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    "# Gabor Filters\n",
    "\n",
    "Gabor filters are used to extract features from an image. They extract spatial frequency content in a certain direction. Brainlit's Gabor implementation can be used for nD images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from brainlit.preprocessing import gabor_filter\n",
    "from skimage import data\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "img = data.brick()\n",
    "\n",
    "frequencies = [0.1, 0.1, 0.25, 0.25]\n",
    "phi = [0, np.pi / 2, 0, np.pi / 2]\n",
    "\n",
    "plt.figure()\n",
    "plt.imshow(img, cmap=\"gray\")\n",
    "plt.axis(\"off\")\n",
    "plt.title(\"Original Image\")\n",
    "plt.show()\n",
    "\n",
    "plt.figure()\n",
    "for i in range(4):\n",
    "    plt.subplot(2, 2, i + 1)\n",
    "    filtered = gabor_filter(img, 5, phi[i], frequencies[i], truncate=3)\n",
    "    plt.imshow(filtered[0], cmap=\"gray\")\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    if i == 0:\n",
    "        plt.title(\"Orientation=0\")\n",
    "        plt.ylabel(\"Frequency=0.1\")\n",
    "    elif i == 1:\n",
    "        plt.title(\"Orientation=\\u03C0/2\")\n",
    "    elif i == 2:\n",
    "        plt.ylabel(\"Frequency=0.25\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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