Created August 1, 2024 © GPL3+

AI circuit designer for racing simulators (fail, for now...)

Well, or a success, if we consider that I have learned how not to make an AI circuit generator...

BeginnerWork in progress1 hour12

AI circuit designer for racing simulators (fail, for now...)

Things used in this project

Hardware components

AMD Instinct™ MI210 accelerators

Software apps and online services

AMD ROCm™ Software

Story

Introduction

Racing simulators provide an exhilarating driving experience, yet they are often limited by the number of available tracks. This project aimed to overcome this limitation by creating an AI-powered tool that can dynamically generate realistic and engaging racing circuits. The goal was to enhance user experience by providing a continuous stream of new challenges, ensuring that both casual gamers and professional drivers remain engaged.

TLDR: It hasn't worked, at least for now

Research and Initial Attempts

Understanding the State of the Art

The journey began with extensive research into existing methods for circuit generation. Some projects relied on procedural generation techniques using splines. These methods could produce circuits, but they often lacked diversity, predominantly generating circular tracks with few straight sections. The limitations of these techniques highlighted the need for a more sophisticated approach.

First Attempts: Procedural Generation

To get hands-on experience, I implemented a basic script (test_procedural_generation.py) to generate racing circuits procedurally, in order to create a dataset to train an AI model. The initial results were underwhelming. The generated circuits were simplistic and lacked the complexity and realism needed to create an engaging racing experience. Also I had been trying some different ideas (test_procedural_algorithms.ipynb) without clear results. This was a valuable learning experience that underscored the limitations of procedural generation for this application.

Trying with satellital images

In my quest to improve the dataset and generate more realistic circuits, I also experimented with using satellite images. The idea was to leverage real-world topographical data to inform the AI model, providing a richer and more varied set of inputs. I collected satellite images of different racing tracks and landscapes, hoping that this approach would introduce more diversity and realism to the generated circuits. Despite the initial enthusiasm, integrating satellite imagery presented significant challenges. The complexity and noise in the satellite images made it difficult for the AI model to extract useful patterns. Also it requires some manual supervision to ensure that images are collected in the right way (scale, orientation, accuracy of the coordinates...)

Exploring Real Circuit Data

Next, I tried obtaining a dataset of drawings existing F1 circuits (https://github.com/f1tenth/f1tenth_racetracks). Although the dataset provided a good starting point, it was limited in size. To augment the data, I applied various transformations such as rotation and scaling to create additional samples (augment_dataset.py). This step was crucial for preparing the dataset for training, but it still didn't address the core challenge of generating realistic and varied circuits dynamically.

Choosing AI Architecture

Selecting DCGAN

Mainly I have been playing with Deep Convolutional Generative Adversarial Network (DCGAN) architecture. DCGANs are well-suited for tasks that involve generating new data that closely resembles a given dataset. I like them particularly because, if I understand well, they don't require images of both domains (when doing image to image conversion) to work, and can replicate an entire domain from a relatively small set of examples. Implementing the DCGAN architecture using PyTorch was the next major step in the project (train_dcgan_pytorch.py, don't take as reference for anything!). Luckily, there is ChatGPT to lend a hand.

Implementation and Challenges

The implementation process involved several stages, from setting up the neural network architecture to training the model on the augmented dataset. Unfortunately, the initial results were far from promising. The generated circuits lacked coherence and realism, often producing abstract shapes that bore little resemblance to actual racing tracks.

Lessons Learned and Final Thoughts

Reflecting on the Journey

The project was an eye-opening experience that highlighted the challenges of using AI for dynamic content generation. Despite the setbacks, several key insights were gained:

Data Quality and Quantity: Having a diverse and comprehensive dataset is crucial for training effective AI models. The limited dataset was a significant bottleneck in this project.
Complexity of AI Models: While DCGANs have great potential, it seems that they require careful tuning and substantial computational resources to produce high-quality results. AMD AAC was really helpful to make long runs, but the experiments that I have been launching never converged...
Iterative Development: AI projects often require multiple iterations and continuous learning from failures to make meaningful progress. At start I was very confident because in the past, I have done some AI projects successfully, but with this one I have to take more time.

Conclusion: The Path Forward

Although the project did not achieve its ultimate goal, it laid a solid foundation for future work. Understanding the complexities involved in dynamic circuit generation has provided a clearer direction for future research. The journey reaffirmed the adage that innovation often comes from persistent effort and learning from failures.

Despite everything, my dream of creating a tool that can dynamically generate endless, realistic racing circuits remains alive =)

import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from scipy.interpolate import splprep, splev

def generate_race_track(num_points=20, track_width=10, image_size=(1000, 1000)):
    # Generate random points around a circle
    angles = np.linspace(0, 2 * np.pi, num_points, endpoint=False)
    radii = image_size[0] / 3 + np.random.rand(num_points) * image_size[0] / 6
    points = np.array([radii * np.cos(angles), radii * np.sin(angles)]).T
    points = np.vstack([points, points[0]])  # Ensure the track loops back to the start
    
    # Interpolate the points using splines for a smooth curve
    tck, u = splprep(points.T, s=2.0, per=True)
    u_new = np.linspace(u.min(), u.max(), 1000)
    x_new, y_new = splev(u_new, tck, der=0)
    
    # Create the plot
    fig, ax = plt.subplots(figsize=(image_size[0] / 100, image_size[1] / 100), dpi=100)
    ax.plot(x_new, y_new, 'k-', lw=track_width)
    ax.set_xlim(0, image_size[0])
    ax.set_ylim(0, image_size[1])
    ax.axis('off')
    
    # Save the track image
    plt.gca().invert_yaxis()  # Invert y-axis to have (0, 0) at top-left corner
    plt.subplots_adjust(left=0, right=1, top=1, bottom=0)
    plt.savefig('race_track.png', bbox_inches='tight', pad_inches=0)
    plt.close(fig)

generate_race_track()

