{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":113558,"databundleVersionId":14174843,"sourceType":"competition"}],"dockerImageVersionId":31153,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"_kg_hide-output":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"#!/usr/bin/env python3\n\"\"\"\nScientific Image Forgery Detection - Complete Working Solution\nDetects and segments copy-move forgeries in biomedical images\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\nimport os\nimport json\nfrom pathlib import Path\nfrom typing import List, Dict, Tuple, Optional\nimport random\nfrom collections import defaultdict\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.data import Dataset, DataLoader, random_split\nfrom torch.optim import AdamW\nfrom torch.optim.lr_scheduler import OneCycleLR\n\nimport cv2\nfrom PIL import Image\nfrom tqdm import tqdm\n\nimport warnings\nwarnings.filterwarnings('ignore')\n\n# Set random seeds for reproducibility\ndef set_seed(seed=42):\n    random.seed(seed)\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n    if torch.cuda.is_available():\n        torch.cuda.manual_seed_all(seed)\n        torch.backends.cudnn.deterministic = True\n        torch.backends.cudnn.benchmark = False\n\nset_seed(42)\n\n# Device configuration\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\nprint(f\"Using device: {device}\")\nif torch.cuda.is_available():\n    print(f\"GPU: {torch.cuda.get_device_name(0)}\")\n    print(f\"Memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.2f} GB\")\n\n# ============================================================================\n# CONFIGURATION\n# ============================================================================\n\nclass Config:\n    \"\"\"Configuration with optimized parameters\"\"\"\n    \n    # Paths\n    BASE_PATH = Path('/kaggle/input/recodai-luc-scientific-image-forgery-detection')\n    TRAIN_IMAGES_DIR = BASE_PATH / 'train_images'\n    TRAIN_MASKS_DIR = BASE_PATH / 'train_masks'\n    TEST_IMAGES_DIR = BASE_PATH / 'test_images'\n    SAMPLE_SUB_PATH = BASE_PATH / 'sample_submission.csv'\n    \n    # Model parameters\n    IMAGE_SIZE = 384\n    BATCH_SIZE = 8 if torch.cuda.is_available() else 2\n    VAL_BATCH_SIZE = 12 if torch.cuda.is_available() else 2\n    NUM_WORKERS = 0\n    \n    # Training parameters\n    EPOCHS = 12\n    LEARNING_RATE = 2e-3\n    WEIGHT_DECAY = 1e-5\n    VALIDATION_SPLIT = 0.15\n    \n    # Loss weights\n    DICE_WEIGHT = 0.6\n    BCE_WEIGHT = 0.4\n    \n    # Detection thresholds\n    CLASSIFICATION_THRESHOLD = 0.35\n    SEGMENTATION_THRESHOLD = 0.45\n    MIN_AREA = 100\n    \n    # Test Time Augmentation\n    TTA_ENABLED = True\n\n# ============================================================================\n# DATA DISCOVERY\n# ============================================================================\n\ndef discover_data():\n    \"\"\"Discover all training and test data\"\"\"\n    \n    config = Config()\n    print(\"\\n\" + \"=\"*70)\n    print(\"DATA DISCOVERY\")\n    print(\"=\"*70)\n    \n    # Discover training images\n    authentic_images = []\n    forged_images = []\n    \n    authentic_dir = config.TRAIN_IMAGES_DIR / 'authentic'\n    forged_dir = config.TRAIN_IMAGES_DIR / 'forged'\n    \n    # Check authentic images\n    if authentic_dir.exists():\n        authentic_images = sorted(list(authentic_dir.glob('*.[jpJP][npNP][gG]*')))\n        print(f\"\\nAuthentic images found: {len(authentic_images)}\")\n    \n    # Check forged images\n    if forged_dir.exists():\n        forged_images = sorted(list(forged_dir.glob('*.[jpJP][npNP][gG]*')))\n        print(f\"Forged images found: {len(forged_images)}\")\n    \n    # Discover mask files\n    mask_mapping = {}\n    \n    if config.TRAIN_MASKS_DIR.exists():\n        mask_files = sorted(list(config.TRAIN_MASKS_DIR.glob('*.npy')))\n        print(f\"Mask files (.npy) found: {len(mask_files)}\")\n        \n        # Create mask mapping\n        for mask_file in mask_files:\n            mask_stem = mask_file.stem\n            \n            # Match with forged images\n            for forged_img in forged_images:\n                if forged_img.stem == mask_stem:\n                    if forged_img.stem not in mask_mapping:\n                        mask_mapping[forged_img.stem] = []\n                    mask_mapping[forged_img.stem].append(mask_file)\n                    break\n    \n    print(f\"Images with masks: {len(mask_mapping)}\")\n    \n    # Discover test images\n    test_images = sorted(list(config.TEST_IMAGES_DIR.glob('*.