Upload Drive to WebP.ipynb

Drive to WebP.ipynb

@@ -1 +1 @@
-{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"6HquiGjaJiYK"},"outputs":[],"source":["# Install required libraries\n","#!pip install pillow google-colab\n","\n","from google.colab import drive\n","from PIL import Image\n","import os\n","import zipfile\n","from pathlib import Path\n","import shutil\n","\n","# Mount Google Drive\n","drive.mount('/content/drive')\n","\n","# Define paths and settings\n","source_dir = '/content/drive/MyDrive/Saved from Chrome/'\n","output_zip = '/content/images_lossless_1024_webp.zip'\n","webp_dir = '/content/temp_lossless_1024_webp'\n","MAX_SIZE = 1024  # Maximum dimension (width or height)\n","\n","# Create temporary directory for resized lossless webp files\n","os.makedirs(webp_dir, exist_ok=True)\n","\n","# Get all image files\n","image_extensions = {'.jpg', '.jpeg', '.png', '.bmp', '.tiff', '.gif', '.webp'}\n","image_files = []\n","\n","print(\"🔍 Scanning for images...\")\n","for file_path in Path(source_dir).rglob('*'):\n","    if file_path.is_file() and file_path.suffix.lower() in image_extensions:\n","        image_files.append(file_path)\n","\n","print(f\"✅ Found {len(image_files)} image files\")\n","\n","# Convert, resize, and save as lossless WebP\n","webp_files = []\n","failed_conversions = []\n","resize_stats = {'resized': 0, 'unchanged': 0}\n","\n","print(\"🔄 Resizing to 1024px max dimension & converting to lossless WebP...\")\n","for i, img_path in enumerate(image_files, 1):\n","    try:\n","        # Open image\n","        with Image.open(img_path) as img:\n","            original_width, original_height = img.size\n","\n","            # Resize if either dimension exceeds MAX_SIZE\n","            if original_width > MAX_SIZE or original_height > MAX_SIZE:\n","                # Calculate scaling factor to fit within MAX_SIZE\n","                scale = min(MAX_SIZE / original_width, MAX_SIZE / original_height)\n","                new_width = int(original_width * scale)\n","                new_height = int(original_height * scale)\n","\n","                # Resize using high-quality LANCZOS resampling\n","                img_resized = img.resize((new_width, new_height), Image.Resampling.LANCZOS)\n","                resize_stats['resized'] += 1\n","            else:\n","                img_resized = img\n","                resize_stats['unchanged'] += 1\n","\n","            # Preserve original mode (including transparency) for lossless conversion\n","            # Save as lossless WebP\n","            webp_path = os.path.join(webp_dir, f\"{i:04d}.webp\")\n","            img_resized.save(webp_path, 'WEBP', lossless=True, method=6)\n","            webp_files.append(webp_path)\n","\n","        if i % 10 == 0 or i == len(image_files):\n","            print(f\"  Processed {i}/{len(image_files)} images\")\n","\n","    except Exception as e:\n","        error_msg = f\"Error processing {img_path.name}: {str(e)}\"\n","        print(f\"❌ {error_msg}\")\n","        failed_conversions.append((img_path, str(e)))\n","        continue\n","\n","print(f\"\\n📏 Resize Statistics:\")\n","print(f\"   Images resized: {resize_stats['resized']}\")\n","print(f\"   Images unchanged: {resize_stats['unchanged']}\")\n","print(f\"✅ Successfully converted {len(webp_files)} images to 1024px lossless WebP\")\n","\n","# Create zip file with maximum compression\n","print(\"📦 Creating ZIP archive...