Upload vertical_slice_prepper.ipynb

vertical_slice_prepper.ipynb

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+{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"hDzdSOe90dAa"},"outputs":[],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"markdown","metadata":{"id":"JrHflBf3CETe"},"source":["Prior to running the gray background cell , you can use an AI image edit tool to set the entire background of all images to gray.  "]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"Bl8b8izvBl3D"},"outputs":[],"source":["#@title 🖼️ **Image Background Processor**\n","#@markdown **Crop left/right empty space + Convert gray background to exact #181818**\n","#@markdown ---\n","#@markdown 1. Run this cell (Shift + Enter)\n","#@markdown 2. Fill the form below\n","#@markdown 3. Click the **Run** button (or Shift + Enter again)\n","#@markdown ---\n","#@markdown **Features**\n","#@markdown • Mounts Google Drive automatically\n","#@markdown • Unzips your file\n","#@markdown • Crops **only left & right** (height stays exactly the same)\n","#@markdown • Gently changes background to **#181818** (prioritizes edges, protects center content)\n","#@markdown • Saves `PROCESSED_yourfile.zip` in the **same Drive folder**\n","\n","# ==================== FORM PARAMETERS ====================\n","\n","zip_path = \"/content/drive/MyDrive/my_images.zip\" #@param {type:\"string\", placeholder:\"Paste full path to your ZIP file here\"}\n","\n","tolerance_crop = 30 #@param {type:\"slider\", min:0, max:100, step:1, title:\"🟦 Crop Tolerance (higher = more aggressive cropping)\"}\n","\n","bg_replace_threshold = 40 #@param {type:\"slider\", min:10, max:100, step:1, title:\"🎨 Background Match Threshold (lower = stricter gray detection)\"}\n","\n","force_remount = False #@param {type:\"boolean\", title:\"🔄 Force remount Drive (recommended)\"}\n","\n","#@markdown ---\n","\n","from google.colab import drive\n","import zipfile\n","import os\n","from PIL import Image\n","\n","# ========================= CONFIG =========================\n","TARGET_GRAY = (24, 24, 24)   # Exact #181818 - do not change\n","# =======================================================\n","\n","print(\"🚀 Starting image processor...\")\n","\n","# 1. Mount Google Drive\n","drive.mount('/content/drive', force_remount=force_remount)\n","\n","# 2. Validate ZIP path\n","if not zip_path or not os.path.exists(zip_path):\n","    raise FileNotFoundError(f\"❌ ZIP file not found:\\n{zip_path}\\n\\nPlease paste the correct full path (e.g. /content/drive/MyDrive/my_photos.zip)\")\n","\n","print(f\"✅ ZIP found: {zip_path}\")\n","\n","# 3. Create working folders\n","extract_dir = '/content/extracted_images'\n","processed_dir = '/content/processed_images'\n","os.makedirs(extract_dir, exist_ok=True)\n","os.makedirs(processed_dir, exist_ok=True)\n","\n","# 4. Unzip\n","print(\"📦 Unzipping...\")\n","with zipfile.ZipFile(zip_path, 'r') as zip_ref:\n","    zip_ref.extractall(extract_dir)\n","print(f\"✅ Unzipped to {extract_dir}\")\n","\n","# Helper 1: Crop only left & right empty space\n","def crop_left_right(img):\n","    if img.mode != 'RGB':\n","        img = img.convert('RGB')\n","\n","    width, height = img.size\n","    bg_color = img.getpixel((0, 0))   # top-left pixel = background reference\n","\n","    left = width\n","    right = 0\n","\n","    for x in range(width):\n","        column_is_empty = True\n","        for y in range(height):\n","            px = img.getpixel((x, y))\n","            if any(abs(a - b) > tolerance_crop for a, b in zip(px, bg_color)):\n","                column_is_empty = False\n","                break\n","        if not column_is_empty:\n","            left = min(left, x)\n","            right = max(right, x)\n","\n","    if left >= right:\n","        return img\n","\n","    return img.crop((left, 0, right + 1, height))\n","\n","# Helper 2: Gently adjust background to exact #181818\n","def adjust_background(img, original_bg_color):\n","    if img.mode != 'RGB':\n","        img = img.convert('RGB')\n","\n","    width, height = img.size\n","    pixels = img.load()\n","\n","    for x in range(width):\n","        for y in range(height):\n","            px = pixels[x, y]\n","            dist = ((px[0] - original_bg_color[0])**2 +\n","                    (px[1] - original_bg_color[1])**2 +\n","                    (px[2] - original_bg_color[2])**2) ** 0.5\n","\n","            if dist <= bg_replace_threshold:\n","                pixels[x, y] = TARGET_GRAY\n","\n","    return img\n","\n","# 5. Process every image\n","print(\"🖼️ Processing images...