{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0",
   "metadata": {},
   "source": [
    "# Create Table from YOLO Object Detection\n",
    "\n",
    "Create a 3LC Table from YOLO-format dataset with normalized bounding box annotations for efficient object detection workflows.\n",
    "\n",
    "![img](../../images/create-yolo-table.png)\n",
    "\n",
    "<!-- Tags: [\"object-detection\", \"yolo\", \"bounding-boxes\", \"beginner\"] -->\n",
    "\n",
    "YOLO format is popular for its simplicity and efficiency in modern object detection pipelines. The normalized coordinate system and straightforward text-based annotation format make it easy to work with and integrate into training workflows.\n",
    "\n",
    "This notebook loads a YOLO-format detection dataset and converts it to a 3LC Table. We process both image files and corresponding text annotation files containing normalized bounding box coordinates and class indices for each detected object.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1",
   "metadata": {},
   "source": [
    "## Project setup\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2",
   "metadata": {},
   "outputs": [],
   "source": [
    "PROJECT_NAME = \"3LC Tutorials - YOLO Detection\"\n",
    "DATASET_NAME = \"YOLO-Detection-Dataset\"\n",
    "TABLE_NAME = \"initial-detection\"\n",
    "DATA_PATH = \"../../../data\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3",
   "metadata": {},
   "source": [
    "## Install dependencies\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4",
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install 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 Detection Table\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_yaml_file = (Path(DATA_PATH) / \"yolo\" / \"simple.yaml\").absolute()\n",
    "\n",
    "assert dataset_yaml_file.exists()\n",
    "\n",
    "train_table = tlc.Table.from_yolo(\n",
    "    dataset_yaml_file=str(dataset_yaml_file),\n",
    "    split=\"train\",\n",
    "    project_name=PROJECT_NAME,\n",
    "    dataset_name=DATASET_NAME,\n",
    "    table_name=TABLE_NAME,\n",
    "    task=\"detect\",\n",
    ")"
   ]
  }
 ],
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