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Set the random seed for reproducibility\n",
    "np.random.seed(42)\n",
    "\n",
    "# Parameters\n",
    "num_points = 20\n",
    "image_size = (1000, 1000)\n",
    "\n",
    "# Generate fully random points within the image size\n",
    "points = np.random.rand(num_points, 2) * image_size\n",
    "points = np.vstack([points, points[0]])  # Ensure the track loops back to the start\n",
    "\n",
    "# Plot the random points\n",
    "fig, ax = plt.subplots(figsize=(image_size[0] / 100, image_size[1] / 100), dpi=100)\n",
    "ax.plot(points[:, 0], points[:, 1], 'bo')  # 'bo' for blue points without lines\n",
    "ax.set_xlim(0, image_size[0])\n",
    "ax.set_ylim(0, image_size[1])\n",
    "ax.set_aspect('equal', 'box')\n",
    "plt.gca().invert_yaxis()  # Invert y-axis to have (0, 0) at top-left corner\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Function to filter out points that are too close to each other\n",
    "def filter_points(points, min_distance):\n",
    "    filtered_points = []\n",
    "    for point in points:\n",
    "        if not filtered_points:\n",
    "            filtered_points.append(point)\n",
    "        else:\n",
    "            distances = np.linalg.norm(np.array(filtered_points) - point, axis=1)\n",
    "            if np.all(distances >= min_distance):\n",
    "                filtered_points.append(point)\n",
    "    return np.array(filtered_points)\n",
    "\n",
    "# Parameters\n",
    "min_distance = 100  # Minimum distance between points\n",
    "\n",
    "# Filter the points\n",
    "filtered_points = filter_points(points, min_distance)\n",
    "filtered_points = np.vstack([filtered_points, filtered_points[0]])  # Ensure the track loops back to the start\n",
    "\n",
    "# Plot the filtered points\n",
    "fig, ax = plt.subplots(figsize=(image_size[0] / 100, image_size[1] / 100), dpi=100)\n",
    "ax.plot(filtered_points[:, 0], filtered_points[:, 1], 'bo')  # 'bo' for blue points without lines\n",
    "ax.set_xlim(0, image_size[0])\n",
    "ax.set_ylim(0, image_size[1])\n",
    "ax.set_aspect('equal', 'box')\n",
    "plt.gca().invert_yaxis()  # Invert y-axis to have (0, 0) at top-left corner\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2500x500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from itertools import permutations\n",
    "\n",
    "# Parameters\n",
    "num_points = 20\n",
    "image_size = (1000, 1000)\n",
    "min_distance = 100\n",
    "\n",
    "# Set the random seed for reproducibility\n",
    "np.random.seed(42)\n",
    "\n",
    "# Step 1: Generate fully random points within the image size\n",
    "points = np.random.rand(num_points, 2) * image_size\n",
    "\n",
    "# Step 2: Filter out points that are too close to each other\n",
    "def filter_points(points, min_distance):\n",
    "    filtered_points = []\n",
    "    for point in points:\n",
    "        if not filtered_points:\n",
    "            filtered_points.append(point)\n",
    "        else:\n",
    "            distances = np.linalg.norm(np.array(filtered_points) - point, axis=1)\n",
    "            if np.all(distances >= min_distance):\n",
    "                filtered_points.append(point)\n",
    "    return np.array(filtered_points)\n",
    "\n",
    "filtered_points = filter_points(points, min_distance)\n",
    "\n",
    "# Step 3: Generate tracks using nearest neighbor heuristic\n",
    "def generate_track(points):\n",
    "    remaining_points = points.tolist()\n",
    "    track = [remaining_points.pop(0)]\n",
    "    while remaining_points:\n",
    "        last_point = track[-1]\n",
    "        distances = np.linalg.norm(np.array(remaining_points) - last_point, axis=1)\n",
    "        nearest_index = np.argmin(distances)\n",
    "        track.append(remaining_points.pop(nearest_index))\n",
    "    return np.array(track)\n",
    "\n",
    "# Generate multiple tracks\n",
    "num_tracks = 5\n",
    "tracks = [generate_track(np.random.permutation(filtered_points)) for _ in range(num_tracks)]\n",
    "\n",
    "# Step 4: Plot the tracks\n",
    "fig, axes = plt.subplots(1, 5, figsize=(25, 5))\n",
    "for i, ax in enumerate(axes):\n",
    "    track = tracks[i]\n",
    "    track = np.vstack([track, track[0]])  # Ensure the track loops back to the start\n",
    "    ax.plot(track[:, 0], track[:, 1], 'k-', lw=2)  # 'k-' for black lines\n",
    "    ax.plot(track[:, 0], track[:, 1], 'bo')  # 'bo' for blue points\n",
    "    ax.set_xlim(0, image_size[0])\n",
    "    ax.set_ylim(0, image_size[1])\n",
    "    ax.set_aspect('equal', 'box')\n",
    "    ax.axis('off')\n",
    "    plt.gca().invert_yaxis()  # Invert y-axis to have (0, 0) at top-left corner\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2500x500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from itertools import permutations\n",
    "\n",
    "def lines_intersect(p1, p2, p3, p4):\n",
    "    def ccw(A, B, C):\n",
    "        return (C[1]-A[1]) * (B[0]-A[0]) > (B[1]-A[1]) * (C[0]-A[0])\n",
    "    return ccw(p1, p3, p4) != ccw(p2, p3, p4) and ccw(p1, p2, p3) != ccw(p1, p2, p4)\n",
    "\n",
    "\n",
    "\n",
    "# Parameters\n",
    "num_points = 15\n",
    "image_size = (1000, 1000)\n",
    "min_distance = 100\n",
    "\n",
    "# Set the random seed for reproducibility\n",
    "#np.random.seed(42)\n",
    "\n",
    "# Step 1: Generate fully random points within the image size\n",
    "points = np.random.rand(num_points, 2) * image_size\n",
    "\n",
    "# Step 2: Filter out points that are too close to each other\n",
    "def filter_points(points, min_distance):\n",
    "    filtered_points = []\n",
    "    for point in points:\n",
    "        if not filtered_points:\n",
    "            filtered_points.append(point)\n",
    "        else:\n",
    "            distances = np.linalg.norm(np.array(filtered_points) - point, axis=1)\n",
    "            if np.all(distances >= min_distance):\n",
    "                filtered_points.append(point)\n",
    "    return np.array(filtered_points)\n",
    "\n",
    "filtered_points = filter_points(points, min_distance)\n",
    "\n",
    "# Step 3: Helper function to check for intersections\n",
    "def has_intersections(track):\n",
    "    for i in range(len(track) - 1):\n",
    "        for j in range(i + 2, len(track) - 1):\n",
    "            if lines_intersect(track[i], track[i + 1], track[j], track[j + 1]):\n",
    "                return True\n",
    "    return False\n",
    "\n",
    "# Step 4: Generate valid tracks\n",
    "valid_tracks = []\n",
    "for perm in permutations(filtered_points):\n",
    "    track = np.vstack([perm, perm[0]])  # Ensure the track loops back to the start\n",
    "    if not has_intersections(track):\n",
    "        valid_tracks.append(track)\n",
    "    if len(valid_tracks) >= 5:\n",
    "        break\n",
    "\n",
    "# Step 5: Plot the valid tracks\n",
    "fig, axes = plt.subplots(1, 5, figsize=(25, 5))\n",
    "for i, ax in enumerate(axes):\n",
    "    track = valid_tracks[i]\n",
    "    ax.plot(track[:, 0], track[:, 1], 'k-', lw=2)  # 'k-' for black lines\n",
    "    ax.plot(track[:, 0], track[:, 1], 'bo')  # 'bo' for blue points\n",
    "    ax.set_xlim(0, image_size[0])\n",
    "    ax.set_ylim(0, image_size[1])\n",
    "    ax.set_aspect('equal', 'box')\n",
    "    ax.axis('off')\n",
    "    plt.gca().invert_yaxis()  # Invert y-axis to have (0, 0) at top-left corner\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2500x500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from itertools import permutations\n",
    "\n",
    "def lines_intersect(p1, p2, p3, p4):\n",
    "    def ccw(A, B, C):\n",
    "        return (C[1]-A[1]) * (B[0]-A[0]) > (B[1]-A[1]) * (C[0]-A[0])\n",
    "    return ccw(p1, p3, p4) != ccw(p2, p3, p4) and ccw(p1, p2, p3) != ccw(p1, p2, p4)\n",
