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    {
      "cell_type": "code",
      "execution_count": null,
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      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "\n# Segmenting the picture of greek coins in regions\n\nThis example uses `spectral_clustering` on a graph created from\nvoxel-to-voxel difference on an image to break this image into multiple\npartly-homogeneous regions.\n\nThis procedure (spectral clustering on an image) is an efficient\napproximate solution for finding normalized graph cuts.\n\nThere are two options to assign labels:\n\n* with 'kmeans' spectral clustering will cluster samples in the embedding space\n  using a kmeans algorithm\n* whereas 'discrete' will iteratively search for the closest partition\n  space to the embedding space.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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      "source": [
        "# Author: Gael Varoquaux <gael.varoquaux@normalesup.org>, Brian Cheung\n# License: BSD 3 clause\n\nimport time\n\nimport numpy as np\nfrom scipy.ndimage.filters import gaussian_filter\nimport matplotlib.pyplot as plt\nimport skimage\nfrom skimage.data import coins\nfrom skimage.transform import rescale\n\nfrom sklearn.feature_extraction import image\nfrom sklearn.cluster import spectral_clustering\nfrom sklearn.utils.fixes import parse_version\n\n# these were introduced in skimage-0.14\nif parse_version(skimage.__version__) >= parse_version(\"0.14\"):\n    rescale_params = {\"anti_aliasing\": False, \"multichannel\": False}\nelse:\n    rescale_params = {}\n\n# load the coins as a numpy array\norig_coins = coins()\n\n# Resize it to 20% of the original size to speed up the processing\n# Applying a Gaussian filter for smoothing prior to down-scaling\n# reduces aliasing artifacts.\nsmoothened_coins = gaussian_filter(orig_coins, sigma=2)\nrescaled_coins = rescale(smoothened_coins, 0.2, mode=\"reflect\", **rescale_params)\n\n# Convert the image into a graph with the value of the gradient on the\n# edges.\ngraph = image.img_to_graph(rescaled_coins)\n\n# Take a decreasing function of the gradient: an exponential\n# The smaller beta is, the more independent the segmentation is of the\n# actual image. For beta=1, the segmentation is close to a voronoi\nbeta = 10\neps = 1e-6\ngraph.data = np.exp(-beta * graph.data / graph.data.std()) + eps\n\n# Apply spectral clustering (this step goes much faster if you have pyamg\n# installed)\nN_REGIONS = 25"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Visualize the resulting regions\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "for assign_labels in (\"kmeans\", \"discretize\"):\n    t0 = time.time()\n    labels = spectral_clustering(\n        graph, n_clusters=N_REGIONS, assign_labels=assign_labels, random_state=42\n    )\n    t1 = time.time()\n    labels = labels.reshape(rescaled_coins.shape)\n\n    plt.figure(figsize=(5, 5))\n    plt.imshow(rescaled_coins, cmap=plt.cm.gray)\n    for l in range(N_REGIONS):\n        plt.contour(labels == l, colors=[plt.cm.nipy_spectral(l / float(N_REGIONS))])\n    plt.xticks(())\n    plt.yticks(())\n    title = \"Spectral clustering: %s, %.2fs\" % (assign_labels, (t1 - t0))\n    print(title)\n    plt.title(title)\nplt.show()"
      ]
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