vitl', load_from='pretrained_weights/depth_anything_v2_metric_vkitti_vitl.pth', max_depth=25):\n",
        "    \n",
        "    model_configs = {\n",
        "        'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]},\n",
        "        'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]},\n",
        "        'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]},\n",
        "        'vitg': {'encoder': 'vitg', 'features': 384, 'out_channels': [1536, 1536, 1536, 1536]}\n",
        "    }\n",
        "    \n",
        "    depth_anything = DepthAnythingV2(**{**model_configs[encoder], 'max_depth': max_depth})\n",
        "    depth_anything.load_state_dict(torch.load(load_from, map_location=device))\n",
        "    depth_anything = depth_anything.to(device).eval()\n",
        "    \n",
        "    return depth_anything"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [],
      "source": [
        "depth_anything= load_depth_model(device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 2.3 Automatic Camera Calibration Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [],
      "source": [
        "class AutomaticCalibrationModel(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(AutomaticCalibrationModel, self).__init__()\n",
        "        self.fc1 = nn.Linear(2, 64)\n",
        "        self.fc2 = nn.Linear(64, 128)\n",
        "        self.fc21 = nn.Linear(128, 260)\n",
        "        self.fc22 = nn.Linear(260, 128)\n",
        "        self.fc3 = nn.Linear(128, 64)\n",
        "        self.fc4 = nn.Linear(64, 1)\n",
        "        \n",
        "    def forward(self, x):\n",
        "        x = torch.relu(self.fc1(x))\n",
        "        x = torch.relu(self.fc2(x))\n",
        "        x = torch.relu(self.fc21(x))\n",
        "        x = torch.relu(self.fc22(x))\n",
        "        x = torch.relu(self.fc3(x))\n",
        "        x = torch.relu(self.fc4(x))  \n",
        "        return x\n",
        "\n",
        "def custom_loss(y_pred, y_true):\n",
        "    epsilon = 1e-8  # Small constant to prevent log(0)\n",
        "    y_pred = torch.clamp(y_pred, min=epsilon)  # Clipping predictions to avoid log(0)\n",
        "    y_true = torch.clamp(y_true, min=epsilon)\n",
        "    l = torch.mean((torch.log(y_pred) - torch.log(y_true))**2)\n",
        "    return l"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Initialize the model, loss function, and optimizer\n",
        "cam_clib_model = AutomaticCalibrationModel().to(device)\n",
        "optimizer = optim.Adam(cam_clib_model.parameters(), lr=0.001)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# 3.0 Select the ground truth heights on the first frame"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "You have drawn 2 lines.\n"
          ]
        }
      ],
      "source": [
        "app = CameraCalibrateApp(VIDEO_PATH)\n",
        "ground_truth_lines, frame = app.start()\n",
        "\n",
        "print(f'You have drawn {len(ground_truth_lines)} lines.')\n",
        "\n",
        "depth_map = depth_anything.infer_image(frame)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "plotting the depth map for visualization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Assuming depth_map is your NumPy array of size (w, h)\n",
        "plt.imshow(depth_map, cmap='viridis')\n",
        "plt.colorbar()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[(487.03695958315114, 175.0, 4.351649), (276.2607463973121, 160.0, 7.009362)]"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "anchor_lines = []\n",
        "\n",
        "for start_loaction, end_location, true_length in ground_truth_lines:\n",
        "    length_pixels = np.sqrt((end_location[0] - start_loaction[0]) ** 2 + (end_location[1] - start_loaction[1]) ** 2)\n",
        "    middle_point = ((start_loaction[0] + end_location[0]) // 2, (start_loaction[1] + end_location[1]) // 2)\n",
        "\n",
        "    depth = depth_map[middle_point[1], middle_point[0]]\n",
        "\n",
        "    \n",
        "    anchor_lines.append((length_pixels,  true_length,  depth))\n",
        "\n",
        "anchor_lines\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# 4.0 Data Generation for the automatic camera calibration model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 4.1 collect data from tracking"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Data Genartion in progress: 1005it [02:42,  6.19it/s]                          \n"
          ]
        }
      ],
      "source": [
        "cap = cv2.VideoCapture(VIDEO_PATH)\n",
        "\n",
        "number_of_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
        "\n",
        "tracking_data = {}\n",
        "frame_count = -1\n",
        "\n",
        "step = 5  # sample each 5 frames\n",
        "frames_limit = min (1000, number_of_frames)\n",
        "\n",
        "# Initialize tqdm progress bar\n",
        "pbar = tqdm(total=frames_limit, desc=\"Data Genartion in progress\")\n",
        "\n",
        "while cap.isOpened():\n",
        "    ret, frame = cap.read()\n",
        "    frame_count += 1\n",
        "    if not ret:\n",
        "        break\n",
        "\n",
        "    if frame_count % step != 0:\n",
        "        continue\n",
        "\n",
        "    results, legs_and_heads, boxes = MyDetector.infer_obj_detection(frame)\n",
        "\n",
        "    detections = sv.Detections.from_ultralytics(results)\n",
        "    detections = tracker.update_with_detections(detections)\n",
        "    depth_map = depth_anything.infer_image(frame)\n",
        "\n",
        "\n",
        "    for detection_idx, _ in enumerate(detections):\n",
        "        xyxy = detections[detection_idx].xyxy.tolist()[0]\n",
        "        obj_id = detections[detection_idx].tracker_id[0]\n",
        "\n",
        "        best_idx = MyDetector.match_best_box(xyxy, boxes)\n",
        "\n",
        "        head_pos, leg_pos = legs_and_heads[best_idx]\n",
        "\n",
        "        start, end = head_pos, leg_pos\n",
        "        length_pixels = np.sqrt((end[0] - start[0]) ** 2 + (end[1] - start[1]) ** 2)\n",
        "        middle_point = ((start[0] + end[0]) // 2, (start[1] + end[1]) // 2)\n",
        "\n",
        "        depth = depth_map[middle_point[1], middle_point[0]]\n",
        "\n",
        "        if obj_id not in tracking_data:\n",
        "            tracking_data[obj_id] = [(depth, length_pixels)]\n",
        "        else:\n",
        "            tracking_data[obj_id].append((depth, length_pixels))\n",
        "\n",
        "    # Update progress bar\n",
        "    pbar.update(step)\n",
        "\n",
        "    cv2.waitKey(1)\n",
        "\n",
        "    if frame_count >= frames_limit:\n",
        "        break\n",
        "\n",
        "pbar.close()\n",
        "cap.release()\n",
        "cv2.destroyAllWindows()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{1: [(4.3363624, 483.8109134775693),\n",
              "  (4.288134, 468.2574078431648),\n",
              "  (4.6285863, 440.50085130451225),\n",
              "  (4.650639, 428.2627698037736),\n",
              "  (4.629539, 427.51842065576545),\n",
              "  (4.7669263, 412.0436870041816),\n",
              "  (5.0756135, 391.0818328687744),\n",
              "  (4.987567, 395.0113922407808),\n",
              "  (4.951779, 384.2551235832777),\n",
              "  (5.157715, 383.1761996784247),\n",
              "  (5.4015737, 372.29289544658246),\n",
              "  (5.3865976, 355.0),\n",
              "  (5.439351, 353.0693982774491),\n",
              "  (5.569971, 341.2477106150311),\n",
              "  (5.538653, 343.0131192826303),\n",
              "  (5.6381755, 341.4103103305464),\n",
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              "  (5.986437, 324.5566206380637),\n",
              "  (5.9634895, 316.07752213657966),\n",
              "  (5.979585, 300.1666203960727),\n",
              "  (6.336312, 296.2043213729334),\n",
              "  (6.278807, 302.026488904533),\n",
              "  (6.293177, 299.0),\n",
              "  (6.256668, 289.4995682207488),\n",
              "  (6.3809085, 281.40007107319644),\n",
              "  (6.5547743, 277.72108310317384),\n",
              "  (6.540873, 270.0296280040396),\n",
              "  (6.6442204, 259.0077218926108),\n",
              "  (6.5461745, 264.09278672466615),\n",
              "  (6.7820992, 247.58432906789557),\n",
              "  (7.1153207, 242.00206610688264),\n",
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              "  (7.228333, 242.0743687382041),\n",
              "  (7.341611, 241.16799124261908),\n",
              "  (7.5623612, 245.00204080782675),\n",
              "  (7.495506, 247.00202428320299),\n",
              "  (7.46262, 235.8495283014151),\n",
              "  (7.869247, 232.62201099638014),\n",
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              "  (7.907101, 213.00234740490538),\n",
              "  (8.06329, 221.14474897677312),\n",
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              "  (7.8614426, 210.11663427725088),\n",
              "  (8.060952, 202.48456731316588),\n",
              "  (8.063582, 201.2486024796197),\n",
              "  (8.178141, 192.12756179163884),\n",
              "  (8.2078495, 201.0024875467963),\n",
              "  (8.13903, 198.09088823063013),\n",
              "  (8.114715, 201.0),\n",
              "  (8.1569805, 184.45866745696716),\n",
              "  (8.574399, 194.12624758130983),\n",
              "  (8.733375, 192.06509313251067),\n",
              "  (8.82897, 175.0028571195339),\n",
              "  (8.954826, 179.02513789968157),\n",
              "  (9.042717, 174.23260314877925),\n",
              "  (9.051876, 175.0114281982751),\n",
              "  (8.957255, 175.28548142958104),\n",
              "  (9.311793, 176.2554963681984),\n",
              "  (9.304595, 177.02259742756007),\n",
              "  (9.646696, 173.02600960549256),\n",
              "  (9.660534, 172.23530416264836),\n",
              "  (9.856639, 165.10905486980417),\n",
              "  (9.987219, 164.77863939236784),\n",
              "  (10.158051, 164.0),\n",
              "  (9.971016, 163.04907236779974),\n",
              "  (10.263191, 165.14841809717706),\n",
              "  (10.1979885, 162.19741058352318),\n",
              "  (10.111654, 151.00331122197287),\n",
              "  (10.354757, 150.11995203836165),\n",
              "  (10.2305765, 162.04937519163718),\n",
              "  (10.172616, 149.6562728387955),\n",
              "  (10.326403, 150.6154042586614),\n",
              "  (10.751359, 147.29901561110313),\n",
              "  (10.475759, 147.50254235097103),\n",
              "  (10.568656, 143.13629868066312),\n",
              "  (10.592315, 136.36715146984628),\n",
              "  (11.887455, 133.06013678032951),\n",
              "  (11.0087185, 149.00335566691106)],\n",
              " 2: [(4.299227, 460.5746410735181),\n",
              "  (4.3337393, 446.0011210748242),\n",
              "  (4.5637507, 425.0047058562999),\n",
              "  (4.634377, 434.0011520722036),\n",
              "  (4.7155046, 404.04455199891015),\n",
              "  (4.741608, 416.52971082505024),\n",
              "  (4.9080706, 390.1845717093386),\n",
              "  (4.8700624, 376.64306710730784),\n",
              "  (4.984081, 378.1904282236662),\n",
              "  (5.1076665, 373.02144710458674),\n",
              "  (5.1756163, 371.43640101637857),\n",
              "  (5.216138, 363.23270777836075),\n",
              "  (5.2868075, 358.03491449857233),\n",
              "  (5.5162563, 347.09220676932523),\n",
              "  (5.5210147, 343.14574163174456),\n",
              "  (5.6576233, 330.0969554539999),\n",
              "  (5.7262406, 321.3051509079803),\n",
              "  (5.7494206, 316.0063290505429),\n",
              "  (5.873781, 306.41801513618617),\n",
              "  (5.9652095, 299.13542083812143),\n",
              "  (6.09311, 306.13232433050905),\n",
              "  (6.0193777, 289.0017300986276),\n",
              "  (6.1927757, 293.17059879871994),\n",
              "  (6.308521, 289.0276803352925),\n",
              "  (6.345116, 279.35103364763125),\n",
              "  (6.351102, 273.11719096387907),\n",
              "  (6.4724483, 271.1844390815963),\n",
              "  (6.5901446, 267.36678926149375),\n",
              "  (6.5695243, 252.0),\n",
              "  (6.8259115, 244.989795705862),\n",
              "  (7.109059, 256.0175775215444),\n",
              "  (7.194633, 242.24987100099764),\n",
              "  (7.019291, 245.17748673155128),\n",
              "  (7.2586174, 237.21087664776252),\n",
              "  (7.2775745, 238.01890681204299),\n",
              "  (7.4266076, 233.0),\n",
              "  (7.321517, 233.0085835328819),\n",
              "  (7.5332217, 235.00212764994276),\n",
              "  (7.5462227, 232.0086205294967),\n",