\")\n","image_extensions = ('.png', '.jpg', '.jpeg', '.bmp', '.tiff', '.webp')\n","\n","processed_count = 0\n","for filename in os.listdir(extract_dir):\n","    if filename.lower().endswith(image_extensions):\n","        img_path = os.path.join(extract_dir, filename)\n","        try:\n","            with Image.open(img_path) as original_img:\n","                cropped = crop_left_right(original_img)\n","                orig_bg = original_img.getpixel((0, 0))\n","                processed = adjust_background(cropped, orig_bg)\n","\n","                out_path = os.path.join(processed_dir, filename)\n","                processed.save(out_path, quality=95 if filename.lower().endswith(('.jpg', '.jpeg')) else None)\n","\n","                processed_count += 1\n","                print(f\"   ✅ {filename}\")\n","        except Exception as e:\n","            print(f\"   ❌ Skipped {filename}: {e}\")\n","\n","print(f\"\\n🎉 Processed {processed_count} images!\")\n","\n","# 6. Create final ZIP in Drive\n","output_zip_name = \"PROCESSED_\" + os.path.basename(zip_path)\n","output_zip_path = os.path.join(os.path.dirname(zip_path), output_zip_name)\n","\n","print(f\"📦 Creating final ZIP: {output_zip_name}\")\n","with zipfile.ZipFile(output_zip_path, 'w', zipfile.ZIP_DEFLATED) as zip_out:\n","    for root, _, files in os.walk(processed_dir):\n","        for file in files:\n","            file_path = os.path.join(root, file)\n","            arcname = os.path.relpath(file_path, processed_dir)\n","            zip_out.write(file_path, arcname)\n","\n","print(\"\\n✅ ALL DONE!\")\n","print(f\"📍 Saved to your Drive at:\")\n","print(f\"   {output_zip_path}\")\n","print(\"\\n🔗 Open the file directly in Google Drive → right-click → Download if needed.\")"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"6dlmgMn_hOeO"},"outputs":[],"source":["# @title Image Processing Pipeline: Unzip → Crop Content → Filter Height → Save COLORED + WHITE Background Zips to Drive\n","# Run this cell after mounting Drive (it will prompt if needed)\n","\n","from google.colab import drive\n","import os\n","import zipfile\n","import shutil\n","from PIL import Image, ImageChops\n","\n","# Mount Google Drive (run once per session)\n","drive.mount('/content/drive', force_remount=False)\n","\n","# ==================== INPUT: ZIP PATH ====================\n","# Replace the path below with your ZIP file location on Drive\n","zip_path = '/content/drive/MyDrive/my_images2.zip'  #@param {type:\"string\"}\n","\n","if not os.path.exists(zip_path):\n","    raise FileNotFoundError(f\"ZIP file not found: {zip_path}\\nPlease update the zip_path variable above.\")\n","\n","# ==================== SETUP TEMP DIRECTORIES ====================\n","extract_dir = '/content/extracted_images'\n","colored_bg_dir = '/content/colored_background_images'\n","white_bg_dir = '/content/white_background_images'\n","\n","for d in [extract_dir, colored_bg_dir, white_bg_dir]:\n","    os.makedirs(d, exist_ok=True)\n","\n","# ==================== HELPER FUNCTIONS ====================\n","def crop_to_content_and_bg(im):\n","    \"\"\"Crop empty space using either alpha (transparent) or solid background color.\n","    Returns (cropped_image, background_color) or (None, None) if empty.\"\"\"\n","    if im.size[0] == 0 or im.size[1] == 0:\n","        return None, None\n","\n","    # Get original top-left pixel as potential background color\n","    bg_color = im.getpixel((0, 0))\n","\n","    # Handle transparent images (RGBA / LA / P with transparency)\n","    if im.mode in ('RGBA', 'LA') or (im.mode == 'P' and 'transparency' in im.info):\n","        try:\n","            alpha = im.split()[-1]  # last channel is alpha\n","            bbox = alpha.getbbox()\n","            if bbox:\n","                return im.crop(bbox), None  # transparent = treated as COLORED background\n","        except:\n","            pass\n","\n","    # Solid color background case (RGB or fallback)\n","    bg = Image.new(im.mode, im.size, bg_color)\n","    diff = ImageChops.difference(im, bg)\n","    bbox = diff.getbbox()\n","    if bbox:\n","        return im.crop(bbox), bg_color\n","    return None, None\n","\n","\n","def is_light_background(bg_color):\n","    \"\"\"Return True if background is white or light-colored (luminance > 200).\"\"\"\n","    if bg_color is None:\n","        return False\n","    # Handle RGBA\n","    if len(bg_color) == 4:\n","        r, g, b, a = bg_color\n","        if a < 200:  # mostly transparent\n","            return False\n","        bg_color = (r, g, b)\n","    r, g, b = bg_color[:3]\n","    # Standard luminance formula\n","    luminance = 0.299 * r + 0.587 * g + 0.114 * b\n","    return luminance > 200\n","\n","\n","# ==================== UNPACK ZIP + CLEAN NON-IMAGE JUNK ====================\n","print(\"📦 Unpacking ZIP...\")\n","with zipfile.ZipFile(zip_path, 'r') as zip_ref:\n","    zip_ref.extractall(extract_dir)\n","\n","# Explicitly remove common non-image junk (Mac __MACOSX folder + ._ files)\n","print(\"🧹 Cleaning non-image items (__MACOSX, ._ files, etc.)...\")\n","macosx_path = os.path.join(extract_dir, '__MACOSX')\n","if os.path.exists(macosx_path):\n","    shutil.rmtree(macosx_path)\n","    print(\"   Removed __MACOSX directory.