{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0",
   "metadata": {},
   "source": [
    "# Create Table from YOLO Instance Segmentation\n",
    "\n",
    "Create a 3LC Table from YOLO-format dataset with polygon-based instance segmentation annotations for modern object detection pipelines.\n",
    "\n",
    "![img](../../images/create-yolo-table.png)\n",
    "\n",
    "<!-- Tags: [\"instance-segmentation\", \"yolo\", \"polygons\", \"beginner\"] -->\n",
    "\n",
    "YOLO format is widely used in modern computer vision pipelines due to its simplicity and efficiency. The polygon-based segmentation format provides precise boundaries while maintaining fast inference speeds.\n",
    "\n",
    "This notebook loads a YOLO-format segmentation dataset and converts it to a 3LC Table. We process both image files and corresponding text annotation files containing normalized polygon coordinates for each instance.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1",
   "metadata": {},
   "source": [
    "## Project setup\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2",
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "PROJECT_NAME = \"3LC Tutorials - YOLO Segmentation\"\n",
    "DATASET_NAME = \"YOLO-Seg-Dataset\"\n",
    "TABLE_NAME = \"initial-segmentation\"\n",
    "DATA_PATH = \"../../../data\"\n",
    "INSTALL_DEPENDENCIES = True"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3",
   "metadata": {},
   "source": [
    "## Install dependencies\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4",
   "metadata": {},
   "outputs": [],
   "source": [
    "if INSTALL_DEPENDENCIES:\n",
    "    %pip install -q 3lc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5",
   "metadata": {},
   "source": [
    "## Imports\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "import tlc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7",
   "metadata": {},
   "source": [
    "## Create Segmentation Table\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8",
   "metadata": {},
   "outputs": [],
   "source": [
    "images_path = (Path(DATA_PATH) / \"yolo\" / \"datasets\" / \"simple\" / \"images\").absolute()\n",
    "\n",
    "assert images_path.exists()\n",
    "\n",
    "train_table = tlc.Table.from_yolo_url(\n",
    "    images_url=images_path,\n",
    "    categories={1.0: \"A\", 2.0: \"B\", 3.0: \"C\"},\n",
    "    project_name=PROJECT_NAME,\n",
    "    dataset_name=DATASET_NAME,\n",
    "    table_name=TABLE_NAME,\n",
    "    task=\"segment\",\n",
    ")"
   ]
  }
 ],
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