{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install pillow datasets pandas pypng uuid\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preproccessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import uuid\n",
    "import shutil\n",
    "\n",
    "def rename_and_move_images(source_dir, target_dir):\n",
    "    # Create the target directory if it doesn't exist\n",
    "    os.makedirs(target_dir, exist_ok=True)\n",
    "\n",
    "    # List of common image file extensions\n",
    "    image_extensions = ('.jpg', '.jpeg', '.png', '.gif', '.bmp', '.tiff')\n",
    "\n",
    "    # Walk through the source directory and its subdirectories\n",
    "    for root, dirs, files in os.walk(source_dir):\n",
    "        for file in files:\n",
    "            # Check if the file has an image extension\n",
    "            if file.lower().endswith(image_extensions):\n",
    "                # Generate a new filename with UUID\n",
    "                new_filename = str(uuid.uuid4()) + os.path.splitext(file)[1]\n",
    "                \n",
    "                # Construct full file paths\n",
    "                old_path = os.path.join(root, file)\n",
    "                new_path = os.path.join(target_dir, new_filename)\n",
    "                \n",
    "                # Move and rename the file\n",
    "                shutil.move(old_path, new_path)\n",
    "                print(f\"Moved and renamed: {old_path} -> {new_path}\")\n",
    "\n",
    "# Usage\n",
    "source_directory = \"images\"\n",
    "target_directory = \"train\"\n",
    "\n",
    "rename_and_move_images(source_directory, target_directory)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Examine Metadata For Stable Diffusion PNGs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "import os\n",
    "\n",
    "# Set the path to the train directory\n",
    "train_directory = \"train\"\n",
    "\n",
    "# Get any PNG image file from the train directory\n",
    "image_file = None\n",
    "for file_name in os.listdir(train_directory):\n",
    "    if file_name.endswith('.png'):\n",
    "        image_file = os.path.join(train_directory, file_name)\n",
    "        break\n",
    "\n",
    "# Check if an image was found\n",
    "if image_file:\n",
    "    # Open the image\n",
    "    with Image.open(image_file) as img:\n",
    "        print(f\"Filename: {image_file}\")\n",
    "        \n",
    "        # Extract and display image size (width, height)\n",
    "        print(f\"Size: {img.size}\")\n",
    "        \n",
    "        # Extract and display image mode (RGB, RGBA, etc.)\n",
    "        print(f\"Mode: {img.mode}\")\n",
    "        \n",
    "        # Extract and display image format\n",
    "        print(f\"Format: {img.format}\")\n",
    "        \n",
    "        # Extract and display image info (metadata)\n",
    "        print(\"Metadata:\")\n",
    "        for key, value in img.info.items():\n",
    "            print(f\"  {key}: {value}\")\n",
    "else:\n",
    "    print(\"No PNG image found in the train directory.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract the Metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import png\n",
    "import pandas as pd\n",
    "import re\n",
    "\n",
    "# Directory containing images\n",
    "image_dir = 'train'\n",
    "metadata_list = []\n",
    "\n",
    "# Mapping of possible keys to standardized field names\n",
    "field_mapping = {\n",
    "    'parameters': 'prompt',\n",
    "    'seed': 'seed',\n",
    "    'steps': 'steps',\n",
    "    'cfg scale': 'cfg',\n",
    "    'cfg_scale': 'cfg',\n",
    "    'cfg-scale': 'cfg',\n",
    "    'guidance': 'guidance',\n",
    "    'sampler': 'sampler_name',\n",
    "    'sampler_name': 'sampler_name',\n",
    "    'version': 'version',\n",
    "    'rng': 'rng',\n",
    "    'size': 'size'\n",
    "}\n",
    "\n",
    "def extract_metadata_from_png(image_path):\n",
    "    \"\"\"\n",
    "    Extracts metadata from all tEXt chunks in a PNG image.\n",
    "    Args:\n",
    "        image_path (str): Path to the PNG image.\n",
    "    Returns:\n",
    "        dict: A dictionary containing extracted metadata.\n",