\")\n","\n","# ==================== FIND ALL IMAGES (recursive + extra safety checks) ====================\n","image_paths = []\n","valid_extensions = ('.png', '.jpg', '.jpeg', '.webp', '.bmp', '.tiff')\n","\n","for root, _, files in os.walk(extract_dir):\n","    # Skip any residual __MACOSX folder (in case it was recreated)\n","    if '__MACOSX' in root:\n","        continue\n","    for file in files:\n","        # Extra safety: ignore Mac hidden files and only keep real images\n","        if (file.lower().endswith(valid_extensions) and\n","            not file.startswith('._') and\n","            not file.startswith('.')):  # also catches .DS_Store etc.\n","            image_paths.append(os.path.join(root, file))\n","\n","print(f\"Found {len(image_paths)} valid images to process (non-image junk excluded).\")\n","\n","# ==================== PROCESS EACH IMAGE ====================\n","colored_count = 0\n","white_count = 0\n","\n","for filepath in image_paths:\n","    filename = os.path.basename(filepath)\n","    try:\n","        with Image.open(filepath) as img:\n","            # Convert palette mode if needed\n","            if img.mode == 'P':\n","                img = img.convert('RGBA')\n","\n","            cropped, bg_color = crop_to_content_and_bg(img)\n","\n","            if cropped is None or cropped.height < 1024:\n","                continue\n","\n","            # Decide which folder based on background\n","            if is_light_background(bg_color):\n","                out_dir = white_bg_dir\n","                white_count += 1\n","            else:\n","                out_dir = colored_bg_dir\n","                colored_count += 1\n","\n","            out_path = os.path.join(out_dir, filename)\n","            cropped.save(out_path)   # PIL auto-detects format from extension\n","\n","    except Exception as e:\n","        print(f\"⚠️ Skipped {filename}: {e}\")\n","\n","total_kept = colored_count + white_count\n","print(f\"✅ Processing complete!\")\n","print(f\"   • Total images kept (height ≥ 1024px): {total_kept}\")\n","print(f\"   • Colored background images: {colored_count}\")\n","print(f\"   • White / light background images: {white_count}\")\n","\n","# ==================== CREATE ZIPS ====================\n","def make_zip(source_dir, zip_filename):\n","    \"\"\"Create ZIP and exclude any non-image files (extra safety).\"\"\"\n","    zip_full_path = f'/content/{zip_filename}'\n","    with zipfile.ZipFile(zip_full_path, 'w', zipfile.ZIP_DEFLATED) as zf:\n","        for root, _, files in os.walk(source_dir):\n","            for file in files:\n","                if file.lower().endswith(valid_extensions) and not file.startswith('._'):\n","                    file_path = os.path.join(root, file)\n","                    arcname = os.path.relpath(file_path, source_dir)\n","                    zf.write(file_path, arcname)\n","    return zip_full_path\n","\n","print(\"🗜️ Creating ZIP files...\")\n","colored_zip = make_zip(colored_bg_dir, 'colored_background_images.zip')\n","white_zip = make_zip(white_bg_dir, 'white_background_images.zip')\n","\n","# ==================== COPY TO GOOGLE DRIVE ====================\n","drive_colored = '/content/drive/MyDrive/colored_background_images.zip'\n","drive_white = '/content/drive/MyDrive/white_background_images.zip'\n","\n","shutil.copy(colored_zip, drive_colored)\n","shutil.copy(white_zip, drive_white)\n","\n","print(\"🎉 DONE!\")\n","print(f\"📁 Colored background images → {drive_colored}\")\n","print(f\"📁 White / light background images → {drive_white}\")\n","print(\"\\nYou can now download the two separate ZIPs from your Google Drive.\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"93JpoQJ77NGu","cellView":"form"},"outputs":[],"source":["# ────────────────────────────────────────────────\n","# 🎨 CLEAN ROW → PURE 1024x1024 FRAMES (NO DISTORTION)\n","# ────────────────────────────────────────────────\n","\n","#@markdown ## ⚙️ Main Settings\n","num_frames = 500  #@param {type:\"slider\", min:10, max:2000, step:10}\n","overlap_percent = 25  #@param {type:\"slider\", min:0, max:50, step:1}\n","min_step_ratio = 0.8  #@param {type:\"slider\", min:0.5, max:1.0, step:0.05}\n","max_step_ratio = 1.0  #@param {type:\"slider\", min:0.8, max:1.5, step:0.05}\n","\n","#@markdown ## 🧱 Border\n","border_width = 8  #@param {type:\"slider\", min:0, max:100}\n","\n","#@markdown ## 💾 Input / Output\n","save_to_google_drive = True  #@param {type:\"boolean\"}\n","drive_folder_name = \"vertical_slices_output\"  #@param {type:\"string\"}\n","zip_file_path = \"/content/drive/MyDrive/output_result.zip\"  #@param {type:\"string\"}\n","\n","# ────────────────────────────────────────────────\n","\n","FRAME_SIZE = 1024\n","INNER_SIZE = FRAME_SIZE - 2 * border_width\n","BORDER_COLOR = (24, 24, 24)\n","\n","import os, random, zipfile\n","import numpy as np\n","from PIL import Image\n","from google.colab import drive\n","\n","# ────────────────────────────────────��───────────\n","# Mount