bounding_box_scatter_plot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0509e4da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import xml.etree.ElementTree as ET\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def read_annotation(annotation_path):\n",
    "    tree = ET.parse(annotation_path)\n",
    "    root = tree.getroot()\n",
    "\n",
    "    boxes = []\n",
    "    labels = []\n",
    "    for obj in root.findall('object'):\n",
    "        label = obj.find('name').text\n",
    "        labels.append(label)\n",
    "        bbox = obj.find('bndbox')\n",
    "        xmin = int(bbox.find('xmin').text)\n",
    "        ymin = int(bbox.find('ymin').text)\n",
    "        xmax = int(bbox.find('xmax').text)\n",
    "        ymax = int(bbox.find('ymax').text)\n",
    "        boxes.append([(xmin + xmax) / 2, (ymin + ymax) / 2])  # Use center coordinates\n",
    "\n",
    "    return boxes, labels\n",
    "\n",
    "def visualize_scatter_plot(boxes, labels):\n",
    "    x, y = zip(*boxes)\n",
    "\n",
    "    # Get unique class labels and assign a unique color to each class\n",
    "    unique_labels = list(set(labels))\n",
    "    num_classes = len(unique_labels)\n",
    "    class_colors = sns.color_palette(\"tab20\", num_classes)\n",
    "\n",
    "    # Map class labels to their corresponding colors\n",
    "    label_to_color = {label: color for label, color in zip(unique_labels, class_colors)}\n",
    "\n",
    "    # Convert class labels to colors for each data point\n",
    "    colors = [label_to_color[label] for label in labels]\n",
    "\n",
    "    # Set up the seaborn style\n",
    "    sns.set(style=\"whitegrid\", font_scale=1.2)\n",
    "\n",
    "    # Create a scatter plot\n",
    "    plt.figure(figsize=(10, 8))\n",
    "    ax = sns.scatterplot(x=x, y=y, hue=labels, palette=label_to_color, alpha=0.8, s=100, edgecolor='none')\n",
    "\n",
    "    # Customizations\n",
    "    plt.xlabel('X', fontweight='bold', fontsize=14, labelpad=10)\n",
    "    plt.ylabel('Y', fontweight='bold', fontsize=14, labelpad=10)\n",
    "    plt.title('Pascal VOC Dataset Scatter Plot', fontweight='bold', fontsize=18, pad=20)\n",
    "    plt.legend(title='Class', title_fontsize=12, loc='upper left', fontsize=12, markerscale=1.5)\n",
    "\n",
    "    # Remove spines\n",
    "    sns.despine()\n",
    "\n",
    "    # Set grid lines and background color\n",
    "    plt.grid(True, linewidth=0.5, linestyle='--', alpha=0.5)\n",
    "    ax.set_facecolor('whitesmoke')\n",
    "\n",
    "    # Tight layout\n",
    "    plt.tight_layout()\n",
    "\n",
    "    # Remove legend border\n",
    "    legend = ax.get_legend()\n",
    "    legend.legendPatch.set_edgecolor('none')\n",
    "\n",
    "    plt.show()\n",
    "    \n",
    "# Function to read annotations from multiple files in a folder\n",
    "def read_annotations_from_folder(folder_path, num_annotations=10):\n",
    "    boxes_list = []\n",
    "    labels_list = []\n",
    "    for i in range(1, num_annotations + 1):\n",
    "        annotation_file = f\"image{i}.xml\"\n",
    "        annotation_path = os.path.join(folder_path, annotation_file)\n",
    "        if os.path.exists(annotation_path):\n",
    "            boxes, labels = read_annotation(annotation_path)\n",
    "            if boxes:  # Check if the boxes list is not empty\n",
    "                boxes_list.extend(boxes)\n",
    "                labels_list.extend(labels)\n",
    "    return boxes_list, labels_list\n",
    "\n",
    "# Usage example\n",
    "folder_path = 'pascal_voc_datasets/VOCdevkit/PlotsEnchanced_Original_NoAugmentation/Annotations'\n",
    "boxes, labels = read_annotations_from_folder(folder_path, num_annotations=10000)\n",
    "\n",
    "# Check if any annotations were found before plotting\n",
    "if boxes and labels:\n",
    "    visualize_scatter_plot(boxes, labels)\n",
    "else:\n",
    "    print(\"No annotations found.\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytorch-cude-env",
   "language": "python",
   "name": "pytorch-cude-env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}