otsu_batch.ipynb

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  {
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   "metadata": {},
   "source": [
    "# [Lab Session 2](otsu.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[Task 2.1.]()** Implement Otsu thresholding as a re-usable function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def otsu_thresholding(imgf):\n",
    "    img8 = (imgf * 255).round().astype(np.uint8)\n",
    "    \n",
    "    ...\n",
    "    \n",
    "    return seg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[Task 2.2.]()** Implement a re-usable function to compute the Dice coefficient:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_dice(bin1, bin2):\n",
    "    assert bin1.dtype == np.bool\n",
    "    assert bin2.dtype == np.bool\n",
    "    \n",
    "    ...\n",
    "    \n",
    "    return dice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[Task 2.3.]()** Test your implementations from above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result should be the same as in your [otsu.ipynb](otsu.ipynb) notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[Task 2.4.]()** Perform batch processing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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