    "\n",
    "\n",
    "\n",
    "# Parameters\n",
    "num_points = 15\n",
    "image_size = (1000, 1000)\n",
    "min_distance = 100\n",
    "\n",
    "# Set the random seed for reproducibility\n",
    "#np.random.seed(42)\n",
    "\n",
    "# Step 1: Generate fully random points within the image size\n",
    "points = np.random.rand(num_points, 2) * image_size\n",
    "\n",
    "# Step 2: Filter out points that are too close to each other\n",
    "def filter_points(points, min_distance):\n",
    "    filtered_points = []\n",
    "    for point in points:\n",
    "        if not filtered_points:\n",
    "            filtered_points.append(point)\n",
    "        else:\n",
    "            distances = np.linalg.norm(np.array(filtered_points) - point, axis=1)\n",
    "            if np.all(distances >= min_distance):\n",
    "                filtered_points.append(point)\n",
    "    return np.array(filtered_points)\n",
    "\n",
    "filtered_points = filter_points(points, min_distance)\n",
    "\n",
    "# Step 3: Helper function to check for intersections\n",
    "def has_intersections(track):\n",
    "    for i in range(len(track) - 1):\n",
    "        for j in range(i + 2, len(track) - 1):\n",
    "            if lines_intersect(track[i], track[i + 1], track[j], track[j + 1]):\n",
    "                return True\n",
    "    return False\n",
    "\n",
    "# Step 4: Generate valid tracks\n",
    "valid_tracks = []\n",
    "for perm in permutations(filtered_points):\n",
    "    track = np.vstack([perm, perm[0]])  # Ensure the track loops back to the start\n",
    "    if not has_intersections(track):\n",
    "        valid_tracks.append(track)\n",
    "    if len(valid_tracks) >= 5:\n",
    "        break\n",
    "\n",
    "# Step 5: Plot the valid tracks\n",
    "fig, axes = plt.subplots(1, 5, figsize=(25, 5))\n",
    "for i, ax in enumerate(axes):\n",
    "    track = valid_tracks[i]\n",
    "    ax.plot(track[:, 0], track[:, 1], 'k-', lw=2)  # 'k-' for black lines\n",
    "    ax.plot(track[:, 0], track[:, 1], 'bo')  # 'bo' for blue points\n",
    "    ax.set_xlim(0, image_size[0])\n",
    "    ax.set_ylim(0, image_size[1])\n",
    "    ax.set_aspect('equal', 'box')\n",
    "    ax.axis('off')\n",
    "    plt.gca().invert_yaxis()  # Invert y-axis to have (0, 0) at top-left corner\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Set the random seed for reproducibility\n",
    "np.random.seed(42)\n",
    "\n",
    "# Parameters\n",
    "num_points = 50\n",
    "image_size = (1000, 1000)\n",
    "\n",
    "# Generate fully random points within the image size\n",
    "points = np.random.rand(num_points, 2) * image_size\n",
    "\n",
    "# Plot the random points\n",
    "fig, ax = plt.subplots(figsize=(image_size[0] / 100, image_size[1] / 100), dpi=100)\n",
    "ax.plot(points[:, 0], points[:, 1], 'bo')  # 'bo' for blue points without lines\n",
    "ax.set_xlim(0, image_size[0])\n",
    "ax.set_ylim(0, image_size[1])\n",
    "ax.set_aspect('equal', 'box')\n",
    "plt.gca().invert_yaxis()  # Invert y-axis to have (0, 0) at top-left corner\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Find two points that are approximately one-third of the image size apart\n",
    "min_distance = image_size[0] / 3\n",
    "max_distance = image_size[0] / 3 * 1.5\n",
    "\n",
    "def find_straight(points, min_distance, max_distance):\n",
    "    num_points = len(points)\n",
    "    for _ in range(1000):  # Try 1000 times to find suitable points\n",
    "        idx1, idx2 = np.random.choice(num_points, 2, replace=False)\n",
    "        p1, p2 = points[idx1], points[idx2]\n",
    "        distance = np.linalg.norm(p1 - p2)\n",
    "        if min_distance <= distance <= max_distance:\n",
    "            return p1, p2\n",
    "    return None, None\n",
    "\n",
    "p1, p2 = find_straight(points, min_distance, max_distance)\n",
    "\n",
    "# Plot the selected points and straight line\n",
    "fig, ax = plt.subplots(figsize=(image_size[0] / 100, image_size[1] / 100), dpi=100)\n",
    "ax.plot(points[:, 0], points[:, 1], 'bo')  # 'bo' for blue points without lines\n",
    "if p1 is not None and p2 is not None:\n",
    "    ax.plot([p1[0], p2[0]], [p1[1], p2[1]], 'ro-', lw=2)  # 'ro-' for red line with points\n",
    "ax.set_xlim(0, image_size[0])\n",
    "ax.set_ylim(0, image_size[1])\n",
    "ax.set_aspect('equal', 'box')\n",
    "plt.gca().invert_yaxis()  # Invert y-axis to have (0, 0) at top-left corner\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[47], line 30\u001b[0m\n\u001b[1;32m     27\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39marray(track)\n\u001b[1;32m     29\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m p1 \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m p2 \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m---> 30\u001b[0m     track \u001b[38;5;241m=\u001b[39m \u001b[43mbuild_track\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpoints\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mp1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mp2\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     32\u001b[0m     \u001b[38;5;66;03m# Plot the track\u001b[39;00m\n\u001b[1;32m     33\u001b[0m     fig, ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(figsize\u001b[38;5;241m=\u001b[39m(image_size[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m100\u001b[39m, image_size[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m100\u001b[39m), dpi\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m100\u001b[39m)\n",
      "Cell \u001b[0;32mIn[47], line 22\u001b[0m, in \u001b[0;36mbuild_track\u001b[0;34m(points, start, end)\u001b[0m\n\u001b[1;32m     20\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m is_valid_track(track, p):\n\u001b[1;32m     21\u001b[0m         track\u001b[38;5;241m.\u001b[39mappend(p)\n\u001b[0;32m---> 22\u001b[0m         \u001b[43mpoints\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mremove\u001b[49m\u001b[43m(\u001b[49m\u001b[43mp\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     23\u001b[0m         \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[1;32m     24\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()"
     ]
    }
   ],
   "source": [
    "def lines_intersect(p1, p2, p3, p4):\n",
    "    def ccw(A, B, C):\n",
    "        return (C[1]-A[1]) * (B[0]-A[0]) > (B[1]-A[1]) * (C[0]-A[0])\n",
    "    return ccw(p1, p3, p4) != ccw(p2, p3, p4) and ccw(p1, p2, p3) != ccw(p1, p2, p4)\n",
    "\n",
    "def is_valid_track(track, new_point):\n",
    "    for i in range(len(track) - 1):\n",
    "        if lines_intersect(track[i], track[i + 1], track[-1], new_point):\n",
    "            return False\n",
    "    return True\n",
    "\n",
    "def build_track(points, start, end):\n",
    "    track = [start, end]\n",
    "    points = [p for p in points if not np.array_equal(p, start) and not np.array_equal(p, end)]\n",
    "    \n",
    "    while points:\n",
    "        last_point = track[-1]\n",
    "        points.sort(key=lambda p: np.linalg.norm(p - last_point))\n",
    "        for p in points:\n",
    "            if is_valid_track(track, p):\n",
    "                track.append(p)\n",
    "                points.remove(p)\n",
    "                break\n",
    "        else:\n",
    "            break  # If no valid point found, exit the loop\n",
    "\n",
    "    return np.array(track)\n",
    "\n",
    "if p1 is not None and p2 is not None:\n",
    "    track = build_track(points, p1, p2)\n",
    "\n",
    "    # Plot the track\n",
    "    fig, ax = plt.subplots(figsize=(image_size[0] / 100, image_size[1] / 100), dpi=100)\n",
    "    ax.plot(points[:, 0], points[:, 1], 'bo')  # 'bo' for blue points without lines\n",
    "    if track is not None:\n",
