bildverarbeitungss18-hausaufgaben/assignments/04_assignment.ipynb

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    "# Image Processing SS 18 - Assignment - 04\n",
    "\n",
    "### Deadline is 16.5.2016 at 8:00 o'clock\n",
    "\n",
    "Please solve the assignments together with a partner.\n",
    "I will run every notebook. Make sure the code runs through. Select `Kernel` -> `Restart & Run All` to test it.\n",
    "Please strip the output from the cells, either select `Cell` -> `All Output` -> `Clear` or use the `nb_strip_output.py` script / git hook."
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# display the plots inside the notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pylab\n",
    "try:\n",
    "    import urllib.request as urllib2\n",
    "except ImportError:\n",
    "    import urllib2\n",
    "\n",
    "import random\n",
    "try:\n",
    "    from StringIO import StringIO as BytesIO\n",
    "except ImportError:\n",
    "    from io import BytesIO\n",
    "    \n",
    "from PIL import Image\n",
    "\n",
    "pylab.rcParams['figure.figsize'] = (12, 12)   # This makes the plot bigger"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 1 - Qualify sharpness and noise - 5 Points\n",
    "\n",
    "Qualify the noise and sharpness in the images. Make a plot images, noise\n",
    "\n",
    "Please download sample picture from [here](http://sipi.usc.edu/database/misc.zip)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the pictures here\n",
    "sample_images = []\n",
    "direc = 'misc/' # directory of the sample pictures realtivly to your notebook\n",
    "for number in [1,3,5,6]:\n",
    "    sample_images.append(\n",
    "        np.array(Image.open(direc+'4.2.0'+str(number)+'.tiff'))\n",
    "    )\n",
    "for name in ['house']:\n",
    "    sample_images.append(\n",
    "        np.array(Image.open(direc+name+'.tiff'))\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def qualify_noise(img):\n",
    "    \"\"\"Qualify the noise based on the std of a gaussian model.\n",
    "       You may find a window that is contant in the images.\n",
    "    \"\"\"\n",
    "    # your code here\n",
    "    return random.randint(0, 10)\n",
    "\n",
    "plt.bar(range(len(sample_images)), [qualify_noise(i) for i in sample_images])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def qualify_sharpness(img):\n",
    "    \"\"\"Qualify the sharpness based on the average pixel differences.\"\"\"\n",
    "    # your code here\n",
    "    return random.randint(0, 10)\n",
    "plt.bar(range(len(sample_images)), [qualify_sharpness(i) for i in sample_images])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Does the result match the expectations? If not what processing step can be done?/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 2 - SSIM JPEG Compression - 5 Points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def jpeg_enocde(img, quality):\n",
    "    pil_img = Image.fromarray(img)\n",
    "    buffer = BytesIO()\n",
    "    pil_img.save(buffer, \"JPEG\", quality=quality)\n",
    "    return buffer\n",
    "\n",
    "def jpeg_decode(buffer):\n",
    "    img = Image.open(buffer)\n",
    "    return np.array(img)\n",
    "\n",
    "def jpeg_quality_filter(img, quality):\n",
    "    as_jpeg = jpeg_enocde(img, quality)\n",
    "    return jpeg_decode(as_jpeg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "images_for_jpeg = sample_images[2::]\n",
    "len(images_for_jpeg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "images10 = [jpeg_quality_filter(img, 10) for img in images_for_jpeg]\n",
    "images50 = [jpeg_quality_filter(img, 10) for img in images_for_jpeg]\n",
    "images80 = [jpeg_quality_filter(img, 10) for img in images_for_jpeg]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ssim(img, filtered_img):\n",
    "    \"\"\"The SSIM similarity measure. Use the parameters from the paper \n",
    "    as on the second to last slide from the lecture\"\"\"\n",
    "    # your code\n",
    "    return random.randint(0, 10)\n",
    "\n",
    "for i, img in enumerate(images_for_jpeg):\n",
    "    print(i)\n",
    "    compressed_images = [images10[i], images50[i], images80[i]]\n",
    "    plt.bar(range(len(compressed_images)),\n",
    "             [ssim(img, comp) for comp in compressed_images])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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