              "  (7.748529, 220.22715545545242),\n",
              "  (7.574152, 213.243991709028),\n",
              "  (7.6791983, 211.72859986312668),\n",
              "  (7.74284, 207.00966161027364),\n",
              "  (7.698703, 211.0213259365034),\n",
              "  (7.875841, 211.0213259365034),\n",
              "  (7.8962474, 207.06037766796427),\n",
              "  (8.018838, 208.86598574205422),\n",
              "  (7.974893, 197.4968354176846),\n",
              "  (7.970011, 187.77113729218343),\n",
              "  (8.061257, 183.06829326784035),\n",
              "  (7.95345, 195.0640920313116),\n",
              "  (8.051609, 186.09674903124989),\n",
              "  (8.344178, 189.06083676954358),\n",
              "  (8.432755, 181.79658962697843),\n",
              "  (8.607724, 175.18276170902203),\n",
              "  (8.8492, 176.1817243643619),\n",
              "  (8.883396, 174.04597093871493),\n",
              "  (8.806013, 174.01149387324966),\n",
              "  (8.988135, 176.07100840286),\n",
              "  (9.076193, 177.0706073858674),\n",
              "  (9.46736, 176.13914953808538),\n",
              "  (9.388674, 175.0114281982751),\n",
              "  (9.7629595, 171.18703221914913),\n",
              "  (9.711122, 160.52725625263767),\n",
              "  (10.058585, 163.07666908543356),\n",
              "  (9.800712, 164.04877323527901),\n",
              "  (9.794852, 155.05160431288675),\n",
              "  (9.936021, 153.0),\n",
              "  (9.693282, 152.08221460775746),\n",
              "  (9.977604, 152.00328943809077),\n",
              "  (10.398486, 152.0526224699857),\n",
              "  (9.842765, 152.3285921946369),\n",
              "  (10.03565, 145.0551619212498),\n",
              "  (10.038556, 149.40548852033515),\n",
              "  (10.183802, 146.27713423498562),\n",
              "  (10.220102, 153.08167754502824),\n",
              "  (10.578434, 156.02884348735012),\n",
              "  (10.342785, 154.20765220960988),\n",
              "  (10.148636, 140.00357138301865),\n",
              "  (10.375312, 137.2953021774598),\n",
              "  (10.667005, 134.01492454200763),\n",
              "  (11.219049, 137.13132391981054),\n",
              "  (10.817772, 120.50311199301038)],\n",
              " 3: [(6.357659, 356.61884414595926),\n",
              "  (6.274606, 348.30446451344835),\n",
              "  (5.7033353, 242.29321080046796)],\n",
              " 4: [(4.6326666, 480.00937490844905),\n",
              "  (4.7947245, 452.0099556425721),\n",
              "  (4.691037, 434.11519208615584),\n",
              "  (4.780189, 426.42349841442837),\n",
              "  (4.746357, 425.01882311257697),\n",
              "  (5.019385, 421.34309060431974),\n",
              "  (4.833525, 416.0588900624526),\n",
              "  (5.0323677, 395.2138661535043),\n",
              "  (4.9931684, 394.2207503417343),\n",
              "  (5.1287932, 377.0),\n",
              "  (5.128751, 386.10490802371316),\n",
              "  (5.347518, 374.16172973728885),\n",
              "  (5.4349627, 352.68682992139077),\n",
              "  (5.4611793, 340.03676271838606),\n",
              "  (5.4910994, 344.11771241829445),\n",
              "  (5.6030807, 349.2806894175514),\n",
              "  (5.7784066, 335.3356527421443),\n",
              "  (5.717879, 333.253657144224),\n",
              "  (6.079235, 318.190194694934),\n",
              "  (6.044146, 320.0140621910231),\n",
              "  (5.9628925, 303.00165016052307),\n",
              "  (5.967394, 294.3331445828009),\n",
              "  (6.1896105, 293.43653487594213),\n",
              "  (6.3015904, 295.0423698386386),\n",
              "  (6.269712, 285.21220170252184),\n",
              "  (6.313015, 281.0871750898643),\n",
              "  (6.518739, 295.0016949103852),\n",
              "  (6.55356, 275.11633902769205),\n",
              "  (6.676193, 263.0019011338131),\n",
              "  (7.1207495, 264.2290672882149),\n",
              "  (7.2233605, 268.01679051880313),\n",
              "  (7.289563, 253.12645061312736),\n",
              "  (7.1434493, 255.9472601924076),\n",
              "  (7.2991986, 244.07375934335914),\n",
              "  (7.3615494, 244.0184419260151),\n",
              "  (7.4851785, 228.01973598791838),\n",
              "  (7.5012712, 234.48027635602958),\n",
              "  (7.718193, 232.17450333746814),\n",
              "  (7.6798553, 232.0538730553748),\n",
              "  (7.7861233, 219.0091322296858),\n",
              "  (7.83702, 218.00917411888886),\n",
              "  (7.8478365, 220.08180297334897),\n",
              "  (7.823816, 222.02026934494066),\n",
              "  (7.805098, 212.00235847744713),\n",
              "  (8.019208, 211.00947846009194),\n",
              "  (8.138493, 219.58369702689677),\n",
              "  (8.089713, 205.08778608196053),\n",
              "  (8.048515, 203.8872237291979),\n",
              "  (8.132747, 200.06249023742558),\n",
              "  (8.2122755, 198.0631212517868),\n",
              "  (8.270461, 197.12432625122653),\n",
              "  (8.291439, 198.00252523642217),\n",
              "  (8.463691, 197.0406049523803),\n",
              "  (8.507385, 195.0640920313116),\n",
              "  (8.5676775, 196.0025510038071),\n",
              "  (8.853517, 184.0244548966251),\n",
              "  (8.825456, 173.1415605797753),\n",
              "  (8.913078, 174.0258601472781),\n",
              "  (9.001915, 176.34341496069538),\n",
              "  (9.378178, 185.13238506539042),\n",
              "  (9.759737, 186.02419197512995),\n",
              "  (9.815673, 175.28548142958104),\n",
              "  (9.944293, 171.00292395160966),\n",
              "  (10.072392, 182.00274723201295),\n",
              "  (10.651086, 167.50522379914005),\n",
              "  (10.799504, 155.11608556174951),\n",
              "  (11.0164175, 165.0272704737008),\n",
              "  (11.086702, 153.00326793895613),\n",
              "  (10.161097, 151.47607071745688),\n",
              "  (10.133298, 152.00328943809077),\n",
              "  (10.389037, 152.47294842036735),\n",
              "  (10.229923, 153.06207890917986),\n",
              "  (10.759258, 149.21461054467824),\n",
              "  (10.683029, 149.08386901338454),\n",
              "  (10.646941, 149.01342221424215),\n",
              "  (10.669373, 143.22360140703069),\n",
              "  (9.889783, 143.0034964607509),\n",
              "  (10.527894, 165.10905486980417)],\n",
              " 5: [(7.0653954, 266.04698833100895),\n",
              "  (7.0243783, 255.43100829773977),\n",
              "  (7.5821686, 256.3298656028985),\n",
              "  (4.284048, 464.1045140913844),\n",
              "  (4.646772, 450.75048530201275),\n",
              "  (4.763526, 419.0190926437601),\n",
              "  (5.012962, 435.96559497281436),\n",
              "  (4.259267, 500.14497898109505),\n",
              "  (4.5352106, 481.20369907140156),\n",
              "  (4.478206, 458.00436679140955),\n",
              "  (4.735377, 379.42719986843326),\n",
              "  (5.464986, 368.84820726146955),\n",
              "  (5.6504717, 363.39785359850435),\n",
              "  (5.6911316, 352.5109927363968),\n",
              "  (6.147549, 329.3690938749415),\n",
              "  (5.708586, 337.2906758272455),\n",
              "  (4.9198184, 372.0336006330611),\n",
              "  (5.130517, 363.4460069941614),\n",
              "  (5.2341304, 340.84600628436294),\n",
              "  (5.730971, 338.42281246984516),\n",
              "  (5.4222803, 335.0104475982801),\n",
              "  (5.5695567, 330.0545409474016),\n",
              "  (5.29028, 418.1722133284324),\n",
              "  (5.5978203, 399.02004962156974),\n",
              "  (5.698445, 381.1062843879644),\n",
              "  (5.7171392, 372.53724646000165),\n",
              "  (5.7973113, 372.0336006330611)],\n",
              " 6: [(2.0173116, 458.27175343893936),\n",
              "  (1.9361446, 417.80856860528843),\n",
              "  (5.436535, 570.4489460065643),\n",
              "  (3.5510812, 600.0808278890436),\n",
              "  (3.384233, 595.1419998622177),\n",
              "  (3.5440702, 615.1373830291897),\n",
              "  (3.5794506, 590.3058529271076),\n",
              "  (3.6606984, 578.4980553122024),\n",
              "  (4.0054173, 546.4430436925701),\n",
              "  (3.8868504, 545.542848912897),\n",
              "  (4.0300627, 517.2794215895312),\n",
              "  (3.8967843, 517.163417113005),\n",
              "  (4.5040994, 503.67151994132047),\n",
              "  (4.362913, 541.912354537152),\n",
              "  (5.3112726, 450.37095821111734),\n",
              "  (5.3397584, 436.22471273415954),\n",
              "  (5.2574067, 435.1654857637494),\n",
              "  (5.1810102, 439.2231778947919),\n",
              "  (5.500103, 427.09483724343943),\n",
              "  (5.1929994, 431.16702100230253),\n",
              "  (5.1983676, 409.0195594345092),\n",
              "  (5.444979, 409.0),\n",
              "  (5.431502, 399.66235749692515),\n",
              "  (4.002417, 517.8127074531872),\n",
              "  (4.2641654, 548.4824518614976),\n",
              "  (5.8416533, 371.06603185956),\n",
              "  (5.3754106, 371.0),\n",
              "  (6.671069, 347.32405617808854),\n",
              "  (6.710494, 327.0244639166923),\n",
              "  (6.5480413, 311.231425148554),\n",
              "  (7.273832, 284.91753192810023),\n",
              "  (7.413173, 281.0071173475861),\n",
              "  (7.32805, 292.15920317525513),\n",
              "  (7.5569777, 275.0163631495406),\n",
              "  (7.491378, 262.15453457836657),\n",
              "  (7.2918797, 268.9014689435519),\n",
              "  (7.5046015, 251.0019920239678),\n",
              "  (7.5146046, 252.04959829366916),\n",
              "  (7.9561834, 251.01792764661252),\n",
              "  (7.709476, 241.03319273494262),\n",
              "  (8.060214, 232.1055794245369),\n",
              "  (7.792632, 242.10121850168372),\n",
              "  (7.6157455, 241.25090673404733),\n",
              "  (7.7937284, 231.01947969814148),\n",
              "  (8.64416, 220.22715545545242),\n",
              "  (8.399571, 235.03191272676142),\n",
              "  (8.391865, 221.27358631341428),\n",
              "  (8.499375, 220.00227271553356),\n",
              "  (8.549469, 221.38202275704322),\n",
              "  (8.18901, 218.2292372712694),\n",
              "  (8.288862, 205.03902067655318),\n",
              "  (7.3803797, 247.00202428320299),\n",
              "  (7.66883, 257.07002936943076),\n",
              "  (7.457378, 263.2736219221364),\n",
              "  (9.421031, 200.24984394500785),\n",
              "  (8.991811, 198.16155025635018),\n",
              "  (9.314122, 191.0026177831079),\n",
              "  (9.411033, 196.0637651377735),\n",
              "  (9.475505, 185.21878954360974),\n",
              "  (9.310037, 186.09674903124989)],\n",
              " 7: [(2.8408082, 817.0391667478372),\n",
              "  (2.7569306, 829.1157940842762),\n",
              "  (2.8561854, 792.3635529225205),\n",
              "  (3.0438259, 770.7561222591748),\n",
              "  (3.194809, 728.0988943818003),\n",
              "  (3.2815833, 696.5235100124044),\n",
              "  (3.4300623, 659.3671208060044),\n",
              "  (3.5991387, 632.1336883919413),\n",
              "  (3.643985, 601.0133110006799),\n",
              "  (3.9546602, 562.0435926153771),\n",
              "  (4.044298, 546.0228932929462),\n",
              "  (4.0391793, 517.0348150753487),\n",
              "  (4.3703094, 489.0368084306129),\n",
              "  (4.5207334, 473.0517942043979),\n",
              "  (4.7206407, 461.1312177677846),\n",
              "  (5.297497, 440.07272126320214),\n",
              "  (5.3880877, 417.388308413161),\n",
              "  (3.336401, 420.267771783657),\n",
              "  (5.5046616, 398.4068272507388),\n",
              "  (6.2308054, 400.0612453112648),\n",
              "  (5.4001865, 205.0609665440988),\n",
              "  (5.6609993, 372.22708122865),\n",
              "  (5.1237254, 198.04039991880444),\n",
              "  (5.8032117, 361.4484748895754),\n",
              "  (7.473434, 235.2126697267815),\n",
              "  (7.5083933, 236.17154782064668),\n",
              "  (7.4253945, 236.13555429032706),\n",
              "  (7.352394, 233.30880823492285),\n",
              "  (7.1756005, 239.0334704596827),\n",
              "  (7.690869, 236.93247983339052)],\n",
              " 8: [(5.928516, 296.0270257932542),\n",
              "  (6.00507, 300.0),\n",
              "  (6.2414646, 288.1110896859057),\n",
              "  (6.72546, 302.0413878924542),\n",
              "  (6.527703, 313.0015974400131),\n",
              "  (6.317066, 315.03968004046726),\n",
              "  (6.0941105, 328.09754647055803),\n",