Drive\n","# ────────────────────────────────────────────────\n","\n","drive_mounted = False\n","if save_to_google_drive:\n","    try:\n","        drive.mount('/content/drive', force_remount=False)\n","        drive_mounted = True\n","        drive_output_dir = os.path.join(\"/content/drive/MyDrive\", drive_folder_name)\n","        os.makedirs(drive_output_dir, exist_ok=True)\n","        print(\"Drive mounted\")\n","    except:\n","        save_to_google_drive = False\n","\n","# ────────────────────────────────────────────────\n","# Extract ZIP\n","# ────────────────────────────────────────────────\n","\n","extract_root = \"/content/extracted_images\"\n","output_dir = \"/content/frames\"\n","\n","os.makedirs(extract_root, exist_ok=True)\n","os.makedirs(output_dir, exist_ok=True)\n","\n","with zipfile.ZipFile(zip_file_path, 'r') as zf:\n","    zf.extractall(extract_root)\n","\n","# ────────────────────────────────────────────────\n","# Load images\n","# ────────────────────────────────────────────────\n","\n","valid_exts = ('.jpg','.jpeg','.png','.webp')\n","all_sources = []\n","\n","for root, _, files in os.walk(extract_root):\n","    for f in files:\n","        if f.lower().endswith(valid_exts):\n","            all_sources.append(os.path.join(root, f))\n","\n","print(\"Images found:\", len(all_sources))\n","\n","# ────────────────────────────────────────────────\n","# Build row UNTIL enough width\n","# ────────────────────────────────────────────────\n","\n","def build_row_until_width(all_sources, target_h, overlap_ratio, required_width):\n","    canvas = Image.new(\"RGB\", (required_width, target_h))\n","    x = 0\n","\n","    while x < required_width:\n","        src = random.choice(all_sources)\n","\n","        try:\n","            im = Image.open(src).convert(\"RGB\")\n","            w, h = im.size\n","\n","            scale = target_h / h\n","            new_w = int(w * scale)\n","\n","            im = im.resize((new_w, target_h), Image.LANCZOS)\n","\n","            # overlap\n","            if x > 0:\n","                x -= int(new_w * overlap_ratio)\n","\n","            canvas.paste(im, (x, 0))\n","            x += new_w\n","\n","        except:\n","            continue\n","\n","    return canvas\n","\n","# ────────────────────────────────────────────────\n","# Calculate required width\n","# ────────────────────────────────────────────────\n","\n","overlap_ratio = overlap_percent / 100.0\n","\n","avg_step = (min_step_ratio + max_step_ratio) / 2\n","estimated_width = int(num_frames * INNER_SIZE * avg_step * 1.2)\n","\n","print(\"Building row width:\", estimated_width)\n","\n","row_img = build_row_until_width(\n","    all_sources,\n","    INNER_SIZE,\n","    overlap_ratio,\n","    estimated_width\n",")\n","\n","row_np = np.array(row_img)\n","row_h, row_w = row_np.shape[:2]\n","\n","# ────────────────────────────────────────────────\n","# Generate frames (NO distortion)\n","# ────────────────────────────────────────────────\n","\n","frame_idx = 0\n","x_cursor = 0\n","\n","while x_cursor + INNER_SIZE <= row_w and frame_idx < num_frames:\n","\n","    step = random.randint(\n","        int(INNER_SIZE * min_step_ratio),\n","        int(INNER_SIZE * max_step_ratio)\n","    )\n","\n","    crop = row_np[:, x_cursor:x_cursor + INNER_SIZE]\n","\n","    final = Image.new(\"RGB\", (FRAME_SIZE, FRAME_SIZE), BORDER_COLOR)\n","    final.paste(Image.fromarray(crop), (border_width, border_width))\n","\n","    fname = f\"frame_{frame_idx:04d}.jpg\"\n","    final.save(os.path.join(output_dir, fname), \"JPEG\", quality=90)\n","\n","    frame_idx += 1\n","    x_cursor += step\n","\n","    if frame_idx % 50 == 0:\n","        print(frame_idx, \"frames done\")\n","\n","print(\"Finished:\", frame_idx)\n","\n","# ────────────────────────────────────────────────\n","# Save ZIP\n","# ─────────────────────────────���──────────────────\n","\n","if save_to_google_drive and drive_mounted:\n","\n","    zip_path = \"/content/output.zip\"\n","    drive_path = os.path.join(drive_output_dir, \"vertical_slices.zip\")\n","\n","    with zipfile.ZipFile(zip_path, 'w', zipfile.ZIP_DEFLATED) as zf:\n","        for f in os.listdir(output_dir):\n","            if f.endswith(\".jpg\"):\n","                zf.write(os.path.join(output_dir, f), arcname=f)\n","\n","    !cp -f \"{zip_path}\" \"{drive_path}\"\n","\n","    print(\"Saved to Drive:\", drive_path)"]},{"cell_type":"markdown","metadata":{"id":"Ct1FG-YemBeK"},"source":["From here the dataset is in your drive and the notebook can be safely disconnected"]},{"cell_type":"markdown","source":["# Other stuff"],"metadata":{"id":"RhSUCuRON_0t"}},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","id":"DCZpKdWX0UNZ"},"outputs":[],"source":["#@title WD Tagger + TOS Filter + Caption Cleaner + Tag Spreading → Drive (single-line + comma spacing) { run: \"auto\" }\n","from google.colab import drive\n","import os\n","import zipfile\n","from