Upload floodfill_background_edits.ipynb

floodfill_background_edits.ipynb

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+{"cells":[{"cell_type":"markdown","source":["# Select Klein processed file to use next"],"metadata":{"id":"s7R3l3-pY5W7"}},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"id":"ijzwEoboHVPR","executionInfo":{"status":"ok","timestamp":1779908921604,"user_tz":-120,"elapsed":15976,"user":{"displayName":"","userId":""}},"outputId":"a4c6907f-ab3e-47ac-c675-2474583a08e1","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"OdcEXFs4oBt_","cellView":"form"},"outputs":[],"source":["import os\n","\n","klein_edited_zip_path = '/content/drive/MyDrive/images.zip' #@param {type:'string'}\n","zip_path = klein_edited_zip_path\n","\n","if not os.path.exists(zip_path):\n","    raise FileNotFoundError(f\"❌ Klein edited zip file not found:\\n{zip_path}\\n\\nPlease ensure the path is correct and the file exists in your Drive.\")"]},{"cell_type":"markdown","source":["# ⚙️ Create 1024x1024 frames from selected klein edited set"],"metadata":{"id":"4PjK9a8Sni3A"}},{"cell_type":"code","source":["#@title 🖼️ **Image Background Processor (v1)**\n","#@markdown ---\n","#@markdown ## Enable Processor V1\n","enable_processor_v1 = False #@param {type:\"boolean\"}\n","\n","#@title 🖼️ **Image Background Processor**\n","#(v2 - Solid #181818 Flood-Fill + Auto-Clear)\n","#**Crop left/right empty space + Convert *any* gray background to exact #181818**\n","#@markdown ---\n","#**New**: Automatically clears all previous processed images on re-run\n","#(no more mixing old files when you run again)\n","\n","#Run this cell (Shift + Enter)\n","#Fill the form below\n","#Click **Run**\n","\n","# ==================== FORM PARAMETERS ====================\n","\n","#input_zip_path = \"/content/drive/MyDrive/klein_processed.zip\" #@param {type:\"string\", placeholder:\"Paste full path to your ZIP file here\"}\n","input_zip_path = zip_path\n","\n","tolerance_crop = 50 #@param {type:\"slider\", min:0, max:100, step:1, title:\"🟦 Crop Tolerance (higher = more aggressive cropping)\"}\n","\n","bg_replace_threshold = 20 #@param {type:\"slider\", min:10, max:100, step:1}\n","\n","force_remount = False #@param {type:\"boolean\", title:\"🔄 Force remount Drive (recommended)\"}\n","\n","#@markdown ---\n","\n","\n","if enable_processor_v1:\n","    from google.colab import drive\n","    import zipfile\n","    import os\n","    import shutil                          # ← NEW for clearing folders\n","    from PIL import Image\n","    from collections import deque\n","\n","    # ========================= CONFIG =========================\n","    TARGET_GRAY = (24, 24, 24)   # Exact #181818 - do not change\n","    # =======================================================\n","\n","    print(\"🚀 Starting image processor (Solid Background v2 + Auto-Clear)...\")\n","\n","    # 1. Mount Google Drive\n","    drive.mount('/content/drive', force_remount=force_remount)\n","\n","    # 2. Validate ZIP path\n","    if not input_zip_path or not os.path.exists(input_zip_path):\n","        raise FileNotFoundError(f\"❌ ZIP file not found:\\n{input_zip_path}\\n\\nPlease paste the correct full path (e.g. /content/drive/MyDrive/my_photos.zip)\")\n","\n","    print(f\"✅ ZIP found: {input_zip_path}\")\n","\n","    # 3. Define working folders\n","    extract_dir = '/content/extracted_images'\n","    processed_dir = '/content/processed_images'\n","\n","    # === NEW: Clear previous processed & extracted images on every run ===\n","    for directory in [extract_dir, processed_dir]:\n","        if os.path.exists(directory):\n","            shutil.rmtree(directory)\n","            print(f\"🧹 Cleared old folder: {directory}\")\n","    os.makedirs(extract_dir, exist_ok=True)\n","    os.makedirs(processed_dir, exist_ok=True)\n","    print(\"✅ Fresh folders ready\")\n","\n","    # 4. Unzip\n","    print(\"📦 Unzipping...