Upload LoRa_vertical_slice.ipynb

LoRa_vertical_slice.ipynb

@@ -1 +1 @@
-{"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","# Configuration\n","# ────────────────────────────────────────────────\n","num_frames = 14  #@param {type:\"slider\", min:1, max:500, step:1}\n","split_images_horizontally = False  #@param {type:\"boolean\"}\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 (and optionally split them)\n","source_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","            source_images.append(img)\n","        except:\n","            print(f\"Could not open image: {filename}\")\n","    elif fname_lower.endswith('.webm'):\n","        try:\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","                source_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 source_images:\n","    print(\"No valid images were loaded. Please upload jpg/jpeg/png/webp/webm files.\")\n","    # Early exit\n","else:\n","    print(f\"Loaded {len(source_images)} images\")\n","\n","    if split_images_horizontally:\n","        print(\"Splitting images horizontally (top + bottom half)...\")\n","        split_images = []\n","        for img in source_images:\n","            w, h = img.size\n","            if h < 4:  # too tiny to split meaningfully\n","                split_images.append(img)\n","                continue\n","            half_h = h // 2\n","            top    = img.crop((0, 0,       w, half_h))\n","            bottom = img.crop((0, half_h,  w, h))\n","            split_images.append(top)\n","            split_images.append(bottom)\n","        source_images = split_images\n","        print(f\"→ Now working with {len(source_images)} half-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_vertical_panels  = 4 #@param{type:'slider',min:1, max:12}\n","    NUM_SLICES = num_vertical_panels\n","    SLICE_WIDTH = INNER_SIZE // NUM_SLICES            # 166 px when NUM_SLICES=6\n","    BORDER_COLOR = (24, 24, 24)                       # #181818\n","\n","    # ────────────────────────────────────────────────\n","    # Extract multiple vertical slices from each (half-)image\n","    # ────────────────────────────────────────────────\n","    all_slices = []\n","    target_w = SLICE_WIDTH\n","    target_h = INNER_SIZE\n","\n","    for photo in source_images:\n","        w, h = photo.size\n","        if h == 0 or w == 0:\n","            continue\n","\n","        # Resize so height matches target_h (full inner height)\n","        scale = target_h / h\n","        new_w = int(w * scale + 0.5)\n","        new_h = target_h\n","\n","        if new_w < 1:\n","            continue\n","\n","        resized = photo.resize((new_w, new_h), Image.LANCZOS)\n","\n","        if new_w < target_w:\n","            # Too narrow → center-pad to one 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","            # Multiple slices possible\n","            num_possible = new_w // target_w\n","            if num_possible < 1:\n","                num_possible = 1\n","\n","            total_width_used = num_possible * target_w\n","            start_left = (new_w - total_width_used) // 2\n","\n","            for i in range(num_possible):\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)} vertical slices\")\n","\n","    if len(all_slices) == 0:\n","        print(\"No usable slices could be extracted. Check image sizes.\")\n","    else:\n","        output_dir = \"vertical_slice_frames\"\n","        os.makedirs(output_dir, exist_ok=True)\n","\n","        frame_idx = 0\n","\n","        while frame_idx < num_frames:\n","            # Random sample with some oversampling to reduce repetition\n","            group = random.sample(all_slices * 4, NUM_SLICES)  # ×4 → good balance for variety\n","            random.shuffle(group)\n","\n","            # Build canvas\n","            canvas = Image.new('RGB', (INNER_SIZE, INNER_SIZE), (0, 0, 0))\n","\n","            for col in range(NUM_SLICES):\n","                crop = group[col]\n","                x = col * SLICE_WIDTH\n","                canvas.paste(crop, (x, 0))\n","\n","            # Add border\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:04d}.jpg\")  # :04d for nicer sorting with 500 frames\n","            final.save(out_path, \"JPEG\", quality=92)\n","            if frame_idx % 50 == 0:\n","                print(f\"Saved {frame_idx}/{num_frames} frames...\")\n","\n","        print(f\"\\nDone. Created {frame_idx} randomized frames in folder: /{output_dir}\")\n","\n","        # Zip & download\n","        zip_name = \"vertical_frames.zip\"\n","        !zip -r -q {zip_name} {output_dir}\n","        files.download(zip_name)"],"metadata":{"id":"_kz2zIO12PQQ"},"execution_count":null,"outputs":[]}],"metadata":{"colab":{"provenance":[{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/LoRa_vertical_slice.ipynb","timestamp":1772831486007},{"file_id":"11rioWNojWzoPp-O4wZK97yjublkR9GtQ","timestamp":1772750616448},{"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}
