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{
"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",
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