\")\n","    with zipfile.ZipFile(input_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 ====================\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))\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: FLOOD-FILL BACKGROUND FROM EDGES ====================\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","        threshold = bg_replace_threshold\n","\n","        visited = set()\n","        queue = deque()\n","        directions = [(dx, dy) for dx in [-1, 0, 1] for dy in [-1, 0, 1] if not (dx == 0 and dy == 0)]\n","\n","        # Seed from borders\n","        for x in range(width):\n","            for y in (0, height - 1):\n","                if (x, y) not in visited:\n","                    px = pixels[x, y]\n","                    dist = ((px[0] - original_bg_color[0])**2 + (px[1] - original_bg_color[1])**2 + (px[2] - original_bg_color[2])**2) ** 0.5\n","                    if dist <= threshold:\n","                        queue.append((x, y))\n","                        visited.add((x, y))\n","                        pixels[x, y] = TARGET_GRAY\n","\n","        for y in range(height):\n","            for x in (0, width - 1):\n","                if (x, y) not in visited:\n","                    px = pixels[x, y]\n","                    dist = ((px[0] - original_bg_color[0])**2 + (px[1] - original_bg_color[1])**2 + (px[2] - original_bg_color[2])**2) ** 0.5\n","                    if dist <= threshold:\n","                        queue.append((x, y))\n","                        visited.add((x, y))\n","                        pixels[x, y] = TARGET_GRAY\n","\n","        # Flood fill\n","        while queue:\n","            x, y = queue.popleft()\n","            for dx, dy in directions:\n","                nx, ny = x + dx, y + dy\n","                if 0 <= nx < width and 0 <= ny < height and (nx, ny) not in visited:\n","                    px = pixels[nx, ny]\n","                    dist = ((px[0] - original_bg_color[0])**2 + (px[1] - original_bg_color[1])**2 + (px[2] - original_bg_color[2])**2) ** 0.5\n","                    if dist <= threshold:\n","                        visited.add((nx, ny))\n","                        pixels[nx, ny] = TARGET_GRAY\n","                        queue.append((nx, ny))\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","    zip_path = output_zip_path\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! Solid #181818 background achieved.\")\n","    print(f\"📍 Saved to your Drive at:\\n   {output_zip_path}\")\n"],"metadata":{"id":"HN4kta-OpZ4W","cellView":"form"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#@title **🖼️ Image Background Processor V2 (WIP)**\n","#@markdown ---\n","#@markdown ## Enable Processor V2\n","enable_processor_v2 = True #@param {type:\"boolean\"}\n","\n","#@title **🖼️ Image Background Processor V2 (WIP)**\n","#(v2.7 - **Inverse speck removal** + Border-protected forward speck + % Margin + Solid #181818)\n","\n","#**Crop left/right empty space + Convert *any* gray background to exact #181818**\n","#**New in v2.7**:\n","#   • **Inverse speck removal**: finds tiny foreground (subject-colored) specks/islands inside the background and replaces them with solid #181818\n","#   • Forward speck removal (previous) still protects any #181818 connected to the outer image edge\n","#   • Anti-aliasing is guaranteed to be the **very last step** (after both forward + inverse speck removal)\n","#   • All previous features (4-corner BG, % margin, checkboxes, distance-based enclosed speck removal) unchanged\n","\n","# ==================== FORM PARAMETERS ====================\n","\n","input_zip_path = zip_path\n","\n","tolerance_crop = 60 #@param {type:\"slider\", min:0, max:100, step:1, title:\"🔵 Crop Tolerance (higher = more aggressive cropping)\"}\n","\n","bg_replace_threshold = 10 #@param {type:\"slider\", min:10, max:100, step:1, title:\"🎨 Legacy absolute threshold (kept for compatibility)\"}\n","\n","# ── % Margin for BG color estimation ──\n","bg_margin_percent = 6 #@param {type:\"slider\", min:0, max:50, step:1, title:\"% Margin for BG color estimation (wider = more tolerant to inner-loop gray variations)\"}\n","\n","# ── Feature Checkboxes ──\n","enable_robust_bg_detection = True #@param {type:\"boolean\", title:\"✅ Use 4-corner robust BG color (best for inner loops)\"}\n","enable_floodfill_background_adjustment = True #@param {type:\"boolean\", title:\"✅ Enable solid #181818 background replacement\"}\n","enable_small_speck_removal = True #@param {type:\"boolean\"}\n","#title:\"✅ Forward speck removal: remove tiny #181818 specks on subject\"}\n","enable_inverse_speck_removal = True #@param {type:\"boolean\"}\n","#title:\"✅ Inverse speck removal: fill tiny subject specks inside background with #181818\"}\n","enable_anti_aliasing = True #@param {type:\"boolean\", title:\"✅ Smooth edges (Gaussian anti-aliasing)\"}\n","\n","anti_aliasing_strength = 6 #@param {type:\"slider\", min:1, max:40, step:1, title:\"   ↳ Anti-aliasing Strength (pixels to blend)\"}\n","\n","# ── Forward speck removal (enclosed only) ──\n","min_floodfill_steps = 8 #@param {type:\"slider\", min:1, max:20, step:1, title:\"   ↳ Min floodfill steps to foreground (N) for ENCLOSED BG only (outer-edge-connected BG always protected)\"}\n","\n","# ── Inverse