added dataset

create-video.ipynb

@@ -0,0 +1,274 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Create Video!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install opencv-python-headless  # If you do not need GUI features\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating the demo\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import subprocess\n",
+    "import logging\n",
+    "from glob import glob\n",
+    "import re\n",
+    "\n",
+    "# Configure logging\n",
+    "logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')\n",
+    "logger = logging.getLogger(__name__)\n",
+    "\n",
+    "def create_near_lossless_h265_video(input_folder, output_file, fps=30, frames_per_image=3, crf=10):\n",
+    "    if not os.path.exists(input_folder):\n",
+    "        logger.error(f\"Input folder '{input_folder}' does not exist.\")\n",
+    "        return\n",
+    "\n",
+    "    png_files = sorted(glob(os.path.join(input_folder, '*.png')))\n",
+    "    if not png_files:\n",
+    "        logger.error(f\"No PNG files found in {input_folder}\")\n",
+    "        return\n",
+    "\n",
+    "    num_images = len(png_files)\n",
+    "    logger.info(f\"Found {num_images} PNG files.\")\n",
+    "\n",
+    "    # Calculate expected duration\n",
+    "    expected_duration = (num_images * frames_per_image) / fps\n",
+    "    logger.info(f\"Expected duration: {expected_duration:.2f} seconds\")\n",
+    "\n",
+    "    # FFmpeg command for near-lossless 10-bit H.265 encoding\n",
+    "    ffmpeg_command = [\n",
+    "        'ffmpeg',\n",
+    "        '-framerate', f'{1/(frames_per_image/fps)}',  # Input framerate\n",
+    "        '-i', os.path.join(input_folder, '%*.png'),   # Input pattern for all PNG files\n",
+    "        '-fps_mode', 'vfr',\n",
+    "        '-pix_fmt', 'yuv420p10le',  # 10-bit pixel format\n",
+    "        '-c:v', 'libx265',          # Use libx265 encoder\n",
+    "        '-preset', 'slow',      # Slowest preset for best compression efficiency\n",
+    "        '-crf', str(crf),           # Constant Rate Factor (0-51, lower is higher quality)\n",
+    "        '-profile:v', 'main10',     # 10-bit profile\n",
+    "        '-x265-params', f\"log-level=error:keyint={2*fps}:min-keyint={fps}:scenecut=0\",  # Ensure consistent encoding\n",
+    "        '-tag:v', 'hvc1',\n",
+    "        '-y',\n",
+    "        output_file\n",
+    "    ]\n",
+    "\n",
+    "    try:\n",
+    "        logger.info(\"Starting near-lossless 10-bit video creation...\")\n",
+    "        process = subprocess.Popen(ffmpeg_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, universal_newlines=True)\n",
+    "        \n",
+    "        encoding_speed = None\n",
+    "        \n",
+    "        for line in process.stderr:\n",
+    "            print(line, end='')  # Print FFmpeg output in real-time\n",
+    "            \n",
+    "            speed_match = re.search(r'speed=\\s*([\\d.]+)x', line)\n",
+    "            if speed_match:\n",
+    "                encoding_speed = float(speed_match.group(1))\n",
+    "        \n",
+    "        process.wait()\n",
+    "        \n",
+    "        if encoding_speed:\n",
+    "            logger.info(f\"Encoding speed: {encoding_speed:.2f}x\")\n",
+    "        \n",
+    "        if process.returncode == 0:\n",
+    "            logger.info(f\"Video created successfully: {output_file}\")\n",
+    "            \n",
+    "            probe_command = ['ffprobe', '-v', 'error', '-show_entries', 'stream=codec_name,width,height,duration,bit_rate,profile', '-of', 'default=noprint_wrappers=1', output_file]\n",
+    "            probe_result = subprocess.run(probe_command, capture_output=True, text=True)\n",
+    "            logger.info(f\"Video properties:\\n{probe_result.stdout}\")\n",
+    "            \n",
+    "            duration_command = ['ffprobe', '-v', 'error', '-show_entries', 'format=duration', '-of', 'default=noprint_wrappers=1:nokey=1', output_file]\n",
+    "            duration_result = subprocess.run(duration_command, capture_output=True, text=True)\n",
+    "            actual_duration = float(duration_result.stdout.strip())\n",
+    "            logger.info(f\"Actual video duration: {actual_duration:.2f} seconds\")\n",
+    "            if abs(actual_duration - expected_duration) > 1:\n",
+    "                logger.warning(f\"Video duration mismatch. Expected: {expected_duration:.2f}, Actual: {actual_duration:.2f}\")\n",
+    "            else:\n",
+    "                logger.info(\"Video duration check passed.\")\n",
+    "        else:\n",
+    "            logger.error(f\"Error during video creation. FFmpeg returned code {process.returncode}\")\n",
+    "\n",
+    "    except subprocess.CalledProcessError as e:\n",
+    "        logger.error(f\"Error during video creation: {e}\")\n",
+    "        logger.error(f\"FFmpeg error output:\\n{e.stderr}\")\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    input_folder = 'train'\n",