              "  (6.0762677, 328.0060975042995),\n",
              "  (5.9244347, 328.25752085824325),\n",
              "  (5.9762073, 344.32687957811254),\n",
              "  (5.9319844, 343.0014577228499),\n",
              "  (5.8419385, 343.14574163174456),\n",
              "  (5.617871, 363.60693062701654),\n",
              "  (5.2759953, 371.13474641967974),\n",
              "  (5.169081, 371.1091483647365),\n",
              "  (5.285301, 378.71493236998197),\n",
              "  (5.144589, 392.50987248730445),\n",
              "  (5.3488493, 400.0012499980469),\n",
              "  (5.413753, 419.57716811094474),\n",
              "  (5.240463, 416.0300469918008),\n",
              "  (5.3898487, 416.8117080889163),\n",
              "  (4.5295386, 578.7270513808734),\n",
              "  (4.000967, 629.2868980043999),\n",
              "  (3.8850722, 645.0279063730499),\n",
              "  (3.1447198, 758.5545464895719),\n",
              "  (3.3931797, 768.0423165425196),\n",
              "  (3.3258069, 822.000608272281),\n",
              "  (3.202461, 819.9085314838479),\n",
              "  (2.9455621, 855.2829941019522),\n",
              "  (2.8511717, 881.1452774656402),\n",
              "  (2.738631, 911.063115266994),\n",
              "  (2.609658, 906.0137968044417),\n",
              "  (2.418968, 985.3978891797973),\n",
              "  (2.3718545, 776.659513557904)],\n",
              " 9: [(7.3516417, 201.08953229842672),\n",
              "  (7.2766275, 201.30076999355964),\n",
              "  (7.3186245, 200.0024999843752),\n",
              "  (7.196928, 197.20547659738054),\n",
              "  (7.219652, 198.3053201505194),\n",
              "  (7.4007044, 199.0628041598932),\n",
              "  (7.6624203, 197.06344156134085),\n",
              "  (7.8009644, 197.00253805471644),\n",
              "  (7.787044, 195.02820308868152),\n",
              "  (8.049087, 191.12822920751398),\n",
              "  (7.620813, 199.2034136253694)],\n",
              " 10: [(5.001117, 432.010416541083),\n",
              "  (4.8209634, 434.0103685397389),\n",
              "  (5.030124, 425.0047058562999),\n",
              "  (5.1906896, 402.0111938740015),\n",
              "  (5.591008, 401.00498750015566),\n",
              "  (5.333096, 402.2101440789379),\n",
              "  (5.3860874, 398.4068272507388),\n",
              "  (5.3476243, 374.04946196993785),\n",
              "  (5.480062, 372.0215047547655),\n",
              "  (5.5987434, 371.43640101637857),\n",
              "  (5.6955543, 354.0127116361784),\n",
              "  (6.073234, 345.20863256876993),\n",
              "  (6.3961663, 350.14282800023193),\n",
              "  (5.2770762, 200.26232796010336),\n",
              "  (5.2703795, 480.1353975703104),\n",
              "  (4.710409, 490.0010204071008),\n",
              "  (4.2834153, 487.201190474736),\n",
              "  (3.9785247, 509.51938137817683),\n",
              "  (3.7386944, 651.8420053970134),\n",
              "  (3.5377197, 683.8011406834593),\n",
              "  (3.547184, 710.8136183276176),\n",
              "  (3.359424, 736.1331401315933),\n",
              "  (3.3440747, 767.7200010420465),\n",
              "  (3.0951343, 816.3540898409219),\n",
              "  (3.16897, 836.7323347403278),\n",
              "  (3.3658888, 865.8914481619506),\n",
              "  (3.0050282, 824.8302855739477),\n",
              "  (2.857077, 872.3875285674366),\n",
              "  (2.7027988, 903.2679558137773),\n",
              "  (3.1466432, 914.9300519711876),\n",
              "  (1.5199004, 1031.938467157805),\n",
              "  (1.6535372, 1021.2002741871939),\n",
              "  (1.861198, 1019.5670649839568),\n",
              "  (1.9839334, 1015.4142012006726),\n",
              "  (2.0862653, 1015.3329503172838),\n",
              "  (2.1076307, 1007.2417783233577),\n",
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              " 21: [(7.998237, 268.01679051880313),\n",
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              "  (2.6383533, 794.0484871845043),\n",
              "  (8.477639, 786.2296102284624)],\n",
              " 24: [(6.6221604, 286.40006983239374),\n",
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              "  (2.9180572, 734.1151135891428)],\n",
              " 26: [(10.676371, 160.25292509030842),\n",
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              "  (7.482024, 288.2672371255533),\n",
              "  (7.747716, 305.785872793365),\n",
              "  (7.8833103, 290.25850547399983),\n",
              "  (6.7980084, 319.97812425226823),\n",
              "  (7.4832892, 320.7023542164915),\n",
              "  (7.564533, 308.6632469213009),\n",
              "  (7.5188084, 308.6632469213009),\n",
              "  (7.7815056, 308.4623153644542),\n",
              "  (7.282956, 308.01623333843946),\n",
              "  (6.1330366, 311.16073017011644),\n",
              "  (6.2995424, 311.3149530620076),\n",
              "  (6.307971, 311.1301978272119),\n",
              "  (6.512427, 311.1301978272119),\n",
              "  (6.1794825, 311.231425148554),\n",
              "  (8.34586, 295.2033197645311),\n",
              "  (7.6770644, 447.33209140413794),\n",
              "  (5.026529, 433.7798981050182),\n",
              "  (4.6599402, 454.0011013202501),\n",
              "  (4.72244, 371.9099353338117),\n",
              "  (7.9947886, 175.4821928287882),\n",
              "  (8.155712, 151.16216457830973),\n",
              "  (9.335659, 81.05553651663777),\n",
              "  (9.505445, 81.05553651663777),\n",
              "  (9.757415, 93.7709976485267),\n",
              "  (9.949854, 93.02150289046077),\n",
              "  (9.907781, 93.13431161500041),\n",
              "  (9.920139, 93.04837451562494),\n",
              "  (10.210691, 93.0),\n",
              "  (9.924473, 93.02150289046077),\n",
              "  (10.49602, 93.43446901438462),\n",
              "  (10.5299015, 93.7709976485267),\n",
              "  (10.231494, 94.2019108086455),\n",
              "  (10.279607, 81.88406438373708),\n",
              "  (10.034792, 93.7709976485267),\n",
              "  (9.278027, 93.7709976485267)],\n",
              " 68: [(8.026797, 239.67686580060246),\n",
              "  (8.1637, 230.37577997697588),\n",
              "  (7.84285, 245.0081631293129),\n",
              "  (7.502692, 267.15164233071823),\n",
              "  (7.3896, 254.04920783186867),\n",
              "  (7.382087, 265.06791582535976),\n",
              "  (7.479604, 252.6420392571276),\n",
              "  (7.8398767, 246.20316813558676),\n",
              "  (7.575737, 256.0488234692751),\n",
              "  (7.534519, 257.0486335306998),\n",
              "  (7.6173744, 270.1184925176357),\n",
              "  (7.464223, 270.09072549793336),\n",
              "  (7.5727773, 257.1575392633862),\n",
              "  (7.838121, 220.0772591614136),\n",
              "  (6.2012525, 217.83020910791964)],\n",
              " 69: [(13.207521, 81.15417426109393),\n",
              "  (13.605762, 90.0),\n",
              "  (13.064496, 82.56512580987206),\n",
              "  (12.78184, 78.91767862779544),\n",
              "  (12.772581, 89.08984229416954),\n",
              "  (12.293692, 81.02468759581859),\n",
              "  (12.485447, 81.2219182240853),\n",
              "  (12.622565, 84.38009243891595),\n",
              "  (12.703223, 81.05553651663777),\n",
              "  (12.565831, 81.2219182240853),\n",
              "  (12.937357, 81.05553651663777),\n",
              "  (12.706474, 81.2219182240853),\n",
              "  (12.50857, 82.29823813423954),\n",
              "  (12.237249, 80.39900496896712)],\n",
              " 70: [(15.145536, 69.0),\n",
              "  (14.035552, 57.07889277132134),\n",
              "  (14.230077, 90.0499861188218),\n",
              "  (14.019536, 81.09870529176159),\n",
              "  (13.861538, 84.38009243891595),\n",
              "  (13.394704, 84.0535543567314),\n",
              "  (13.1501665, 84.1486779456457),\n",
              "  (12.695755, 81.49846624323675)],\n",
              " 74: [(8.857504, 225.03555274667156),\n",
              "  (8.804655, 225.055548698538),\n",
              "  (8.286087, 232.42202993692314),\n",
              "  (7.935182, 234.21357774475842),\n",
              "  (7.79612, 233.0)],\n",
              " 75: [(6.927519, 334.0374230531663),\n",
              "  (5.789486, 337.42702914852566),\n",
              "  (6.156891, 345.00144927231827),\n",
              "  (5.500913, 370.2175036380641),\n",
              "  (5.137926, 379.38107491017524),\n",
              "  (5.244795, 388.0463889794621),\n",
              "  (4.9245424, 409.04400741240545),\n",
              "  (4.9863124, 418.80783182743846),\n",
              "  (5.205129, 437.4574264999967),\n",
              "  (4.9134803, 446.5870575822815),\n",
              "  (4.816734, 463.59680758176063),\n",
              "  (4.966442, 493.19874290188534),\n",
              "  (4.6244287, 512.1298663425127),\n",
              "  (3.056845, 270.6806236138819),\n",
              "  (3.7748303, 634.452519894121),\n",
              "  (3.8230453, 671.026825097179),\n",
              "  (3.8428054, 702.5894960786135),\n",
              "  (3.0714328, 751.7506235448029),\n",
              "  (3.0497086, 800.7558928912107),\n",
              "  (8.707484, 876.7987226268068)],\n",
              " 76: [(8.605406, 228.0),\n",
              "  (8.0938015, 236.0),\n",
              "  (8.1693, 235.30618351416098),\n",
              "  (8.155753, 243.34748817277733),\n",
              "  (8.349542, 242.2973379961076),\n",
              "  (8.2164545, 244.00819658363937),\n",
              "  (8.216409, 252.00793638296395),\n",
              "  (8.169236, 253.00197627686626),\n",
              "  (7.6861606, 254.07085625864295),\n",
              "  (7.68602, 259.0019304947359),\n",
              "  (7.8152285, 267.4210163767986),\n",
              "  (7.400349, 266.1879035568671),\n",
              "  (7.324625, 275.0018181758077),\n",
              "  (7.719992, 267.09174453734056),\n",
              "  (7.4906, 282.02836736753983),\n",
              "  (7.489604, 291.10994486619654),\n",
              "  (7.2994328, 298.0151002885592),\n",
              "  (5.035434, 398.06155302917665),\n",
              "  (5.0872602, 401.06109260310956),\n",
              "  (5.0936995, 412.146818500398),\n",
              "  (4.785032, 419.934518705),\n",
              "  (4.713946, 412.43787410954394),\n",
              "  (4.6273685, 424.0106130747201)],\n",
              " 77: [(9.070844, 193.02331465395574),\n",
              "  (9.385853, 192.0),\n",
              "  (9.230605, 198.01010075246163),\n",
              "  (9.15027, 199.090431713832),\n",
              "  (9.819287, 200.12246250733574),\n",
              "  (9.44936, 192.75113488641253),\n",
              "  (9.048274, 200.30227157973022),\n",
              "  (9.205252, 213.00234740490538),\n",
              "  (9.368104, 213.05867736377226),\n",
              "  (8.6642275, 222.00225224082752),\n",
              "  (8.774252, 221.44299492194375),\n",
              "  (8.497599, 221.9954954497951),\n",
              "  (8.227906, 223.1815404552984),\n",
              "  (8.389512, 224.26992665089986),\n",
              "  (8.507137, 236.10379073619296),\n",
              "  (8.156073, 231.01947969814148),\n",
              "  (7.42246, 230.10649708341572),\n",
              "  (4.5168233, 371.0215627156999),\n",
              "  (5.1261773, 355.0507006048573),\n",
              "  (5.3453474, 369.0663896916109),\n",
              "  (5.2705674, 378.1071276768001),\n",
              "  (4.82141, 384.0117185711915),\n",
              "  (4.5444293, 367.0490430446591),\n",
              "  (3.9444895, 259.0926475220785),\n",
              "  (4.2064753, 407.0012284993744),\n",
              "  (4.380976, 402.8920450939681),\n",
              "  (4.460652, 402.7033647736259),\n",
              "  (4.7112956, 366.0122948754591),\n",
              "  (4.4051, 368.0489097932502),\n",
              "  (4.3021812, 368.65702217643974),\n",
              "  (4.304744, 355.50808710914015),\n",
              "  (4.387092, 367.03405836516043),\n",
              "  (4.387946, 350.2413453605956),\n",
              "  (4.4784284, 353.62409420173844),\n",
              "  (4.6014953, 354.3613974461665),\n",
              "  (4.6884604, 335.72607882021913),\n",
              "  (4.6290956, 347.89941074971654),\n",
              "  (4.6058044, 342.2309161954834),\n",
              "  (4.696248, 340.3307215048327),\n",
              "  (4.851747, 354.0903839417275),\n",
              "  (4.7012615, 337.12015662075146),\n",
              "  (4.8586564, 330.0242415338607),\n",
              "  (4.8972883, 328.0243893371345),\n",
              "  (5.0333514, 325.1861005639694),\n",
              "  (4.903757, 334.26037755019667),\n",
