Clahe Filter and median filter.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a27c73ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Noise removal and PNG conversion completed.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import cv2\n",
    "import os\n",
    "\n",
    "# Path to the directory containing the Herlev dataset\n",
    "dataset_dir = 'Herlev dataset/test/severe_dysplastic'\n",
    "\n",
    "# Create a directory to save the filtered images\n",
    "output_dir = 'Median and clahe Filters/test/severe_dysplastic_MC_filter'\n",
    "os.makedirs(output_dir, exist_ok=True)\n",
    "\n",
    "# List all image files in the dataset directory\n",
    "image_files = [os.path.join(dataset_dir, filename) for filename in os.listdir(dataset_dir) if filename.lower().endswith('.bmp')]\n",
    "\n",
    "# Define the CLAHE filter function\n",
    "def clahe_filter(image):\n",
    "    # Apply median filter\n",
    "    image_median = cv2.medianBlur(image, 3)  # Adjust the kernel size as needed\n",
    "\n",
    "    # Apply CLAHE filter to the median filtered image\n",
    "    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))\n",
    "    lab = cv2.cvtColor(image_median, cv2.COLOR_RGB2LAB)\n",
    "    lab_planes = cv2.split(lab)\n",
    "    lab_planes_list = list(lab_planes)\n",
    "    lab_planes_list[0] = clahe.apply(lab_planes_list[0])\n",
    "    lab = cv2.merge(lab_planes_list)\n",
    "    filtered_image = cv2.cvtColor(lab, cv2.COLOR_LAB2RGB)\n",
    "    \n",
    "    return filtered_image\n",
    "\n",
    "# Apply CLAHE filtering to each image, convert to PNG, and save the filtered images\n",
    "for image_file in image_files:\n",
    "    # Load the image using OpenCV\n",
    "    image = cv2.imread(image_file)\n",
    "\n",
    "    # Convert the image to RGB (OpenCV reads images in BGR format)\n",
    "    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "\n",
    "    # Apply CLAHE filtering\n",
    "    filtered_image = clahe_filter(image_rgb)\n",
    "\n",
    "    # Get the file name without extension\n",
    "    filename = os.path.splitext(os.path.basename(image_file))[0]\n",
    "\n",
    "    # Save the filtered image as PNG format\n",
    "    output_file = os.path.join(output_dir, f'{filename}_filtered.png')\n",
    "    cv2.imwrite(output_file, filtered_image)\n",
    "\n",
    "print(\"Noise removal and PNG conversion completed.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6a1e24b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "153826963-153826970-001_filtered_filtered.png\n",
      "153826963-153826976-001_filtered_filtered.png\n",
      "153826963-153826976-002_filtered_filtered.png\n",
      "153826963-153826976-003_filtered_filtered.png\n",
      "153826963-153826982-001_filtered_filtered.png\n",
      "153826963-153826982-002_filtered_filtered.png\n",
      "153826963-153826982-003_filtered_filtered.png\n",
      "153826963-153826989-001_filtered_filtered.png\n",
      "153826963-153826989-002_filtered_filtered.png\n",
      "153827595-153827604-001_filtered_filtered.png\n",
      "153827595-153827604-002_filtered_filtered.png\n",
      "153827595-153827604-004_filtered_filtered.png\n",
      "153827595-153827611-001_filtered_filtered.png\n",
      "153827595-153827611-002_filtered_filtered.png\n",
      "153827595-153827618-001_filtered_filtered.png\n",
      "153827595-153827618-002_filtered_filtered.png\n",
      "153828877-153828890-001_filtered_filtered.png\n",
      "153828877-153828890-002_filtered_filtered.png\n",
      "153828877-153828897-001_filtered_filtered.png\n",
      "153828877-153828902-002_filtered_filtered.png\n",
      "153828952-153828970-001_filtered_filtered.png\n",
      "153828952-153828980-001_filtered_filtered.png\n",
      "153828952-153829000-001_filtered_filtered.png\n",
      "153828952-153829000-002_filtered_filtered.png\n",
      "153828952-153829000-003_filtered_filtered.png\n",
      "153828952-153829005-001_filtered_filtered.png\n",
      "153828952-153829005-002_filtered_filtered.png\n",
      "153828952-153829009-002_filtered_filtered.png\n",
      "153829063-153829073-001_filtered_filtered.png\n",
      "153829063-153829073-002_filtered_filtered.png\n",
      "153829063-153829078-001_filtered_filtered.png\n",
      "153829063-153829086-001_filtered_filtered.png\n",
      "153829745-153829768-001_filtered_filtered.png\n",
      "153829745-153829768-002_filtered_filtered.png\n",
      "153829745-153829768-003_filtered_filtered.png\n",
      "153830261-153830340-001_filtered_filtered.png\n",
      "153830261-153830340-002_filtered_filtered.png\n",
      "153830680-153830827-001_filtered_filtered.png\n",
      "153830680-153830827-002_filtered_filtered.png\n",
      "153830680-153830827-003_filtered_filtered.png\n",
      "153831352-153831372-001_filtered_filtered.png\n",
      "153831352-153831372-002_filtered_filtered.png\n",
      "153831352-153831372-003_filtered_filtered.png\n",
      "153831352-153831372-004_filtered_filtered.png\n",
      "153915316-153915330-003_filtered_filtered.png\n",
      "153915432-153915441-002_filtered_filtered.png\n",
      "153915432-153915472-001_filtered_filtered.png\n",
      "153915634-153915665-001_filtered_filtered.png\n",
      "153915634-153915665-002_filtered_filtered.png\n",
      "153915634-153915665-003_filtered_filtered.png\n",
      "153915634-153915665-005_filtered_filtered.png\n",
      "153915634-153915665-006_filtered_filtered.png\n",
      "153915634-153915665-007_filtered_filtered.png\n",
      "153915726-153915736-001_filtered_filtered.png\n",
      "153915726-153915774-001_filtered_filtered.png\n",
      "153916001-153916020-001_filtered_filtered.png\n",
      "153916001-153916020-003_filtered_filtered.png\n",
      "153916114-153916129-002_filtered_filtered.png\n",
      "153916922-153916963-001_filtered_filtered.png\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "# Path to the directory containing the filtered images\n",
    "output_dir = 'severe_dysplastic_filtered_clahe'\n",
    "\n",
    "# List all image files in the output directory\n",
    "filtered_files = os.listdir(output_dir)\n",
    "\n",
    "# Print the filtered image filenames\n",
    "for file_name in filtered_files:\n",
    "    print(file_name)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}