    "        ax.plot(track[:, 0], track[:, 1], 'k-', lw=2)  # 'k-' for black lines\n",
    "        ax.plot(track[:, 0], track[:, 1], 'ro')  # 'ro' for red points\n",
    "    ax.set_xlim(0, image_size[0])\n",
    "    ax.set_ylim(0, image_size[1])\n",
    "    ax.set_aspect('equal', 'box')\n",
    "    plt.gca().invert_yaxis()  # Invert y-axis to have (0, 0) at top-left corner\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Set the random seed for reproducibility\n",
    "#np.random.seed(42)\n",
    "\n",
    "# Parameters\n",
    "num_points = 100\n",
    "image_size = (1000, 1000)\n",
    "\n",
    "# Generate fully random points within the image size\n",
    "points = np.random.rand(num_points, 2) * image_size\n",
    "\n",
    "# Convert points to a list of tuples for easier comparison\n",
    "points_list = [tuple(point) for point in points]\n",
    "\n",
    "# Plot the random points\n",
    "fig, ax = plt.subplots(figsize=(image_size[0] / 100, image_size[1] / 100), dpi=100)\n",
    "ax.plot(points[:, 0], points[:, 1], 'bo')  # 'bo' for blue points without lines\n",
    "ax.set_xlim(0, image_size[0])\n",
    "ax.set_ylim(0, image_size[1])\n",
    "ax.set_aspect('equal', 'box')\n",
    "plt.gca().invert_yaxis()  # Invert y-axis to have (0, 0) at top-left corner\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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b4qtf/WrcfPPN8fTTT8cb3vCGeOyxx6b7/MEf/EF86Utfis9//vNx++23x49+9KMoFArT18vlclxwwQXx1FNPxVe+8pX4zGc+E9dff31cddVV9R4uAABAzTJJkiSN/AQ//vGPY8WKFXH77bfHL/3SL8Xk5GQ8//nPjxtuuCFGRkYiIuK73/1uvPjFL449e/bEq1/96viHf/iH+JVf+ZX40Y9+FAMDAxERcd1118V73/ve+PGPfxxLliw55uedmpqK/v7+mJycjL6+vkZ+iQBtp1yOGBuLKJUiBgcj8vmIbLbVowKAxmt2Dmj4HqrJycmIiDjttNMiImLv3r3x9NNPx4YNG6b7nHXWWXHmmWfGnj17IiJiz549cc4550yHqYiI888/P6ampuL++++v+nmefPLJmJqamvUHoBcVixHDwxHr10ds2pQ+Dg+n7QBAfTU0UB0+fDhGR0fjta99bbzkJS+JiIgDBw7EkiVLYvny5bP6DgwMxIEDB6b7zAxTleuVa9Vs27Yt+vv7p/8MDQ3V+asBaH/FYsTISMT+/bPbJybSdqEKAOqroYFqy5Ytcd9998XnPve5Rn6aiIi48sorY3JycvrP+Ph4wz8nQDsplyO2bo2otpC70jY6mvYDAOqjYYHqsssuixtvvDFuu+22yOVy0+0rV66Mp556Kg4dOjSr/8GDB2PlypXTfeZW/as8r/SZa+nSpdHX1zfrD0AvGRubPzM1U5JEjI+n/QCA+qh7oEqSJC677LL4whe+ELfeemusWbNm1vVXvOIV8ZznPCduueWW6bYHHnggHnrooVi7dm1ERKxduzbuvffeePjhh6f73HzzzdHX1xdnn312vYcM0BVKpfr2AwCObXG9P+CWLVvihhtuiL/927+N5z73udN7nvr7+2PZsmXR398fl156aVx++eVx2mmnRV9fX/ze7/1erF27Nl796ldHRMQb3vCGOPvss+Mtb3lLXHPNNXHgwIH4wz/8w9iyZUssXbq03kMG6AqDg/XtBwAcW93Lpmcymartn/70p+O3fuu3IiI92Ped73xn7Ny5M5588sk4//zz4+Mf//is5Xw//OEP4x3veEfs3r07TjnllLjkkkvi6quvjsWLF5YBlU0Hek25nFbzm5iovo8qk4nI5SL27VNCHYDu1ewc0PBzqFpFoAJ6UaXKX8TsUFW517VrV8SMc9QBoOt03TlUADRPoZCGptWrZ7fncsIUADRC3fdQAdBahULExo1pNb9SKd0zlc9b5gcAjSBQAXShbDZi3bpWjwIAup8lfwAAADUyQwXQAcplS/gAoB0JVABtrliM2Lo1Yv/+I225XMSOHYpMAECrWfIH0MYqZdBnhqmI9KypkZH0OgDQOgIVQJsql9OZqWqnBVbaRkfTftCpyuWI3bsjdu5MH72egU4jUAG0qbGx+TNTMyVJxPh42g86UbEYMTwcsX59xKZN6ePwsJlXoLMIVABtqlSqbz9oJ5azAt1CoAJoU4OD9e0H7cJyVqCbCFQAbSqfT6v5ZTLVr2cyEUNDaT/oJJazAt1EoAJoU9lsWho9Yn6oqjzfvt15VHQey1mBbiJQAbSxQiFi166I1atnt+dyabtzqOhElrMC3SSTJNVWMHe+qamp6O/vj8nJyejr62v1cABOSLmcLn8qldI3mfm8mSk6V7mcVvObmKi+jyqTSW8a7NvndQ4cv2bngMUN/wxQZ736xrJXv25S2WzEunWtHgXUR2U568hIGp5mhirLWYFOY8kfHaVXzyzp1a8b6F6WswLdwpI/OkblzJK5r9jK3cxu/QXcq1830BvMvgP11uwcIFDRESrr7Y9WZrdb19v36tcNAFCrZucAS/7oCL16Zkmvft0AAJ1CoKIj9OqZJb36dQMAdAqBio7Qq2eW9OrXDQDQKQQqOkI+n+4VqhRimCuTiRgaSvt1k179ugEAOoVARUeonFkSMT9cdPOZJb36dQMAdAqBio7Rq2eW9OrXDQDQCZRNp+P06pklvfp1AwAcj2bngMUN/wxQZ9lsxLp1rR5F8/Xq1w0A0M4s+QMAAKiRQAUAAFAjS/4AAIAFsad7PoEKAAA4pmIxYuvWiP37j7TlcukRL71cddiSPwAA4FkVixEjI7PDVETExETaXixW/3vlcsTu3RE7d6aP5XKjR9p8AhUAAHBU5XI6M1XtsKVK2+jo/LBULEYMD0esXx+xaVP6ODx89PDVqQQqAADgqMbG5s9MzZQkEePjab+KWme0OpFABQAAHFWpdHz9ap3R6lQCFQAAcFSDg8fXr5YZrU4mUAEAAEeVz6fV/DKZ6tczmYihobRfxPHPaHU6gQoAADiqbDYtjR4xP1RVnm/ffuQ8quOd0ep0AhUAAPCsCoWIXbsiVq+e3Z7Lpe0zz6E63hmtTudgXwAA4JgKhYiNG9O9T6VSOsOUzx+ZmaqozGiNjKThaWZximozWp1OoAIAABYkm41Yt+7Y/SozWlu3zi5QkculYWrmjFanE6gAAIC6W+iMVqcTqAAAgIZY6IxWJ1OUAgAAoEYCFQAAQI0EKgAAgBoJVAAAADUSqAAAAGokUAEAANRIoAIAAKiRQAUAAFAjgQoAAKBGAhUAAECNFrd6AAAAAMejXI4YG4solSIGByPy+YhstjVjEagAAICOUSxGbN0asX//kbZcLmLHjohCofnjseQPAADoCMVixMjI7DAVETExkbYXi80fk0AFAAC0vXI5nZlKkvnXKm2jo2m/ZhKoAACAtjc2Nn9maqYkiRgfj/jKV5o3pgiBCgAA6ACl0sL6HTjQ2HHMJVABAABtb3BwYf1WrmzsOOYSqAAAgLaXz6fV/DKZ6tczmYihoYjXvKa54xKoAACAtpfNpqXRI+aHqsrz7dubfx6VQAUAAHSEQiFi166I1atnt+dyaXsrzqFysC8AANAxCoWIjRvTqn+lUrq3Kp9v/sxUhUAFACxIudw+b2CA3pbNRqxb1+pRpAQqAOCYisX0QM2ZZ8Dkcul+hlYssQFoF/ZQAQDPqliMGBmZf6DmxETaXiy2ZlwA7UCgAgCOqlxOZ6aSZP61StvoaNoPoBcJVADAUY2NzZ+ZmilJIsbH034AvUigAgCOqlSqbz+AbiNQAQBHNThY334A3UagAgCOKp9Pq/llMtWvZzIRQ0NpP4BeJFABAEeVzaal0SPmh6rK8+3bnUcF9C6BCgB4VoVCxK5dEatXz27P5dJ251ABvczBvgDAMRUKERs3ptX8SqV0z1Q+b2YKQKACABYkm41Yt675n7dcFuSA9iVQAQBtq1hMDxaeeRZWLpfu67LUEGgH9lABAG2pWIwYGZl/sPDERNpeLLZmXAAzCVQAQNspl9OZqSSZf63SNjqa9gNoJYEKAJilXI7YvTti5870sRWhZWxs/szUTEkSMT6e9gNoJXuoAIBp7bJnqVSqbz+ARjFDBQBERHvtWRocrG8/gEYRqACAttuzlM+nM2OZTPXrmUzE0FDaD6CVBKqfaYf14gDQKu22ZymbTZcZRswPVZXn27c7jwpoPYEq0iUMw8MR69dHbNqUPg4PK8cKQO9oxz1LhULErl0Rq1fPbs/l0nbnUAHtoOeLUlTWi89d4lBZL+4HNgC9oF33LBUKERs3pjNjpVL6+fN5M1NA+8gkSbXV0p1vamoq+vv7Y3JyMvr6+qr2KZfTmaijLXHIZNK7YPv2+cENQHer/E6cmKi+j8rvRKBTLCQH1FNPL/lrt/XiANAq9iwB1KanA1U7rhcHgFZZ6J4lhZwAjujpPVTtul4cAFrlWHuW2uXgX4B2YQ/VsPXiALAQRyvkVFkSqJAT0A7soWoi68UBYGHa7eBfgHbR04EqwhkXALAQCjkBVNfTe6gqnHEBAM9OISeA6gSqn8lmI9ata/UoAKA9KeQEUF3PL/kDAI4tn0+Xw8/dc1yRyUQMDaX9AHqJQAUAHJNCTgDVCVQAwIIo5AQwnz1UAMCCKeQEMJtABQAcF4WcAI6w5A8AAKBGZqigQcplS2IAALqdQAUNUCxGbN0asX//kbZcLq2QZdM2AED3sOQP6qxYjBgZmR2mIiImJtL2YrE14wIAoP4EKqijcjmdmUqS+dcqbaOjaT8AADqfQAV1NDY2f2ZqpiSJGB9P+wEA0PkEKqijUqm+/QAAaG8CFdTR4GB9+wEA0N4EKqijfD6t5pfJVL+eyUQMDaX9AADofAIV1FE2m5ZGj5gfqirPt293HhXQ3srliN27I3buTB8V0gE4OoEK6qxQiNi1K2L16tntuVza7hwqoJ0VixHDwxHr10ds2pQ+Dg878oH2IvTTTjJJUq3Ac+ebmpqK/v7+mJycjL6+vlYPhx5ULqfV/EqldM9UPm9mCmhvlXP05r4zqMywuylEOygW0yNKZlbVzeXSFSJen0Q0PwcIVABAlMvpTNTRjn7IZNI3rfv2uTlE6wj9LESzc4AlfwCAc/Roe+VyOjNVbSqg0jY6avkfzSdQAQDO0aPtCf20K4EKAHCOHm1P6KddCVQAgHP0aHtCP+1KoAIAnKNH2xP6aVcCFQAQEc7Ro70J/bQrZdMBgFmco0c7q3YO1dBQGqaEfiKcQ1U3AhUAQHcS+nk2zc4Bixv+GQCAnucNMPWUzUasW9fqUUBKoAIAGqraEq1cLt0PY4kW0OkUpQAAGqZYjBgZmX8g68RE2l4stmZcAPUiUAEADVEupzNT1XZrV9pGR9N+AJ1KoAIAGmJsbP7M1ExJEjE+nvYD6FT2UAE8CxvpoXalUn37AbQjgQrgKGykhxMzOFjffgDtyJI/gCpspIcTl8+nNyEymerXM5n0QNZ8vrnjAqgngQpgDhvpoT6y2XRGN2J+qKo8377dMlqgswlUAHPYSA/1UyhE7NoVsXr17PZcLm23fBbodPZQAcxhIz3UV6EQsXGjAi9AdxKoAOawkR7qL5uNWLeu1aMAqD9L/gDmeM1rjn3nPJtN+wEAvU2gApjjK185dsGJcjntBwD0NoEKYA57qACAhRKoAOawhwoAWCiBCmAOh5ECAAslUAHM4TBSAGChBKrjUC5H7N4dsXNn+nisTetA53IYKQCwEM6hWqBiMWLr1oj9+4+05XLpXWxvrKA7OYyUXlAue43P5XsCHI9MkiRJqwfRCFNTU9Hf3x+Tk5PR19d3Qh+rWIwYGYmY+52qLP1xtxqATuRm4Xy+J9D56pkDFkKgOoZyOWJ4ePYP1pkymfQH7b597l4B0DncLJzP9wS6Q7MDVcP3UF199dWRyWRidHR0uu2JJ56ILVu2xOmnnx6nnnpqXHjhhXHw4MFZf++hhx6KCy64IE4++eRYsWJFvPvd745nnnmm0cOdZ2zs6GEqIv2hOz6e9gOATlAup7Mw1W6pVtpGR3trr7DvCVCrhgaqu+++Oz75yU/GL/zCL8xq/4M/+IP40pe+FJ///Ofj9ttvjx/96EdRmHHLp1wuxwUXXBBPPfVUfOUrX4nPfOYzcf3118dVV13VyOFW5YBPALqNm4Xz+Z4AtWpYoHr00Udj8+bN8Rd/8RfxvOc9b7p9cnIy/vIv/zI+8pGPxH/6T/8pXvGKV8SnP/3p+MpXvhJf/epXIyLin/7pn+Lb3/52/NVf/VW87GUvize+8Y3xwQ9+MD72sY/FU0891aghV+WATwC6jZuF8/meALVqWKDasmVLXHDBBbFhw4ZZ7Xv37o2nn356VvtZZ50VZ555ZuzZsyciIvbs2RPnnHNODAwMTPc5//zzY2pqKu6///6qn+/JJ5+MqampWX/qodsP+FQKHqD3uFk4n+8JUKuGBKrPfe5z8fWvfz22bds279qBAwdiyZIlsXz58lntAwMDceDAgek+M8NU5XrlWjXbtm2L/v7+6T9DQ0N1+Eq6+4DPYjEtuLF+fcSmTenj8HDaDkD36vabhbXwPQFqVfdANT4+Hlu3bo3PfvazcdJJJ9X7wx/VlVdeGZOTk9N/xsfH6/axu/GAz0olo7nrxScm0nahCqB7dfPNwlr5ngC1qnug2rt3bzz88MPxi7/4i7F48eJYvHhx3H777XHttdfG4sWLY2BgIJ566qk4dOjQrL938ODBWLlyZURErFy5cl7Vv8rzSp+5li5dGn19fbP+1FOhEPGDH0TcdlvEDTekj/v2dWaYUskIgG68WXiifE+AWiyu9wd8/etfH/fee++stre+9a1x1llnxXvf+94YGhqK5zznOXHLLbfEhRdeGBERDzzwQDz00EOxdu3aiIhYu3ZtfOhDH4qHH344VqxYERERN998c/T19cXZZ59d7yEvWDYbsW5dyz593RxPJaNu+HoBqK5QiNi4Mf15Xyql+4Py+d6ehfE9AY5X3QPVc5/73HjJS14yq+2UU06J008/fbr90ksvjcsvvzxOO+206Ovri9/7vd+LtWvXxqtf/eqIiHjDG94QZ599drzlLW+Ja665Jg4cOBB/+Id/GFu2bImlS5fWe8g9RyUjACq65WZhPfmeAMej7oFqIf7sz/4sFi1aFBdeeGE8+eSTcf7558fHP/7x6evZbDZuvPHGeMc73hFr166NU045JS655JL4n//zf7ZiuF1HJSOAdFmzWQgATlQmSartpOl8U1NT0d/fH5OTk3XfT9XpyuW0mt/ERPV9VJlMul583z5vLoDuVCyme0lnLn/O5dKiBPbJAHS2ZueAhp1DRftSyQjoZaqcAlBPAlWPUskI6EWqnAI0R7kcsXt3xM6d6WM3/1xtyR4q2oNKRkCvUeUUoPF6bVm1QNXjVDICeokqpwCNVVlWPXclQGVZdTeuhLLkD4CeocopQOP06rJqgQqAnpHPp8tO5hbkqchkIoaG0n4AHJ/jWVbdTQQqAHqGKqcAjdOry6oFKgB6iiqnAI3Rq8uqHewLQE8ql1U5BaincjlieDgtQFEtYWQy6c2rffsa+/O22TlAlT8AepIqpwD1VVlWPTKShqeZoaqbl1Vb8gcAANRFLy6rNkMFAADUTaEQsXFj7yyrFqgAAIC66qVl1Zb8AQAA1MgMFQAAnABVQ3ubQAUAADUqFiO2bo3Yv/9IWy6XVrvrxgIMzGfJHwAA1KBYTEuEzwxTEek5TCMj6XW6n0DVIcrliN27I3buTB/L5VaPCACgd5XL6cxUtQNsK22jo96z9QJL/jqAqeSFsX4ZAGiWsbH5M1MzJUnE+Hjar12q3Xmv1BhmqNqcqeSFKRYjhocj1q+P2LQpfRwe9v0BABqjVKpvv0bzXqlxBKo2Zip5YYROAKDZBgfr26+RvFdqrEySVHu73vmmpqaiv78/Jicno6+vr9XDqcnu3endg2O57bb2mUputnI5vbtytCn3TCZdHrlvnyltAKB+Ku9BJiaq3/ye+x6kVcvtevG9UrNzgBmqNtZpU8mtcDzrlwEA6iWbTfezR6ShZKbK8+3b036tXG7nvVLjCVRtrJOmkltF6AQAWqVQiNi1K2L16tntuVzaXii0frmd90qNp8pfG8vn03+Qx5pKzuebP7Z2IXQC0A5UT+tdhULExo3V//8faz98JpPuh9+4sXGvF++VGs8MVRs7nqnkXlUJnXO/PxWZTMTQUG+HTgAaS/U0stl0P/vFF6ePlfdm7bDcznulxhOo2txCppJ7mdDJTA7ABpqt1cu5aG/tsNzOe6XGE6g6QKEQ8YMfpNX8brghfdy3T5iqEDqJcIcYaD7Hm3As7bLcznulxlI2na5h/XrvqtwhnvvTrHLnzS8LoBEcb8KxHG9p9WaMpxfeKzU7ByhKQdeorF+mt7TDhl+gN7XDci7aW2W53chI+vto5u+qViy3816pMSz5AzpaO2z4BXpTuyznor1Zbtf9zFABHc0dYqBVHG/CQj1baXU6n0AFdDR3iIFWabflXLQ3y+26lyV/QEdzvgbQSpZzAWaogI7mDjHQapZzQW8TqICOV7lDvHXr7AIVuVwaptwhBhrNci7oXQIV0BXcIQYAWkGgArqGO8QAQLMpSgEAAFAjgQoAAKBGAhUAAECNBCoAAIAaCVQAAAA1EqgAAABqJFABAADUSKACAACokUAFAABQI4EKAACgRgIVAABAjRa3egAAAEdTLkeMjUWUShGDgxH5fEQ22+pRARwhUAEAbalYjNi6NWL//iNtuVzEjh0RhULrxgUwkyV/AEDbKRYjRkZmh6mIiImJtL1YbM24AOYSqGYolyN2747YuTN9LJdbPSIA6D3lcjozlSTzr1XaRkf9ngbag0D1M8VixPBwxPr1EZs2pY/Dw+6AAUBEc286jo3Nn5maKUkixsfTfgCtJlCFZQUA8GyafdOxVKpvP4BG6vlAZVkBABxdK246Dg7Wtx9AI/V8oLKsAACqa9VNx3w+reaXyVS/nslEDA2l/QBarecDlWUFAFBdq246ZrNpafSI+aGq8nz7dudRAe2h5wOVZQUAUF0rbzoWChG7dkWsXj27PZdL251DBbSLnj/Yt7KsYGKi+pKGTCa9blkBAL2m1TcdC4WIjRvTGbBSKf08+byZKaC99HygqiwrGBlJw9PMUGVZAQC9rB1uOmazEevWNe7jA5yonl/yF2FZAQBUYy8TwLFlkqTaPafONzU1Ff39/TE5ORl9fX0L+jvlsmUFADBXsZhW+5tZoGJoKA1TbjoC7aaWHHAiBCoA4JjcdAQ6RbNzQM/voQIAjs1eJoDq7KECAACokUAFAABQI4EKAACgRgIVAABAjQQqAACAGglUAAAANRKoAAAAaiRQAQAA1EigAgAAqJFABQAAUCOBCgAAoEYCFQAAQI0EKgAAgBoJVAAAADUSqAAAAGokUAEAANRIoAIAAKjR4lYPAGiscjlibCyiVIoYHIzI5yOy2VaPCgCgOwhU0MWKxYitWyP27z/SlstF7NgRUSi0blwAAN3Ckj/oUsVixMjI7DAVETExkbYXi60ZFwBANxGooAuVy+nMVJLMv1ZpGx1N+wEAUDuBCrrQ2Nj8mamZkiRifDztBwBA7QQq6EKlUn37AQBQnUAFXWhwsL79AACoTqCCLpTPp9X8Mpnq1zOZiKGhtB8AALUTqKALZbNpafSI+aGq8nz79t48j6pcjti9O2LnzvRRYQ4A4EQIVNClCoWIXbsiVq+e3Z7Lpe29eA5VsRgxPByxfn3Epk3p4/CwEvIAQO0ySVKtsHLnm5qaiv7+/picnIy+vr5WD6euyuW0OluplO6Byed7c6aBhfF6SVXO5Zr7E68yY9erIRMAuk2zc4BA1WGKxfR8oZklsXO5dHmXN4NQXbmczkQdrZR8JpP+O9q3rzfDJgB0k2bnAEv+OkjlDvvcN4UTE2m7ZUtQnXO5AIBGEag6RLmczkxVm0+stI2O2mAP1TiXCwBoFIGqQ7jDDrVzLhcA0CgCVYdwhx1q51wuAKBRBKoO4Q47LEy1c6acywUANIpA1SHcYYdje7ZzppzL1bsc5gxAIy1u9QBYmMod9pGRNDzNLE7hDjsc/ZypShXMSmjauNG5XL3EURMANJpzqDpMtTcHQ0NpmPLmgF7lnCmqcZgzQG9ysG+ddGugikjfPLrDDkfs3p0u7zuW226LWLeu0aOhHQjZAL2r2TnAkr8OlM16UwgzqYLJXMdz1ISfpwCcCEUpgI6nCiZzCdkANItABXQ8VTCZS8gGoFkEKqDjOWeKuYTs3qEsPtBqAhXQFZwzxUxCdm94trPnAJpFlT+gq6iCyUyOmuheyuIDR6Nsep0IVABECNndSFl84Nkomw4AdeSoie6jLD6t4gYN1QhUAEBHURafVqi2hDiXS/drWl7a2xSlAAA6irL4NFtlz97cmdGJibRdIZTeJlABAB1FWXyaqVxOZ6aqVR2otI2OKtnfywQqAKCjKItPMx3Pnj16k0AFAHQcZ8/RLPbscSyKUgAAHalQiNi4UdU1GsuePY5FoAIAOpay+DRaZc/exET1fVSVc8/s2etdlvwBAMBR2LPHsQhUAADwLOzZ49lY8scs5afKce/Hx+Lx/1eKk184GOf8bj6yS9xyAQB6mz17HI1AxbSvvqcYZ35ka7ysfKQ26I/elYuHLt8Rr77GrRcAoLfZs0c1lvwREWmYetWfjsTK8uyDFlaWJ+JVfzoSX32PI8ABAGAuM1RE+alynPmRrRGRzEvYiyKJwxHxH/7326P8mudE9nl9EaecEnHyyelj5b+XLTv6kfUAANClBCri3o+PzVrmN9eiiDjt8L9G/PqvHf2DZDJpsJobtOrx36ecEnHSSQIbAABtR6AiHv9/dTjaO0kiHnss/fPjH5/4x5urEtjqGdRm/rfABtAVymVFA4DmEqiIk1+4sKO9S+f/Vgy+dEXE448fCU9H++/K82on4NViZmBrhJmBrRGhTWAD2kC3h41iMWLr1oj9MxZd5HLpGULKWgONkkmSer3jbS9TU1PR398fk5OT0dfX1+rhtLXyU+U4ePJwrCxPxKKY/3I4HJkoZXOx8vF9x1dCPUkinnji2YPX8f73zOf1DGyNNjew1Tu0CWzAMXR72CgWI0ZG5v9aqPxodFYQtJ9G3eRpdg4QqIiII1X+ImJWqDoc6W+iu969q/1Kp88MbPUMbZ0Y2BYtmr+HrZ6hbelSgQ06WLeHjXI5Ynh4dlicKZNJw+O+fd01IwedrJE3eQSqOhGojl/lHKpVMwpUTGSHYvzy7e0XppohSSJ++tP6B7WZ/90pKoHtWMVDag1tAhs0TC+Ejd27I9avP3a/225zhhC0g0bf5Gl2DrCHimmvvqYQT31gY+x691g8+mApTn3RYPzan+Zj9bIO/Q17omZWLjzjjPp//GqBrd6hrV4OH4549NH0TyPMDGyNCG0CGz1sbOzoYSoi/VE0Pp7269SwUVpgbaWF9gMap1xOZ6aqTekkSfrrenQ0YuPGzrnJI1AxLZ16zcb+/evShn+KyP1t96yvbzvNCmzPtg/tRJdE1kszAtvxlOk/3tC2ZInARtvqhbAxuLDaSgvuBzRON97kEaiIiKNPvU5MpO2dvr6+J80MbI1w+PDR97DVI7TVO7D95Cfpn0bIZmsv67+QfgIbJ6AXwkY+ny5bnJiofte7sqwxn2/+2IDZuvEmT88Fqm4vGVuLbpx6pQlmFsJ4/vPr//EPH174HrZaQttPf1q/sZbLzQtsJ1pgpNq1GgJbt/wsLT9Vjns/PhaP/79SnPzCwTjnd/PHV820A/RC2Mhm09UUIyPp1zPz66y8tLdv78zXKHSbbrzJ01NFKbq9ZGytbOalJ1UCWy0l+xfy3/UMbI2WzR5XOLv/h6fErr8/JX40eXI8FqfEY3FKnHLGyfG20VPiP76pyt95znPacoatWiGeH2Vz8dDlO7quEE9lFUJE9bDRLasQqv2eHxpKw1Q3fH3QDSqFco51k+dECuWo8lcnc7+R3V4y9kTs3BmxadOx+91wQ8TFFzd+PNAVZga2Ez1zrdp/d2Jgq2cp/7lLIo/TkaMiklg0o72tj4o4Qb0SNrpl9hS6WaNv8ghUdTLzG3nKKX1dXzL2RJihgg50+PCRvWaNCG1PPNHqr3DhFi8+rkB2eOmy+MkH/nf0JZNRbd6s5sPMO4CwAbSLRt7kEajqZOY38utf7xMYnkUzpl6BDlMJbDOC1t47Hot3bXn8Z4v8HouTo/p///obHo+Vzz1GgOuAwHbPn90WLxtd1+ph0AMEXXpVo177zqFqgG6sJlJPNvMC8yxaFHHqqemfn/nevRG7F/BXl//WApYHl8sntiTyWLNudQhsj/+/Hv2lQFPZ300vy2a7YzKjJwJVN1YTqbdCIV2vWu2HeretrwdqU9efpdnsvMBWV+XyUZdEfv+Gr8a/+8v3HfNDnPzCHv6lQFM4sgS6Q08s+avsobKk7dgsOwCOpluWB5efKsfBk4djZXkiFsX8L6Sb91DRPir/nuzvhvpr9pK/Rcfu0vkqS9oi5lfutaRttsrU68UXp4++J0BFt/wszS7JxkOXp1/I4TllKSrPxy/f3vNhqlxOixbt3Jk+lsutHlF3GRs7epiKSG9ajI+n/YD21hOBKuLIkrbVq2e353Km1AEWqlt+lr76mkLc9e5dcSA7+wspZXNdWTL9eBWL6ezJ+vXpsRrr16fPi8VWj6x72N8N3aMhgWpiYiJ+8zd/M04//fRYtmxZnHPOOfG1r31t+nqSJHHVVVfF4OBgLFu2LDZs2BAPPvjgrI/xyCOPxObNm6Ovry+WL18el156aTz66KMnNK5CIeIHP0ir+d1wQ/q4b1/nvAEAaAfd8rP01dcUYuDxH8Q9f3ZbfOWyG+KeP7stVj6+T5j62b6eubMnlX09QlV92N9NI5lhbq6676H6t3/7t3j5y18e69evj3e84x3x/Oc/Px588MF44QtfGC984QsjIuLDH/5wbNu2LT7zmc/EmjVr4v3vf3/ce++98e1vfztOOumkiIh44xvfGKVSKT75yU/G008/HW9961vjla98Zdxwww0LGkez104CNIN9jjSSfT3N0y17Emk/Kkd2wTlUV1xxRfzzP/9zjB1l0W+SJLFq1ap45zvfGe9617siImJycjIGBgbi+uuvj4suuii+853vxNlnnx133313nHvuuRERcdNNN8Wb3vSm2L9/f6xateqY4xCogG7jlySN5qD35qrMBkZUP7Kkk5bR0h6OVjmy115THV+U4u/+7u/i3HPPjd/4jd+IFStWxMtf/vL4i7/4i+nr+/btiwMHDsSGDRum2/r7++O8886LPXv2RETEnj17Yvny5dNhKiJiw4YNsWjRorjzzjurft4nn3wypqamZv0B6BaWYdEM9vU0V7fsSaQ9lMvpTbdqUyWVttFRy/8aoe6B6vvf/3584hOfiBe96EXxj//4j/GOd7wjfv/3fz8+85nPRETEgQMHIiJiYGBg1t8bGBiYvnbgwIFYsWLFrOuLFy+O0047bbrPXNu2bYv+/v7pP0NDQ/X+0gBawi9JmsW+nubrlj2JtJ7Kka1T94N9Dx8+HOeee278yZ/8SUREvPzlL4/77rsvrrvuurjkkkvq/emmXXnllXH55ZdPP5+amhKqgK5wPL8kLcPiROTz6ezIsfb15PPNH1s3qxxZAifCDHPr1H2GanBwMM4+++xZbS9+8YvjoYceioiIlStXRkTEwYMHZ/U5ePDg9LWVK1fGww8/POv6M888E4888sh0n7mWLl0afX19s/4AdAO/JGmWbjlrDHqRGebWqXugeu1rXxsPPPDArLbvfe978YIXvCAiItasWRMrV66MW265Zfr61NRU3HnnnbF27dqIiFi7dm0cOnQo9u7dO93n1ltvjcOHD8d5551X7yEDtDW/JGkm+3qgM1VmmOfeDKnIZCKGhswwN0Ldq/zdfffd8ZrXvCY+8IEPxH/+z/857rrrrnjb294Wn/rUp2Lz5s0RkZZNv/rqq2eVTf/Wt741r2z6wYMH47rrrpsum37uuecqmw70HOWVaQUl+qHzqByZ6viy6RERN954Y1x55ZXx4IMPxpo1a+Lyyy+Pt73tbdPXkySJP/qjP4pPfepTcejQoXjd614XH//4x+M//If/MN3nkUceicsuuyy+9KUvxaJFi+LCCy+Ma6+9Nk499dQFjUGgArqJX5IALES1IzaGhtLlur3ye6IrAlU7EKiAbuOXJAAL0eszzAJVnQhUQDfq9V+SAHAszc4BdS+bDkDjKK9MPQjmAPUjUAFAD6m2dDSXS8ulWzoKcPzqXjYdAGhPleImcw+KnphI24vF1owLoJMJVADQA8rldGaq2s7pStvoaNoPgIUTqACgB4yNzZ+ZmilJIsbH034ALJxABQA9oFSqbz8AUgIVAPSAwcH69gMgJVABQA/I59NqfplM9euZTHpQdD7f3HEBdDqBCgB6QDablkaPmB+qKs+3b3ceFcDxEqgAoEcUChG7dkWsXj27PZdL251DRa8olyN2747YuTN9VN2SE+FgXwDoIYVCxMaNaTW/UindM5XPm5midzjcmnrLJEm1Eyk639TUVPT398fk5GT09fW1ejgAALRY5XDrue9+K8tezdR2h2bnAEv+AADoeg63plEEKgAAup7DrWkUgQoAgK7ncGsaRaACAKDrOdyaRhGoAADoeg63plEEKgAAup7DrWkUgQoAgJ7gcGsawcG+AAD0DIdbU28CFQAAPSWbjVi3rtWjoFtY8gcAAFAjgQoAAKBGAhUAAECNBCoAAIAaCVQAAAA1EqgAAABqJFABAADUyDlUVZTLDnsDAACOTaCao1iM2Lo1Yv/+I225XMSOHenJ2gAAABWW/M1QLEaMjMwOUxERExNpe7HYmnEBAHSKcjli9+6InTvTx3K51SOCxhKofqZcTmemkmT+tUrb6KgfCgAAR1MsRgwPR6xfH7FpU/o4POymNN1NoPqZsbH5M1MzJUnE+HjaDwCA2az0oVcJVD9TKtW3HwBAr7DSh14mUP3M4GB9+wEA9AorfehlAtXP5PNpNb9Mpvr1TCZiaCjtBwDAEVb60MsEqp/JZtPS6BHzQ1Xl+fbtzqMCAJjLSh96mUA1Q6EQsWtXxOrVs9tzubTdOVQAAPNZ6UMvc7DvHIVCxMaN6RrfUim9k5LPm5kCADiaykqfkZE0PM0sTmGlD91OoKoim41Yt67VowAA6ByVlT5bt84uUJHLpWHKSh+6lUAFAEBdWOlDLxKoAACoGyt96DWKUgAAANRIoAIAAKiRQAUAAFAjgQoAAKBGAhUAAECNBCoAAIAaCVQAAAA1EqgAAABqJFABAADUSKACAACokUAFAABQo8WtHgAAzFQuR4yNRZRKEYODEfl8RDbb6lEBQHUCFQBto1iM2Lo1Yv/+I225XMSOHRGFQuvGBQBHY8kfAG2hWIwYGZkdpiIiJibS9mKxNeMCgGcjUAHQcuVyOjOVJPOvVdpGR9N+ANBOBCoAWm5sbP7M1ExJEjE+nvYDgHYiUAHQcqVSffsBQLMoSgEnQDUyqI/Bwfr2A4BmMUMFNSoWI4aHI9avj9i0KX0cHrZxHmqRz6fV/DKZ6tczmYihobQfALQTgQpqoBoZ1Fc2m5ZGj5gfqirPt283AwxA+xGo4DipRgaNUShE7NoVsXr17PZcLm13DhUA7cgeKjhOx1ONbN26pg0LukKhELFxo72JAHQOgQqOk2pk0FjZrJsRAHQOS/7gOKlGBgBAhUAFx0k1MgAAKgQqOE6qkQEAUCFQQQ1UIwMAIEJRCqiZamQAAAhUcAJUIwMA6G2W/AEAANRIoAIAAKiRQAUAAFAjgQoAAKBGAhUAAECNBCoAAIAaCVQAAAA1EqgAAABqJFABAADUSKACAACokUAFAABQI4EKAACgRgIVAABAjQQqAACAGglUAAAANRKoAAAAaiRQAQAA1GhxqwcA7ahcjhgbiyiVIgYHI/L5iGy21aMCAKDdCFQwR7EYsXVrxP79R9pyuYgdOyIKhdaNCwCA9mPJH8xQLEaMjMwOUxERExNpe7HYmnEBANCeBCr4mXI5nZlKkvnXKm2jo2k/AACIEKhg2tjY/JmpmZIkYnw87QcAABECFUwrlerbDwCA7idQwc8MDta3HwAA3U+ggp/J59NqfplM9euZTMTQUNoPAAAiBCqYls2mpdEj5oeqyvPt251HBQDAEQIVzFAoROzaFbF69ez2XC5tdw4VAAAzOdgX5igUIjZuTKv5lUrpnql83swU1FO57N8YAN1BoIIqstmIdetaPQroTsVieubbzGMKcrl0ya1ZYAA6jSV/ADRNsRgxMjL/zLeJibS9WGzNuACgVgIVAE1RLqczU0ky/1qlbXQ07QcAnUKgAqApxsbmz0zNlCQR4+NpPwDoFAIVAE1RKtW3HwC0A4EKgKYYHKxvPwBoBwIVAE2Rz6fV/OYenF2RyUQMDaX9AKBTCFQANEU2m5ZGj5gfqirPt293HhUAnUWgAqBpCoWIXbsiVq+e3Z7Lpe3OoQKg0zjYF4CmKhQiNm5Mq/mVSumeqXzezBQAnUmgAqDpstmIdetaPQoAOHGW/AEAANRIoAIAAKiRQAUAAFAjgQoAAKBGilIAAG2rXFYREmhvAhUA0JaKxYitWyP27z/SlsulB0Q7swxoF5b8AQBtp1iMGBmZHaYiIiYm0vZisTXjaoVyOWL37oidO9PHcrnVIwJmEqgAgLZSLqczU0ky/1qlbXS0N4JFsRgxPByxfn3Epk3p4/BwbwXKdiPgMpdABQC0lbGx+TNTMyVJxPh42q+bmaVrPwIu1QhUAEBbKZXq268TmaVrPwIuRyNQAQBtZXCwvv06kVm69iLg8mwEKgCgreTzaTW/TKb69UwmYmgo7detzNK1FwGXZyNQAQBtJZtNS6NHzA9Vlefbt3f3eVRm6dqLgMuzEaigA6goBPSaQiFi166I1atnt+dyaXu3n0Nllq69CLg8m0ySVFsN2vmmpqaiv78/Jicno6+vr9XDgZo52BLoZeVyuoyqVErfrObz3T0zNVOlCELE7L07lZDVC8GyXZTLaTW/iYnq+6gymfR38759vfP6bGfNzgFmqKCNqSgE9LpsNmLduoiLL04fe+nNaq/P0rUTy1B5NmaooE1V7oYdbROsu2EAvaGXZ+naTbVVI0NDaZgScNtHs3OAQAVtavfu9MDAY7nttvSuLdBc3uRCb/Jvv/01OwcsbvhnAGqiohC0L3sboXdVlqFChUAFbUpFIWhPlb2Nc9d3VPY2dtLelkbcaXf3Hug1ilJAm1IyF9pPuZzOTFVbLF9pGx3tjKMNisV0n+b69RGbNqWPw8MnVuymER8ToN0JVNCmVBSC9jM2dvRCMRFpqBofT/u1s0ZUEFWVFOhVAhW0MSVzob10w97GRsyyddPMHcDxEqigzRUKET/4QVrN74Yb0sd9+4QpaIVu2NvYiFm2bpm5A6iFohTQAVQUgvZQ2ds4MVF9NqZyPlw7721sxCxbN8zcAdTKDBUALFA37G1sxCxbN8zcAdRKoAKA49DpexsbUUFUVVKglwlUAHCcOnlvYyNm2bph5g6gVgIVx6Vcjti9O2LnzvRRxSagV1X2Nl58cfrYSWGhEbNsnT5zB1CrTJJU21bb+aampqK/vz8mJyejr6+v1cPpCsViWhZ3ZiWnXC69K+kXJUDnKZfTynulUrq/KZ8/8WDYiI8JcDyanQMEKhakcmDj3FdLZSmHu48AALSDZucAS/44Jgc2AgBAdXUPVOVyOd7//vfHmjVrYtmyZfHCF74wPvjBD8bMibAkSeKqq66KwcHBWLZsWWzYsCEefPDBWR/nkUceic2bN0dfX18sX748Lr300nj00UfrPVwWwIGNAABQXd0D1Yc//OH4xCc+EX/+538e3/nOd+LDH/5wXHPNNfHRj350us8111wT1157bVx33XVx5513ximnnBLnn39+PPHEE9N9Nm/eHPfff3/cfPPNceONN8Ydd9wRb3/72+s9XBbAgY0AAFBd3fdQ/cqv/EoMDAzEX/7lX063XXjhhbFs2bL4q7/6q0iSJFatWhXvfOc7413veldERExOTsbAwEBcf/31cdFFF8V3vvOdOPvss+Puu++Oc889NyIibrrppnjTm94U+/fvj1WrVh1zHPZQ1c/u3RHr1x+73223pZWuAACgVTp+D9VrXvOauOWWW+J73/teRER885vfjC9/+cvxxje+MSIi9u3bFwcOHIgNGzZM/53+/v4477zzYs+ePRERsWfPnli+fPl0mIqI2LBhQyxatCjuvPPOqp/3ySefjKmpqVl/qA8HNgIAQHWL6/0Br7jiipiamoqzzjorstlslMvl+NCHPhSbN2+OiIgDBw5ERMTAwMCsvzcwMDB97cCBA7FixYrZA128OE477bTpPnNt27YtPvCBD9T7yyGOHNg4MpKGp5lzmg5sBACgl9V9hupv/uZv4rOf/WzccMMN8fWvfz0+85nPxP/6X/8rPvOZz9T7U81y5ZVXxuTk5PSf8fHxhn6+XuPARgAAmK/uM1Tvfve744orroiLLrooIiLOOeec+OEPfxjbtm2LSy65JFauXBkREQcPHozBwcHpv3fw4MF42cteFhERK1eujIcffnjWx33mmWfikUcemf77cy1dujSWLl1a7y+HGQqFiI0bHdgIAAAVdZ+hevzxx2PRotkfNpvNxuHDhyMiYs2aNbFy5cq45ZZbpq9PTU3FnXfeGWvXro2IiLVr18ahQ4di7969031uvfXWOHz4cJx33nn1HjLHIZtNC09cfHH6KEwBANDL6j5D9au/+qvxoQ99KM4888z4+Z//+fjGN74RH/nIR+K3f/u3IyIik8nE6Oho/PEf/3G86EUvijVr1sT73//+WLVqVbz5zW+OiIgXv/jF8cu//Mvxtre9La677rp4+umn47LLLouLLrpoQRX+AAAAmqHugeqjH/1ovP/974/f/d3fjYcffjhWrVoV/+2//be46qqrpvu85z3vicceeyze/va3x6FDh+J1r3td3HTTTXHSSSdN9/nsZz8bl112Wbz+9a+PRYsWxYUXXhjXXnttvYcLAABQs7qfQ9UunEMFAAC9p+PPoQIAAOgVAhUAAECNBCoAAIAaCVQAAAA1EqgAAABqJFABAADUSKACAACokUAFAABQI4EKAACgRotbPQAAAKC9lMsRY2MRpVLE4GBEPh+RzbZ6VO1JoAIAAKYVixFbt0bs33+kLZeL2LEjolBo3bjalSV/AABARKRhamRkdpiKiJiYSNuLxdaMq50JVNDhyuWI3bsjdu5MH8vlVo8IAOhE5XI6M5Uk869V2kZHvdeYS6CCDlYsRgwPR6xfH7FpU/o4POzuEQBw/MbG5s9MzZQkEePjaT+OEKigQ5mSBwDqqVSqb79eIVBBBzIlDwDU2+Bgffv1CoEKOpApeTqR/X4A7S2fT6v5ZTLVr2cyEUNDaT+OEKigA5mSp9PY7wfQ/rLZtDR6xPxQVXm+fbvzqOYSqKADmZKnk9jvB9A5CoWIXbsiVq+e3Z7Lpe3OoZovkyTVdmF0vqmpqejv74/Jycno6+tr9XCgrsrl9O7+xET1fVSZTPqDb98+d5Forcpr9WhLVL1WAdpTuZxuHSiV0hu0+Xzn/Jxudg5Y3PDPANRdZUp+ZCR9QzozVJmSp50cz36/deuaNiwAjiGb9XN5oSz5gw5lSp5OYL8fAN3ODBV0sEIhYuPGzp2Sp/vZ7wdAtxOooMOZkqedVUrwHmu/nxK8AHQqS/4AaBgleAHodgIVAA1lvx8A3cySPwAazn4/ALqVQAVAU9jvB0A3suQPAACgRgIVAABAjQQqAACAGglUAAAANRKoAAAAaqTKH0CbKpeVGQeAdidQAbShYjFi69aI/fuPtOVyETt2OAgXANqJJX8AbaZYjBgZmR2mIiImJtL2YrE14wIA5hOoANpIuZzOTCXJ/GuVttHRtB8A0HoCFUAbGRubPzM1U5JEjI+n/QCA1hOoANpIqVTffgBAYwlUAG1kcLC+/QCAxhKoANpIPp9W88tkql/PZCKGhtJ+AEDrCVQAbSSbTUujR8wPVZXn27c7jwoA2oVABdBmCoWIXbsiVq+e3Z7Lpe3OoQKA9uFgX4A2VChEbNyYVvMrldI9U/m8mSkAaDcCFUCbymYj1q1r9SgAgGdjyR8AAECNBCoAAIAaCVQAAAA1EqgAAABqJFABAADUSKACAACokUAFAABQI4EKAACgRgIVAABAjQQqAACAGglUAAAANRKoAAAAaiRQAQAA1EigAgAAqJFABQAAUCOBCgAAoEYCFQAAQI0Wt3oAdJdyOWJsLKJUihgcjMjnI7LZVo8KAAAaQ6CiborFiK1bI/bvP9KWy0Xs2BFRKLRuXAAA0CiW/FEXxWLEyMjsMBURMTGRtheLrRkXAAA0kkDFCSuX05mpJJl/rdI2Opr2AwCAbiJQccLGxubPTM2UJBHj42k/AADoJgIVJ6xUqm8/AADoFAIVJ2xwsL79AACgUwhUnLB8Pq3ml8lUv57JRAwNpf0AAKCbCFScsGw2LY0eMT9UVZ5v3+48KgAAuo9ARV0UChG7dkWsXj27PZdL251DBQBAN3KwL3VTKERs3JhW8yuV0j1T+byZKQAAupdARV1lsxHr1rV6FAAA0ByW/AEAANRIoAIAAKiRQAUAAFAjgQoAAKBGAhUAAECNBCoAAIAaCVQAAAA1EqgAAABqJFABAADUSKACAACokUAFAABQI4EKAACgRgIVAABAjQQqAACAGglUAAAANRKoAAAAaiRQAQAA1EigAgAAqJFABQAAUCOBCgAAoEYCFQAAQI0EKgAAgBotbvUAoKJcjhgbiyiVIgYHI/L5iGy21aMCAICjE6hoC8VixNatEfv3H2nL5SJ27IgoFFo3LgDoZW52wrFZ8kfLFYsRIyOzw1RExMRE2l4stmZcANDLisWI4eGI9esjNm1KH4eH/V6GuQQqWqpcTmemkmT+tUrb6GjaDwBoDjc7YeEEKlpqbGz+D+uZkiRifDztBwA0npudcHwEKlqqVKpvPwDgxLjZCcdHUQpaanCwvv0AgBPjZmdvUoCkdmaoaKl8Pq3ml8lUv57JRAwNpf0AgMZzs7P3KEByYgQqWiqbTUujR8wPVZXn27e7QwIAzeJmZ29RgOTECVS0XKEQsWtXxOrVs9tzubTdOVQA1KJcjti9O2LnzvRREYWFcbOzdyhAUh8CFW2hUIj4wQ8ibrst4oYb0sd9+4QpAGpjCdOJcbOzNyhAUh+KUtA2stmIdetaPQoAOl1lCdPcu+6VJUwCwcIUChEbNypU0M0UIKkPgQoA6BrHWsKUyaRLmDZuFAwWws3O7qYASX1Y8gcAdA1LmGDhFCCpD4EKAOgaljDBwilAUh8CFQDQNSxhguOjAMmJyyRJtVXGnW9qair6+/tjcnIy+vr6Wj0cAKAJyuW0mt/ERPV9VJlM+kZx3z533WGmcrl7CpA0OwcoSgEAdI3KEqaRkTQ8zQxVljDB0SlAUjtL/gCArmIJE9BMZqgAgK7jDCWgWQQqAKArWcIENIMlfwAAADUSqAAAAGokUAEAANRIoAIAAKiRQAUAAFAjgQoAAKBGAhUAAECNBCoAAIAaOdgX6CjlcsTYWESpFDE4GJHPp4d3AgC0gkAFdIxiMWLr1oj9+4+05XIRO3ZEFAqtGxfQOm6yAK1myR/QEYrFiJGR2WEqImJiIm0vFlszLqB1isWI4eGI9esjNm1KH4eH/TwAmkugAtpeuZzOTCXJ/GuVttHRtB/QG9xkAdqFQAW0vbGx+W+aZkqSiPHxtB/Q/dxkAdqJQAW0vVKpvv2AzuYmC9BOBCqg7Q0O1rcf0NncZAHaiUAFtL18Pq3ml8lUv57JRAwNpf2A7ucmC9BOBCqg7WWzaWn0iPmhqvJ8+3alkqFXuMkCtBOBCugIhULErl0Rq1fPbs/l0nbnUEHvcJMFaCeZJKlWI6fzTU1NRX9/f0xOTkZfX1+rhwPUiUM8gYpqh30PDaVhyk0W6F3NzgECFQDQsdxkAeZqdg5Y3PDPAADQINlsxLp1rR4FvUaQZyaBCgAAFqjaUtNcLt3XZ6lpb1KUAgAAFqBYjBgZmX+w9MRE2l4stmZctJZABQAAx1AupzNT1aoPVNpGR9N+9BaBCgAAjmFsbP7M1ExJEjE+nvajtwhUAABwDKVSffvRPY47UN1xxx3xq7/6q7Fq1arIZDLxxS9+cdb1JEniqquuisHBwVi2bFls2LAhHnzwwVl9Hnnkkdi8eXP09fXF8uXL49JLL41HH310Vp9vfetbkc/n46STToqhoaG45pprjv+rAwCAOhgcrG8/usdxB6rHHnssXvrSl8bHPvaxqtevueaauPbaa+O6666LO++8M0455ZQ4//zz44knnpjus3nz5rj//vvj5ptvjhtvvDHuuOOOePvb3z59fWpqKt7whjfEC17wgti7d2/86Z/+afyP//E/4lOf+lQNXyIAAJyYfD6t5pfJVL+eyaQHS+fzzR0XrXdCB/tmMpn4whe+EG9+85sjIp2dWrVqVbzzne+Md73rXRERMTk5GQMDA3H99dfHRRddFN/5znfi7LPPjrvvvjvOPffciIi46aab4k1velPs378/Vq1aFZ/4xCfife97Xxw4cCCWLFkSERFXXHFFfPGLX4zvfve7Cxqbg30BAKinSpW/iNnFKSoha9cupdPbQUcf7Ltv3744cOBAbNiwYbqtv78/zjvvvNizZ09cdNFFsWfPnli+fPl0mIqI2LBhQyxatCjuvPPO+PVf//XYs2dP/NIv/dJ0mIqIOP/88