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dataset.yaml

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+path: .
+train: images/train
+val: 
+names:
+  0: Armored_Personnel_Carrier
+  1: Bulldozer
+  2: Cement_Mixer_Truck
+  3: Commercial_Airplane
+  4: Dump_Truck
+  5: Fighter_Jet
+  6: Microcar
+  7: Military_Helicopter
+  8: Military_Tank
+  9: Military_Truck
+  10: Motorcycle
+  11: Pickup_Truck
+  12: Sedan_Car
+  13: Sport_Car
+  14: SUV
+  15: Transport_Helicopter

process.ipynb

@@ -0,0 +1,133 @@
+{
+ "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"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "sgrs",
+   "language": "python",
+   "name": "python3"
+  },
+  "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": 2
+}