[jpJP][npNP][gG]*')))\n    print(f\"Test images found: {len(test_images)}\")\n    \n    print(\"\\n\" + \"=\"*70)\n    print(f\"Total training images: {len(authentic_images) + len(forged_images)}\")\n    print(f\"  - Authentic: {len(authentic_images)}\")\n    print(f\"  - Forged: {len(forged_images)}\")\n    print(\"=\"*70)\n    \n    return authentic_images, forged_images, mask_mapping, test_images\n\n# ============================================================================\n# DATASET - FIXED VERSION\n# ============================================================================\n\nclass ForgeryDataset(Dataset):\n    \"\"\"Dataset for copy-move forgery detection with FIXED authentic/forged handling\"\"\"\n    \n    def __init__(self, authentic_paths: List[Path], forged_paths: List[Path], \n                 mask_mapping: Dict, image_size: int = 384, augment: bool = False):\n        # Store authentic and forged separately\n        self.authentic_paths = authentic_paths\n        self.forged_paths = forged_paths\n        self.all_paths = authentic_paths + forged_paths\n        self.mask_mapping = mask_mapping\n        self.image_size = image_size\n        self.augment = augment\n        \n        print(f\"\\nDataset initialized:\")\n        print(f\"  Total images: {len(self.all_paths)}\")\n        print(f\"  Authentic images: {len(self.authentic_paths)}\")\n        print(f\"  Forged images: {len(self.forged_paths)}\")\n    \n    def __len__(self):\n        return len(self.all_paths)\n    \n    def augment_image(self, image, mask):\n        \"\"\"Apply random augmentations\"\"\"\n        # Random horizontal flip\n        if random.random() > 0.5:\n            image = cv2.flip(image, 1)\n            mask = cv2.flip(mask, 1)\n        \n        # Random vertical flip\n        if random.random() > 0.5:\n            image = cv2.flip(image, 0)\n            mask = cv2.flip(mask, 0)\n        \n        # Random rotation (90, 180, 270)\n        if random.random() > 0.5:\n            k = random.choice([1, 2, 3])\n            image = np.rot90(image, k)\n            mask = np.rot90(mask, k)\n        \n        # Random brightness/contrast\n        if random.random() > 0.5:\n            alpha = random.uniform(0.8, 1.2)  # Contrast\n            beta = random.uniform(-20, 20)     # Brightness\n            image = np.clip(alpha * image + beta, 0, 255).astype(np.uint8)\n        \n        return image, mask\n    \n    def __getitem__(self, idx):\n        img_path = self.all_paths[idx]\n        \n        # Load image\n        img = cv2.imread(str(img_path))\n        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n        orig_h, orig_w = img.shape[:2]\n        \n        # Determine if this is an authentic or forged image\n        is_forged = img_path in self.forged_paths\n        has_mask = img_path.stem in self.mask_mapping\n        \n        # Load mask\n        if is_forged and has_mask:\n            mask_paths = self.mask_mapping[img_path.stem]\n            mask = np.zeros((orig_h, orig_w), dtype=np.uint8)\n            \n            for mask_path in mask_paths:\n                # Load .npy mask\n                single_mask = np.load(str(mask_path))\n                \n                # Ensure mask is 2D\n                if single_mask.ndim > 2:\n                    single_mask = single_mask[:, :, 0] if single_mask.shape[2] == 1 else single_mask.max(axis=2)\n                \n                # Resize if needed\n                if single_mask.shape[:2] != (orig_h, orig_w):\n                    single_mask = cv2.resize(single_mask, (orig_w, orig_h), interpolation=cv2.INTER_NEAREST)\n                \n                # Binary threshold\n                