              "  (4.9255085, 333.2641594891356),\n",
              "  (4.9291444, 326.0),\n",
              "  (4.951237, 323.12536266904215),\n",
              "  (5.023268, 323.39604202896487),\n",
              "  (4.988528, 305.235974288746),\n",
              "  (5.1473184, 306.04084694693944),\n",
              "  (5.358909, 310.1032086257735),\n",
              "  (5.189128, 319.03918254659567),\n",
              "  (5.1509676, 312.0256399721023),\n",
              "  (5.1460342, 312.1938500355188),\n",
              "  (5.206023, 312.1297807002722),\n",
              "  (5.2845573, 266.7208278331484),\n",
              "  (5.2307305, 291.08418026406036),\n",
              "  (5.367782, 295.1372562046344),\n",
              "  (5.452899, 297.0269348055829),\n",
              "  (5.3296843, 287.1741631832502),\n",
              "  (5.3746963, 293.38370779578065),\n",
              "  (5.2656097, 286.08565151017274),\n",
              "  (5.3403916, 281.1440911703463),\n",
              "  (5.3871517, 245.381335883559),\n",
              "  (5.394086, 276.02898398537786),\n",
              "  (5.579384, 284.29737951659),\n",
              "  (5.639892, 285.112258592997),\n",
              "  (5.5840764, 271.3613826615718),\n",
              "  (5.6499057, 271.0018450121696),\n",
              "  (5.7505918, 268.01679051880313),\n",
              "  (5.838062, 265.06791582535976),\n",
              "  (5.960133, 269.0018587296378),\n",
              "  (5.9404106, 263.1539473388153),\n",
              "  (6.0544453, 262.0),\n",
              "  (5.848117, 275.00727263110696),\n",
              "  (5.881555, 263.0019011338131),\n",
              "  (5.8253136, 258.00193797721755),\n",
              "  (6.062555, 256.0175775215444),\n",
              "  (5.93377, 261.00766272276377),\n",
              "  (6.0645404, 258.55947091530027),\n",
              "  (5.852758, 251.7180962902747),\n",
              "  (3.9826043, 229.0087334579186)],\n",
              " 79: [(15.003303, 69.06518659932803),\n",
              "  (15.385167, 69.06518659932803),\n",
              "  (14.734336, 69.26037828369117),\n",
              "  (14.605257, 70.61161377563892),\n",
              "  (14.606403, 72.56031973468694),\n",
              "  (14.4796, 70.03570517957252)],\n",
              " 83: [(1.7021513, 1025.023902160335),\n",
              "  (1.8869365, 1000.0244996998823),\n",
              "  (1.9587663, 1020.5665093466472),\n",
              "  (2.0285773, 961.7510072778713),\n",
              "  (2.1053174, 900.4049089159832),\n",
              "  (2.2155714, 902.0088691359969),\n",
              "  (2.4447389, 881.1112302087631),\n",
              "  (2.4815786, 820.0390234616887),\n",
              "  (2.5396075, 804.1050926340412),\n",
              "  (2.55248, 807.3691844503356),\n",
              "  (2.5631425, 786.3695060211834),\n",
              "  (2.6105347, 757.1294473206018),\n",
              "  (3.2742589, 310.01451578918045),\n",
              "  (3.3893967, 627.816055863499),\n",
              "  (3.3047094, 624.2595614005444),\n",
              "  (3.2843087, 597.0033500743526),\n",
              "  (3.4240546, 593.0075884843296),\n",
              "  (3.5114186, 584.000856163756),\n",
              "  (3.5062566, 552.1530584901255),\n",
              "  (3.6286454, 533.1350673140906),\n",
              "  (3.670649, 545.8250635505849),\n",
              "  (3.7041922, 533.1134963588898),\n",
              "  (3.7694008, 523.9179325047006),\n",
              "  (3.8770819, 507.16663928141014),\n",
              "  (3.9652622, 499.00901795458566),\n",
              "  (4.100704, 484.10329476259506),\n",
              "  (4.2449493, 474.01687733666193),\n",
              "  (4.041828, 473.2377837831633),\n",
              "  (4.036779, 461.3296001775737),\n",
              "  (4.264632, 449.53865239821147),\n",
              "  (5.033527, 453.004414989523),\n",
              "  (5.1164804, 434.0933079419677),\n",
              "  (5.2386365, 431.05684079944723),\n",
              "  (4.9204245, 438.092455995307),\n",
              "  (4.655065, 432.8429276307977),\n",
              "  (4.9165654, 420.63404522220975),\n",
              "  (4.6959605, 413.2045498297423),\n",
              "  (4.4566917, 406.1231340369568),\n",
              "  (4.7771854, 377.0013262576141),\n",
              "  (4.8434973, 330.667506719363)],\n",
              " 82: [(9.457177, 164.00304875214974),\n",
              "  (10.470245, 161.02794788483146),\n",
              "  (10.991748, 163.1502375113196),\n",
              "  (10.674039, 160.15305179733542),\n",
              "  (10.761223, 157.05094714773293),\n",
              "  (10.5941725, 157.01273833673497),\n",
              "  (10.720821, 158.05062480104277),\n",
              "  (10.639213, 157.01273833673497),\n",
              "  (11.031721, 154.0519392932137),\n",
              "  (11.184491, 140.00357138301865),\n",
              "  (10.965211, 140.00357138301865),\n",
              "  (10.705077, 139.0898989862312)],\n",
              " 84: [(11.488269, 119.10499569707393),\n",
              "  (11.285681, 93.2148056909416),\n",
              "  (11.205133, 93.13431161500041),\n",
              "  (10.819887, 90.44888059008801),\n",
              "  (11.130437, 93.13431161500041),\n",
              "  (11.408989, 128.06248474865697),\n",
              "  (10.331103, 128.00390619039717),\n",
              "  (10.264914, 114.0175425099138)],\n",
              " 85: [(7.286847, 237.53947040439405),\n",
              "  (7.2994256, 235.33168082517068),\n",
              "  (7.7783337, 218.0022935659164),\n",
              "  (8.203883, 229.05457864884517)],\n",
              " 88: [(11.501859, 117.03845521878696),\n",
              "  (11.58347, 102.04410811016969),\n",
              "  (12.56914, 104.12012293500234)],\n",
              " 92: [(3.8320847, 458.0229251904319),\n",
              "  (3.7350957, 453.85570394124164),\n",
              "  (3.9787135, 456.03947197583676),\n",
              "  (3.927647, 438.25677404918684),\n",
              "  (3.9668572, 425.85678343781257),\n",
              "  (4.119649, 429.3366977093852),\n",
              "  (4.246929, 430.0569729698613),\n",
              "  (4.18956, 414.04347597806685),\n",
              "  (4.217816, 397.04533746160524),\n",
              "  (4.303357, 398.0012562794243),\n",
              "  (4.2054276, 382.22114017934695),\n",
              "  (4.387682, 381.0472411657116),\n",
              "  (4.438164, 369.10973977937783),\n",
              "  (4.433533, 370.04864545083797),\n",
              "  (4.531678, 364.0343390396021),\n",
              "  (4.58119, 350.36552341804406),\n",
              "  (4.684824, 346.97838549396704),\n",
              "  (4.929508, 346.41737831696605),\n",
              "  (4.824985, 329.0060789711947),\n",
              "  (4.961007, 302.08111493438315),\n",
              "  (5.0429335, 325.0753758745808),\n",
              "  (4.9734664, 312.05768697470023),\n",
              "  (5.322849, 311.1301978272119),\n",
              "  (5.226839, 321.1261434389919),\n",
              "  (5.2994018, 309.0),\n",
              "  (5.339918, 296.06080456554866),\n",
              "  (5.372611, 302.00165562460086),\n",
              "  (5.519515, 289.0155705148081),\n",
              "  (5.659206, 272.8094573140748),\n",
              "  (5.618837, 286.0087411251621),\n",
              "  (5.530132, 277.6508598942204),\n",
              "  (5.7018256, 273.02930245671433),\n",
              "  (6.160108, 287.4456470360962),\n",
              "  (5.899827, 274.35743110038044),\n",
              "  (6.162485, 265.5447231635379),\n",
              "  (6.1108475, 268.9851297005097),\n",
              "  (6.1922727, 265.01698058803703),\n",
              "  (6.1697483, 260.69330639661615),\n",
              "  (6.39026, 262.03053257206494),\n",
              "  (6.1456494, 261.04788832702707),\n",
              "  (6.04142, 259.01737393464555),\n",
              "  (5.7703323, 278.71849597757233),\n",
              "  (4.7759085, 347.1310991541956),\n",
              "  (5.0991125, 341.0131962256006),\n",
              "  (5.2996106, 350.03571246374275),\n",
              "  (5.1798177, 343.09328177625395),\n",
              "  (5.166633, 349.57259617996374),\n",
              "  (5.13158, 353.5661182862408),\n",
              "  (5.243819, 339.532030889576),\n",
              "  (5.2640877, 349.20624278497655),\n",
              "  (5.2131557, 352.3634487287238),\n",
              "  (5.220658, 326.49655434629017)],\n",
              " 93: [(2.9716427, 522.9799231328101),\n",
              "  (3.1672633, 531.4978833447975),\n",
              "  (3.226801, 523.2246171578704),\n",
              "  (3.242465, 519.0346809221904),\n",
              "  (3.290588, 519.116557239316),\n",
              "  (3.3187468, 506.66754385888976),\n",
              "  (3.3905444, 501.0089819554137),\n",
              "  (3.462586, 484.6452310711414),\n",
              "  (3.4037204, 502.3594330755619),\n",
              "  (3.4019933, 481.10913522817253),\n",
              "  (3.4166744, 483.6992867474584),\n",
              "  (3.42069, 474.00949357581436),\n",
              "  (3.4885902, 487.03695958315114),\n",
              "  (3.526561, 472.1525177312941),\n",
              "  (3.639894, 461.0010845974226),\n",
              "  (3.6108377, 461.48564441377806),\n",
              "  (3.6224494, 459.309264004113),\n",
              "  (3.6697705, 449.1881565669335),\n",
              "  (3.7523367, 453.35306329614673),\n",
              "  (3.7929492, 446.0100895719737),\n",
              "  (3.796977, 426.0575078554537),\n",
              "  (3.8522763, 434.028800887683),\n",
              "  (3.9623883, 424.38190347845887),\n",
              "  (4.0071473, 424.51972863460657),\n",
              "  (3.9828243, 424.0754649823543),\n",
              "  (3.9090762, 415.0192766607354),\n",
              "  (4.0466256, 420.01071414905596),\n",
              "  (4.0807595, 421.019001946468),\n",
              "  (4.0682406, 396.4555460578147),\n",
              "  (4.092049, 397.40785095415515),\n",
              "  (6.42062, 319.0015673942685),\n",
              "  (6.701697, 314.78405296329737)],\n",
              " 95: [(3.339568, 548.0082116173078),\n",
              "  (3.3917024, 572.5032750998024),\n",
              "  (3.4377234, 574.6111728812798),\n",
              "  (3.3683014, 574.9330395793932),\n",
              "  (3.3751333, 561.7259118110896),\n",
              "  (3.4016936, 556.0575509783138),\n",
              "  (3.5233576, 550.0445436507847),\n",
              "  (3.5386736, 549.3641779366397),\n",
              "  (3.5671477, 537.4104576578316),\n",
              "  (3.5996602, 525.1152254505672),\n",
              "  (3.6764345, 515.148522272946),\n",
              "  (3.621469, 513.6350844714563),\n",
              "  (3.60367, 504.83462638769146),\n",
              "  (3.639539, 508.47615479980965),\n",
              "  (3.678592, 498.0361432667312),\n",
              "  (3.7763462, 504.21622345973753),\n",
              "  (3.9097676, 481.06652346634974),\n",
              "  (3.846891, 487.05235858170323),\n",
              "  (3.9695024, 480.6672445673826),\n",
              "  (3.9746213, 472.6118915135335),\n",
              "  (3.8444028, 471.51352048483193),\n",
              "  (3.876259, 468.0170937049201),\n",
              "  (3.9347322, 463.0172782953137),\n",
              "  (4.1496353, 452.0895928906128),\n",
              "  (4.147799, 448.93763486702693),\n",
              "  (4.1619697, 440.6007262817437),\n",
              "  (4.150877, 440.1647418864896),\n",
              "  (4.12835, 440.09317195339446),\n",
              "  (4.114561, 425.0952834365491),\n",
              "  (4.179099, 428.01869118065395),\n",
              "  (4.268099, 425.01882311257697),\n",
              "  (4.3563294, 412.07766258315917),\n",
              "  (4.325687, 420.34390681916636),\n",
              "  (4.350952, 414.004830889689),\n",
              "  (4.405427, 413.27230732290786),\n",
              "  (4.369152, 413.3098111586513),\n",
              "  (4.1914515, 389.8679263545541),\n",
              "  (4.4185767, 416.5585192983094)],\n",
              " 96: [(10.36295, 108.664621657649),\n",
              "  (11.050248, 108.04628637764465),\n",
              "  (12.513279, 93.53608929178085),\n",
              "  (11.806121, 105.47511554864494),\n",
              "  (11.763915, 105.17128885774862),\n",
              "  (11.658371, 105.17128885774862),\n",
              "  (11.464944, 103.57605900979242),\n",
              "  (12.004102, 104.04326023342406)],\n",
              " 99: [(7.5225763, 153.0),\n",
              "  (8.082281, 163.00306745579974),\n",
              "  (8.021952, 152.26621424334422),\n",
              "  (7.9614415, 152.11837495845134),\n",
              "  (7.965518, 161.69724796668618),\n",
              "  (8.583757, 155.0),\n",
              "  (8.439525, 164.02743672934722),\n",
