Denoising.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import skimage.restoration as sr\n",
    "import numpy as np\n",
    "import dicom\n",
    "import glob\n",
    "import h5py\n",
    "import os\n",
    "import scipy.io as scio\n",
    "from skimage import exposure\n",
    "from skimage.io import imsave, imread\n",
    "from scipy.misc import imresize\n",
    "from medpy.filter.noise import immerkaer\n",
    "from scipy.io import savemat\n",
    "from scipy import ndimage, misc\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def atoi(text) : \n",
    "    return int(text) if text.isdigit() else text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def natural_keys(text) :\n",
    "    return [atoi(c) for c in re.split('(\\d+)', text)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "root_path = \"\"\n",
    "filenames = []\n",
    "for root, dirnames, filenames in os.walk(\"Dataset/Resized_train//\"):\n",
    "    filenames.sort(key = natural_keys)\n",
    "    rootpath = root"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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     ]
    }
   ],
   "source": [
    "print(filenames)"
   ]
  },
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   "metadata": {
    "collapsed": false
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     ]
    }
   ],
   "source": [
    "images = []\n",
    "for filename in filenames :\n",
    "    filepath = os.path.join(root,filename)\n",
    "    image = ndimage.imread(filepath, mode = \"L\")\n",
    "    images.append(image)\n",
    "    print(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "770\n"
     ]
    }
   ],
   "source": [
    "print (len(images))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "denoisedimages = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hval = 10\n",
    "alpha = 15\n",
    "beta = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def denoiseImage(image):\n",
    "    maxvalue = np.max(image)\n",
    "    newimage = image*(255.0/maxvalue).astype(np.uint8)\n",
    "    denoised = sr.denoise_nl_means(newimage, multichannel=False, h=hval)\n",
    "    denoised = denoised - (alpha*beta)\n",
    "    denoised[denoised<0]=0\n",
    "    denoised = denoised.astype(np.uint8)\n",
    "    denoisedimages.append(denoised)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for image in images:\n",
    "    denoiseImage(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "770\n"
     ]
    }
   ],
   "source": [
    "print (len(denoisedimages))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fefd9890450>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xQOlSa5p1/fWDKmkUqBs3bqTOJ1jUJD6IgsE/ArK5uZl+ekh3rbQ//9DtdrG/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\ngFoTuQJqTeT/A/7GoCZcGwLRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fefd99e8190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(denoisedimages[0],cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "233\n"
     ]
    }
   ],
   "source": [
    "print (np.max(denoisedimages[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "imsave('/home/iplab/Desktop/denoised.png',denoisedimages[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for item in range(770):\n",
    "    imsave('/home/iplab/Desktop/DenoisedTrain/denoised_'+str(item+1)+'.png',denoisedimages[item])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}