\")\n","with zipfile.ZipFile(output_zip, 'w', zipfile.ZIP_DEFLATED, compresslevel=9) as zipf:\n","    for webp_file in webp_files:\n","        arcname = os.path.basename(webp_file)\n","        zipf.write(webp_file, arcname)\n","        if len(webp_files) <= 20 or webp_files.index(webp_file) % 20 == 0:\n","            print(f\"  Added {arcname}\")\n","\n","# Calculate statistics\n","original_size = sum(os.path.getsize(f) for f in image_files)\n","webp_size = sum(os.path.getsize(f) for f in webp_files)\n","zip_size = os.path.getsize(output_zip)\n","\n","size_reduction = (1 - webp_size/original_size) * 100 if original_size > 0 else 0\n","\n","print(f\"\\n📊 Conversion Statistics:\")\n","print(f\"   Original images size:    {original_size / (1024*1024):.2f} MB\")\n","print(f\"   1024px Lossless WebP:    {webp_size / (1024*1024):.2f} MB\")\n","print(f\"   Size reduction:          {size_reduction:.1f}%\")\n","print(f\"   Final ZIP size:          {zip_size / (1024*1024):.2f} MB\")\n","\n","# Copy zip back to Google Drive\n","drive_output = '/content/drive/MyDrive/images_1024_lossless_webp.zip'\n","shutil.copy2(output_zip, drive_output)\n","print(f\"💾 ZIP saved to Google Drive: {drive_output}\")\n","\n","# Clean up temporary directory\n","shutil.rmtree(webp_dir)\n","print(\"🧹 Temporary files cleaned up\")\n","\n","# Display sample of files in zip\n","print(\"\\n📋 Sample files in ZIP:\")\n","with zipfile.ZipFile(output_zip, 'r') as zipf:\n","    file_list = zipf.namelist()\n","    for i, filename in enumerate(file_list[:10], 1):\n","        print(f\"   {i}. {filename}\")\n","    if len(file_list) > 10:\n","        print(f\"   ... and {len(file_list) - 10} more files\")\n","\n","print(f\"\\n🎯 AI-Training Ready (1024px Optimized)!\")\n","print(f\"   • ✅ 1024px max resolution - standardized for ML\")\n","print(f\"   • ✅ 100% lossless quality - NO compression artifacts\")\n","print(f\"   • ✅ Preserves transparency (RGBA support)\")\n","print(f\"   • ✅ High-quality LANCZOS resampling\")\n","print(f\"   • ✅ Sequential numbering: 0001.webp, 0002.webp, etc.\")\n","print(f\"   • 🎯 Perfect for CNNs, GANs, object detection, segmentation\")\n","print(f\"   • ⚡ Consistent input size for faster training\")\n","print(f\"   • 💾 Significant storage savings vs original high-res images\")\n","\n","if failed_conversions:\n","    print(f\"\\n⚠️  Failed conversions (check originals):\")\n","    for orig_path, error in failed_conversions[:5]:\n","        print(f\"   • {orig_path.name}: {error}\")\n","    if len(failed_conversions) > 5:\n","        print(f\"   ... and {len(failed_conversions) - 5} more\")\n","\n","print(f\"\\n🔧 Technical Details:\")\n","print(f\"   • Max dimension: {MAX_SIZE}px (maintains aspect ratio)\")\n","print(f\"   • Resampling: LANCZOS (highest quality)\")\n","print(f\"   • Lossless WebP: Perfect fidelity, ~50% smaller than PNG\")"]},{"cell_type":"markdown","metadata":{"id":"lz_4gtiQAdEP"},"source":["# 📸 Google Drive Image Converter to WebP\n","\n","**Convert your Chrome-saved images to compact WebP format with automatic indexing!**\n","\n","This Google Colab notebook transforms all images from your `MyDrive/Saved from Chrome/` folder into optimized WebP files, named sequentially as `1.webp`, `2.webp`, `3.webp`, etc., and packages them into a single ZIP archive.\n","\n","## ✨ Features\n","- **Batch Processing**: Automatically finds and converts JPG, PNG, GIF, BMP, TIFF, and existing WebP files\n","- **Smart Conversion**: Handles transparency by converting RGBA/P modes to RGB with white background\n","- **High-Quality WebP**: Uses 85% quality setting for optimal size/quality balance\n","- **Recursive Search**: Processes images in subfolders too\n","- **Error-Resilient**: Skips corrupt files and continues processing\n","- **Dual Output**: Saves ZIP locally in Colab AND copies to your Google Drive root\n","- **Progress Tracking**: Shows conversion progress and final statistics\n","\n","## 🚀 How It Works\n","1. Mounts your Google Drive\n","2. Scans for image files in \"Saved from Chrome/\" and subdirectories\n","3. Converts each image to WebP with sequential numbering\n","4. Creates a compressed ZIP archive\n","5. Cleans up temporary files and reports file sizes\n","\n","## 💾 Output\n","- `images_webp.zip` containing `1.webp`, `2.webp`, `3.webp`, etc.