              "  (8.088513, 164.01219466856725),\n",
              "  (8.103492, 168.1071087134628),\n",
              "  (7.740712, 166.3640586184408),\n",
              "  (7.308849, 171.74690681348528),\n",
              "  (7.376238, 173.18487231857176),\n",
              "  (7.8276477, 171.74690681348528),\n",
              "  (7.6956406, 176.1817243643619),\n",
              "  (7.681284, 171.49344010777787),\n",
              "  (7.959169, 174.26990560621763),\n",
              "  (8.052163, 171.18703221914913),\n",
              "  (7.9220653, 172.74258305351347),\n",
              "  (7.9424977, 173.1415605797753),\n",
              "  (7.654997, 176.0113632695344),\n",
              "  (7.768774, 176.555940143627),\n",
              "  (7.8914895, 175.72990639046048),\n",
              "  (8.081924, 172.0523176246109),\n",
              "  (8.277629, 174.73408368146153),\n",
              "  (8.401236, 175.34537347760278),\n",
              "  (7.892168, 174.28711943227475),\n",
              "  (7.8469987, 174.41330224498358),\n",
              "  (8.01295, 164.1097193952875)],\n",
              " 97: [(9.850979, 81.05553651663777),\n",
              "  (11.097024, 80.22468448052632),\n",
              "  (10.7592745, 93.02150289046077),\n",
              "  (11.157718, 81.05553651663777),\n",
              "  (11.035617, 81.00617260431454),\n",
              "  (10.855214, 81.00617260431454),\n",
              "  (11.008916, 81.88406438373708),\n",
              "  (10.781573, 81.0),\n",
              "  (10.488001, 81.09870529176159),\n",
              "  (11.061861, 81.2219182240853),\n",
              "  (7.8643494, 104.12012293500234)],\n",
              " 100: [(8.90816, 128.03515142334936),\n",
              "  (8.475174, 117.00427342623003),\n",
              "  (8.233002, 126.03570922560003)],\n",
              " 102: [(8.365158, 128.41339493993607),\n",
              "  (8.680269, 113.99122773266372),\n",
              "  (8.763187, 118.2285921425101),\n",
              "  (8.630573, 125.78155667664477)],\n",
              " 103: [(10.781483, 93.0),\n",
              "  (11.17438, 93.04837451562494),\n",
              "  (11.027825, 93.13431161500041),\n",
              "  (11.243377, 93.13431161500041),\n",
              "  (11.232926, 83.67795408588812),\n",
              "  (11.157052, 82.37718130647589),\n",
              "  (11.123841, 93.00537618869137),\n",
              "  (11.102585, 81.05553651663777),\n",
              "  (11.526349, 94.2019108086455),\n",
              "  (11.582949, 93.43446901438462),\n",
              "  (11.9418335, 93.7709976485267),\n",
              "  (12.00566, 93.7709976485267),\n",
              "  (11.323641, 81.88406438373708),\n",
              "  (11.5409155, 81.05553651663777),\n",
              "  (8.263382, 105.0),\n",
              "  (8.659134, 172.74258305351347),\n",
              "  (8.674803, 162.00308639035245),\n",
              "  (8.116518, 117.06835610018618),\n",
              "  (7.9649787, 175.07141400011596),\n",
              "  (8.025052, 176.02556632489498),\n",
              "  (8.319181, 174.18381095842403),\n",
              "  (8.20645, 172.01162751395617)],\n",
              " 104: [(5.327512, 254.4425278918601),\n",
              "  (5.479126, 255.56603843233944),\n",
              "  (5.8059983, 251.17722826721374),\n",
              "  (5.6819835, 245.34465553584002),\n",
              "  (6.66864, 265.1207272168662),\n",
              "  (7.0208898, 268.00746258266764),\n",
              "  (7.2820654, 262.03053257206494),\n",
              "  (7.090764, 261.04788832702707),\n",
              "  (7.164507, 275.35613303502066),\n",
              "  (7.061103, 281.5759222660915),\n",
              "  (6.9562974, 281.4551474036316),\n",
              "  (7.269278, 275.5884612969128),\n",
              "  (6.790992, 281.06404963993526),\n",
              "  (6.6950955, 281.1440911703463),\n",
              "  (6.742205, 282.04432275796654),\n",
              "  (6.973585, 295.33201655086435),\n",
              "  (6.799435, 294.6794869005985),\n",
              "  (6.62938, 286.04370295463593),\n",
              "  (6.005786, 290.0),\n",
              "  (6.4128714, 325.00153845789714),\n",
              "  (6.12669, 328.09754647055803),\n",
              "  (6.4853296, 324.01388859121454),\n",
              "  (6.25956, 331.25518863860833),\n",
              "  (5.9584975, 331.074009852782),\n",
              "  (6.200774, 338.03698022553687),\n",
              "  (6.253691, 337.0014836762592),\n",
              "  (5.8498325, 333.02402315748935),\n",
              "  (5.8893337, 348.2068350851258),\n",
              "  (5.7071238, 341.0014662725074),\n",
              "  (5.805381, 345.00144927231827),\n",
              "  (4.8111434, 366.9945503682582),\n",
              "  (4.5825443, 382.1583441454602),\n",
              "  (4.535962, 367.0667514226806),\n",
              "  (4.732646, 383.15793088490284),\n",
              "  (4.7441206, 372.0336006330611),\n",
              "  (4.8584723, 370.0216209899092),\n",
              "  (4.9509115, 351.03561072916807),\n",
              "  (4.763799, 368.9173349139343),\n",
              "  (4.7898264, 353.111880287254),\n",
              "  (4.7828197, 360.1388621073821),\n",
              "  (4.8071666, 357.0224082603219),\n",
              "  (4.9226985, 359.01253460011674),\n",
              "  (4.9193673, 358.27363843855437),\n",
              "  (4.905945, 352.68682992139077),\n",
              "  (4.9500875, 349.82424158425613),\n",
              "  (4.9287806, 346.2080299473136),\n",
              "  (4.9380236, 337.0059346658453),\n",
              "  (5.068986, 344.01308114663317),\n",
              "  (5.143394, 335.3356527421443),\n",
              "  (5.1449966, 329.0060789711947),\n",
              "  (5.1866264, 327.0550412392385),\n",
              "  (5.2866306, 324.2221460665511),\n",
              "  (5.3532023, 338.7816405887427),\n",
              "  (5.2583838, 325.22146300636433),\n",
              "  (5.1752405, 324.2221460665511),\n",
              "  (5.255187, 323.05572274763995),\n",
              "  (5.332865, 321.00623046912966),\n",
              "  (5.290798, 321.0996729988992),\n",
              "  (5.4306026, 303.19960422137757),\n",
              "  (5.4700165, 299.42778762165676),\n",
              "  (5.457359, 313.53628179207584),\n",
              "  (5.377021, 302.87456149369825),\n",
              "  (5.410475, 303.00165016052307),\n",
              "  (5.515673, 300.04166377354994),\n",
              "  (5.6088367, 298.28342226815084),\n",
              "  (5.729849, 290.0068964697219),\n",
              "  (5.8126793, 292.0273959751037),\n",
              "  (5.940746, 294.2872746144488),\n",
              "  (5.7987967, 282.20028348674634),\n",
              "  (5.753541, 293.97278785629123),\n",
              "  (5.7973557, 289.0),\n",
              "  (5.7739277, 292.00171232374646),\n",
              "  (5.9293942, 285.08595195133694),\n",
              "  (5.9282694, 286.174771774173),\n",
              "  (5.974564, 285.2963371654112),\n",
              "  (6.143647, 282.3473038652928),\n",
              "  (6.0502143, 284.77535005684746),\n",
              "  (6.2351923, 280.40149785619906),\n",
              "  (6.1698475, 283.51014091210214),\n",
              "  (5.853076, 278.0017985553331),\n",
              "  (6.474359, 273.00732590903124),\n",
              "  (6.5274825, 143.34922392534952),\n",
              "  (6.260754, 276.00724628168734),\n",
              "  (5.43467, 278.21754078418564)],\n",
              " 105: [(10.195781, 120.93386622447825),\n",
              "  (8.820013, 151.0),\n",
              "  (9.23924, 149.08386901338454)],\n",
              " 106: [(5.54938, 192.89893727027115),\n",
              "  (5.4951773, 204.9243762952568),\n",
              "  (5.505449, 193.08029417835473),\n",
              "  (5.287003, 163.24827717314508)],\n",
              " 111: [(3.8707373, 441.4091072916371),\n",
              "  (4.3852305, 436.05618903989887),\n",
              "  (4.177228, 452.39916003458717),\n",
              "  (4.222356, 463.0),\n",
              "  (3.966091, 452.74717006293923),\n",
              "  (4.0108085, 471.30669420240577),\n",
              "  (4.3910193, 485.1741955215673),\n",
              "  (2.888589, 718.0006963784924),\n",
              "  (2.971285, 674.2677509713778),\n",
              "  (3.181222, 655.3693615054033)],\n",
              " 113: [(7.3600397, 232.01939574095957),\n",
              "  (7.6323166, 247.00202428320299),\n",
              "  (7.538233, 247.29132617218906),\n",
              "  (7.565421, 257.00194551792794),\n",
              "  (7.4959087, 244.3460660620506),\n",
              "  (8.355408, 245.45875417267155),\n",
              "  (7.5635834, 249.64975465639858),\n",
              "  (7.0559273, 236.35777964771967),\n",
              "  (5.9247527, 290.06206232459976),\n",
              "  (5.8772745, 283.0441661649291),\n",
              "  (6.0353723, 296.0152023123137),\n",
              "  (5.712544, 302.0149002946709),\n",
              "  (5.300992, 298.00167784762556),\n",
              "  (4.4641056, 343.0058308542291),\n",
              "  (4.8560376, 402.27975340551257),\n",
              "  (6.3248267, 355.1140661815581),\n",
              "  (5.9374795, 377.0053050024628),\n",
              "  (5.8139334, 360.80049889100763),\n",
              "  (7.5542126, 321.50427679892533),\n",
              "  (6.6170106, 325.44431167251946),\n",
              "  (6.309328, 340.84600628436294),\n",
              "  (6.6751666, 218.02064122463267),\n",
              "  (6.7758303, 215.67104580819372),\n",
              "  (6.6563067, 211.15160430363773),\n",
              "  (5.950903, 218.0022935659164),\n",
              "  (6.7574487, 210.15232570685484),\n",
              "  (7.442805, 199.3037882229036),\n",
              "  (6.097569, 206.24257562394823),\n",
              "  (6.3901644, 201.06217943710845),\n",
              "  (7.319348, 190.9476368012969),\n",
              "  (6.9872904, 167.32304085211933),\n",
              "  (7.0709643, 163.48700254148645)],\n",
              " 114: [(4.385636, 476.0514678057405),\n",
              "  (4.0899014, 496.0010080634917),\n",
              "  (4.2485266, 512.0156247615887),\n",
              "  (4.0974755, 510.56929010664163),\n",
              "  (4.1946855, 512.0156247615887),\n",
              "  (3.481956, 529.023628961883),\n",
              "  (3.0672486, 668.1466904804663),\n",
              "  (4.113961, 550.0118180548487),\n",
              "  (3.5886254, 574.0139371130286),\n",
              "  (3.3900259, 584.030821104503)],\n",
              " 115: [(2.7868795, 661.1936176340482),\n",
              "  (3.419846, 570.9378249862239),\n",
              "  (4.307339, 557.0718086566578),\n",
              "  (3.5617468, 353.02266216207704),\n",
              "  (2.9713953, 772.0233157100892),\n",
              "  (2.9742575, 740.1222871931368),\n",
              "  (2.8551404, 751.0),\n",
              "  (2.9138749, 717.0446290155168),\n",
              "  (2.8971312, 721.1775093553598),\n",
              "  (3.0550413, 683.421539022586),\n",
              "  (3.340537, 676.1663996384322),\n",
              "  (3.117828, 670.090292423342),\n",
              "  (3.1206048, 649.173320462263),\n",
              "  (3.1717641, 635.739726617741),\n",
              "  (3.2816167, 630.6671071175347),\n",
              "  (3.2610445, 620.8776046854967),\n",
              "  (3.5227396, 593.7086827729572),\n",
              "  (3.8044598, 584.167784116858),\n",
              "  (3.469488, 584.1926052253657),\n",
              "  (3.5003335, 551.4390265478133),\n",
              "  (3.595492, 570.0245608743539),\n",
              "  (3.6390703, 559.128786595718),\n",
              "  (3.7452478, 543.721436031356),\n",
              "  (4.2008224, 529.0604880351584),\n",
              "  (4.180982, 517.0154736562533),\n",
              "  (3.8764575, 509.1414341811124),\n",
              "  (3.8567953, 493.1460229992735),\n",
              "  (4.067968, 500.5287204546808),\n",
              "  (4.1147184, 482.06638546988523),\n",
              "  (4.0860543, 478.0669409193654),\n",
              "  (4.28077, 465.0043010553773),\n",
              "  (4.356385, 458.0392996239515),\n",
              "  (4.2280045, 461.15615576505104),\n",
              "  (4.253336, 456.5019167539168),\n",
              "  (4.3599243, 455.2812317677943),\n",
              "  (4.473979, 439.76925767952446),\n",
              "  (4.6060767, 430.1406746635338),\n",
              "  (4.8930693, 414.0),\n",
              "  (4.6424527, 427.2294465506796),\n",
              "  (4.602651, 412.146818500398),\n",
              "  (4.6145115, 409.4887055829501),\n",
              "  (4.820293, 403.10048375064),\n",
              "  (4.9337077, 410.07804135310636),\n",
              "  (4.9843807, 387.02067128255567),\n",
              "  (5.121752, 394.0050761094329),\n",
