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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Table from COCO Instance Segmentation\n",
    "\n",
    "Create a 3LC Table from COCO-format dataset containing images with instance segmentation annotations for object detection and segmentation tasks.\n",
    "\n",
    "![img](../../images/instance-segmentation.jpg)\n",
    "\n",
    "<!-- Tags: [\"instance-segmentation\", \"coco\"] -->\n",
    "\n",
    "COCO is one of the most popular formats for instance segmentation datasets, providing precise pixel-level masks for each object instance. This format is essential for tasks requiring both object detection and precise boundary delineation.\n",
    "\n",
    "This notebook demonstrates loading a COCO-format dataset and converting it to a 3LC Table. The resulting table contains image and segmentation columns with properly structured polygon or mask annotations from the COCO JSON format.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Project setup\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "PROJECT_NAME = \"3LC Tutorials - COCO128\"\n",
    "DATASET_NAME = \"COCO128\"\n",
    "TABLE_NAME = \"initial-segmentation\"\n",
    "DATA_PATH = \"../../../data\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Install dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install 3lc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "import tlc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Segmentation Table\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "annotations_file = (Path(DATA_PATH) / \"coco128\" / \"annotations.json\").absolute()\n",
    "image_folder = (Path(DATA_PATH) / \"coco128\" / \"images\").absolute()\n",
    "\n",
    "assert annotations_file.exists()\n",
    "assert image_folder.exists()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "seg_table = tlc.Table.from_coco(\n",
    "    annotations_file=annotations_file,\n",
    "    image_folder=image_folder,\n",
    "    table_name=TABLE_NAME,\n",
    "    dataset_name=DATASET_NAME,\n",
    "    project_name=PROJECT_NAME,\n",
    "    task=\"segment\",\n",
    ")"
   ]
  }
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