speck removal ──\n","max_inverse_speck_size = 10 #@param {type:\"slider\", min:1, max:100, step:1, title:\"   ↳ Max size of foreground specks to fill with #181818\"}\n","\n","force_remount = False #@param {type:\"boolean\", title:\"🔄 Force remount Drive\"}\n","\n","#@markdown ---\n","\n","\n","if enable_processor_v2:\n","    from google.colab import drive\n","    import zipfile\n","    import os\n","    import shutil\n","    from PIL import Image, ImageFilter\n","    from collections import deque\n","\n","    # ========================= CONFIG =========================\n","    TARGET_GRAY = (24, 24, 24)   # Exact #181818\n","    # =======================================================\n","\n","    print(\"🚀 Starting Image Background Processor v2.7 (Forward + Inverse speck removal)...\")\n","\n","    # 1. Mount Google Drive\n","    drive.mount('/content/drive', force_remount=force_remount)\n","\n","    # 2. Validate ZIP path\n","    if not input_zip_path or not os.path.exists(input_zip_path):\n","        raise FileNotFoundError(f\"❌ ZIP file not found:\\n{input_zip_path}\\n\\nPlease paste the correct full path\")\n","\n","    print(f\"✅ ZIP found: {input_zip_path}\")\n","\n","    # 3. Define working folders\n","    extract_dir = '/content/extracted_images'\n","    processed_dir = '/content/processed_images'\n","\n","    # Clear previous runs\n","    for directory in [extract_dir, processed_dir]:\n","        if os.path.exists(directory):\n","            shutil.rmtree(directory)\n","            print(f\"﹅ Cleared old folder: {directory}\")\n","    os.makedirs(extract_dir, exist_ok=True)\n","    os.makedirs(processed_dir, exist_ok=True)\n","    print(\"✅ Fresh folders ready\")\n","\n","    # 4. Unzip\n","    print(\"🚀 Unzipping...\")\n","    with zipfile.ZipFile(input_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 ====================\n","    def crop_left_right(img):\n","        if img.mode != 'RGB':\n","            img = img.convert('RGB')\n","        width, height = img.size\n","        bg_color = img.getpixel((0, 0))\n","        left = width\n","        right = 0\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","        if left >= right:\n","            return img\n","        return img.crop((left, 0, right + 1, height))\n","\n","    # ==================== HELPER 2: FULL BACKGROUND PROCESSOR (v2.7) ====================\n","    def adjust_background(img, original_bg_color,\n","                          enable_floodfill_background_adjustment,\n","                          enable_small_speck_removal,\n","                          enable_inverse_speck_removal,\n","                          enable_anti_aliasing,\n","                          anti_aliasing_strength,\n","                          min_floodfill_steps,\n","                          max_inverse_speck_size,\n","                          bg_margin_percent):\n","        if img.mode != 'RGB':\n","            img = img.convert('RGB')\n","\n","        width, height = img.size\n","        pixels_access = img.load()\n","        target_gray = TARGET_GRAY\n","        directions = [(dx, dy) for dx in [-1, 0, 1] for dy in [-1, 0, 1] if not (dx == 0 and dy == 0)]\n","\n","        # 1. Identify foreground using % margin\n","        per_channel_margin = int(255 * (bg_margin_percent / 100.0))\n","        is_foreground = [[False for _ in range(height)] for _ in range(width)]\n","        for y in range(height):\n","            for x in range(width):\n","                px = pixels_access[(x, y)]\n","                is_bg = all(abs(px[i] - original_bg_color[i]) <= per_channel_margin for i in range(3))\n","                if not is_bg:\n","                    is_foreground[x][y] = True\n","\n","        # 2. Base image: solid #181818 + foreground\n","        base_processed_img = Image.new(\"RGB\", (width, height), target_gray)\n","        base_processed_pixels = base_processed_img.load()\n","        for y in range(height):\n","            for x in range(width):\n","                if is_foreground[x][y]:\n","                    base_processed_pixels[(x, y)] = pixels_access[(x, y)]\n","\n","        # 3. Forward speck removal (border-protected, enclosed only)\n","        if enable_floodfill_background_adjustment and enable_small_speck_removal:\n","            print(\"   🔍 Running forward speck removal (border-protected)...