added notebook

label_studio2yolo.ipynb

@@ -0,0 +1,112 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "1bba141c-5345-4833-960e-59a5e65f08b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import shutil\n",
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57ee26a6-ba9b-4228-847a-8fdb9dca42bd",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "https://harminder.dev/projects/ai-powered-property-surveillance/training/#create-a-data-configuration-file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f4ee3ad6-2555-403f-a68b-c2a102649fe0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset successfully split into train, val, and test sets.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Set the seed for reproducibility\n",
+    "\n",
+    "random.seed(42)\n",
+    "\n",
+    "# Paths\n",
+    "\n",
+    "base_path = 'yolo'\n",
+    "images_path = os.path.join(base_path, 'images')\n",
+    "labels_path = os.path.join(base_path, 'labels')\n",
+    "\n",
+    "# Split Ratios\n",
+    "test_ratio = 0.20\n",
+    "\n",
+    "# Create directories for train, val, and test sets\n",
+    "\n",
+    "for set_type in ['train', 'test']:\n",
+    "    for content_type in ['images', 'labels']:\n",
+    "        os.makedirs(os.path.join(base_path, set_type, content_type), exist_ok=True)\n",
+    "\n",
+    "# Get all image filenames\n",
+    "\n",
+    "all_files = [f for f in os.listdir(images_path) if os.path.isfile(os.path.join(images_path, f))]\n",
+    "random.shuffle(all_files)\n",
+    "\n",
+    "# Calculate split indices\n",
+    "\n",
+    "total_files = len(all_files)\n",
+    "train_end = int(total_files*test_ratio)\n",
+    "\n",
+    "# Split files\n",
+    "\n",
+    "test_files = all_files[:train_end]\n",
+    "train_files = all_files[train_end:]\n",
+    "\n",
+    "# Function to copy files\n",
+    "\n",
+    "def copy_files(files, set_type):\n",
+    "    for file in files: # Copy image\n",
+    "        shutil.copy(os.path.join(images_path, file), os.path.join(base_path, set_type, 'images')) # Copy corresponding label\n",
+    "        label_file = file.rsplit('.', 1)[0] + '.txt'\n",
+    "        shutil.copy(os.path.join(labels_path, label_file), os.path.join(base_path, set_type, 'labels'))\n",
+    "\n",
+    "# Copy files to respective directories\n",
+    "\n",
+    "copy_files(train_files, 'train')\n",
+    "copy_files(test_files, 'test')\n",
+    "\n",
+    "print(\"Dataset successfully split into train, val, and test sets.\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "torch",
+   "language": "python",
+   "name": "torch"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}