+PDH/5w/Nu//Vs873nPm/e5n3zyyXjyySenn09OTkZE+g0FAIATtWFDxP/3/0W8970RP/rRkfZVqyKuvjq97q1n61Xe/5/AvNFxqWugOnDgQEREDAwMzGofGBiYvnbgwIFYsWLF7EEsXhynnXbarD5r1qyZ9zEq16oFqm3btsUHPvCBee1DQ0M1fjUAAHBsExMRb3lLq0fBXP/6r/8a/f39Df88dQ1UrXTllVfG5ZdfPv380KFD8YIXvCAeeuihpnwj6U1TU1MxNDQU4+PjlpbSMF5nNIPXGc3gdUYzTE5OxplnnhmnnXZaUz5fXQPVypUrIyLi4MGDMTijxMnBgwfjZS972XSfhx9+eNbfe+aZZ+KRRx6Z/vsrV66MgwcPzupTeV7pM9fSpUtj6dKl89r7+/v9g6Xh+vr6vM5oOK8zmsHrjGbwOqMZFi1qzglRdf0sa9asiZUrV8Ytt9wy3TY1NRV33nlnrF27NiIi1q5dG4cOHYq9e/dO97n11lvj8OHDcd555033ueOOO+Lpp5+e7nPzzTfHz/3cz1Vd7gcAANAKxx2oHn300bjnnnvinnvuiYi0EMU999wTDz30UGQymRgdHY0//uM/jr/7u7+Le++9N/7rf/2vsWrVqulKgC9+8Yvjl3/5l+Ntb3tb3HXXXfHP//zPcdlll8VFF10Uq1atioiITZs2xZIlS+LSSy+N+++/P/76r/86duzYMWtJHwAAQKsd95K/r33ta7F+/frp55WQc8kll8T1118f73nPe+Kxxx6Lt7/97XHo0KF43eteFzfddFOcdNJJ03/ns5/9bFx22WXx+te/PhYtWhQXXnhhXHvttdPX+/v745/+6Z9iy5Yt8YpXvCLOOOOMuOqqq2adVXUsS5cujT/6oz+qugwQ6sXrjGbwOqMZvM5oBq8zmqHZr7MTOocKAACglzVnpxYAAEAXEqgAAABqJFABAADUSKACAACoUVcGqo997GMxPDwcJ510Upx33nlx1113tXpIdJBt27bFK1/5ynjuc58bK1asiDe/+c3xwAMPzOrzxBNPxJYtW+L000+PU089NS688MJ5h1E/9NBDccEFF8TJJ58cK1asiHe/+93xzDPPNPNLoUNcffXV08dOVHiNUS8TExPxm7/5m3H66afHsmXL4pxzzomvfe1r09eTJImrrroqBgcHY9myZbFhw4Z48MEHZ32MRx55JDZv3hx9fX2xfPnyuPTSS+PRRx9t9pdCmyqXy/H+978/1qxZE8uWLYsXvvCF8cEPfjBm1j3zOuN43XHHHfGrv/qrsWrVqshkMvHFL35x1vV6vaa+9a1vRT6fj5NOOimGhobimmuuOf7BJl3mc5/7XLJkyZLk//yf/5Pcf//9ydve9rZk+fLlycGDB1s9NDrE+eefn3z6059O7rvvvuSee+5J3vSmNyVnnnlm8uijj073+Z3f+Z1kaGgoueWWW5Kvfe1ryatf/erkNa95zfT1Z555JnnJS16SbNiwIfnGN76R/P3f/31yxhlnJFdeeWUrviTa2F133ZUMDw8nv/ALv5Bs3bp1ut1rjHp45JFHkhe84AXJb/3WbyV33nln8v3vfz/5x3/8x+T//t//O93n6quvTvr7+5MvfvGLyTe/+c3k137t15I1a9YkP/3pT6f7/PIv/3Ly0pe+NPnqV7+ajI2NJf/+3//75OKLL27Fl0Qb+tCHPpScfvrpyY033pjs27cv+fznP5+ceuqpyY4dO6b7eJ1xvP7+7/8+ed/73pcUi8UkIpIvfOELs67X4zU1OTmZDAwMJJs3b07uu+++ZOfOncmyZcuST37yk8c11q4LVK961auSLVu2TD8vl8vJqlWrkm3btrVwVHSyhx9+OImI5Pbbb0+SJEkOHTqUPOc5z0k+//nPT/f5zne+k0REsmfPniRJ0h8CixYtSg4cODDd5xOf+ETS19eXPPnkk839AmhbP/nJT5IXvehFyc0335z8x//4H6cDldcY9fLe9743ed3rXnfU64cPH05WrlyZ/Omf/ul026FDh5KlS5cmO3fuTJIkSb797W8nEZHcfffd033+4R/+IclkMsnExETjBk/HuOCCC5Lf/u3fntVWKBSSzZs3J0nidcaJmxuo6vWa+vjHP54873nPm/V7873vfW/ycz/3c8c1vq5a8vfUU0/F3r17Y8OGDdNtixYtig0bNsSePXtaODI62eTkZEREnHbaaRERsXfv3nj66adnvc7OOuusOPPMM6dfZ3v27IlzzjknBgYGpvucf/75MTU1Fffff38TR08727JlS1xwwQWzXksRXmPUz9/93d/FueeeG7/xG78RK1asiJe//OXxF3/xF9PX9+3bFwcOHJj1Wuvv74/zzjtv1mtt+fLlce6550732bBhQyxatCjuvPPO5n0xtK3XvOY1ccstt8T3vve9iIj45je/GV/+8pfjjW98Y0R4nVF/9XpN7dmzJ37pl34plixZMt3n/PPPjwceeCD+7d/+bcHjWXyiX1A7+Zd/+Zcol8uz3mBERAwMDMR3v/vdFo2KTnb48OEYHR2N1772tfGSl7wkIiIOHDgQS5YsieXLl8/qOzAwEAcOHJjuU+11WLkGn/vc5+LrX/963H333fOueY1RL9///vfjE5/4RFx++eXx3//7f4+77747fv/3fz+WLFkSl1xyyfRrpdpraeZrbcWKFbOuL168OE477TSvNSIi4oorroipqak466yzIpvNRrlcjg996EOxefPmiAivM+quXq+pAwcOxJo1a+Z9jMq15z3veQsaT1cFKqi3LVu2xH333Rdf/vKXWz0Uusj4+Hhs3bo1br755jjppJNaPRy62OHDh+Pcc8+NP/mTP4mIiJe//OVx3333xXXXXReXXHJJi0dHt/ibv/mb+OxnPxs33HBD/PzP/3zcc889MTo6GqtWrfI6oyd01ZK/M844I7LZ7LxKWAcPHoyVK1e2aFR0qssuuyxuvPHGuO222yKXy023r1y5Mp566qk4dOjQrP4zX2crV66s+jqsXKO37d27Nx5++OH4xV/8xVi8eHEsXrw4br/99rj22mtj8eLFMTAw4DVGXQwODsbZZ589q+3FL35xPPTQQxFx5LXybL83V65cGQ8//PCs688880w88sgjXmtERMS73/3uuOKKK+Kiiy6Kc845J97ylrfEH/zBH8S2bdsiwuuM+qvXa6pev0u7KlAtWbIkXvGKV8Qtt9wy3Xb48OG45ZZbYu3atS0cGZ0kSZK47LLL4gtf+ELceuut86aCX/GKV8RznvOcWa+zBx54IB566KHp19natWvj3nvvnfUP+eabb46+vr55b27oPa9//evj3nvvjXvuuWf6z7nnnhubN2+e/m+vMerhta997bxjH773ve/FC17wgoiIWLNmTaxcuXLWa21qairuvPPOWa+1Q4cOxd69e6f73HrrrXH48OE477zzmvBV0O4ef/zxWLRo9lvKbDYbhw8fjgivM+qvXq+ptWvXxh133BFPP/30dJ+bb745fu7nfm7By/0iojvLpi9dujS5/vrrk29/+9vJ29/+9mT58uWzKmHBs3nHO96R9Pf3J7t3705KpdL0n8cff3y6z+/8zu8kZ555ZnLrrbcmX/va15K1a9cma9eunb5eKWn9hje8IbnnnnuSm266KXn+85+vpDVHNbPKX5J4jVEfd911V7J48eLkQx/6UPLggw8mn/3sZ5OTTz45+au/+qvpPldffXWyfPny5G//9m+Tb33rW8nGjRurlh5++ctfntx5553Jl7/85eRFL3qRctZMu+SSS5LVq1dPl00vFovJGWeckbznPe+Z7uN1xvH6yU9+knzjG99IvvGNbyQRkXzkIx9JvvGNbyQ//OEPkySpz2vq0KFDycDAQPKWt7wlue+++5LPfe5zycknn6xsepIkyUc/+tHkzDPPTJYsWZK86lWvSr761a+2ekh0kIio+ufTn/70dJ+f/vSnye/+7u8mz3ve85KTTz45+fVf//WkVCrN+jg/+MEPkje+8Y3JsmXLkjPOOCN55zvfmTz99NNN/mroFHMDldcY9fKlL30peclLXpIsXbo0Oeuss5JPfepTs64fPnw4ef/7358MDAwkS5cuTV7/+tcnDzzwwKw+//qv/5pcfPHFyamnnpr09fUlb33rW5Of/OQnzfwyaGNTU1PJ1q1bkzPPPDM56aSTkn/37/5d8r73vW9WKWqvM47XbbfdVvX92CWXXJIkSf1eU9/85jeT173udcnSpUuT1atXJ1dfffVxjzWTJDOOsQYAAGDBumoPFQAAQDMJVAAAADUSqAAAAGokUAEAANRIoAIAAKiRQAUAAFAjgQoAAKBGAhUAAECNBCoAAIAaCVQAAAA1EqgAAABqJFABAADU6P8HPsE2D50ttEgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Find two points that are approximately one-third of the image size apart\n",
    "min_distance = image_size[0] / 3\n",
    "max_distance = image_size[0] / 3 * 1.5\n",
    "\n",
    "def find_straight(points, min_distance, max_distance):\n",
    "    num_points = len(points)\n",
    "    for _ in range(1000):  # Try 1000 times to find suitable points\n",
    "        idx1, idx2 = np.random.choice(num_points, 2, replace=False)\n",
    "        p1, p2 = points[idx1], points[idx2]\n",
    "        distance = np.linalg.norm(np.array(p1) - np.array(p2))\n",
    "        if min_distance <= distance <= max_distance:\n",
    "            return p1, p2\n",
    "    return None, None\n",
    "\n",
    "p1, p2 = find_straight(points_list, min_distance, max_distance)\n",
    "\n",
    "# Plot the selected points and straight line\n",
    "fig, ax = plt.subplots(figsize=(image_size[0] / 100, image_size[1] / 100), dpi=100)\n",
    "ax.plot(points[:, 0], points[:, 1], 'bo')  # 'bo' for blue points without lines\n",
    "if p1 is not None and p2 is not None:\n",
    "    ax.plot([p1[0], p2[0]], [p1[1], p2[1]], 'ro-', lw=2)  # 'ro-' for red line with points\n",
    "ax.set_xlim(0, image_size[0])\n",
    "ax.set_ylim(0, image_size[1])\n",
    "ax.set_aspect('equal', 'box')\n",
    "plt.gca().invert_yaxis()  # Invert y-axis to have (0, 0) at top-left corner\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def lines_intersect(p1, p2, p3, p4):\n",
    "    def ccw(A, B, C):\n",
    "        return (C[1]-A[1]) * (B[0]-A[0]) > (B[1]-A[1]) * (C[0]-A[0])\n",
    "    return ccw(p1, p3, p4) != ccw(p2, p3, p4) and ccw(p1, p2, p3) != ccw(p1, p2, p4)\n",
    "\n",
    "def is_valid_track(track, new_point):\n",
    "    for i in range(len(track) - 1):\n",
    "        if lines_intersect(track[i], track[i + 1], track[-1], new_point):\n",
    "            return False\n",
    "    return True\n",
    "\n",
    "def build_track(points, start, end):\n",
    "    track = [start, end]\n",
    "    points = [p for p in points if p != start and p != end]\n",
    "    \n",
    "    while points:\n",
    "        last_point = track[-1]\n",
    "        points.sort(key=lambda p: np.linalg.norm(np.array(p) - np.array(last_point)))\n",
    "        for p in points:\n",
    "            if is_valid_track(track, p):\n",
    "                track.append(p)\n",
    "                points.remove(p)\n",
    "                break\n",
    "        else:\n",
    "            break  # If no valid point found, exit the loop\n",
    "\n",
    "    return track\n",
    "\n",
    "if p1 is not None and p2 is not None:\n",
    "    track = build_track(points_list, p1, p2)\n",
    "    track = np.array(track)\n",
    "\n",
    "    # Plot the track\n",
    "    fig, ax = plt.subplots(figsize=(image_size[0] / 100, image_size[1] / 100), dpi=100)\n",
    "    ax.plot(points[:, 0], points[:, 1], 'bo')  # 'bo' for blue points without lines\n",
    "    if track is not None:\n",
    "        ax.plot(track[:, 0], track[:, 1], 'k-', lw=2)  # 'k-' for black lines\n",
    "        ax.plot(track[:, 0], track[:, 1], 'ro')  # 'ro' for red points\n",
    "    ax.set_xlim(0, image_size[0])\n",
    "    ax.set_ylim(0, image_size[1])\n",
    "    ax.set_aspect('equal', 'box')\n",
    "    plt.gca().invert_yaxis()  # Invert y-axis to have (0, 0) at top-left corner\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

