image_clean.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "from tqdm import tqdm\n",
    "import cv2\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import imutils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data(dir_path):\n",
    "    X = []\n",
    "    for file in tqdm((os.listdir(dir_path))):\n",
    "        if not file.startswith('.'):\n",
    "            img = cv2.imread(dir_path + '/' + file)\n",
    "            X.append(img)\n",
    "    X = np.array(X)\n",
    "    print(f'{len(X)} images loaded from {dir_path} directory.')\n",
    "    return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = load_data('E:/MSCS/Semester 1/Neural Networks/Project/Tumor Detection/Dataset/glioma')\n",
    "X = load_data('E:/MSCS/Semester 1/Neural Networks/Project/Tumor Detection/Dataset/meningioma')\n",
    "X = load_data('E:/MSCS/Semester 1/Neural Networks/Project/Tumor Detection/Dataset/pituitary tumor')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_samples(X, n=50):\n",
    "    \"\"\"\n",
    "    Creates a gridplot for desired number of images (n) from the specified set\n",
    "    \"\"\"\n",
    "    np.random.shuffle(X)\n",
    "    imgs = X[:n]\n",
    "    j = 10\n",
    "    i = int(n/j)\n",
    "\n",
    "    plt.figure(figsize=(15,6))\n",
    "    c = 1\n",
    "    for img in imgs:\n",
    "        plt.subplot(i,j,c)\n",
    "        plt.imshow(img)\n",
    "\n",
    "        plt.xticks([])\n",
    "        plt.yticks([])\n",
    "        c += 1\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_samples(X, n=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def crop_imgs(set_name, add_pixels_value=0):\n",
    "    \"\"\"\n",
    "    Finds the extreme points on the image and crops the rectangular out of them\n",
    "    \"\"\"\n",
    "    set_new = []\n",
    "    for img in set_name:\n",
    "        gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n",
    "        gray = cv2.GaussianBlur(gray, (5, 5), 0)\n",
    "\n",
    "        # threshold the image, then perform a series of erosions +\n",
    "        # dilations to remove any small regions of noise\n",
    "        thresh = cv2.threshold(gray, 45, 255, cv2.THRESH_BINARY)[1]\n",
    "        thresh = cv2.erode(thresh, None, iterations=2)\n",
    "        thresh = cv2.dilate(thresh, None, iterations=2)\n",
    "\n",
    "        # find contours in thresholded image, then grab the largest one\n",
    "        cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
    "        cnts = imutils.grab_contours(cnts)\n",
    "        c = max(cnts, key=cv2.contourArea)\n",
    "\n",
    "        # find the extreme points\n",
    "        extLeft = tuple(c[c[:, :, 0].argmin()][0])\n",
    "        extRight = tuple(c[c[:, :, 0].argmax()][0])\n",
    "        extTop = tuple(c[c[:, :, 1].argmin()][0])\n",
    "        extBot = tuple(c[c[:, :, 1].argmax()][0])\n",
    "\n",
    "        ADD_PIXELS = add_pixels_value\n",
    "        new_img = img[extTop[1]-ADD_PIXELS:extBot[1]+ADD_PIXELS, extLeft[0]-ADD_PIXELS:extRight[0]+ADD_PIXELS].copy()\n",
    "        set_new.append(new_img)\n",
    "\n",
    "    return np.array(set_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_crop = crop_imgs(X, add_pixels_value=0)\n",
    "plot_samples(X_crop, n=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "IMG_SIZE = 256\n",
    "X_crop_resized = []\n",
    "for img in X_crop:\n",
    "    img_new = cv2.resize(img,(IMG_SIZE,IMG_SIZE))\n",
    "    X_crop_resized.append(img_new)\n",
    "X_crop_resized = np.array(X_crop_resized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_samples(X_crop_resized, n=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_crop_resized_unique = np.unique(X_crop_resized, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_new_images(X, dir_name):\n",
    "    for (i,img) in enumerate(X):\n",
    "        cv2.imwrite(dir_name+'/'+str(i)+'.jpg', img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!mkdir all_cleaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_new_images(X_crop_resized_unique, 'all_cleaned/')"
   ]
  }
 ],
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