nonblanktilingcv2labeled.ipynb

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    "# Extract non-blank tiles from PNG WSIs, and save it in labels-specific folders\n",
    "Fast and simple method to produce relatively balanced dataset for adjusting and validating classification models before generalise it on the whole train_images dataset."
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   "source": [
    "import os\n",
    "import cv2\n",
    "import numpy as np\n",
    "import pandas as pd"
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    "# Function to generate tiles from images based on provided parameters\n",
    "def generate_tiles(image_id, label, image_dir, output_dir, tile_size, threshold_mean, threshold_std):\n",
    "    # Create label-specific output directory\n",
    "    label_output_dir = os.path.join(output_dir, label)\n",
    "    os.makedirs(label_output_dir, exist_ok=True)\n",
    "\n",
    "    # Construct the path for the image using its ID\n",
    "    image_path = os.path.join(image_dir, f\"{image_id}.png\")\n",
    "    \n",
    "    # Check if the image exists\n",
    "    if not os.path.exists(image_path):\n",
    "        print(f\"Image not found: {image_path}\")\n",
    "        return\n",
    "\n",
    "    try:\n",
    "        # Read the image using OpenCV\n",
    "        img = cv2.imread(image_path)\n",
    "        height, width, _ = img.shape\n",
    "    except Exception as e:\n",
    "        print(f\"Error reading image {image_path}: {e}\")\n",
    "        return\n",
    "\n",
    "    # Calculate the number of rows and columns for tiles\n",
    "    rows_count = height // tile_size\n",
    "    cols_count = width // tile_size\n",
    "\n",
    "    # Iterate through rows and columns to generate tiles\n",
    "    for row_idx in range(rows_count):\n",
    "        for col_idx in range(cols_count):\n",
    "            # Calculate coordinates for each tile\n",
    "            x = col_idx * tile_size\n",
    "            y = row_idx * tile_size\n",
    "\n",
    "            # Extract the tile from the image\n",
    "            tile = img[y:y+tile_size, x:x+tile_size]\n",
    "\n",
    "            # Define the name for the tiles\n",
    "            tile_name = f\"{image_id}_tile_{col_idx}_{row_idx}.png\"\n",
    "            \n",
    "            # Convert the tile to a NumPy array for mean and std calculation\n",
    "            tile_np = np.array(tile)\n",
    "\n",
    "            # Check if the tile is not mostly blank based on mean and std thresholds\n",
    "            if tile_np.mean() >= threshold_mean and tile_np.std() >= threshold_std:\n",
    "                # Define the path for the tiles\n",
    "                tile_path = os.path.join(label_output_dir, tile_name)\n",
    "                # Save the tile as a PNG files\n",
    "                cv2.imwrite(tile_path, tile)"
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    "# Define directory paths and CSV file\n",
    "image_dir = \"/train_images\"\n",
    "output_dir = \"/output_tiles\"\n",
    "csv_file_path = \"/train.csv\"\n",
    "\n",
    "# Read the CSV file containing image IDs and labels\n",
    "data = pd.read_csv(csv_file_path)\n",
    "\n",
    "# Iterate through each row in the CSV file\n",
    "for index, dataset_row in data.iterrows():\n",
    "    # Extract image ID, label, and generate tiles for each entry\n",
    "    generate_tiles(dataset_row['image_id'], dataset_row['label'], image_dir, output_dir, tile_size=256, threshold_mean=170, threshold_std=15)"
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