single_mask = (single_mask > 0).astype(np.uint8)\n                mask = np.maximum(mask, single_mask)\n            \n            label = 1  # Forged\n        else:\n            # Authentic image - no mask\n            mask = np.zeros((orig_h, orig_w), dtype=np.uint8)\n            label = 0  # Authentic\n        \n        # Apply augmentation\n        if self.augment:\n            img, mask = self.augment_image(img, mask)\n        \n        # Resize\n        img = cv2.resize(img, (self.image_size, self.image_size))\n        mask = cv2.resize(mask, (self.image_size, self.image_size), interpolation=cv2.INTER_NEAREST)\n        \n        # Normalize image\n        img = img.astype(np.float32) / 255.0\n        \n        # Convert to tensors\n        img_tensor = torch.from_numpy(img).permute(2, 0, 1).float()\n        mask_tensor = torch.from_numpy(mask).unsqueeze(0).float()\n        label_tensor = torch.tensor([label], dtype=torch.float32)\n        \n        return img_tensor, mask_tensor, label_tensor\n\n# ============================================================================\n# MODEL ARCHITECTURE\n# ============================================================================\n\nclass ConvBlock(nn.Module):\n    \"\"\"Convolutional block with BatchNorm and ReLU\"\"\"\n    def __init__(self, in_channels, out_channels):\n        super().__init__()\n        self.conv = nn.Sequential(\n            nn.Conv2d(in_channels, out_channels, 3, padding=1, bias=False),\n            nn.BatchNorm2d(out_channels),\n            nn.ReLU(inplace=True),\n            nn.Conv2d(out_channels, out_channels, 3, padding=1, bias=False),\n            nn.BatchNorm2d(out_channels),\n            nn.ReLU(inplace=True)\n        )\n    \n    def forward(self, x):\n        return self.conv(x)\n\nclass ForgeryDetectionModel(nn.Module):\n    \"\"\"U-Net style model for segmentation with classification head\"\"\"\n    \n    def __init__(self, in_channels=3, num_classes=1):\n        super().__init__()\n        \n        # Encoder\n        self.enc1 = ConvBlock(in_channels, 32)\n        self.pool1 = nn.MaxPool2d(2)\n        \n        self.enc2 = ConvBlock(32, 64)\n        self.pool2 = nn.MaxPool2d(2)\n        \n        self.enc3 = ConvBlock(64, 128)\n        self.pool3 = nn.MaxPool2d(2)\n        \n        self.enc4 = ConvBlock(128, 256)\n        self.pool4 = nn.MaxPool2d(2)\n        \n        # Bottleneck\n        self.bottleneck = ConvBlock(256, 512)\n        \n        # Decoder\n        self.upconv4 = nn.ConvTranspose2d(512, 256, 2, stride=2)\n        self.dec4 = ConvBlock(512, 256)\n        \n        self.upconv3 = nn.ConvTranspose2d(256, 128, 2, stride=2)\n        self.dec3 = ConvBlock(256, 128)\n        \n        self.upconv2 = nn.ConvTranspose2d(128, 64, 2, stride=2)\n        self.dec2 = ConvBlock(128, 64)\n        \n        self.upconv1 = nn.ConvTranspose2d(64, 32, 2, stride=2)\n        self.dec1 = ConvBlock(64, 32)\n        \n        # Segmentation head\n        self.seg_head = nn.Conv2d(32, num_classes, 1)\n        \n        # Classification head\n        self.global_pool = nn.AdaptiveAvgPool2d(1)\n        self.classifier = nn.Sequential(\n            nn.Linear(512, 256),\n            nn.ReLU(inplace=True),\n            nn.Dropout(0.3),\n            nn.Linear(256, 1)\n        )\n    \n    def forward(self, x):\n        # Encoder\n        enc1 = self.enc1(x)\n        enc2 = self.enc2(self.pool1(enc1))\n        enc3 = self.enc3(self.pool2(enc2))\n        enc4 = self.enc4(self.pool3(enc3))\n        \n        # Bottleneck\n        bottleneck = self.bottleneck(self.pool4(enc4))\n        \n        # Decoder with skip connections\n        dec4 = self.upconv4(bottleneck)\n        dec4 = torch.cat([dec4, enc4], dim=1)\n        dec4 = self.dec4(dec4)\n        \n        dec3 = self.upconv3(dec4)\n        dec3 = torch.cat([dec3, enc3], dim=1)\n        dec3 = self.dec3(dec3)\n        \n        dec2 = self.upconv2(dec3)\n        dec2 = torch.cat([dec2, enc2], dim=1)\n        dec2 = self.dec2(dec2)\n        \n        dec1 = self.upconv1(dec2)\n        dec1 = torch.cat([dec1, enc1], dim=1)\n        dec1 = self.dec1(dec1)\n        \n        # Segmentation output\n        seg_out = self.seg_head(dec1)\n        \n        # Classification output\n        pooled = self.global_pool(bottleneck)\n        pooled = pooled.view(pooled.size(0), -1)\n        cls_out = self.classifier(pooled)\n        \n        return seg_out, cls_out\n\n# ============================================================================\n# LOSS FUNCTIONS\n# ============================================================================\n\nclass DiceLoss(nn.Module):\n    \"\"\"Dice loss for segmentation\"\"\"\n    def __init__(self, smooth=1.0):\n        super().