pathlib import Path\n","import shutil\n","from tqdm.auto import tqdm\n","import re\n","!pip install timm pillow pandas requests nltk -q\n","\n","import timm\n","import torch\n","from PIL import Image\n","import torchvision.transforms as transforms\n","import pandas as pd\n","import requests\n","from io import StringIO\n","import nltk\n","from collections import defaultdict\n","\n","# Fix for recent NLTK versions\n","nltk.download('punkt', quiet=True)\n","nltk.download('punkt_tab', quiet=True)\n","\n","# ────────────────────────────────────────────────\n","#@markdown ### Settings\n","drive.mount('/content/drive', force_remount=False)\n","\n","zip_path = \"/content/drive/MyDrive/vertical_slices_output/vertical_slices.zip\"                        #@param {type:\"string\"}\n","output_zip_name = \"cleaned_tagged_dataset.zip\"  #@param {type:\"string\"}\n","output_folder_on_drive = \"/content/drive/MyDrive/Cleaned_Datasets\"  #@param {type:\"string\"}\n","\n","case_sensitive_loli_check = False     #@param {type:\"boolean\"}\n","tag_probability_threshold = 0.35     #@param {type:\"slider\", min:0.1, max:0.6, step:0.05}\n","\n","# ────────────────────────────────────────────────\n","if not zip_path or not os.path.isfile(zip_path):\n","    print(\"❌ Please provide a valid zip file path\")\n","    raise SystemExit\n","\n","print(f\"📦 Input zip:  {zip_path}\")\n","print(f\"📤 Will save:  {output_folder_on_drive}/{output_zip_name}\\n\")\n","\n","# ────────────────────────────────────────────────\n","extract_dir = Path(\"/content/extracted\")\n","cleaned_dir = Path(\"/content/cleaned_dataset\")\n","\n","shutil.rmtree(extract_dir, ignore_errors=True)\n","shutil.rmtree(cleaned_dir, ignore_errors=True)\n","extract_dir.mkdir(exist_ok=True, parents=True)\n","cleaned_dir.mkdir(exist_ok=True, parents=True)\n","\n","print(\"📂 Extracting archive...\")\n","with zipfile.ZipFile(zip_path, 'r') as zf:\n","    zf.extractall(extract_dir)\n","\n","# ────────────────────────────────────────────────\n","# Load WD tagger\n","print(\"🔧 Loading WD tagger model...\")\n","tags_url = \"https://huggingface.co/SmilingWolf/wd-vit-tagger-v3/resolve/main/selected_tags.csv\"\n","tags_df = pd.read_csv(StringIO(requests.get(tags_url).text))\n","tags = tags_df['name'].tolist()\n","\n","model = timm.create_model(\"hf_hub:SmilingWolf/wd-vit-tagger-v3\", pretrained=True)\n","\n","device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","model = model.eval().to(device)\n","\n","preprocess = transforms.Compose([\n","    transforms.Resize((448, 448)),\n","    transforms.ToTensor(),\n","    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n","])\n","\n","def get_wd_tags(img_path):\n","    try:\n","        img = Image.open(img_path).convert(\"RGB\")\n","        x = preprocess(img).unsqueeze(0).to(device)\n","        with torch.no_grad():\n","            logits = model(x)\n","        probs = torch.sigmoid(logits).squeeze(0).cpu().numpy()\n","        selected = [tags[i] for i, p in enumerate(probs) if p > tag_probability_threshold]\n","        return selected\n","    except Exception as e:\n","        print(f\"  tagging failed: {img_path.name} → {str(e)}\")\n","        return []\n","\n","# ────────────────────────────────────────────────\n","def is_junk_file(path: Path) -> bool:\n","    \"\"\"Skip macOS metadata files and common junk\"\"\"\n","    name = path.name\n","    path_str = str(path)\n","    return (\n","        name.startswith('._') or\n","        '__MACOSX' in path_str or\n","        name in {'.DS_Store', 'Thumbs.db', 'desktop.ini', '.Spotlight-V100', '.Trashes'}\n","    )\n","\n","def normalize_skin_tags(tag_list: list) -> list:\n","    \"\"\"Replace blue_skin / colored_skin with plain 'skin' (only once)\"\"\"\n","    new_list = []\n","    has_skin = False\n","    for t in tag_list:\n","        if t in (\"blue_skin\", \"colored_skin\"):\n","            if not has_skin:\n","                new_list.append(\"skin\")\n","                has_skin = True\n","        else:\n","            new_list.append(t)\n","    return new_list\n","\n","def clean_caption(text: str) -> str:\n","    if not text.strip():\n","        return \"\"\n","\n","    # Remove unwanted characters & patterns\n","    text = text.replace('`', '')\n","    text = text.replace('(', '').replace(')', '')\n","    text = text.replace('*', '')\n","\n","    # Remove word 'small' (standalone, case insensitive)\n","    text = re.sub(r'\\bsmall\\b', '', text, flags=re.IGNORECASE)\n","\n","    # Collapse all whitespace including newlines, tabs, etc.