\n","- Significantly smaller file sizes (WebP compression typically 25-35% better than JPEG)\n","- Ready for web use, storage, or sharing\n","\n","## 🎯 Perfect For\n","- Organizing Chrome download clutter\n","- Web developers needing optimized images\n","- Anyone wanting to save Google Drive storage space\n","- Creating indexed image collections for ML datasets or galleries\n","\n","**Run this notebook to instantly compress and organize your Chrome-saved images into a clean, numbered WebP collection!**\n","\n","*Note: Requires Google Drive authentication when first run.*"]}],"metadata":{"colab":{"provenance":[{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1760450712160},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1756712618300},{"file_id":"https://huggingface.co/codeShare/JupyterNotebooks/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1747490904984},{"file_id":"https://huggingface.co/codeShare/JupyterNotebooks/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1740037333374},{"file_id":"https://huggingface.co/codeShare/JupyterNotebooks/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1736477078136},{"file_id":"https://huggingface.co/codeShare/JupyterNotebooks/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1725365086834}]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}
+{"cells":[{"cell_type":"code","source":["# --------------------------------------------------------------\n","# Google Colab: Convert a folder of PDFs → 1256×1256 PNGs\n","# --------------------------------------------------------------\n","\n","# 1. Install required libraries\n","!apt-get -qq update\n","!apt-get -qq install -y poppler-utils   # provides pdftoppm\n","!pip install -q pdf2image tqdm\n","\n","# 2. Import modules\n","import os\n","from pathlib import Path\n","from pdf2image import convert_from_path\n","from PIL import Image\n","from tqdm import tqdm\n","import zipfile\n","import shutil\n","\n","# --------------------------------------------------------------\n","# 3. Choose source: upload files OR mount Google Drive\n","# --------------------------------------------------------------\n","USE_DRIVE = True  # Set True to read PDFs from Drive\n","\n","if USE_DRIVE:\n","    from google.colab import drive\n","    drive.mount('/content/drive')\n","    pdf_dir = Path('/content/drive/MyDrive/PDFs')   # <-- change to your folder\n","else:\n","    from google.colab import files\n","    uploaded = files.upload()   # <-- upload PDFs here\n","    pdf_dir = Path('/content/uploaded_pdfs')\n","    pdf_dir.mkdir(exist_ok=True)\n","    for name, data in uploaded.items():\n","        (pdf_dir / name).write_bytes(data)\n","\n","# --------------------------------------------------------------\n","# 4. Output folder & zip\n","# --------------------------------------------------------------\n","out_dir = Path('/content/png_output')\n","out_dir.mkdir(exist_ok=True)\n","\n","zip_path = '/content/pdf_pngs_1256x1256.zip'\n","\n","# --------------------------------------------------------------\n","# 5. Conversion function\n","# --------------------------------------------------------------\n","def pdf_to_1256_png(pdf_path: Path, out_dir: Path):\n","    \"\"\"\n","    Convert each page of a PDF into a 1256×1256 PNG.\n","    The page is scaled to fit inside the square while keeping aspect ratio.