    "    \"\"\"\n",
    "    metadata = {}\n",
    "    try:\n",
    "        with open(image_path, 'rb') as f:\n",
    "            reader = png.Reader(file=f)\n",
    "            for chunk_type, chunk_data in reader.chunks():\n",
    "                if chunk_type == b'tEXt':\n",
    "                    # Decode the tEXt chunk\n",
    "                    chunk_text = chunk_data.decode('latin1')\n",
    "                    # Split into key and value using null byte as separator\n",
    "                    if '\\x00' in chunk_text:\n",
    "                        key, value = chunk_text.split('\\x00', 1)\n",
    "                        key = key.lower().strip()\n",
    "                        value = value.strip()\n",
    "                        # Map the key to standardized field name if it exists\n",
    "                        if key in field_mapping:\n",
    "                            standardized_key = field_mapping[key]\n",
    "                            metadata[standardized_key] = value\n",
    "    except Exception as e:\n",
    "        print(f\"Error reading PNG file {image_path}: {e}\")\n",
    "    return metadata\n",
    "\n",
    "def parse_parameters(parameters):\n",
    "    \"\"\"\n",
    "    Parses the 'parameters' string to extract individual fields.\n",
    "    \"\"\"\n",
    "    # Extract prompt (everything before the first recognized field)\n",
    "    prompt_end = min((parameters.find(field) for field in ['Steps:', 'CFG scale:', 'Guidance:', 'Seed:'] if field in parameters), default=-1)\n",
    "    prompt = parameters[:prompt_end].strip() if prompt_end != -1 else parameters\n",
    "\n",
    "    # Extract other fields\n",
    "    fields = {\n",
    "        'steps': r'Steps: (\\d+)',\n",
    "        'cfg': r'CFG scale: ([\\d.]+)',\n",
    "        'guidance': r'Guidance: ([\\d.]+)',\n",
    "        'seed': r'Seed: (\\d+)',\n",
    "        'width': r'Size: (\\d+)x\\d+',\n",
    "        'height': r'Size: \\d+x(\\d+)',\n",
    "        'rng': r'RNG: (\\w+)',\n",
    "        'sampler_name': r'Sampler: (\\w+)',\n",
    "        'version': r'Version: (.+)$'\n",
    "    }\n",
    "\n",
    "    parsed = {'prompt': prompt}\n",
    "    for key, pattern in fields.items():\n",
    "        match = re.search(pattern, parameters)\n",
    "        parsed[key] = match.group(1).strip() if match else 'N/A'\n",
    "\n",
    "    return parsed\n",
    "\n",
    "# Loop through all PNG images in the directory\n",
    "for file_name in os.listdir(image_dir):\n",
    "    if file_name.lower().endswith('.png'):\n",
    "        image_path = os.path.join(image_dir, file_name)\n",
    "        metadata = extract_metadata_from_png(image_path)\n",
    "        \n",
    "        # Parse the 'parameters' field\n",
    "        if 'prompt' in metadata:\n",
    "            parsed_metadata = parse_parameters(metadata['prompt'])\n",
    "        else:\n",
    "            parsed_metadata = {field: 'N/A' for field in field_mapping.values()}\n",
    "        \n",
    "        # Add file name to metadata\n",
    "        parsed_metadata['file_name'] = file_name\n",
    "        \n",
    "        metadata_list.append(parsed_metadata)\n",
    "\n",
    "# Convert the metadata list to a DataFrame\n",
    "metadata_df = pd.DataFrame(metadata_list)\n",
    "\n",
    "# Define the desired column order\n",
    "desired_columns = ['file_name', 'seed', 'prompt', 'steps', 'cfg', 'sampler_name', 'guidance', 'version', 'width', 'height', 'rng']\n",
    "\n",
    "# Ensure all desired columns are present\n",
    "for col in desired_columns:\n",
    "    if col not in metadata_df.columns:\n",
    "        metadata_df[col] = 'N/A'\n",
    "\n",
    "# Reorder the DataFrame columns\n",
    "metadata_df = metadata_df[desired_columns]\n",
    "\n",
    "# Save the metadata to a CSV file\n",
    "metadata_csv_path = os.path.join(image_dir, 'metadata.csv')\n",
    "metadata_df.to_csv(metadata_csv_path, index=False)\n",
    "print(\"Metadata extraction complete. Metadata saved to:\", metadata_csv_path)\n"
   ]
  }
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