__init__()\n        self.smooth = smooth\n    \n    def forward(self, pred, target):\n        pred = torch.sigmoid(pred)\n        pred = pred.view(-1)\n        target = target.view(-1)\n        \n        intersection = (pred * target).sum()\n        dice = (2. * intersection + self.smooth) / (pred.sum() + target.sum() + self.smooth)\n        \n        return 1 - dice\n\nclass CombinedLoss(nn.Module):\n    \"\"\"Combined loss for segmentation and classification\"\"\"\n    def __init__(self, dice_weight=0.6, bce_weight=0.4):\n        super().__init__()\n        self.dice_loss = DiceLoss()\n        self.bce_loss = nn.BCEWithLogitsLoss()\n        self.dice_weight = dice_weight\n        self.bce_weight = bce_weight\n    \n    def forward(self, seg_pred, cls_pred, seg_target, cls_target):\n        # Segmentation loss (only for forged images)\n        seg_loss = self.dice_loss(seg_pred, seg_target)\n        seg_bce = self.bce_loss(seg_pred, seg_target)\n        seg_combined = self.dice_weight * seg_loss + self.bce_weight * seg_bce\n        \n        # Classification loss\n        cls_loss = self.bce_loss(cls_pred, cls_target)\n        \n        # Combine losses\n        total_loss = seg_combined + cls_loss\n        \n        return total_loss, seg_loss, cls_loss\n\n# ============================================================================\n# TRAINING\n# ============================================================================\n\ndef train_epoch(model, dataloader, criterion, optimizer, scheduler, device):\n    \"\"\"Train for one epoch\"\"\"\n    model.train()\n    total_loss = 0\n    total_seg_loss = 0\n    total_cls_loss = 0\n    \n    pbar = tqdm(dataloader, desc=\"Training\")\n    for images, masks, labels in pbar:\n        images = images.to(device)\n        masks = masks.to(device)\n        labels = labels.to(device)\n        \n        optimizer.zero_grad()\n        \n        seg_out, cls_out = model(images)\n        loss, seg_loss, cls_loss = criterion(seg_out, cls_out, masks, labels)\n        \n        loss.backward()\n        optimizer.step()\n        scheduler.step()\n        \n        total_loss += loss.item()\n        total_seg_loss += seg_loss.item()\n        total_cls_loss += cls_loss.item()\n        \n        pbar.set_postfix({\n            'loss': f'{loss.item():.4f}',\n            'seg': f'{seg_loss.item():.4f}',\n            'cls': f'{cls_loss.item():.4f}',\n            'lr': f'{scheduler.get_last_lr()[0]:.6f}'\n        })\n    \n    return total_loss / len(dataloader)\n\ndef validate(model, dataloader, criterion, device):\n    \"\"\"Validate the model\"\"\"\n    model.eval()\n    total_loss = 0\n    \n    with torch.no_grad():\n        for images, masks, labels in tqdm(dataloader, desc=\"Validation\"):\n            images = images.to(device)\n            masks = masks.to(device)\n            labels = labels.to(device)\n            \n            seg_out, cls_out = model(images)\n            loss, _, _ = criterion(seg_out, cls_out, masks, labels)\n            \n            total_loss += loss.item()\n    \n    return total_loss / len(dataloader)\n\n# ============================================================================\n# INFERENCE\n# ============================================================================\n\ndef predict_with_tta(model, image, config):\n    \"\"\"Prediction with Test Time Augmentation\"\"\"\n    model.eval()\n    \n    if not config.TTA_ENABLED:\n        with torch.no_grad():\n            seg_out, cls_out = model(image.unsqueeze(0))\n        