\n","    \"\"\"\n","    # Convert PDF pages to PIL images (dpi≈300 → good quality)\n","    pages = convert_from_path(str(pdf_path), dpi=300)\n","\n","    base_name = pdf_path.stem\n","    for i, page in enumerate(pages, start=1):\n","        # Create a 1256×1256 white canvas\n","        canvas = Image.new('RGB', (1256, 1256), (255, 255, 255))\n","\n","        # Compute scaling to fit the page inside the canvas\n","        page_ratio = page.width / page.height\n","        target_ratio = 1256 / 1256\n","\n","        if page_ratio > target_ratio:\n","            # fit width\n","            new_w = 1256\n","            new_h = int(1256 / page_ratio)\n","        else:\n","            # fit height\n","            new_h = 1256\n","            new_w = int(1256 * page_ratio)\n","\n","        resized = page.resize((new_w, new_h), Image.LANCZOS)\n","\n","        # Center the resized image on the canvas\n","        offset = ((1256 - new_w) // 2, (1256 - new_h) // 2)\n","        canvas.paste(resized, offset)\n","\n","        # Save\n","        png_name = f\"{base_name}_page{i:03d}.png\"\n","        canvas.save(out_dir / png_name, 'PNG')\n","    return len(pages)\n","\n","# --------------------------------------------------------------\n","# 6. Run conversion for all PDFs\n","# --------------------------------------------------------------\n","pdf_files = list(pdf_dir.glob('*.pdf'))\n","if not pdf_files:\n","    raise FileNotFoundError(\"No PDF files found in the selected folder.\")\n","\n","total_pages = 0\n","for pdf_path in tqdm(pdf_files, desc=\"Converting PDFs\"):\n","    total_pages += pdf_to_1256_png(pdf_path, out_dir)\n","\n","print(f\"\\nDone! Converted {len(pdf_files)} PDFs → {total_pages} PNGs\")\n","print(f\"PNG folder: {out_dir}\")\n","\n","# --------------------------------------------------------------\n","# 7. Zip & download\n","# --------------------------------------------------------------\n","print(\"Creating zip archive...\")\n","with zipfile.ZipFile(zip_path, 'w', zipfile.ZIP_DEFLATED) as z:\n","    for png_file in out_dir.rglob('*.png'):\n","        z.write(png_file, arcname=png_file.relative_to(out_dir.parent))\n","\n","print(f\"Zip ready: {zip_path}\")\n","#files.download(zip_path)"],"metadata":{"id":"6vhJw80FW6Db"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"6HquiGjaJiYK"},"outputs":[],"source":["# Install required libraries\n","#!pip install pillow google-colab\n","\n","from google.colab import drive\n","from PIL import Image\n","import os\n","import zipfile\n","from pathlib import Path\n","import shutil\n","\n","# Mount Google Drive\n","drive.mount('/content/drive')\n","\n","# Define paths and settings\n","source_dir = '/content/drive/MyDrive/Saved from Chrome/'\n","output_zip = '/content/images_lossless_1024_webp.zip'\n","webp_dir = '/content/temp_lossless_1024_webp'\n","MAX_SIZE = 1024  # Maximum dimension (width or height)\n","\n","# Create temporary directory for resized lossless webp files\n","os.makedirs(webp_dir, exist_ok=True)\n","\n","# Get all image files\n","image_extensions = {'.jpg', '.jpeg', '.png', '.bmp', '.tiff', '.gif', '.webp'}\n","image_files = []\n","\n","print(\"🔍 Scanning for images...\")\n","for file_path in Path(source_dir).rglob('*'):\n","    if file_path.is_file() and file_path.suffix.lower() in image_extensions:\n","        image_files.append(file_path)\n","\n","print(f\"✅ Found {len(image_files)} image files\")\n","\n","# Convert, resize, and save as lossless WebP\n","webp_files = []\n","failed_conversions = []\n","resize_stats = {'resized': 0, 'unchanged': 0}\n","\n","print(\"🔄 Resizing to 1024px max dimension & converting to lossless WebP...