\")\n","            refined_img = base_processed_img.copy()\n","            refined_pixels = refined_img.load()\n","\n","            # Mark border-connected #181818\n","            connected_to_border = [[False] * height for _ in range(width)]\n","            queue = deque()\n","            for x in range(width):\n","                for y_border in [0, height-1]:\n","                    if refined_pixels[(x, y_border)] == target_gray and not connected_to_border[x][y_border]:\n","                        connected_to_border[x][y_border] = True\n","                        queue.append((x, y_border))\n","            for y in range(height):\n","                for x_border in [0, width-1]:\n","                    if refined_pixels[(x_border, y)] == target_gray and not connected_to_border[x_border][y]:\n","                        connected_to_border[x_border][y] = True\n","                        queue.append((x_border, y))\n","            while queue:\n","                cx, cy = queue.popleft()\n","                for dx, dy in directions:\n","                    nx, ny = cx + dx, cy + dy\n","                    if 0 <= nx < width and 0 <= ny < height and not connected_to_border[nx][ny] and refined_pixels[(nx, ny)] == target_gray:\n","                        connected_to_border[nx][ny] = True\n","                        queue.append((nx, ny))\n","\n","            # Distance to foreground (for enclosed BG only)\n","            distance = [[-1] * height for _ in range(width)]\n","            queue = deque()\n","            for y in range(height):\n","                for x in range(width):\n","                    if is_foreground[x][y]:\n","                        for dx, dy in directions:\n","                            nx, ny = x + dx, y + dy\n","                            if 0 <= nx < width and 0 <= ny < height and not is_foreground[nx][ny] and distance[nx][ny] == -1:\n","                                distance[nx][ny] = 1\n","                                queue.append((nx, ny))\n","                                break\n","            while queue:\n","                cx, cy = queue.popleft()\n","                for dx, dy in directions:\n","                    nx, ny = cx + dx, cy + dy\n","                    if 0 <= nx < width and 0 <= ny < height and not is_foreground[nx][ny] and distance[nx][ny] == -1:\n","                        distance[nx][ny] = distance[cx][cy] + 1\n","                        queue.append((nx, ny))\n","\n","            # Revert only enclosed + too-close BG pixels\n","            for y in range(height):\n","                for x in range(width):\n","                    if refined_pixels[(x, y)] == target_gray:\n","                        if not connected_to_border[x][y] and (distance[x][y] == -1 or distance[x][y] < min_floodfill_steps):\n","                            refined_pixels[(x, y)] = pixels_access[(x, y)]\n","\n","            base_processed_img = refined_img\n","            base_processed_pixels = base_processed_img.load()\n","\n","        # 4. INVERSE speck removal (tiny foreground specks inside background → #181818)\n","        if enable_floodfill_background_adjustment and enable_inverse_speck_removal:\n","            print(\"   🔍 Running inverse speck removal (small FG specks → #181818)...\")\n","            refined_img = base_processed_img.copy()\n","            refined_pixels = refined_img.load()\n","            visited = [[False] * height for _ in range(width)]\n","\n","            for y in range(height):\n","                for x in range(width):\n","                    if refined_pixels[(x, y)] != target_gray and not visited[x][y]:\n","                        component = []\n","                        queue = deque([(x, y)])\n","                        visited[x][y] = True\n","                        while queue:\n","                            cx, cy = queue.popleft()\n","                            component.append((cx, cy))\n","                            for dx, dy in directions:\n","                                nx, ny = cx + dx, cy + dy\n","                                if 0 <= nx < width and 0 <= ny < height and not visited[nx][ny] and refined_pixels[(nx, ny)] != target_gray:\n","                                    visited[nx][ny] = True\n","                                    queue.append((nx, ny))\n","                        # Fill small foreground specks with solid #181818\n","                        if len(component) < max_inverse_speck_size:\n","                            for px, py in component:\n","                                refined_pixels[(px, py)] = target_gray\n","\n","            base_processed_img = refined_img\n","            base_processed_pixels = base_processed_img.load()\n","\n","        # 5. Anti-aliasing – ALWAYS LAST (as requested)\n","        if not enable_anti_aliasing:\n","            