{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing Samples: 100%|██████████| 4931/4931 [6:09:48<00:00,  4.50s/it]  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset preparation complete.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import json\n",
    "import requests\n",
    "from tqdm import tqdm\n",
    "\n",
    "# Load the JSON file\n",
    "json_file_path = \"Train-v2.json\"  # Update this with the actual file path\n",
    "with open(json_file_path, 'r') as f:\n",
    "    data = json.load(f)\n",
    "\n",
    "# Create directories for images, labels, and segments\n",
    "images_dir = \"images/train\"\n",
    "labels_dir = \"labels/train\"\n",
    "segments_dir = \"segments/train\"\n",
    "os.makedirs(images_dir, exist_ok=True)\n",
    "os.makedirs(labels_dir, exist_ok=True)\n",
    "os.makedirs(segments_dir, exist_ok=True)\n",
    "\n",
    "# Helper function to download images or segmentations\n",
    "def download_image(url, output_path):\n",
    "    response = requests.get(url)\n",
    "    if response.status_code == 200:\n",
    "        with open(output_path, 'wb') as f:\n",
    "            f.write(response.content)\n",
    "\n",
    "# Prepare YOLO labels\n",
    "category_id_mapping = {cat['id']: i-1 for i, cat in enumerate(data['dataset']['task_attributes']['categories'], 1)}  # Mapping category_id to YOLO class id\n",
    "category_names = {i-1: cat['name'] for i, cat in enumerate(data['dataset']['task_attributes']['categories'], 1)}  # YOLO class names\n",
    "\n",
    "# Iterate through the dataset samples\n",
    "for sample in tqdm(data['dataset']['samples'], desc=\"Processing Samples\"):\n",
    "    label_status = sample['labels'].get('ground-truth', {}).get('label_status', 'SKIPPED')\n",
    "    \n",
    "    if label_status == \"LABELED\":\n",
    "        image_url = sample['attributes']['image']['url']\n",
    "        image_name = sample['name']\n",
    "        image_output_path = os.path.join(images_dir, image_name)\n",
    "\n",
    "        # Download the image\n",
    "        download_image(image_url, image_output_path)\n",
    "\n",
    "        # Download segmentation bitmap (if any)\n",
    "        segmentation_url = sample['labels']['ground-truth']['attributes'].get('segmentation_bitmap', {}).get('url')\n",
    "        if segmentation_url:\n",
    "            seg_output_path = os.path.join(segments_dir, image_name)\n",
    "            download_image(segmentation_url, seg_output_path)\n",
    "\n",
    "        # Write annotations to label files in YOLO format\n",
    "        label_output_path = os.path.join(labels_dir, os.path.splitext(image_name)[0] + \".txt\")\n",
    "        annotations = sample['labels']['ground-truth']['attributes']['annotations']\n",
    "\n",
    "        with open(label_output_path, 'w') as f:\n",
    "            for annotation in annotations:\n",
    "                category_id = annotation['category_id']\n",
    "                yolo_class_id = category_id_mapping[category_id]\n",
    "                # YOLO format typically expects: class_id, x_center, y_center, width, height\n",
    "                # Since we have segmentation, we may only record class_id here for simplicity.\n",
    "                # Modify this part to handle polygon or bounding box coordinates if necessary.\n",
    "                f.write(f\"{yolo_class_id}\\n\")\n",
    "\n",
    "# Create YAML file for YOLO segmentation\n",
    "yaml_content = {\n",
    "    'path': '.',  # Root path\n",
    "    'train': 'images/train',  # Path to training images\n",
    "    'val': '',  # No validation set\n",
    "    'names': category_names\n",
    "}\n",
    "\n",
    "yaml_file_path = \"dataset.yaml\"\n",
    "with open(yaml_file_path, 'w') as yaml_file:\n",
    "    yaml_file.write(f\"path: {yaml_content['path']}\\n\")\n",
    "    yaml_file.write(f\"train: {yaml_content['train']}\\n\")\n",
    "    yaml_file.write(f\"val: {yaml_content['val']}\\n\")\n",
    "    yaml_file.write(\"names:\\n\")\n",
    "    for i, name in yaml_content['names'].items():\n",
    "        yaml_file.write(f\"  {i}: {name}\\n\")\n",
    "\n",
    "print(\"Dataset preparation complete.\")\n"
   ]
  }
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