\")\n","for i, img_path in enumerate(image_files, 1):\n","    try:\n","        # Open image\n","        with Image.open(img_path) as img:\n","            original_width, original_height = img.size\n","\n","            # Resize if either dimension exceeds MAX_SIZE\n","            if original_width > MAX_SIZE or original_height > MAX_SIZE:\n","                # Calculate scaling factor to fit within MAX_SIZE\n","                scale = min(MAX_SIZE / original_width, MAX_SIZE / original_height)\n","                new_width = int(original_width * scale)\n","                new_height = int(original_height * scale)\n","\n","                # Resize using high-quality LANCZOS resampling\n","                img_resized = img.resize((new_width, new_height), Image.Resampling.LANCZOS)\n","                resize_stats['resized'] += 1\n","            else:\n","                img_resized = img\n","                resize_stats['unchanged'] += 1\n","\n","            # Preserve original mode (including transparency) for lossless conversion\n","            # Save as lossless WebP\n","            webp_path = os.path.join(webp_dir, f\"{i:04d}.webp\")\n","            img_resized.save(webp_path, 'WEBP', lossless=True, method=6)\n","            webp_files.append(webp_path)\n","\n","        if i % 10 == 0 or i == len(image_files):\n","            print(f\"  Processed {i}/{len(image_files)} images\")\n","\n","    except Exception as e:\n","        error_msg = f\"Error processing {img_path.name}: {str(e)}\"\n","        print(f\"❌ {error_msg}\")\n","        failed_conversions.append((img_path, str(e)))\n","        continue\n","\n","print(f\"\\n📏 Resize Statistics:\")\n","print(f\"   Images resized: {resize_stats['resized']}\")\n","print(f\"   Images unchanged: {resize_stats['unchanged']}\")\n","print(f\"✅ Successfully converted {len(webp_files)} images to 1024px lossless WebP\")\n","\n","# Create zip file with maximum compression\n","print(\"📦 Creating ZIP archive...\")\n","with zipfile.ZipFile(output_zip, 'w', zipfile.ZIP_DEFLATED, compresslevel=9) as zipf:\n","    for webp_file in webp_files:\n","        arcname = os.path.basename(webp_file)\n","        zipf.write(webp_file, arcname)\n","        if len(webp_files) <= 20 or webp_files.index(webp_file) % 20 == 0:\n","            print(f\"  Added {arcname}\")\n","\n","# Calculate statistics\n","original_size = sum(os.path.getsize(f) for f in image_files)\n","webp_size = sum(os.path.getsize(f) for f in webp_files)\n","zip_size = os.path.getsize(output_zip)\n","\n","size_reduction = (1 - webp_size/original_size) * 100 if original_size > 0 else 0\n","\n","print(f\"\\n📊 Conversion Statistics:\")\n","print(f\"   Original images size:    {original_size / (1024*1024):.2f} MB\")\n","print(f\"   1024px Lossless WebP:    {webp_size / (1024*1024):.2f} MB\")\n","print(f\"   Size reduction:          {size_reduction:.1f}%\")\n","print(f\"   Final ZIP size:          {zip_size / (1024*1024):.2f} MB\")\n","\n","# Copy zip back to Google Drive\n","drive_output = '/content/drive/MyDrive/images_1024_lossless_webp.zip'\n","shutil.copy2(output_zip, drive_output)\n","print(f\"💾 ZIP saved to Google Drive: {drive_output}\")\n","\n","# Clean up temporary directory\n","shutil.rmtree(webp_dir)\n","print(\"🧹 Temporary files cleaned up\")\n","\n","# Display sample of files in zip\n","print(\"\\n📋 Sample files in ZIP:\")\n","with zipfile.ZipFile(output_zip, 'r') as zipf:\n","    file_list = zipf.namelist()\n","    for i, filename in enumerate(file_list[:10], 1):\n","        print(f\"   {i}. {filename}\")\n","    if len(file_list) > 10:\n","        print(f\"   ... and {len(file_list) - 10} more files\")\n","\n","print(f\"\\n🎯 AI-Training Ready (1024px Optimized)!\")\n","print(f\"   • ✅ 1024px max resolution - standardized for ML\")\n","print(f\"   • ✅ 100% lossless quality - NO compression artifacts\")\n","print(f\"   • ✅ Preserves transparency (RGBA support)\")\n","print(f\"   • ✅ High-quality LANCZOS resampling\")\n","print(f\"   • ✅ Sequential numbering: 0001.webp, 0002.webp, etc.