return base_processed_img\n","\n","        mask = Image.new(\"L\", (width, height), 0)\n","        mask_pixels = mask.load()\n","        for y in range(height):\n","            for x in range(width):\n","                if base_processed_pixels[(x, y)] != target_gray:\n","                    mask_pixels[(x, y)] = 255\n","\n","        blur_radius = anti_aliasing_strength / 2.0\n","        blurred_mask = mask.filter(ImageFilter.GaussianBlur(radius=blur_radius))\n","\n","        solid_bg = Image.new(\"RGB\", (width, height), target_gray)\n","        final_img_with_aa = Image.composite(base_processed_img, solid_bg, blurred_mask)\n","\n","        return final_img_with_aa\n","\n","    # ==================== MAIN PROCESSING LOOP ====================\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","                    # === ROBUST BACKGROUND COLOR DETECTION ===\n","                    cropped = crop_left_right(original_img)\n","                    if cropped.mode != 'RGB':\n","                        cropped = cropped.convert('RGB')\n","\n","                    if enable_robust_bg_detection:\n","                        w, h = cropped.size\n","                        samples = [\n","                            cropped.getpixel((0, 0)),\n","                            cropped.getpixel((w-1, 0)),\n","                            cropped.getpixel((0, h-1)),\n","                            cropped.getpixel((w-1, h-1))\n","                        ]\n","                        orig_bg = tuple(sum(c) // len(samples) for c in zip(*samples))\n","                        print(f\"   📍 Using robust 4-corner BG color for {filename}\")\n","                    else:\n","                        orig_bg = cropped.getpixel((0, 0))\n","\n","                    # === PROCESS IMAGE ===\n","                    if enable_floodfill_background_adjustment:\n","                        processed = adjust_background(\n","                            cropped,\n","                            orig_bg,\n","                            enable_floodfill_background_adjustment,\n","                            enable_small_speck_removal,\n","                            enable_inverse_speck_removal,\n","                            enable_anti_aliasing,\n","                            anti_aliasing_strength,\n","                            min_floodfill_steps,\n","                            max_inverse_speck_size,\n","                            bg_margin_percent\n","                        )\n","                    else:\n","                        processed = cropped\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","\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\n","    output_zip_name = \"PROCESSED_\" + os.path.basename(input_zip_path)\n","    output_zip_path = os.path.join(os.path.dirname(input_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! Solid #181818 everywhere (forward + inverse specks cleaned) + smooth AA last\")\n","    print(f\"📍 Saved to your Drive at:\\n   {output_zip_path}\")\n"],"metadata":{"id":"tFGWg7jNv4mo"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# ⚙️ Create composites"],"metadata":{"id":"kVAe29swHo4I"}},{"cell_type":"code","source":["#@markdown Set zip file input path for making composites\n","input_zip_path = \"/content/drive/MyDrive/PROCESSED_klein_processed.zip\"   #@param {type:'string'}\n","zip_file_path = input_zip_path"],"metadata":{"id":"IXQKiBVrNPcE"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"93JpoQJ77NGu","cellView":"form"},"outputs":[],"source":["# ────────────────────────────────────────────────\n","# 🎨 CLEAN ROW → PURE 1024x1024 FRAMES (NO DISTORTION)\n","# ────────────────────────────────────────────────\n","\n","#@markdown ## ⚙️ Main Settings\n","num_frames = 100  #@param {type:\"slider\", min:10, max:2000, step:10}\n","overlap_percent = 0  #@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","# ────────────────────────────────────────────────\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","source":["# Done!\n","\n","In the zip file /content/drive/MyDrive/vertical_slices_output/vertical_slices.zip  ,  there you will find 100 image composites to choose for lora training\n","\n","//-----//"],"metadata":{"id":"tJlvhf9xDmA7"}}],"metadata":{"colab":{"provenance":[{"file_id":"https://huggingface.co/codeShare/FLUX.2-klein-AIO-SDNQ-4bit-dynamic/blob/main/colab_notebooks/post_edit_tools/post_processing_tasks.ipynb","timestamp":1779908978810}],"collapsed_sections":["s7R3l3-pY5W7","4PjK9a8Sni3A","kVAe29swHo4I"]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}