+{"cells":[{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"collapsed":true,"id":"at9ImGTxYqn6"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import os\n","import io\n","import random\n","import zipfile\n","from google.colab import drive\n","from PIL import Image\n","import cv2\n","import numpy as np\n","from datetime import datetime\n","import gc\n","\n","# ────────────────────────────────────────────────\n","# Configuration\n","# ────────────────────────────────────────────────\n","num_frames = 1000  #@param {type:\"slider\", min:1, max:1500, step:1}\n","split_images_horizontally = False  #@param {type:\"boolean\"}\n","use_manual_upload = False  #@param {type:\"boolean\"}\n","\n","save_to_google_drive = True   #@param {type:\"boolean\"}\n","drive_folder_name = \"vertical_slices_output\"  #@param {type:\"string\"}\n","\n","#@markdown ---\n","#@markdown **ZIP MODE** (when `use_manual_upload` is OFF)\n","#@markdown Paste full path to your zip file\n","#@markdown (example: `/content/my_images.zip` or `/content/drive/MyDrive/photos.zip`)\n","zip_file_path = \"/content/drive/MyDrive/fetch.zip\"  #@param {type:\"string\"}\n","\n","# ────────────────────────────────────────────────\n","# Mount Google Drive if selected\n","# ────────────────────────────────────────────────\n","drive_mounted = False\n","drive_output_dir = None\n","\n","if save_to_google_drive:\n","    try:\n","        drive.mount('/content/drive', force_remount=False)\n","        drive_mounted = True\n","        drive_base = \"/content/drive/MyDrive\"\n","        drive_output_dir = os.path.join(drive_base, drive_folder_name)\n","        os.makedirs(drive_output_dir, exist_ok=True)\n","        print(f\"→ Google Drive mounted. Zip will be saved to: {drive_output_dir}\")\n","    except Exception as e:\n","        print(f\"Google Drive mount failed: {e}\")\n","        print(\"→ No output will be saved (browser download was removed).\")\n","        save_to_google_drive = False\n","else:\n","    print(\"Google Drive saving is disabled → no output will be saved!\")\n","\n","# Early exit if nowhere to save\n","if not save_to_google_drive:\n","    print(\"\\n!!! WARNING: No saving method is active. Frames will be created but lost after runtime ends.\")\n","    print(\"     → Enable 'save_to_google_drive' or the work is temporary only.\\n\")\n","\n","# ────────────────────────────────────────────────\n","# Helpers to reduce memory usage\n","# ────────────────────────────────────────────────\n","def extract_vertical_slices(photo, target_w, target_h, border_color):\n","    \"\"\"Extract slices from one image → return list of small crops only\"\"\"\n","    slices = []\n","    w, h = photo.size\n","    if h <= 0 or w <= 0:\n","        return slices\n","\n","    scale = target_h / h\n","    new_w = int(w * scale + 0.5)\n","    if new_w < 1:\n","        return slices\n","\n","    resized = photo.resize((new_w, target_h), Image.LANCZOS)\n","\n","    if new_w < target_w:\n","        sl = Image.new('RGB', (target_w, target_h), border_color)\n","        sl.paste(resized, ((target_w - new_w)//2, 0))\n","        slices.append(sl)\n","    else:\n","        num = max(1, new_w // target_w)\n","        used = num * target_w\n","        start = (new_w - used) // 2\n","        for i in range(num):\n","            left = start + i * target_w\n","            crop = resized.crop((left, 0, left + target_w, target_h))\n","            slices.append(crop)\n","\n","    # Free resized immediately\n","    del resized\n","    return slices\n","\n","# ────────────────────────────────────────────────\n","# Load & process images one-by-one (memory friendly)\n","# ────────────────────────────────────────────────\n","all_slices = []\n","BORDER_COLOR = (24, 24, 24)\n","\n","# ────────────────────────────────────────────────\n","# Frame composition settings - moved up to define target_w earlier\n","# ────────────────────────────────────────────────\n","FRAME_SIZE = 1024\n","BORDER_PX  = 14\n","INNER_SIZE = FRAME_SIZE - 2 * BORDER_PX\n","num_vertical_panels = 4 #@param {type:\"slider\", min:1, max:12}\n","NUM_SLICES = num_vertical_panels\n","SLICE_WIDTH = INNER_SIZE // NUM_SLICES\n","target_h = INNER_SIZE # target_h was also defined later, ensuring it's available\n","target_w = SLICE_WIDTH   # now we know it\n","\n","extract_root = \"/content/extracted_images\"\n","os.makedirs(extract_root, exist_ok=True)\n","\n","print(\"Starting image loading and slice extraction...\\n\")\n","\n","if use_manual_upload:\n","    from google.colab import files\n","    print(\"Upload your jpg, jpeg, png, webp or webm files\")\n","    uploaded = files.upload()\n","\n","    for filename, filedata in uploaded.items():\n","        fname_lower = filename.lower()\n","        if fname_lower.startswith('._') or '__macosx' in fname_lower:\n","            continue\n","\n","        if fname_lower.endswith(('.jpg','.jpeg','.png','.webp')):\n","            try:\n","                img = Image.open(io.BytesIO(filedata)).convert('RGB')\n","                # Process immediately\n","                temp_slices = extract_vertical_slices(img, target_w, target_h, BORDER_COLOR)\n","                all_slices.extend(temp_slices)\n","                del img\n","                del temp_slices\n","                gc.collect()\n","            except Exception as e:\n","                print(f\"Skip {filename}: {e}\")\n","\n","        elif fname_lower.endswith('.webm'):\n","            # Video handling skipped for memory — too heavy; can re-add if needed\n","            pass # Added pass statement here to fix the IndentationError\n","\n","else:\n","    if not zip_file_path.strip() or not os.path.isfile(zip_file_path):\n","        print(\"No valid zip path provided.