\")\n","print(f\"   • 🎯 Perfect for CNNs, GANs, object detection, segmentation\")\n","print(f\"   • ⚡ Consistent input size for faster training\")\n","print(f\"   • 💾 Significant storage savings vs original high-res images\")\n","\n","if failed_conversions:\n","    print(f\"\\n⚠️  Failed conversions (check originals):\")\n","    for orig_path, error in failed_conversions[:5]:\n","        print(f\"   • {orig_path.name}: {error}\")\n","    if len(failed_conversions) > 5:\n","        print(f\"   ... and {len(failed_conversions) - 5} more\")\n","\n","print(f\"\\n🔧 Technical Details:\")\n","print(f\"   • Max dimension: {MAX_SIZE}px (maintains aspect ratio)\")\n","print(f\"   • Resampling: LANCZOS (highest quality)\")\n","print(f\"   • Lossless WebP: Perfect fidelity, ~50% smaller than PNG\")"]},{"cell_type":"markdown","metadata":{"id":"lz_4gtiQAdEP"},"source":["# 📸 Google Drive Image Converter to WebP\n","\n","**Convert your Chrome-saved images to compact WebP format with automatic indexing!**\n","\n","This Google Colab notebook transforms all images from your `MyDrive/Saved from Chrome/` folder into optimized WebP files, named sequentially as `1.webp`, `2.webp`, `3.webp`, etc., and packages them into a single ZIP archive.\n","\n","## ✨ Features\n","- **Batch Processing**: Automatically finds and converts JPG, PNG, GIF, BMP, TIFF, and existing WebP files\n","- **Smart Conversion**: Handles transparency by converting RGBA/P modes to RGB with white background\n","- **High-Quality WebP**: Uses 85% quality setting for optimal size/quality balance\n","- **Recursive Search**: Processes images in subfolders too\n","- **Error-Resilient**: Skips corrupt files and continues processing\n","- **Dual Output**: Saves ZIP locally in Colab AND copies to your Google Drive root\n","- **Progress Tracking**: Shows conversion progress and final statistics\n","\n","## 🚀 How It Works\n","1. Mounts your Google Drive\n","2. Scans for image files in \"Saved from Chrome/\" and subdirectories\n","3. Converts each image to WebP with sequential numbering\n","4. Creates a compressed ZIP archive\n","5. Cleans up temporary files and reports file sizes\n","\n","## 💾 Output\n","- `images_webp.zip` containing `1.webp`, `2.webp`, `3.webp`, etc.\n","- Significantly smaller file sizes (WebP compression typically 25-35% better than JPEG)\n","- Ready for web use, storage, or sharing\n","\n","## 🎯 Perfect For\n","- Organizing Chrome download clutter\n","- Web developers needing optimized images\n","- Anyone wanting to save Google Drive storage space\n","- Creating indexed image collections for ML datasets or galleries\n","\n","**Run this notebook to instantly compress and organize your Chrome-saved images into a clean, numbered WebP collection!**\n","\n","*Note: Requires Google Drive authentication when first run.*"]}],"metadata":{"colab":{"provenance":[{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/Drive to WebP.ipynb","timestamp":1760993725927},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1760450712160},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1756712618300},{"file_id":"https://huggingface.co/codeShare/JupyterNotebooks/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1747490904984},{"file_id":"https://huggingface.co/codeShare/JupyterNotebooks/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1740037333374},{"file_id":"https://huggingface.co/codeShare/JupyterNotebooks/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1736477078136},{"file_id":"https://huggingface.co/codeShare/JupyterNotebooks/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1725365086834}]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}