\n","    text = re.sub(r'\\s+', ' ', text)\n","\n","    # Common safety / TOS replacements\n","    text = re.sub(r'\\byoung girl\\b', 'young woman', text, flags=re.IGNORECASE)\n","    text = re.sub(r'\\bswastika\\b', 'manji', text, flags=re.IGNORECASE)\n","    text = re.sub(r'\\byoung\\b', '', text, flags=re.IGNORECASE)\n","\n","    return text.strip()\n","\n","def spread_tags_into_caption(caption: str, new_tags: list) -> str:\n","    new_tags = normalize_skin_tags(new_tags)\n","\n","    if not new_tags:\n","        cleaned = clean_caption(caption)\n","        return ' , '.join(cleaned.split(',')).strip() if cleaned else ''\n","\n","    base = clean_caption(caption)\n","    if not base:\n","        return ' , '.join(new_tags)\n","\n","    sentences = nltk.sent_tokenize(base)\n","    if len(sentences) <= 1:\n","        combined = base + \" \" + \" , \".join(new_tags)\n","    else:\n","        # Distribute tags between sentences\n","        num_gaps = len(sentences) - 1\n","        tags_per_gap = max(1, len(new_tags) // num_gaps)\n","        extra = len(new_tags) % num_gaps\n","\n","        parts = []\n","        tag_idx = 0\n","        for i, sent in enumerate(sentences):\n","            parts.append(sent.strip())\n","            if i < num_gaps:\n","                cnt = tags_per_gap + (1 if i < extra else 0)\n","                if cnt > 0:\n","                    group = new_tags[tag_idx : tag_idx + cnt]\n","                    tag_idx += cnt\n","                    parts.append(\" , \".join(group))\n","\n","        # Remaining tags at the end\n","        if tag_idx < len(new_tags):\n","            parts.append(\" , \".join(new_tags[tag_idx:]))\n","\n","        combined = \" \".join(parts)\n","\n","    # Final cleanup: normalize comma spacing\n","    combined = re.sub(r'\\s*,\\s*', ' , ', combined)\n","    combined = re.sub(r'\\s+', ' ', combined).strip()\n","\n","    return combined\n","\n","# ────────────────────────────────────────────────\n","print(\"\\n🔍 Processing files...\\n\")\n","\n","removed = 0\n","kept = 0\n","\n","groups = defaultdict(list)\n","for f in extract_dir.rglob(\"*\"):\n","    if f.is_file() and not is_junk_file(f):\n","        groups[f.stem].append(f)\n","\n","for stem, files in tqdm(groups.items(), desc=\"Groups\"):\n","    # Filter again just in case\n","    valid_files = [f for f in files if not is_junk_file(f)]\n","\n","    imgs = [\n","        f for f in valid_files\n","        if f.suffix.lower() in {'.jpg', '.jpeg', '.png', '.webp', '.gif', '.bmp', '.tiff'}\n","    ]\n","    txts = [f for f in valid_files if f.suffix.lower() == '.txt']\n","\n","    if not imgs:\n","        continue\n","\n","    img = imgs[0]  # take the first valid image\n","    wd_tags = get_wd_tags(img)\n","\n","    # Loli check (case sensitive or not)\n","    joined_tags = ' '.join(wd_tags)\n","    has_loli = 'loli' in (joined_tags.lower() if not case_sensitive_loli_check else joined_tags)\n","\n","    if has_loli:\n","        removed += 1\n","        # Optional: remove files from temp dir (not strictly needed)\n","        # for f in valid_files: f.unlink(missing_ok=True)\n","        continue\n","\n","    kept += 1\n","\n","    # Read original caption if exists\n","    orig_caption = \"\"\n","    if txts:\n","        try:\n","            orig_caption = txts[0].read_text(encoding=\"utf-8\", errors=\"replace\").strip()\n","        except:\n","            pass\n","\n","    # Create final caption\n","    final_caption = spread_tags_into_caption(orig_caption, wd_tags)\n","\n","    # Copy only non-junk files to cleaned folder\n","    for f in valid_files:\n","        rel = f.relative_to(extract_dir)\n","        dst = cleaned_dir / rel\n","        dst.parent.mkdir(parents=True, exist_ok=True)\n","        shutil.copy2(f, dst)\n","\n","    # Write cleaned caption next to the image\n","    txt_name = img.stem + \".txt\"\n","    txt_rel = img.relative_to(extract_dir).parent / txt_name\n","    txt_dst = cleaned_dir / txt_rel\n","    txt_dst.parent.mkdir(parents=True, exist_ok=True)\n","    with open(txt_dst, \"w\", encoding=\"utf-8\") as fw:\n","        fw.write(final_caption)\n","\n","print(f\"\\n✅ Done processing\")\n","print(f\"   Removed (loli detected): {removed}\")\n","print(f\"   Kept & cleaned         : {kept}\")\n","\n","# ────────────────────────────────────────────────\n","print(\"\\n🗜️  Creating output zip...\")\n","\n","final_zip = Path(f\"/content/{output_zip_name}\")\n","\n","with zipfile.ZipFile(final_zip, \"w\", zipfile.ZIP_DEFLATED) as zf:\n","    for item in tqdm(cleaned_dir.rglob(\"*\"), desc=\"Zipping\"):\n","        if item.is_file() and not is_junk_file(item):\n","            arc = item.relative_to(cleaned_dir)\n","            zf.write(item, arc)\n","\n","# ────────────────────────────────────────────────\n","print(\"\\n💾 Copying to Drive...\")\n","os.makedirs(output_folder_on_drive, exist_ok=True)\n","drive_dest = Path(output_folder_on_drive) / output_zip_name\n","shutil.copy2(final_zip, drive_dest)\n","\n","size_mb = final_zip.stat().st_size / (1024 * 1024)\n","print(f\"→ Saved: {drive_dest}\")\n","print(f\"   Size: {size_mb:.1f} MiB\")\n","\n","# ────────────────────────────────────────────────\n","print(\"\\n🧹 Cleaning up temp folders...