return seg_out[0], cls_out[0]\n    \n    predictions_seg = []\n    predictions_cls = []\n    \n    # Original\n    with torch.no_grad():\n        seg, cls = model(image.unsqueeze(0))\n        predictions_seg.append(seg[0])\n        predictions_cls.append(cls[0])\n    \n    # Horizontal flip\n    img_flip = torch.flip(image, [2])\n    with torch.no_grad():\n        seg, cls = model(img_flip.unsqueeze(0))\n        seg = torch.flip(seg[0], [2])\n        predictions_seg.append(seg)\n        predictions_cls.append(cls[0])\n    \n    # Vertical flip\n    img_flip = torch.flip(image, [1])\n    with torch.no_grad():\n        seg, cls = model(img_flip.unsqueeze(0))\n        seg = torch.flip(seg[0], [1])\n        predictions_seg.append(seg)\n        predictions_cls.append(cls[0])\n    \n    # Average predictions\n    seg_final = torch.stack(predictions_seg).mean(0)\n    cls_final = torch.stack(predictions_cls).mean(0)\n    \n    return seg_final, cls_final\n\ndef rle_encode(mask):\n    \"\"\"Run-length encode a binary mask\"\"\"\n    dots = np.where(mask.T.flatten() == 1)[0]\n    run_lengths = []\n    prev = -2\n    for b in dots:\n        if b > prev + 1:\n            run_lengths.extend((b + 1, 0))\n        run_lengths[-1] += 1\n        prev = b\n    return run_lengths\n\n# ============================================================================\n# MAIN PIPELINE\n# ============================================================================\n\ndef main():\n    config = Config()\n    \n    print(\"\\n\" + \"=\"*70)\n    print(\"SCIENTIFIC IMAGE FORGERY DETECTION\")\n    print(\"=\"*70)\n    \n    # Discover data\n    authentic_images, forged_images, mask_mapping, test_images = discover_data()\n    \n    # Create dataset\n    all_authentic = authentic_images\n    all_forged = forged_images\n    \n    # Split into train and validation\n    num_val_authentic = int(len(all_authentic) * config.VALIDATION_SPLIT)\n    num_val_forged = int(len(all_forged) * config.VALIDATION_SPLIT)\n    \n    # Shuffle\n    random.shuffle(all_authentic)\n    random.shuffle(all_forged)\n    \n    train_authentic = all_authentic[num_val_authentic:]\n    val_authentic = all_authentic[:num_val_authentic]\n    \n    train_forged = all_forged[num_val_forged:]\n    val_forged = all_forged[:num_val_forged]\n    \n    print(f\"\\nDataset split:\")\n    print(f\"  Training: {len(train_authentic)} authentic + {len(train_forged)} forged = {len(train_authentic) + len(train_forged)}\")\n    print(f\"  Validation: {len(val_authentic)} authentic + {len(val_forged)} forged = {len(val_authentic) + len(val_forged)}\")\n    \n    # Create datasets\n    train_dataset = ForgeryDataset(\n        train_authentic, train_forged, mask_mapping,\n        image_size=config.IMAGE_SIZE, augment=True\n    )\n    \n    val_dataset = ForgeryDataset(\n        val_authentic, val_forged, mask_mapping,\n        image_size=config.IMAGE_SIZE, augment=False\n    )\n    \n    # Create dataloaders\n    train_loader = DataLoader(\n        train_dataset, batch_size=config.BATCH_SIZE,\n        shuffle=True, num_workers=config.NUM_WORKERS, pin_memory=True\n    )\n    \n    val_loader = DataLoader(\n        val_dataset, batch_size=config.VAL_BATCH_SIZE,\n        shuffle=False, num_workers=config.NUM_WORKERS, pin_memory=True\n    )\n    \n    # Initialize model\n    print(\"\\nInitializing model...\")\n    model = ForgeryDetectionModel().to(device)\n    total_params = sum(p.numel() for p in model.parameters())\n    print(f\"Model parameters: {total_params:,}\")\n    \n    # Loss and optimizer\n    criterion = CombinedLoss(\n        dice_weight=config.DICE_WEIGHT,\n        bce_weight=config.BCE_WEIGHT\n    )\n    \n    optimizer = AdamW(\n        model.parameters(),\n        lr=config.LEARNING_RATE,\n        weight_decay=config.WEIGHT_DECAY\n    )\n    \n    scheduler = OneCycleLR(\n        optimizer,\n        max_lr=config.LEARNING_RATE,\n        epochs=config.EPOCHS,\n        steps_per_epoch=len(train_loader),\n        pct_start=0.3\n    )\n    \n    # Training loop\n    print(\"\\nStarting training...