\")\n","    else:\n","        try:\n","            with zipfile.ZipFile(zip_file_path, 'r') as zf:\n","                zf.extractall(extract_root)\n","            print(\"Extraction done.\")\n","        except Exception as e:\n","            print(f\"Zip error: {e}\")\n","            raise\n","\n","        valid_exts = ('.jpg', '.jpeg', '.png', '.webp')\n","        count = 0\n","        for root, _, files in os.walk(extract_root):\n","            if '__MACOSX' in root:\n","                continue\n","            for fname in files:\n","                if fname.startswith('._'):\n","                    continue\n","                if fname.lower().endswith(valid_exts):\n","                    path = os.path.join(root, fname)\n","                    try:\n","                        with Image.open(path) as img:\n","                            img.verify()  # quick corruption check\n","                        img = Image.open(path).convert('RGB')  # reopen properly\n","                        temp_slices = extract_vertical_slices(img, target_w, target_h, BORDER_COLOR)\n","                        all_slices.extend(temp_slices)\n","                        del img\n","                        del temp_slices\n","                        count += 1\n","                        if count % 20 == 0:\n","                            print(f\"Processed {count} images...\")\n","                            gc.collect()\n","                    except Exception as e:\n","                        print(f\"Skip {fname}: {e}\")\n","\n","print(f\"\\nExtracted {len(all_slices)} vertical slices in total.\")\n","\n","    # ────────────────────────────────────────────────\n","    # Frame composition settings (original location - not needed here anymore)\n","    # ────────────────────────────────────────────────\n","    # FRAME_SIZE = 1024\n","    # BORDER_PX  = 14\n","    # INNER_SIZE = FRAME_SIZE - 2 * BORDER_PX\n","    # num_vertical_panels = 4 #@param {type:\"slider\", min:1, max:12}\n","    # NUM_SLICES = num_vertical_panels\n","    # SLICE_WIDTH = INNER_SIZE // NUM_SLICES\n","    # target_w = SLICE_WIDTH   # now we know it\n","\n","if len(all_slices) == 0:\n","    print(\"No slices → nothing to do.\")\n","else:\n","    output_dir = \"/content/vertical_slice_frames\"\n","    os.makedirs(output_dir, exist_ok=True)\n","\n","    frame_idx = 0\n","    while frame_idx < num_frames:\n","        k = min(NUM_SLICES, len(all_slices))\n","        group = random.sample(all_slices * 4, k)\n","        random.shuffle(group)\n","\n","        canvas = Image.new('RGB', (INNER_SIZE, INNER_SIZE), (0,0,0))\n","        for col in range(NUM_SLICES):\n","            crop = group[col % len(group)]\n","            canvas.paste(crop, (col * SLICE_WIDTH, 0))\n","\n","        final = Image.new('RGB', (FRAME_SIZE, FRAME_SIZE), BORDER_COLOR)\n","        final.paste(canvas, (BORDER_PX, BORDER_PX))\n","\n","        frame_idx += 1\n","        fname = f\"frame_{frame_idx:04d}.jpg\"\n","        final.save(os.path.join(output_dir, fname), \"JPEG\", quality=85)  # lower quality = less disk/RAM pressure\n","\n","        del canvas, final\n","        if frame_idx % 50 == 0:\n","            print(f\"Saved {frame_idx}/{num_frames} frames...\")\n","            gc.collect()\n","\n","    print(f\"\\nCreated {frame_idx} frames in {output_dir}\")\n","\n","    # ─── Zip & save only to Drive ───\n","    if save_to_google_drive and drive_mounted:\n","        timestamp = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n","        zip_name = f\"vertical_frames_{timestamp}.zip\"\n","        zip_path_local = f\"/content/{zip_name}\"\n","\n","        print(\"Creating zip...\")\n","        !zip -r -q \"{zip_path_local}\" \"{output_dir}\"\n","\n","        drive_zip_path = os.path.join(drive_output_dir, zip_name)\n","        print(\"Copying to Google Drive...\")\n","        !cp \"{zip_path_local}\" \"{drive_zip_path}\"\n","\n","        print(f\"\\nDone! Zip saved here:\")\n","        print(f\"→ {drive_zip_path}\")\n","\n","        # Optional: clean up local files to free space\n","        # !rm -rf \"{output_dir}\" \"{zip_path_local}\"\n","        print(\"Local files kept in /content/ — you can delete them manually via Files panel.\")\n","    else:\n","        print(\"\\nNo save location active → frames are in /content/vertical_slice_frames but will be lost when runtime ends.\")"],"metadata":{"id":"eJLdI4TvcLyz"},"execution_count":null,"outputs":[]}],"metadata":{"colab":{"provenance":[{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/LoRa_vertical_slice.ipynb","timestamp":1772923478843},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/LoRa_vertical_slice.ipynb","timestamp":1772831486007},{"file_id":"11rioWNojWzoPp-O4wZK97yjublkR9GtQ","timestamp":1772750616448},{"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}