\")\n","shutil.rmtree(extract_dir, ignore_errors=True)\n","shutil.rmtree(cleaned_dir, ignore_errors=True)\n","\n","print(\"\\nAll finished ✓\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"A3uVQAHkkJXU"},"outputs":[],"source":["# @title Recompose Frames – Variable Columns – Save ZIP to Drive\n","import os\n","import random\n","import zipfile\n","from google.colab import drive\n","from PIL import Image\n","import numpy as np\n","import gc\n","from tqdm.notebook import tqdm\n","\n","# ────────────────────────────────────────────────\n","# Configuration\n","# ────────────────────────────────────────────────\n","#@markdown Input zip path (your previous 1024×1024 frames)\n","input_zip_path = \"/content/drive/MyDrive/my_set.zip\"  #@param {type:\"string\"}\n","\n","#@markdown Output folder name on Google Drive\n","drive_output_folder = \"recomposed_variable_columns\"  #@param {type:\"string\"}\n","\n","#@markdown How many new frames to generate?\n","num_new_frames = 300  #@param {type:\"slider\", min:200, max:8000, step:100}\n","\n","#@markdown Allowed column counts (at least one must be selected)\n","allow_2_columns = True   #@param {type:\"boolean\"}\n","allow_3_columns = False   #@param {type:\"boolean\"}\n","allow_4_columns = False   #@param {type:\"boolean\"}\n","allow_5_columns = False  #@param {type:\"boolean\"}\n","allow_1_column  = False  #@param {type:\"boolean\"}\n","\n","#@markdown ─── Composition settings ───\n","keep_original_height = True      #@param {type:\"boolean\"}  # 1024 px tall\n","add_border               = True  #@param {type:\"boolean\"}\n","border_px                = 12\n","border_color             = (18, 18, 18)\n","\n","#@markdown JPEG quality\n","save_quality = 83  #@param {type:\"slider\", min:65, max:95, step:1}\n","\n","# ────────────────────────────────────────────────\n","# Mount Drive\n","# ────────────────────────────────────────────────\n","drive.mount('/content/drive', force_remount=False)\n","print()\n","\n","drive_base = \"/content/drive/MyDrive\"\n","output_dir_drive = os.path.join(drive_base, drive_output_folder)\n","os.makedirs(output_dir_drive, exist_ok=True)\n","\n","# ────────────────────────────────────────────────\n","# Prepare local temp folders\n","# ────────────────────────────────────────────────\n","extract_dir = \"/content/extracted_frames\"\n","local_output_dir = \"/content/recomposed_temp\"\n","os.makedirs(extract_dir, exist_ok=True)\n","os.makedirs(local_output_dir, exist_ok=True)\n","\n","# ────────────────────────────────────────────────\n","# 1. Extract input zip\n","# ────────────────────────────────────────────────\n","if not os.path.isfile(input_zip_path):\n","    print(f\"❌ File not found: {input_zip_path}\")\n","else:\n","    print(f\"Extracting {os.path.basename(input_zip_path)} …\")\n","    with zipfile.ZipFile(input_zip_path, 'r') as zf:\n","        zf.extractall(extract_dir)\n","    print(\"Extraction done.\\n\")\n","\n","    # ────────────────────────────────────────────────\n","    # 2. Collect all clean vertical strips (~256×1024)\n","    # ────────────────────────────────────────────────\n","    all_strips = []\n","    valid_exts = ('.jpg', '.jpeg', '.png')\n","\n","    print(\"Extracting vertical strips from frames…\")\n","    frame_files = [f for f in os.listdir(extract_dir)\n","                   if f.lower().endswith(valid_exts) and not f.startswith('._')]\n","\n","    for fname in tqdm(frame_files):\n","        try:\n","            img = Image.open(os.path.join(extract_dir, fname)).convert('RGB')\n","            w, h = img.size\n","            if w != 1024 or h != 1024:\n","                continue\n","\n","            inner_w = 1024 - 2 * border_px\n","            strip_w = inner_w // 4\n","\n","            for i in range(4):\n","                left = border_px + i * strip_w\n","                strip = img.crop((left, border_px, left + strip_w, 1024 - border_px))\n","                all_strips.append(strip)\n","\n","            del img\n","            gc.collect()\n","\n","        except:\n","            pass\n","\n","    print(f\"\\nCollected {len(all_strips):,} vertical strips.\\n\")\n","\n","    if len(all_strips) < 2:\n","        print(\"❌ Too few strips to create compositions.\")\n","    else:\n","        # ────────────────────────────────────────────────\n","        # 3. Prepare allowed column counts\n","        # ────────────────────────────────────────────────\n","        possible_cols = []\n","        if allow_1_column:  possible_cols.append(1)\n","        if allow_2_columns: possible_cols.append(2)\n","        if allow_3_columns: possible_cols.append(3)\n","        if allow_4_columns: possible_cols.append(4)\n","        if allow_5_columns: possible_cols.append(5)\n","\n","        if not possible_cols:\n","            print(\"❌ Please enable at least one column count.