import os
import shutil
from PIL import Image, ImageOps

# Define paths
source_base_dir = './f1tenth_racetracks'
extracted_circuits = './extracted_circuits'
augmented_dataset = './dataset/tracks'
image_size = (64, 64)
rotation_steps = 20
rotation_angle = 360 / rotation_steps

# Create destination and augmented directories if they don't exist
os.makedirs(extracted_circuits, exist_ok=True)
os.makedirs(augmented_dataset, exist_ok=True)

# Step 1: Copy .png images from subfolders to destination folder
for root, _, files in os.walk(source_base_dir):
    for file in files:
        if file.endswith('.png'):
            src_path = os.path.join(root, file)
            dst_path = os.path.join(extracted_circuits, file)
            shutil.copy(src_path, dst_path)
            print(f"Copied {src_path} to {dst_path}")

# Step 2: Augment data by rotating each image 20 times
for file in os.listdir(extracted_circuits):
    if file.endswith('.png'):
        image_path = os.path.join(extracted_circuits, file)
        with Image.open(image_path) as img:
            # Create a new image with white background to accommodate rotation
            max_dim = max(img.size)
            new_img = Image.new('RGB', (max_dim, max_dim), 'white')
            new_img.paste(img, ((max_dim - img.width) // 2, (max_dim - img.height) // 2))

            for i in range(rotation_steps):
                rotated_img = new_img.rotate(i * rotation_angle, resample=Image.BICUBIC, expand=True, fillcolor='white')
                resized_img = rotated_img.resize(image_size, Image.ANTIALIAS)
                augmented_image_path = os.path.join(augmented_dataset, f"{os.path.splitext(file)[0]}_rot{i}.png")
                resized_img.save(augmented_image_path)
                print(f"Saved rotated image {augmented_image_path}")

print("Data augmentation completed.")

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.utils as vutils
import torchvision.datasets as dset
import torchvision.transforms as transforms
from torch.utils.data import DataLoader

# Define paths
data_dir = 'datasets'
image_size = 64
batch_size = 128

# Data transformations
transform = transforms.Compose([
    transforms.Resize(image_size),
    transforms.CenterCrop(image_size),
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])

# Load dataset
dataset = dset.ImageFolder(root=data_dir, transform=transform)
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)

# Define the Generator and Discriminator (as previously defined)
class Generator(nn.Module):
    def __init__(self, nz, ngf, nc):
        super(Generator, self).__init__()
        self.main = nn.Sequential(
            nn.ConvTranspose2d(nz, ngf * 8, 4, 1, 0, bias=False),
            nn.BatchNorm2d(ngf * 8),
            nn.ReLU(True),
            nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf * 4),
            nn.ReLU(True),
            nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf * 2),
            nn.ReLU(True),
            nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf),
            nn.ReLU(True),
            nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False),
            nn.Tanh()
        )

    def forward(self, input):
        return self.main(input)

class Discriminator(nn.Module):
    def __init__(self, nc, ndf):
        super(Discriminator, self).__init__()
        self.main = nn.Sequential(
            nn.Conv2d(nc, ndf, 4, 2, 1, bias=False),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Dropout(0.3),  # Add dropout
            nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ndf * 2),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Dropout(0.3),  # Add dropout
            nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ndf * 4),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Dropout(0.3),  # Add dropout
            nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ndf * 8),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(ndf * 8, 1, 4, 1, 0, bias=False),
            nn.Sigmoid()
        )

    def forward(self, input):
        return self.main(input)

# Initialize models
nz = 100
ngf = 64
ndf = 64
nc = 3

netG = Generator(nz, ngf, nc).cuda()
netD = Discriminator(nc, ndf).cuda()

# Adjusted learning rates
optimizerD = optim.Adam(netD.parameters(), lr=0.00002, betas=(0.5, 0.999))  # Further reduce learning rate for Discriminator
optimizerG = optim.Adam(netG.parameters(), lr=0.0001, betas=(0.5, 0.999))  # Slightly reduce learning rate for Generator

# Loss function
criterion = nn.BCELoss()

# Training loop
num_epochs = 10000  # Increase the number of epochs
real_label = 1.0
fake_label = 0.0
fixed_noise = torch.randn(64, nz, 1, 1).cuda()

for epoch in range(num_epochs):
    for i, data in enumerate(dataloader, 0):
        # Update Discriminator: maximize log(D(x)) + log(1 - D(G(z)))
        netD.zero_grad()
        real_images = data[0].cuda()
        batch_size = real_images.size(0)
        labels = torch.full((batch_size,), real_label, dtype=torch.float, device='cuda')
        
        output = netD(real_images).view(-1)
        lossD_real = criterion(output, labels)
        lossD_real.backward()
        
        noise = torch.randn(batch_size, nz, 1, 1, device='cuda')
        fake_images = netG(noise)
        labels.fill_(fake_label)
        
        output = netD(fake_images.detach()).view(-1)
        lossD_fake = criterion(output, labels)
        lossD_fake.backward()
        optimizerD.step()

        # Update Generator: maximize log(D(G(z)))
        netG.zero_grad()
        labels.fill_(real_label)
        
        output = netD(fake_images).view(-1)
        lossG = criterion(output, labels)
        lossG.backward()
        optimizerG.step()

        if i % 50 == 0:
            print(f'Epoch [{epoch}/{num_epochs}] Batch [{i}/{len(dataloader)}] '
                  f'Loss_D: {lossD_real.item() + lossD_fake.item()}, Loss_G: {lossG.item()}')

    # Save generated images
    if epoch % 10 == 0:
        with torch.no_grad():
            fake = netG(fixed_noise).detach().cpu()
        vutils.save_image(fake, f'output/fake_samples_epoch_{epoch}.png', normalize=True)

    # Save model checkpoints
    if epoch % 50 == 0:
        torch.save(netG.state_dict(), f'netG_epoch_{epoch}.pth')
        torch.save(netD.state_dict(), f'netD_epoch_{epoch}.pth')

Credits

fsayas

1 project • 0 followers

Thanks to f1tenth.

AI circuit designer for racing simulators (fail, for now...)

Things used in this project

Hardware components

Software apps and online services

Story

Introduction

Research and Initial Attempts

Choosing AI Architecture

Lessons Learned and Final Thoughts

Code

test_procedural_generation.py

test_procedural_algorithms.ipynb

augment_dataset.py

train_dcgan_pytorch.py

Credits

fsayas

Comments

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AI circuit designer for racing simulators (fail, for now...)

AI circuit designer for racing simulators (fail, for now...)

Things used in this project

Hardware components

Software apps and online services

Story

Introduction

Research and Initial Attempts

Choosing AI Architecture

Lessons Learned and Final Thoughts

Code

test_procedural_generation.py

test_procedural_algorithms.ipynb

augment_dataset.py

train_dcgan_pytorch.py

Credits

fsayas

Comments

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