\")\n    best_val_loss = float('inf')\n    \n    for epoch in range(config.EPOCHS):\n        print(f\"\\nEpoch {epoch + 1}/{config.EPOCHS}\")\n        \n        train_loss = train_epoch(model, train_loader, criterion, optimizer, scheduler, device)\n        val_loss = validate(model, val_loader, criterion, device)\n        \n        print(f\"Epoch {epoch + 1}: Train Loss = {train_loss:.4f}, Val Loss = {val_loss:.4f}\")\n        \n        if val_loss < best_val_loss:\n            best_val_loss = val_loss\n            torch.save(model.state_dict(), 'best_model.pth')\n            print(f\"  -> Best model saved (Val Loss: {val_loss:.4f})\")\n    \n    # Load best model for inference\n    print(\"\\nLoading best model for inference...\")\n    model.load_state_dict(torch.load('best_model.pth'))\n    model.eval()\n    \n    # Generate predictions\n    print(\"\\nGenerating predictions...\")\n    \n    sample_sub = pd.read_csv(config.SAMPLE_SUB_PATH)\n    results = []\n    \n    for idx, row in tqdm(sample_sub.iterrows(), total=len(sample_sub), desc=\"Predicting\"):\n        case_id = str(row['case_id'])\n        \n        # Find test image\n        test_img_path = None\n        for img_path in test_images:\n            if img_path.stem == case_id:\n                test_img_path = img_path\n                break\n        \n        if test_img_path and test_img_path.exists():\n            # Load image\n            img = cv2.imread(str(test_img_path))\n            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n            orig_h, orig_w = img.shape[:2]\n            \n            # Preprocess\n            img_resized = cv2.resize(img, (config.IMAGE_SIZE, config.IMAGE_SIZE))\n            img_normalized = img_resized.astype(np.float32) / 255.0\n            img_tensor = torch.from_numpy(img_normalized).permute(2, 0, 1).float().to(device)\n            \n            # Predict with TTA\n            seg_output, cls_output = predict_with_tta(model, img_tensor, config)\n            \n            cls_prob = torch.sigmoid(cls_output).cpu().item()\n            \n            if cls_prob < config.CLASSIFICATION_THRESHOLD:\n                results.append({'case_id': int(case_id), 'annotation': 'authentic'})\n            else:\n                seg_prob = torch.sigmoid(seg_output).cpu().numpy()[0]\n                seg_mask = (seg_prob > config.SEGMENTATION_THRESHOLD).astype(np.uint8)\n                seg_mask = cv2.resize(seg_mask, (orig_w, orig_h), interpolation=cv2.INTER_NEAREST)\n                \n                # Morphological operations\n                kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))\n                seg_mask = cv2.morphologyEx(seg_mask, cv2.MORPH_CLOSE, kernel, iterations=2)\n                seg_mask = cv2.morphologyEx(seg_mask, cv2.MORPH_OPEN, kernel)\n                \n                if seg_mask.sum() > config.MIN_AREA:\n                    # RLE encoding\n                    run_lengths = rle_encode(seg_mask)\n                    \n                    if len(run_lengths) > 0:\n                        results.append({\n                            'case_id': int(case_id),\n                            'annotation': json.dumps([int(x) for x in run_lengths])\n                        })\n                    else:\n                        results.append({'case_id': int(case_id), 'annotation': 'authentic'})\n                else:\n                    results.append({'case_id': int(case_id), 'annotation': 'authentic'})\n        else:\n            results.append({'case_id': int(case_id), 'annotation': 'authentic'})\n    \n    # Create submission\n    submission_df = pd.DataFrame(results)\n    submission_df.to_csv('submission.csv', index=False)\n    \n    print(\"\\n\" + \"=\"*70)\n    print(\"SUBMISSION COMPLETE\")\n    print(\"=\"*70)\n    print(f\"Total predictions: {len(submission_df)}\")\n    print(f\"Authentic: {(submission_df['annotation'] == 'authentic').sum()}\")\n    print(f\"Forgeries: {(submission_df['annotation'] != 'authentic').sum()}\")\n    \n    return submission_df\n\nif __name__ == \"__main__\":\n    submission = main()","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}