\")\n","        else:\n","            print(f\"Allowed column counts: {possible_cols}\\n\")\n","\n","            # ────────────────────────────────────────────────\n","            # 4. Generate new variable-width frames\n","            # ────────────────────────────────────────────────\n","            base_strip_w = all_strips[0].width   # usually ~256\n","            target_h = 1024 if keep_original_height else None\n","\n","            print(f\"Generating {num_new_frames} new frames…\")\n","\n","            for i in tqdm(range(num_new_frames)):\n","                num_cols = random.choice(possible_cols)\n","                chosen_strips = random.choices(all_strips, k=num_cols)\n","\n","                # Optional light variation\n","                if random.random() < 0.18:\n","                    chosen_strips = [s.transpose(Image.FLIP_LEFT_RIGHT) if random.random() < 0.5 else s\n","                                     for s in chosen_strips]\n","\n","                canvas_w = num_cols * base_strip_w\n","                canvas_h = target_h if target_h else max(s.height for s in chosen_strips)\n","\n","                canvas = Image.new('RGB', (canvas_w, canvas_h), (0,0,0))\n","\n","                for col, strip in enumerate(chosen_strips):\n","                    paste_img = strip\n","                    if target_h and strip.height != target_h:\n","                        paste_img = strip.resize((base_strip_w, target_h), Image.LANCZOS)\n","\n","                    paste_y = (canvas_h - paste_img.height) // 2\n","                    canvas.paste(paste_img, (col * base_strip_w, paste_y))\n","\n","                # Add border if requested\n","                if add_border:\n","                    bordered = Image.new('RGB', (canvas_w + 2*border_px, canvas_h + 2*border_px), border_color)\n","                    bordered.paste(canvas, (border_px, border_px))\n","                    final = bordered\n","                else:\n","                    final = canvas\n","\n","                # Save\n","                fname = f\"recomp_{i+1:05d}_cols{num_cols}.jpg\"\n","                final.save(os.path.join(local_output_dir, fname), \"JPEG\", quality=save_quality)\n","\n","                if (i+1) % 300 == 0:\n","                    gc.collect()\n","\n","            print(f\"\\nCreated {num_new_frames} frames in {local_output_dir}\")\n","\n","            # ────────────────────────────────────────────────\n","            # 5. Zip and copy to Drive\n","            # ────────────────────────────────────────────────\n","            zip_name = f\"recomposed_{len(possible_cols)}options.zip\"\n","            local_zip = f\"/content/{zip_name}\"\n","            drive_zip = os.path.join(output_dir_drive, zip_name)\n","\n","            print(\"\\nCreating zip (flat structure)…\")\n","            with zipfile.ZipFile(local_zip, 'w', zipfile.ZIP_DEFLATED, compresslevel=6) as zf:\n","                for fname in os.listdir(local_output_dir):\n","                    if fname.endswith(('.jpg','.jpeg','.png')):\n","                        zf.write(os.path.join(local_output_dir, fname), arcname=fname)\n","\n","            print(\"Copying to Google Drive…\")\n","            !cp -f \"{local_zip}\" \"{drive_zip}\"\n","\n","            print(f\"\\nSuccess! ZIP saved to:\")\n","            print(f\"→ {drive_zip}\")\n","\n","            # Optional: clean up local files (uncomment if needed)\n","            # !rm -rf \"{local_output_dir}\" \"{local_zip}\"\n","            print(\"\\nTemporary files kept in /content — delete manually if disk space is low.\")"]}],"metadata":{"colab":{"provenance":[{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/lora_vertical_slice_dataset_creator.ipynb","timestamp":1776287741995},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/lora_vertical_slice_dataset_creator.ipynb","timestamp":1776178739426},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/lora_vertical_slice_dataset_creator.ipynb","timestamp":1776027716448},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/lora_vertical_slice_dataset_creator.ipynb","timestamp":1773663661932},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/civit_caption_prepper.ipynb","timestamp":1773663290922},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/civit_caption_prepper.ipynb","timestamp":1773264797996},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/civit_caption_prepper.ipynb","timestamp":1773163850245},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/civit_caption_prepper.ipynb","timestamp":1773090196076},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/civit_caption_prepper.ipynb","timestamp":1773089575687},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/civit_caption_prepper.ipynb","timestamp":1773080355474},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/Drive to WebP.ipynb","timestamp":1772998638620},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/Drive to WebP.ipynb","timestamp":1763646205520},{"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}],"collapsed_sections":["RhSUCuRON_0t"]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}