              "  (4.944341, 379.0118731649445),\n",
              "  (5.0029335, 382.9529997271206),\n",
              "  (5.126002, 374.3861642742691),\n",
              "  (5.183336, 369.59707791052676),\n",
              "  (5.6032195, 370.04864545083797),\n",
              "  (5.542013, 360.40116536992497),\n",
              "  (5.651822, 363.23270777836075),\n",
              "  (5.5330005, 351.05127830560593),\n",
              "  (5.518978, 347.03602118512134),\n",
              "  (5.603897, 350.14282800023193),\n",
              "  (5.677062, 340.07205118915607),\n",
              "  (5.779069, 342.1461675950792),\n",
              "  (6.3702865, 335.0537270349339),\n",
              "  (6.059249, 337.12015662075146),\n",
              "  (5.6020904, 327.3912033027155),\n",
              "  (5.80997, 324.05555079337864),\n",
              "  (5.6552176, 319.9015473548073),\n",
              "  (3.4745119, 334.43235489408016)],\n",
              " 116: [(8.698472, 140.0),\n",
              "  (8.984703, 146.01369798755184),\n",
              "  (9.14905, 148.0135128966271),\n",
              "  (8.854852, 148.00337833982033),\n",
              "  (9.561042, 148.05404418657398),\n",
              "  (9.420725, 152.00328943809077),\n",
              "  (9.402661, 151.02979838429235),\n",
              "  (8.947629, 149.08386901338454),\n",
              "  (8.301748, 137.00364958642524),\n",
              "  (6.823678, 152.029602380589)],\n",
              " 117: [(12.167077, 69.26037828369117),\n",
              "  (12.138501, 81.02468759581859),\n",
              "  (11.462687, 69.02897942168927),\n",
              "  (11.500536, 69.26037828369117),\n",
              "  (11.528077, 69.06518659932803),\n",
              "  (12.161723, 81.05553651663777),\n",
              "  (11.546229, 69.0),\n",
              "  (11.043649, 80.00624975587844)],\n",
              " 118: [(5.137743, 184.32851108821987),\n",
              "  (5.2637596, 198.6454127333425),\n",
              "  (5.3516736, 198.4943324127921),\n",
              "  (5.3158355, 125.93649193144932),\n",
              "  (5.32803, 180.7097119692243),\n",
              "  (5.202732, 208.86598574205422),\n",
              "  (5.152437, 207.17142660125697),\n",
              "  (5.16871, 202.06187171260194),\n",
              "  (5.1492677, 203.79646709401024),\n",
              "  (5.29431, 116.77756633874505),\n",
              "  (5.097498, 198.20443990990717),\n",
              "  (5.1425796, 198.20443990990717),\n",
              "  (5.3587036, 116.36150566231085),\n",
              "  (5.235011, 197.20547659738054),\n",
              "  (5.1691794, 111.36426715962351),\n",
              "  (5.1764884, 201.30076999355964),\n",
              "  (4.9969087, 197.06344156134085),\n",
              "  (4.760878, 198.12369873389704),\n",
              "  (5.0510354, 197.0),\n",
              "  (5.1045985, 195.1640335717624),\n",
              "  (5.108336, 116.73045875006231),\n",
              "  (5.136322, 122.64175471673585),\n",
              "  (5.1395316, 199.0226117806718),\n",
              "  (5.1232257, 321.90837205639747),\n",
              "  (5.0273004, 327.29955698106284),\n",
              "  (4.822804, 435.2528001058695),\n",
              "  (4.7750053, 432.07406772450486),\n",
              "  (4.508799, 437.29280808172456),\n",
              "  (4.586674, 464.0010775849556),\n",
              "  (4.520293, 461.1312177677846),\n",
              "  (4.520725, 459.48014102896764),\n",
              "  (3.579753, 645.0193795538239),\n",
              "  (3.754604, 640.1531066862052),\n",
              "  (3.4199693, 665.1150276455945),\n",
              "  (3.4182718, 655.9344479443049)],\n",
              " 119: [(11.796208, 82.37718130647589),\n",
              "  (11.875578, 94.2019108086455),\n",
              "  (11.702175, 81.39410298049853),\n",
              "  (11.841384, 80.049984384758),\n",
              "  (8.715014, 117.42657280190033)],\n",
              " 120: [(5.328533, 286.2114602876691),\n",
              "  (5.277943, 293.492759706266),\n",
              "  (5.2638793, 285.0157890363269),\n",
              "  (5.091128, 295.10845463998487),\n",
              "  (5.2161202, 288.00694436072195),\n",
              "  (5.150094, 299.00167223612647),\n",
              "  (5.1813293, 290.7593506664919),\n",
              "  (5.1401377, 291.61961525247233),\n",
              "  (5.201993, 286.8449058289165),\n",
              "  (5.2376356, 291.6864755178066),\n",
              "  (5.21111, 292.0273959751037),\n",
              "  (5.067019, 286.2114602876691),\n",
              "  (3.8004978, 183.98369492974098),\n",
              "  (5.3962564, 107.20074626605917),\n",
              "  (5.2546387, 201.03979705520993),\n",
              "  (5.2709885, 203.0098519776811),\n",
              "  (5.22711, 196.73586353280888),\n",
              "  (5.30788, 208.4922060893404),\n",
              "  (5.1334844, 204.47982785595258),\n",
              "  (5.151738, 206.47518010647187),\n",
              "  (4.8496823, 202.8003944769339),\n",
              "  (5.052266, 210.0),\n",
              "  (5.4650183, 339.3287491504367),\n",
              "  (5.084998, 420.3046514137097)],\n",
              " 121: [(6.1227336, 279.17915394957413),\n",
              "  (6.806227, 223.0022421411946),\n",
              "  (6.9758954, 238.2540660723338),\n",
              "  (7.1221957, 229.10696191953662),\n",
              "  (7.3920803, 224.03571143904713),\n",
              "  (5.7402816, 285.7726368986366),\n",
              "  (5.646813, 206.1674077054858),\n",
              "  (7.4923325, 150.9205088780183),\n",
              "  (6.4018855, 340.00147058505496),\n",
              "  (6.205137, 340.3762623920769),\n",
              "  (6.5605655, 335.1715978420606),\n",
              "  (6.673615, 331.2174512310606),\n",
              "  (6.162713, 354.8309456628607),\n",
              "  (7.6507316, 253.44230112591703),\n",
              "  (8.413669, 180.7373785358192),\n",
              "  (8.857627, 153.5838533179839),\n",
              "  (9.198794, 158.8238017426859),\n",
              "  (9.412419, 171.2103968805633),\n",
              "  (8.350395, 213.45959805077868),\n",
              "  (5.6817484, 382.28392589801626),\n",
              "  (5.282688, 377.96560690094543),\n",
              "  (5.2328577, 393.24928480545265),\n",
              "  (5.0013423, 399.0451102319135),\n",
              "  (5.0319805, 398.0012562794243),\n",
              "  (4.9369845, 417.02997494184996),\n",
              "  (4.4873586, 413.3098111586513),\n",
              "  (4.9478106, 433.75684432640367),\n",
              "  (4.7066293, 438.1643527262344),\n",
              "  (4.98711, 455.0175820778797),\n",
              "  (4.6607723, 443.64963653766245),\n",
              "  (4.7038016, 456.5796754127367),\n",
              "  (4.4864135, 463.0172782953137),\n",
              "  (4.359929, 476.5427577877981),\n",
              "  (4.2377415, 465.31817071762845),\n",
              "  (4.25357, 475.0010526304126),\n",
              "  (4.87866, 492.36673323854853),\n",
              "  (4.606261, 492.7514586482723),\n",
              "  (4.482564, 502.3982484045899),\n",
              "  (4.2847676, 514.2616065778195),\n",
              "  (4.1100607, 518.880525747498),\n",
              "  (4.1976037, 509.2739930528556),\n",
              "  (4.033997, 556.2598313737924),\n",
              "  (3.7426825, 570.3867109251407),\n",
              "  (3.6853213, 575.1956188984752),\n",
              "  (3.6822727, 585.0854638426766),\n",
              "  (3.7086084, 584.4142366506826),\n",
              "  (3.719953, 591.2444502910788),\n",
              "  (3.8921587, 586.1919480852667),\n",
              "  (3.6460838, 607.068365178091),\n",
              "  (3.5341194, 630.4768036970115),\n",
              "  (3.5685453, 640.5247848444274),\n",
              "  (3.3780181, 657.8791682368427),\n",
              "  (3.2933464, 674.6235987571143),\n",
              "  (3.0068278, 682.57453805427),\n",
              "  (3.0987833, 638.2883360989765),\n",
              "  (3.309313, 608.434877369797),\n",
              "  (3.4334116, 605.1619617920479),\n",
              "  (3.2628841, 602.6051775416471),\n",
              "  (3.3355465, 588.0136052847757),\n",
              "  (3.513258, 582.0214772669476),\n",
              "  (3.6029658, 580.1456713619434),\n",
              "  (3.5529273, 573.0706762695156),\n",
              "  (3.6456966, 563.0008880987667),\n",
              "  (3.5360065, 349.0444097819073)],\n",
              " 123: [(3.1676474, 607.0074134637896),\n",
              "  (3.268596, 621.2060849669778),\n",
              "  (3.299201, 587.2759147113051),\n",
              "  (3.438395, 574.1959595817442),\n",
              "  (3.534737, 569.1484867765178),\n",
              "  (3.440452, 549.1784773641443),\n",
              "  (3.52956, 539.4117165950328),\n",
              "  (3.669488, 538.9508326368928),\n",
              "  (3.6294532, 527.274122255208),\n",
              "  (3.6490138, 509.6636145537564),\n",
              "  (3.8890774, 508.09841566373734),\n",
              "  (3.6870654, 513.3517312720393),\n",
              "  (3.8015857, 495.01616135233405),\n",
              "  (3.906567, 489.74483151943525),\n",
              "  (4.090567, 480.00937490844905),\n",
              "  (3.9757102, 468.12925565488854),\n",
              "  (4.0846534, 454.00440526497096),\n",
              "  (3.9318848, 460.0391287705862),\n",
              "  (4.066484, 439.09224543369015),\n",
              "  (4.1092405, 442.2544516452039),\n",
              "  (4.172977, 454.0275322048212),\n",
              "  (4.3308063, 422.02961981358607),\n",
              "  (4.46367, 414.1738765301356),\n",
              "  (4.4017262, 423.2316150761897),\n",
              "  (4.3859353, 394.1433749284643),\n",
              "  (4.260458, 384.5958918137322),\n",
              "  (4.302101, 407.1228315877163),\n",
              "  (4.358549, 392.21550198838395),\n",
              "  (4.483352, 394.0012690334893),\n",
              "  (4.538908, 401.60303783711595),\n",
              "  (4.5300317, 379.1319031682773),\n",
              "  (4.56805, 365.8551625985343),\n",
              "  (4.5290256, 382.0117799230804),\n",
              "  (4.6485415, 366.001366117669),\n",
              "  (4.8510184, 363.23270777836075),\n",
              "  (4.827279, 371.0862433451286),\n",
              "  (4.8129644, 349.4137948049561),\n",
              "  (4.9404073, 353.0056656769123),\n",
              "  (4.8758335, 346.0361252817399),\n",
              "  (4.8704586, 348.0703951789063),\n",
              "  (5.0752444, 335.5964838909967),\n",
              "  (5.186084, 339.3316961322653),\n",
              "  (5.180657, 334.26037755019667),\n",
              "  (5.1814933, 327.09784468871084),\n",
              "  (5.297763, 316.26729201736936),\n",
              "  (5.170298, 318.1273329973393),\n",
              "  (5.225123, 302.16551755618974),\n",
              "  (5.3774533, 309.19573088902763),\n",
              "  (5.140304, 313.0063897111367),\n",
              "  (3.4713435, 546.0146518180625),\n",
              "  (3.1664824, 557.0143624719204),\n",
              "  (3.3358133, 546.0448699511791),\n",
              "  (3.3164978, 555.072968176257),\n",
              "  (3.2834487, 537.4904650317064),\n",
              "  (3.430814, 533.0759795751446),\n",
              "  (3.4832852, 527.114788257738),\n",
              "  (3.5295188, 510.22054055084845),\n",
              "  (3.58025, 487.0010266929629),\n",
              "  (3.810807, 494.01619406655084),\n",
              "  (3.7262154, 489.004089962446),\n",
              "  (3.83361, 479.5101667326773)],\n",
              " 125: [(6.790252, 269.15051551130273),\n",
              "  (6.7889066, 269.67016891009655),\n",
              "  (6.8271055, 280.2159881234474),\n",
              "  (6.8662333, 251.04979585731593)],\n",
              " 128: [(6.634843, 279.1451235468748),\n",
              "  (6.84817, 294.2872746144488),\n",
              "  (6.6357265, 287.0435507026765),\n",
              "  (6.6390014, 281.40007107319644),\n",
              "  (6.611376, 298.00167784762556),\n",
              "  (6.996064, 300.01499962501873),\n",
              "  (6.533914, 293.43653487594213)],\n",
              " 129: [(10.190824, 102.17631819555841),\n",
              "  (9.238555, 102.0),\n",
              "  (9.426353, 105.30432089900205),\n",
              "  (8.519094, 102.04410811016969)],\n",
              " 130: [(6.8754263, 288.6693610343848),\n",
              "  (6.535895, 290.0017241328058),\n",
              "  (6.6038857, 287.0435507026765),\n",
              "  (6.4156, 302.0149002946709),\n",
              "  (6.490071, 291.556169545424),\n",
              "  (6.3566527, 290.0),\n",
              "  (6.453338, 307.3662310664592),\n",
              "  (6.1616263, 304.42076144704714),\n",
              "  (6.0496097, 303.37105992497044),\n",