+    "    output_file = 'near_lossless_output.mp4'\n",
+    "    fps = 30\n",
+    "    frames_per_image = 3\n",
+    "    crf = 18  # Very low CRF for near-lossless quality (0 is lossless, but often overkill)\n",
+    "\n",
+    "    create_near_lossless_h265_video(input_folder, output_file, fps, frames_per_image, crf)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Transfer File"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import subprocess\n",
+    "import logging\n",
+    "from glob import glob\n",
+    "import re\n",
+    "\n",
+    "# Configure logging\n",
+    "logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')\n",
+    "logger = logging.getLogger(__name__)\n",
+    "\n",
+    "def create_high_quality_video(input_folder, output_file, fps=60, frames_per_image=3, codec='ffv1'):\n",
+    "    if not os.path.exists(input_folder):\n",
+    "        logger.error(f\"Input folder '{input_folder}' does not exist.\")\n",
+    "        return\n",
+    "\n",
+    "    png_files = sorted(glob(os.path.join(input_folder, '*.png')))\n",
+    "    if not png_files:\n",
+    "        logger.error(f\"No PNG files found in {input_folder}\")\n",
+    "        return\n",
+    "\n",
+    "    num_images = len(png_files)\n",
+    "    logger.info(f\"Found {num_images} PNG files.\")\n",
+    "\n",
+    "    # Calculate expected duration\n",
+    "    expected_duration = (num_images * frames_per_image) / fps\n",
+    "    logger.info(f\"Expected duration: {expected_duration:.2f} seconds\")\n",
+    "\n",
+    "    # Base FFmpeg command\n",
+    "    ffmpeg_command = [\n",
+    "        'ffmpeg',\n",
+    "        '-framerate', f'{1/(frames_per_image/fps)}',  # Input framerate\n",
+    "        '-i', os.path.join(input_folder, '%*.png'),   # Input pattern for all PNG files\n",
+    "        '-fps_mode', 'vfr',\n",
+    "    ]\n",
+    "\n",
+    "    # Codec-specific settings\n",
+    "    if codec == 'ffv1':\n",
+    "        output_file = output_file.rsplit('.', 1)[0] + '.mkv'  # FFV1 is typically used with MKV container\n",
+    "        ffmpeg_command.extend([\n",
+    "            '-c:v', 'ffv1',\n",
+    "            '-level', '3',\n",
+    "            '-coder', '1',\n",
+    "            '-context', '1',\n",
+    "            '-g', '1',\n",
+    "            '-slices', '24',\n",
+    "            '-slicecrc', '1'\n",
+    "        ])\n",
+    "        logger.info(\"Using FFV1 codec (lossless)\")\n",
+    "    elif codec == 'prores':\n",
+    "        output_file = output_file.rsplit('.', 1)[0] + '.mov'  # ProRes is typically used with MOV container\n",
+    "        ffmpeg_command.extend([\n",
+    "    '-c:v', 'prores_ks',\n",
+    "    '-profile:v', 'proxy',  # Use ProRes 422 Proxy profile\n",
+    "    '-qscale:v', '11'  # Adjust quality scale; higher values mean lower quality. 11 is typical for proxy quality.\n",
+    "])\n",
+    "\n",
+    "        logger.info(\"Using ProRes codec (near-lossless)\")\n",
+    "    else:\n",
+    "        logger.error(f\"Unsupported codec: {codec}\")\n",
+    "        return\n",
+    "\n",
+    "    ffmpeg_command.extend(['-y', output_file])\n",
+    "\n",
+    "    try:\n",
+    "        logger.info(f\"Starting high-quality video creation with {codec} codec...\")\n",
+    "        process = subprocess.Popen(ffmpeg_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, universal_newlines=True)\n",
+    "        \n",
+    "        encoding_speed = None\n",
+    "        \n",
+    "        for line in process.stderr:\n",
+    "            print(line, end='')  # Print FFmpeg output in real-time\n",
+    "            \n",
+    "            speed_match = re.search(r'speed=\\s*([\\d.]+)x', line)\n",
+    "            if speed_match:\n",
+    "                encoding_speed = float(speed_match.group(1))\n",
+    "        \n",
+    "        process.wait()\n",
+    "        \n",
+    "        if encoding_speed:\n",
+    "            logger.info(f\"Encoding speed: {encoding_speed:.4f}x\")\n",
+    "        \n",
+    "        if process.returncode == 0:\n",
+    "            logger.info(f\"Video created successfully: {output_file}\")\n",
+    "            \n",
+    "            probe_command = ['ffprobe', '-v', 'error', '-show_entries', 'stream=codec_name,width,height,duration,bit_rate', '-of', 'default=noprint_wrappers=1', output_file]\n",
+    "            probe_result = subprocess.run(probe_command, capture_output=True, text=True)\n",
+    "            logger.info(f\"Video properties:\\n{probe_result.stdout}\")\n",
+    "            \n",
+    "            duration_command = ['ffprobe', '-v', 'error', '-show_entries', 'format=duration', '-of', 'default=noprint_wrappers=1:nokey=1', output_file]\n",
+    "            duration_result = subprocess.run(duration_command, capture_output=True, text=True)\n",
+    "            actual_duration = float(duration_result.stdout.strip())\n",
+    "            logger.info(f\"Actual video duration: {actual_duration:.2f} seconds\")\n",
