Upload Vertical_slice.ipynb

Vertical_slice.ipynb

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+{"cells":[{"cell_type":"code","source":["import os\n","import io\n","import random\n","from google.colab import files\n","from PIL import Image\n","import cv2\n","import numpy as np\n","\n","# ────────────────────────────────────────────────\n","# Number of frames to produce\n","# ────────────────────────────────────────────────\n","num_frames = 125  #@param {type:\"slider\", min:1, max:150, step:1}\n","\n","# ────────────────────────────────────────────────\n","# Upload your images/videos\n","# ────────────────────────────────────────────────\n","print(\"Upload your jpg, jpeg, png, webp or webm files\")\n","uploaded = files.upload()\n","\n","# Collect images\n","images = []\n","\n","for filename, filedata in uploaded.items():\n","    fname_lower = filename.lower()\n","    if fname_lower.endswith(('.jpg', '.jpeg', '.png', '.webp')):\n","        try:\n","            img = Image.open(io.BytesIO(filedata)).convert('RGB')\n","            images.append(img)\n","        except:\n","            print(f\"Could not open image: {filename}\")\n","    elif fname_lower.endswith('.webm'):\n","        try:\n","            # Read first frame from webm\n","            cap = cv2.VideoCapture(filename)\n","            ret, frame = cap.read()\n","            cap.release()\n","            if ret:\n","                frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n","                img = Image.fromarray(frame_rgb)\n","                images.append(img)\n","            else:\n","                print(f\"Could not read video frame: {filename}\")\n","        except:\n","            print(f\"Error processing video: {filename}\")\n","\n","if not images:\n","    print(\"No valid images were loaded. Please upload jpg/jpeg/png/webp/webm files.\")\n","else:\n","    print(f\"Loaded {len(images)} images\")\n","\n","    # ────────────────────────────────────────────────\n","    # Settings\n","    # ────────────────────────────────────────────────\n","    FRAME_SIZE = 1024\n","    BORDER_PX   = 14\n","    INNER_SIZE  = FRAME_SIZE - 2 * BORDER_PX          # 996\n","    NUM_SLICES  = 12\n","    SLICE_WIDTH = INNER_SIZE // NUM_SLICES            # 83 px (996 // 12 = 83)\n","    BORDER_COLOR = (24, 24, 24)                       # #181818\n","\n","    # ────────────────────────────────────────────────\n","    # Extract multiple vertical slices from each image\n","    # ────────────────────────────────────────────────\n","    all_slices = []\n","    target_w = SLICE_WIDTH\n","    target_h = INNER_SIZE\n","\n","    for idx, photo in enumerate(images):\n","        w, h = photo.size\n","        if h == 0 or w == 0:\n","            continue  # skip invalid\n","\n","        scale = float(target_h) / h\n","        new_w = int(w * scale)\n","        new_h = target_h\n","\n","        if new_w <= 0:\n","            continue\n","\n","        resized = photo.resize((new_w, new_h), Image.LANCZOS)\n","\n","        if new_w < target_w:\n","            # Pad to one centered slice\n","            slice_img = Image.new('RGB', (target_w, target_h), BORDER_COLOR)\n","            paste_x = (target_w - new_w) // 2\n","            slice_img.paste(resized, (paste_x, 0))\n","            all_slices.append(slice_img)\n","        else:\n","            num_slices_from_this = new_w // target_w\n","            if num_slices_from_this < 1:\n","                num_slices_from_this = 1  # shouldn't happen\n","            total_slices_width = num_slices_from_this * target_w\n","            start_left = (new_w - total_slices_width) // 2\n","            for i in range(num_slices_from_this):\n","                left = start_left + i * target_w\n","                crop = resized.crop((left, 0, left + target_w, target_h))\n","                all_slices.append(crop)\n","\n","    print(f\"Extracted {len(all_slices)} unique slices from {len(images)} images\")\n","\n","    if not all_slices:\n","        print(\"No slices extracted. Check image dimensions.\")\n","    else:\n","        output_dir = \"vertical_slice_frames\"\n","        os.makedirs(output_dir, exist_ok=True)\n","\n","        # To ensure uniqueness to best ability: shuffle the slices once\n","        # Then sample without replacement for each frame; if not enough, reshuffle and reuse\n","        total_needed = num_frames * NUM_SLICES\n","        if len(all_slices) < total_needed:\n","            print(f\"Note: Only {len(all_slices)} slices available, but {total_needed} needed. Will reuse slices as necessary.\")\n","\n","        # Create a shuffled copy of slices for selection\n","        shuffled_slices = all_slices[:]\n","        random.shuffle(shuffled_slices)\n","\n","        frame_idx = 0\n","        used_slices = set()  # Track used slices across frames (by id)\n","\n","        while frame_idx < num_frames:\n","            # Select 12 unique slices for this frame, preferring unused ones\n","            group = []\n","            available = [slc for slc in shuffled_slices if id(slc) not in used_slices]\n","\n","            if len(available) < NUM_SLICES:\n","                # Not enough unused; reshuffle all and reset used if necessary\n","                shuffled_slices = all_slices[:]\n","                random.shuffle(shuffled_slices)\n","                used_slices.clear()\n","                available = shuffled_slices[:]\n","\n","            # Take up to 12\n","            for _ in range(NUM_SLICES):\n","                if available:\n","                    slc = available.pop(0)\n","                    group.append(slc)\n","                    used_slices.add(id(slc))\n","                else:\n","                    # Fallback: use a random one if somehow exhausted\n","                    group.append(random.choice(all_slices))\n","\n","            # Create new 996×996 inner canvas\n","            canvas = Image.new('RGB', (INNER_SIZE, INNER_SIZE), (0, 0, 0))\n","\n","            for col in range(NUM_SLICES):\n","                if col < len(group):\n","                    crop = group[col]\n","                else:\n","                    # filler for missing (shouldn't happen)\n","                    crop = Image.new('RGB', (target_w, target_h), BORDER_COLOR)\n","\n","                # Paste into canvas\n","                x = col * SLICE_WIDTH\n","                canvas.paste(crop, (x, 0))\n","\n","            # ── Add gray border around the whole thing ───────────────────────────\n","            final = Image.new('RGB', (FRAME_SIZE, FRAME_SIZE), BORDER_COLOR)\n","            final.paste(canvas, (BORDER_PX, BORDER_PX))\n","\n","            # Save\n","            frame_idx += 1\n","            out_path = os.path.join(output_dir, f\"frame_{frame_idx:03d}.jpg\")\n","            final.save(out_path, \"JPEG\", quality=92)\n","            print(f\"Saved: {out_path}\")\n","\n","        print(f\"\\nDone. Created {frame_idx} frames in folder: /{output_dir}\")\n","\n","        # Optional: zip and download\n","        zip_name = \"vertical_frames.zip\"\n","        !zip -r -q {zip_name} {output_dir}\n","        files.download(zip_name)"],"metadata":{"id":"uQNPVVghmKza"},"execution_count":null,"outputs":[]}],"metadata":{"colab":{"provenance":[{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/Drive to WebP.ipynb","timestamp":1772741185417},{"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}]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}