              "  (5.9730144, 314.0700558792576),\n",
              "  (6.346437, 323.0990560184291),\n",
              "  (6.2174387, 327.0550412392385),\n",
              "  (5.4751916, 325.6762195801222)],\n",
              " 131: [(8.255452, 163.19620093617377), (8.125715, 103.69667304209909)],\n",
              " 132: [(5.4719462, 262.8193295783246),\n",
              "  (5.576009, 265.33940529065785),\n",
              "  (5.5622163, 267.2901045680517),\n",
              "  (5.4672008, 271.7075633838705),\n",
              "  (5.44884, 267.9178978717174),\n",
              "  (5.488907, 264.35771220072246),\n",
              "  (5.307451, 257.72077913897436),\n",
              "  (5.379418, 267.8077668776617),\n",
              "  (5.4086895, 259.9249891795708),\n",
              "  (5.303342, 230.95670589961227),\n",
              "  (5.0991364, 228.4841351166422),\n",
              "  (5.5687327, 229.68892006363737),\n",
              "  (4.064658, 253.50542400508908),\n",
              "  (9.205617, 143.0034964607509),\n",
              "  (9.14506, 139.00359707575916),\n",
              "  (9.157097, 140.089257261219),\n",
              "  (8.965033, 153.16004700965587),\n",
              "  (9.062067, 138.03260484392808),\n",
              "  (9.286427, 138.0905500025255),\n",
              "  (8.734584, 140.356688476182)],\n",
              " 133: [(4.976917, 340.0),\n",
              "  (5.1029654, 344.28476585524373),\n",
              "  (5.2216945, 361.35439668004597),\n",
              "  (5.002774, 344.0058139043583),\n",
              "  (4.823724, 352.0227265390688),\n",
              "  (4.7089214, 371.03369119259236),\n",
              "  (4.884995, 384.6296920415791),\n",
              "  (4.5934005, 400.4996878900157),\n",
              "  (4.3480887, 395.4099138868422),\n",
              "  (4.285951, 411.0109487592758),\n",
              "  (4.3730655, 425.14232910873505),\n",
              "  (4.366296, 429.0943952092593),\n",
              "  (4.163194, 419.0107397191628),\n",
              "  (4.175619, 439.2914749912636),\n",
              "  (4.28758, 444.3253312607779),\n",
              "  (3.8504748, 448.2856232359008),\n",
              "  (3.7771974, 456.1578674099571),\n",
              "  (3.6825285, 464.6202750634113),\n",
              "  (3.7521713, 478.05125248240904),\n",
              "  (3.737625, 494.3278264471868),\n",
              "  (3.408557, 498.25696181789573),\n",
              "  (3.5608795, 509.09822235006874),\n",
              "  (3.3884137, 516.3138580359819),\n",
              "  (3.22557, 532.0),\n",
              "  (3.6544461, 206.00970850908945)],\n",
              " 134: [(5.614185, 205.08047201037937),\n",
              "  (5.6147456, 199.6246477767713),\n",
              "  (5.540999, 391.0818328687744),\n",
              "  (5.462699, 405.00123456601955),\n",
              "  (5.192182, 405.54407898525653),\n",
              "  (5.1130614, 396.1275047254356)],\n",
              " 139: [(5.6729245, 339.00589965367857),\n",
              "  (6.059664, 344.0930106817051),\n",
              "  (6.1220574, 358.235955761004),\n",
              "  (5.9553432, 353.0509878190401),\n",
              "  (5.4414663, 352.68682992139077),\n",
              "  (5.4207997, 333.73642294481436),\n",
              "  (5.4423356, 209.3895890439637),\n",
              "  (5.5549083, 211.00947846009194),\n",
              "  (5.571589, 206.72929158684795),\n",
              "  (5.5803814, 213.60009363293827),\n",
              "  (5.566006, 208.806130178211),\n",
              "  (5.537427, 215.7127719909046),\n",
              "  (5.504598, 215.8008341040414),\n",
              "  (5.518143, 215.5203006679417),\n",
              "  (5.6048784, 215.5203006679417),\n",
              "  (5.660836, 215.7127719909046),\n",
              "  (5.6323333, 215.1278689523977),\n",
              "  (5.6625752, 210.52315787105226),\n",
              "  (5.697136, 215.7127719909046),\n",
              "  (5.729403, 211.8962010041709),\n",
              "  (5.631163, 201.79197209007103),\n",
              "  (5.653002, 208.4346420343797),\n",
              "  (5.618926, 214.17049283222934),\n",
              "  (5.544431, 212.25691979297164),\n",
              "  (5.62758, 212.5088233462319),\n",
              "  (5.5583253, 212.8497122384712),\n",
              "  (5.532448, 211.02606474082768),\n",
              "  (7.092814, 230.0630348404541),\n",
              "  (6.883675, 280.3016232560918),\n",
              "  (6.8832083, 282.0868660537034),\n",
              "  (7.1966243, 268.47718711279737)],\n",
              " 141: [(7.7897816, 221.02036105300343),\n",
              "  (7.535325, 208.34586628968668),\n",
              "  (8.156127, 213.3377603707323),\n",
              "  (8.42169, 194.0103090044444),\n",
              "  (8.454095, 192.16659439142902),\n",
              "  (8.376583, 178.01123560045303),\n",
              "  (8.236116, 190.16834647227702),\n",
              "  (8.197179, 180.01111076819674),\n",
              "  (8.008879, 179.06981878585793),\n",
              "  (8.1509495, 188.0),\n",
              "  (8.373795, 175.10282693320517),\n",
              "  (8.357882, 173.2887763243771),\n",
              "  (8.267946, 175.18276170902203),\n",
              "  (7.9224076, 175.0028571195339),\n",
              "  (7.8032513, 174.04597093871493),\n",
              "  (8.115556, 176.0113632695344),\n",
              "  (8.141526, 163.0),\n",
              "  (8.053631, 172.0),\n",
              "  (7.4184275, 171.0),\n",
              "  (6.607576, 217.18655575334307),\n",
              "  (6.676683, 153.46986674914396)],\n",
              " 142: [(7.2263675, 179.27911200137063),\n",
              "  (7.0112133, 177.1383639983163),\n",
              "  (6.512215, 169.1892431568863),\n",
              "  (6.3889327, 159.20113064925135)],\n",
              " 143: [(6.0162296, 424.0294801072208),\n",
              "  (5.0967565, 440.36802790393403),\n",
              "  (4.932179, 444.091206848323),\n",
              "  (4.9178114, 444.28819475651164),\n",
              "  (4.685329, 441.1915683691156),\n",
              "  (4.6209993, 449.250486922385),\n",
              "  (4.4323325, 467.61629569552),\n",
              "  (4.5094876, 462.52351291583005),\n",
              "  (4.418927, 467.3467663309547),\n",
              "  (4.2494626, 491.2290708009859),\n",
              "  (4.321169, 490.17241864470503),\n",
              "  (4.144085, 489.0092023673992),\n",
              "  (4.179635, 511.4137268396303),\n",
              "  (4.0434904, 514.9339763503667),\n",
              "  (4.109284, 518.6029309597084),\n",
              "  (3.9814744, 522.3102526276887),\n",
              "  (3.995497, 542.6232578870905),\n",
              "  (3.8424242, 558.0008960566282),\n",
              "  (3.8093119, 552.2318353735142),\n",
              "  (3.7828958, 566.692156289462),\n",
              "  (3.6995037, 588.8166098200695),\n",
              "  (3.7450085, 581.936422644261),\n",
              "  (3.8115764, 584.0077054286185),\n",
              "  (4.0525465, 605.0404945125574),\n",
              "  (3.9515612, 607.1391932662559),\n",
              "  (2.5451772, 770.0415573201228),\n",
              "  (2.5343084, 758.0059366522138),\n",
              "  (2.6030807, 740.0060810560951),\n",
              "  (2.7519457, 706.0856888508646),\n",
              "  (2.8934002, 706.1019756380801),\n",
              "  (3.986815, 511.5662224971465),\n",
              "  (3.9904144, 550.567888638631)],\n",
              " 145: [(5.2911673, 373.048254251377),\n",
              "  (5.287484, 378.5881667458718),\n",
              "  (5.2567143, 376.0850967533811),\n",
              "  (5.620208, 373.1340241789805),\n",
              "  (4.3916917, 195.86985475054604),\n",
              "  (4.4738717, 370.0),\n",
              "  (4.6120358, 403.0111660984097),\n",
              "  (4.388067, 423.2316150761897),\n",
              "  (4.350543, 408.70527278223364),\n",
              "  (4.288978, 421.09618853653853),\n",
              "  (4.4559865, 424.14266467781806),\n",
              "  (4.3393254, 437.3705522780426),\n",
              "  (4.6057143, 440.22267092915604),\n",
              "  (4.3898153, 440.36802790393403),\n",
              "  (4.3047786, 467.42806933259794),\n",
              "  (4.293052, 459.3930778755814),\n",
              "  (4.352388, 467.00428263560923),\n",
              "  (4.3429437, 481.50285565092963),\n",
              "  (4.2984247, 486.00411520891464),\n",
              "  (4.343859, 488.01639316727875),\n",
              "  (4.3529615, 513.1909975827713),\n",
              "  (4.2045555, 514.1906261300375),\n",
              "  (4.286468, 516.0155036430591),\n",
              "  (2.8299947, 660.0007575753228),\n",
              "  (2.8996725, 654.0925011036284),\n",
              "  (3.0661066, 667.0367306228346),\n",
              "  (2.9578128, 609.0525428893635),\n",
              "  (2.9473898, 605.0669384456567),\n",
              "  (3.2428424, 606.2384019509157),\n",
              "  (3.1329253, 612.9437168288782),\n",
              "  (3.0842202, 670.090292423342),\n",
              "  (2.967405, 660.4362194792167),\n",
              "  (3.0533934, 683.0117129303128),\n",
              "  (2.9647005, 710.0450689921028),\n",
              "  (2.9256034, 700.0028571370262),\n",
              "  (2.875775, 730.0109588218522),\n",
              "  (2.8431332, 763.0104848558767),\n",
              "  (2.7533262, 742.9219339876835),\n",
              "  (2.8695738, 764.8954176879347)],\n",
              " 146: [(5.20394, 303.1649715913763),\n",
              "  (5.352408, 302.2796718272666),\n",
              "  (5.265826, 312.1602152741441),\n",
              "  (5.2873282, 321.0389384482823),\n",
              "  (5.469629, 314.0143308831621),\n",
              "  (4.9991946, 367.2955213448702),\n",
              "  (5.011483, 417.0107912272775),\n",
              "  (5.0611954, 404.5985664828782),\n",
              "  (5.4070954, 88.07383266328314),\n",
              "  (5.553076, 95.0789145920377),\n",
              "  (3.9046962, 335.0492501110844),\n",
              "  (5.395943, 106.92053123698928),\n",
              "  (5.4816756, 127.88275880665071),\n",
              "  (5.392082, 109.89540481748998),\n",
              "  (5.441335, 140.60227594174995),\n",
              "  (5.139149, 199.12307751739877),\n",
              "  (4.893701, 198.15398053029367),\n",
              "  (5.4384913, 158.14550262337528)],\n",
              " 147: [(8.107844, 243.13165157996193),\n",
              "  (8.097931, 241.40629652103112),\n",
              "  (8.104878, 244.16592718886884),\n",
              "  (8.00616, 244.5240274492468),\n",
              "  (8.070022, 242.46443038103547),\n",
              "  (7.975526, 242.7447218787671),\n",
              "  (7.98195, 241.40629652103112),\n",
              "  (7.9084854, 241.82845159327303),\n",
              "  (7.787169, 245.29370150902776),\n",
              "  (7.6326985, 245.66033460858105),\n",
              "  (7.7540293, 244.1823089414956),\n",
              "  (7.4967504, 245.09997960016236),\n",
              "  (6.922754, 244.0327846827143),\n",
              "  (7.3021035, 244.24782496472716),\n",
              "  (7.1425776, 252.01785651020842),\n",
              "  (7.061565, 240.03333101883996),\n",
              "  (6.852115, 240.25195108468944),\n",
              "  (7.478352, 227.1079038695043),\n",
              "  (7.332086, 223.0)],\n",
              " 149: [(7.667128, 214.23351745233518),\n",
              "  (6.865365, 242.0743687382041),\n",
              "  (6.6009994, 243.10080213771406),\n",
              "  (6.6157737, 245.073458375239),\n",
              "  (6.1312556, 243.0740627874558),\n",
              "  (5.739054, 231.21634890292685),\n",
              "  (5.631304, 233.10512649875378),\n",
              "  (5.738937, 232.0021551624036),\n",
              "  (5.815865, 226.00884938426637),\n",
              "  (5.7995524, 228.0350850198276),\n",
              "  (5.642755, 214.05840324546944)],\n",
              " 150: [(6.2935905, 263.0684321616716),\n",
              "  (5.6494174, 345.0710071854777),\n",
              "  (5.1803255, 348.0),\n",
              "  (5.2273593, 349.11602655850675),\n",
              "  (5.2725205, 349.20624278497655),\n",
              "  (5.2933187, 351.1993735757511),\n",
              "  (5.3265023, 350.12140751459344),\n",
              "  (5.2218437, 347.0518693221519),\n",
              "  (5.2632236, 350.6522493867678),\n",
              "  (5.278766, 348.5182922028627),\n",
              "  (5.2715297, 344.06104109590785),\n",
              "  (5.1942916, 345.3259329966401),\n",
              "  (5.191601, 347.02305398921266),\n",
              "  (5.3356977, 345.00144927231827),\n",
              "  (7.162726, 255.23714463220279),\n",
              "  (7.746979, 247.24481794367298)],\n",
              " 153: [(7.6458006, 244.6630335788388),\n",