+    "            if abs(actual_duration - expected_duration) > 1:\n",
+    "                logger.warning(f\"Video duration mismatch. Expected: {expected_duration:.2f}, Actual: {actual_duration:.2f}\")\n",
+    "            else:\n",
+    "                logger.info(\"Video duration check passed.\")\n",
+    "        else:\n",
+    "            logger.error(f\"Error during video creation. FFmpeg returned code {process.returncode}\")\n",
+    "\n",
+    "    except subprocess.CalledProcessError as e:\n",
+    "        logger.error(f\"Error during video creation: {e}\")\n",
+    "        logger.error(f\"FFmpeg error output:\\n{e.stderr}\")\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    input_folder = 'train'\n",
+    "    output_file = 'high_quality_output.mp4'\n",
+    "    fps = 60\n",
+    "    frames_per_image = 3\n",
+    "    codec = 'prores'  # Options: 'ffv1' (lossless) or 'prores' (near-lossless)\n",
+    "\n",
+    "    create_high_quality_video(input_folder, output_file, fps, frames_per_image, codec)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

image_metadata_extraction.ipynb

@@ -0,0 +1,173 @@
+{
+ "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": [
+    "Extract the Metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import json\n",
+    "import png\n",
+    "import pandas as pd\n",
+    "\n",
+    "# Directory containing images\n",
+    "image_dir = 'train'\n",
+    "metadata_list = []\n",
+    "\n",
+    "# Function to extract the JSON data from the tEXt chunk in PNG images\n",
+    "def extract_metadata_from_png(image_path):\n",
+    "    with open(image_path, 'rb') as f:\n",
+    "        reader = png.Reader(file=f)\n",
+    "        chunks = reader.chunks()\n",
+    "        for chunk_type, chunk_data in chunks:\n",
+    "            if chunk_type == b'tEXt':\n",
+    "                # Convert bytes to string\n",
+    "                chunk_text = chunk_data.decode('latin1')\n",
+    "                if 'prompt' in chunk_text:\n",
+    "                    try:\n",
+    "                        # Extract JSON string after \"prompt\\0\"\n",
+    "                        json_str = chunk_text.split('prompt\\0', 1)[1]\n",
+    "                        json_data = json.loads(json_str)\n",
+    "                        inputs = json_data.get('3', {}).get('inputs', {})\n",
+    "                        seed = inputs.get('seed', 'N/A')\n",
+    "                        positive_prompt = json_data.get('6', {}).get('inputs', {}).get('text', 'N/A')\n",
+    "                        negative_prompt = json_data.get('7', {}).get('inputs', {}).get('text', 'N/A')\n",
+    "                        model = json_data.get('4', {}).get('inputs', {}).get('ckpt_name', 'N/A')\n",
+    "                        steps = inputs.get('steps', 'N/A')\n",
+    "                        cfg = inputs.get('cfg', 'N/A')\n",
+    "                        sampler_name = inputs.get('sampler_name', 'N/A')\n",
+    "                        scheduler = inputs.get('scheduler', 'N/A')\n",
+    "                        denoise = inputs.get('denoise', 'N/A')\n",
+    "                        return {\n",
+    "                            'seed': seed,\n",
+    "                            'positive_prompt': positive_prompt,\n",
+    "                            'negative_prompt': negative_prompt,\n",
+    "                            'model': model,\n",
+    "                            'steps': steps,\n",
+    "                            'cfg': cfg,\n",
+    "                            'sampler_name': sampler_name,\n",
+    "                            'scheduler': scheduler,\n",
+    "                            'denoise': denoise\n",
+    "                        }\n",
+    "                    except json.JSONDecodeError:\n",
+    "                        pass\n",
+    "    return {}\n",
+    "\n",
+    "# Loop through all images in the directory\n",
+    "for file_name in os.listdir(image_dir):\n",
+    "    if file_name.endswith('.png'):\n",
+    "        image_path = os.path.join(image_dir, file_name)\n",
+    "        metadata = extract_metadata_from_png(image_path)\n",
+    "        metadata['file_name'] = file_name\n",
+    "        metadata_list.append(metadata)\n",
+    "\n",
+    "# Convert metadata to DataFrame\n",
+    "metadata_df = pd.DataFrame(metadata_list)\n",
+    "\n",
+    "# Ensure 'file_name' is the first column\n",
+    "columns_order = ['file_name', 'seed', 'positive_prompt', 'negative_prompt', 'model', 'steps', 'cfg', 'sampler_name', 'scheduler', 'denoise']\n",
+    "metadata_df = metadata_df[columns_order]\n",
+    "\n",
+    "# Save metadata to a CSV file\n",
+    "metadata_csv_path = 'train/metadata.csv'\n",
+    "metadata_df.to_csv(metadata_csv_path, index=False)\n",
+    "\n",
+    "print(\"Metadata extraction complete. Metadata saved to:\", metadata_csv_path)\n",
+    "\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}