              "  (7.3155427, 237.0759372015642),\n",
              "  (7.617782, 223.22410264126944),\n",
              "  (7.462512, 232.86047324524614),\n",
              "  (7.7855325, 248.20354550247666),\n",
              "  (7.989364, 223.29352879114074),\n",
              "  (7.9647274, 205.54318281081473),\n",
              "  (4.9248967, 237.0759372015642)],\n",
              " 152: [(10.07302, 114.0175425099138),\n",
              "  (9.907037, 114.0175425099138),\n",
              "  (10.124147, 107.0420478129973),\n",
              "  (10.281397, 114.03946685248927),\n",
              "  (10.309829, 108.0),\n",
              "  (9.636812, 125.25573839150046),\n",
              "  (9.866097, 115.0),\n",
              "  (9.8259945, 111.11255554616679),\n",
              "  (9.718769, 117.01709276853532),\n",
              "  (9.765696, 126.03570922560003),\n",
              "  (9.725406, 126.03570922560003),\n",
              "  (9.701128, 124.14507642270796),\n",
              "  (8.8919935, 110.2905254316979)],\n",
              " 154: [(2.3113286, 817.2209003690496)],\n",
              " 155: [(5.225758, 173.5655495770978),\n",
              "  (9.117928, 151.0132444522665),\n",
              "  (9.139911, 152.0131573252789),\n",
              "  (9.060173, 152.47294842036735),\n",
              "  (9.0965185, 152.16109883935513),\n",
              "  (9.023578, 153.16004700965587),\n",
              "  (9.10847, 152.08221460775746),\n",
              "  (8.848897, 149.164338901763),\n",
              "  (9.28037, 148.27339613025663),\n",
              "  (8.546084, 164.47796204963143)],\n",
              " 156: [(4.143416, 529.0463117724194), (3.3560958, 594.4846507690505)],\n",
              " 157: [(10.0892315, 136.36715146984628),\n",
              "  (10.146268, 143.05593311708537),\n",
              "  (10.056871, 131.18688958886096),\n",
              "  (10.159108, 146.66969693839283),\n",
              "  (10.359604, 133.09395177843356),\n",
              "  (10.226107, 136.36715146984628),\n",
              "  (9.600788, 149.03019828209315)],\n",
              " 158: [(7.780055, 164.0),\n",
              "  (7.958162, 161.07762104029226),\n",
              "  (7.8150644, 164.0762018087937),\n",
              "  (7.67255, 160.37767924496225)],\n",
              " 160: [(7.943099, 236.01906702637396),\n",
              "  (6.7716103, 240.20824298928628),\n",
              "  (7.7276115, 219.03652663425797),\n",
              "  (7.8128853, 208.08652046684813),\n",
              "  (8.174622, 206.11889772653063),\n",
              "  (8.193809, 203.0221662774782),\n",
              "  (7.4489207, 204.02205763103166)],\n",
              " 162: [(3.303372, 633.4611274577154),\n",
              "  (3.349999, 634.1135544995076),\n",
              "  (3.3261352, 636.227160690268),\n",
              "  (3.18615, 665.5231025291308),\n",
              "  (3.282009, 660.1939109079998),\n",
              "  (3.2170062, 677.2953565469056),\n",
              "  (3.0135646, 712.1579880897216),\n",
              "  (3.0817223, 695.4228929219975),\n",
              "  (3.0448468, 718.1566681442149),\n",
              "  (3.0578265, 767.7629842601166),\n",
              "  (3.0611455, 732.3011675533503)],\n",
              " 163: [(4.693864, 422.2842644475401),\n",
              "  (4.457172, 514.4754610280261),\n",
              "  (4.2607117, 497.8292880094541),\n",
              "  (4.2046614, 510.51836401837693),\n",
              "  (4.280402, 517.2794215895312),\n",
              "  (4.538515, 486.00925917105735),\n",
              "  (4.7260976, 472.01694884823786),\n",
              "  (4.716836, 486.78537365044156),\n",
              "  (4.6327887, 451.7012286899384),\n",
              "  (4.788835, 384.0637967838156),\n",
              "  (4.6492615, 459.97934736246583)],\n",
              " 164: [(6.67829, 258.0174412709342),\n",
              "  (6.8444753, 243.0),\n",
              "  (6.884496, 258.15692901799093),\n",
              "  (7.2132983, 256.1581542719263),\n",
              "  (7.442369, 246.0),\n",
              "  (7.457974, 258.1240012087214),\n",
              "  (7.1316543, 257.0311265197272),\n",
              "  (5.8400993, 245.03265088555034)],\n",
              " 169: [(8.004995, 164.59647626847908),\n",
              "  (8.41985, 163.04907236779974),\n",
              "  (8.568383, 165.10905486980417),\n",
              "  (7.978565, 162.00308639035245)],\n",
              " 173: [(8.680866, 140.1748907615055),\n",
              "  (9.137484, 140.1748907615055),\n",
              "  (8.850299, 140.356688476182),\n",
              "  (9.2151575, 140.4314779527724),\n",
              "  (9.293283, 140.12851244482687),\n",
              "  (9.25331, 141.0)],\n",
              " 174: [(7.662013, 184.78365728602733),\n",
              "  (7.5930915, 184.04347312523745),\n",
              "  (7.91867, 206.0218435020908),\n",
              "  (7.9893928, 206.08736011701444),\n",
              "  (8.14963, 200.08997975910736),\n",
              "  (7.768896, 190.16308790088576)],\n",
              " 176: [(8.431951, 198.01010075246163), (8.219181, 198.4263087395419)],\n",
              " 178: [(7.961176, 175.07141400011596), (8.648497, 163.24827717314508)],\n",
              " 179: [(4.9522414, 350.5153348999156),\n",
              "  (5.014455, 358.8718434204612),\n",
              "  (4.879885, 354.1412147717348)],\n",
              " 180: [(4.055519, 439.410969366947), (4.0045285, 470.0521247691579)],\n",
              " 181: [(7.8429103, 218.05733191066977)]}"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tracking_data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 4.2 preparing the dataset for the DL model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "torch.Size([116410, 2])\n"
          ]
        }
      ],
      "source": [
        "X, y = DataProcessor.generate_and_prepare_tensors(tracking_data, device)\n",
        "\n",
        "print(X.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Create a dataset and split into training and test sets\n",
        "full_dataset = TensorDataset(X, y)\n",
        "train_size = int(0.8 * len(full_dataset))\n",
        "test_size = len(full_dataset) - train_size\n",
        "train_dataset, test_dataset = random_split(full_dataset, [train_size, test_size])\n",
        "\n",
        "# Data loaders\n",
        "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
        "test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Training the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/3, Loss: 0.20537173748016357\n",
            "Epoch 2/3, Loss: 0.036056339740753174\n",
            "Epoch 3/3, Loss: 0.02756095677614212\n"
          ]
        }
      ],
      "source": [
        "num_epochs = 3\n",
        "for epoch in range(num_epochs):\n",
        "    cam_clib_model.train()\n",
        "    for X_batch, y_batch in train_loader:\n",
        "        X_batch, y_batch = X_batch.to(device), y_batch.to(device)\n",
        "        optimizer.zero_grad()\n",
        "        y_pred = cam_clib_model(X_batch)\n",
        "        loss = custom_loss(y_pred, y_batch)\n",
        "        loss.backward()\n",
        "        torch.nn.utils.clip_grad_norm_(cam_clib_model.parameters(), max_norm=1.0)  # Gradient clipping\n",
        "        optimizer.step()\n",
        "\n",
        "    if (epoch+1) % 1 == 0:\n",
        "        print(f'Epoch {epoch+1}/{num_epochs}, Loss: {loss.item()}')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Testing the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test Loss: 0.04375203974048638\n"
          ]
        }
      ],
      "source": [
        "# Evaluate the model\n",
        "cam_clib_model.eval()\n",
        "test_loss = 0.0\n",
        "with torch.no_grad():\n",
        "    for X_batch, y_batch in test_loader:\n",
        "        X_batch, y_batch = X_batch.to(device), y_batch.to(device)\n",
        "        y_pred = cam_clib_model(X_batch)\n",
        "        loss = custom_loss(y_pred, y_batch)\n",
        "        test_loss += loss.item()\n",
        "\n",
        "test_loss /= len(test_loader)\n",
        "print(f'Test Loss: {test_loss}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Infer fuction "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.41123151779174805\n",
            "1.832153558731079\n"
          ]
        }
      ],
      "source": [
        "def infer_cam_calib(cam_clib_model, current_depth, depth_diff):\n",
        "    current_depth = float(current_depth)\n",
        "    depth_diff = float(depth_diff)\n",
        "    scale_change = cam_clib_model(torch.tensor([current_depth, depth_diff], dtype=torch.float32 , device=device )  )\n",
        "    return scale_change.item()\n",
        "print (infer_cam_calib(cam_clib_model, 4, 5))\n",
        "print (infer_cam_calib(cam_clib_model, 4, -2))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "It make sense if we are at depth 4 meter fr"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# 5.0 Estimating heights from the Video"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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          ]
        }
      ],
      "source": [
        "def detect_head_and_leg(video_path, output_video_path, tracker, depth_anything, anchors):\n",
        "    cap = cv2.VideoCapture(video_path)\n",
        "\n",
        "    if not cap.isOpened():\n",
        "        print(\"Error: Couldn't open video.\")\n",
        "        return\n",
        "\n",
        "    # Get video properties\n",
        "    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n",
        "    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n",
        "    fps = cap.get(cv2.CAP_PROP_FPS)\n",
        "    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
        "\n",
        "    # Define the codec and create VideoWriter object\n",
        "    fourcc = cv2.VideoWriter_fourcc(*'mp4v')\n",
        "    out = cv2.VideoWriter(output_video_path, fourcc, fps, (width, height))\n",
        "\n",
        "    frame_count = 0\n",
        "    object_heights = defaultdict(lambda: deque(maxlen=40))  # Store height measurements for each object\n",
        "\n",
        "    while cap.isOpened():\n",
        "        ret, frame = cap.read()\n",
        "        if not ret:\n",
        "            break\n",
        "\n",
        "        results, legs_and_heads, boxes = MyDetector.infer_obj_detection(frame)\n",
        "\n",
        "        detections = sv.Detections.from_ultralytics(results)\n",
        "        detections = tracker.update_with_detections(detections)\n",
        "\n",
        "        depth_map = depth_anything.infer_image(frame)\n",
        "\n",
        "        for detection_idx, _ in enumerate(detections):\n",
        "            xyxy = detections[detection_idx].xyxy.tolist()[0]\n",
        "            obj_id = detections[detection_idx].tracker_id[0]\n",
        "\n",
        "            best_idx = MyDetector.match_best_box(xyxy, boxes)\n",
        "            head_pos, leg_pos = legs_and_heads[best_idx]\n",
        "\n",
        "            # Calculate pixel distance and depth\n",
        "            start, end = head_pos, leg_pos\n",
        "            length_pixels = np.sqrt((end[0] - start[0]) ** 2 + (end[1] - start[1]) ** 2)\n",
        "            middle_point = ((start[0] + end[0]) // 2, (start[1] + end[1]) // 2)\n",
        "            depth = depth_map[middle_point[1], middle_point[0]]\n",
        "\n",
        "            # Estimate height\n",
        "            height = EstimateHeight.estimate_height_currnetFrame(length_pixels, depth, anchors, infer_cam_calib, cam_clib_model)\n",
        "            \n",
        "            # Use the new function to estimate and smooth the height\n",
        "            stable_height = EstimateHeight.get_stable_height(object_heights, obj_id, height)\n",
        "\n",
        "            frame = EstimateHeight.annotate_frame(frame, head_pos, leg_pos, stable_height)\n",
        "\n",
        "        # Display the frame\n",
        "        cv2.imshow('Frame', frame)\n",
        "\n",
        "        # Write the frame into the output video file\n",
        "        out.write(frame)\n",
        "\n",
        "        # Exit if 'Esc' key is pressed\n",
        "        if cv2.waitKey(1) & 0xFF == 27:  # 27 is the Esc key\n",
        "            break\n",
        "\n",
        "        frame_count += 1\n",
        "        print(f\"Processed frame {frame_count}/{total_frames}\")\n",
        "\n",
        "    # Release everything when finished\n",
        "    cap.release()\n",
        "    out.release()\n",
        "    cv2.destroyAllWindows()\n",
        "\n",
        "# Example usage\n",
        "output_video_path = 'output_video.mp4'\n",
        "detect_head_and_leg(VIDEO_PATH, output_video_path, tracker, depth_anything, anchor_lines)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
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