.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/cluster/plot_color_quantization.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_cluster_plot_color_quantization.py>`
        to download the full example code. or to run this example in your browser via Binder

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_cluster_plot_color_quantization.py:


==================================
使用K均值的颜色量化
==================================

对颐和园（中国）图像执行逐像素的矢量量化（VQ），将显示图像所需的颜色数量从96,615种独特颜色减少到64种，同时保留整体外观质量。

在此示例中，像素在3D空间中表示，并使用K均值找到64个颜色簇。在图像处理文献中，从K均值获得的代码簿（簇中心）称为调色板。使用单字节最多可以表示256种颜色，而RGB编码每个像素需要3个字节。例如，GIF文件格式使用这种调色板。

为了比较，还显示了使用随机代码簿（随机选择颜色）的量化图像。

.. GENERATED FROM PYTHON SOURCE LINES 13-86



.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_color_quantization_001.png
         :alt: Original image (96,615 colors)
         :srcset: /auto_examples/cluster/images/sphx_glr_plot_color_quantization_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_color_quantization_002.png
         :alt: Quantized image (64 colors, K-Means)
         :srcset: /auto_examples/cluster/images/sphx_glr_plot_color_quantization_002.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_color_quantization_003.png
         :alt: Quantized image (64 colors, Random)
         :srcset: /auto_examples/cluster/images/sphx_glr_plot_color_quantization_003.png
         :class: sphx-glr-multi-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Fitting model on a small sub-sample of the data
    done in 0.010s.
    Predicting color indices on the full image (k-means)
    done in 0.031s.
    Predicting color indices on the full image (random)
    done in 0.012s.






|

.. code-block:: Python


    # 作者：scikit-learn 开发者
    # SPDX 许可证标识符：BSD-3-Clause

    from time import time

    import matplotlib.pyplot as plt
    import numpy as np

    from sklearn.cluster import KMeans
    from sklearn.datasets import load_sample_image
    from sklearn.metrics import pairwise_distances_argmin
    from sklearn.utils import shuffle

    n_colors = 64

    # 加载颐和园照片
    # 
    # 
    china = load_sample_image("china.jpg")

    # 转换为浮点数，而不是默认的8位整数编码。除以255很重要，这样plt.imshow在处理浮点数据时才能正常工作（需要在[0-1]范围内）。
    china = np.array(china, dtype=np.float64) / 255

    # 加载图像并转换为二维 numpy 数组。
    w, h, d = original_shape = tuple(china.shape)
    assert d == 3
    image_array = np.reshape(china, (w * h, d))

    print("Fitting model on a small sub-sample of the data")
    t0 = time()
    image_array_sample = shuffle(image_array, random_state=0, n_samples=1_000)
    kmeans = KMeans(n_clusters=n_colors, random_state=0).fit(image_array_sample)
    print(f"done in {time() - t0:0.3f}s.")

    # 获取所有点的标签
    print("Predicting color indices on the full image (k-means)")
    t0 = time()
    labels = kmeans.predict(image_array)
    print(f"done in {time() - t0:0.3f}s.")


    codebook_random = shuffle(image_array, random_state=0, n_samples=n_colors)
    print("Predicting color indices on the full image (random)")
    t0 = time()
    labels_random = pairwise_distances_argmin(codebook_random, image_array, axis=0)
    print(f"done in {time() - t0:0.3f}s.")


    def recreate_image(codebook, labels, w, h):
        """从代码簿和标签中重建（压缩的）图像"""
        return codebook[labels].reshape(w, h, -1)


    # 显示所有结果，并列出原始图像
    plt.figure(1)
    plt.clf()
    plt.axis("off")
    plt.title("Original image (96,615 colors)")
    plt.imshow(china)

    plt.figure(2)
    plt.clf()
    plt.axis("off")
    plt.title(f"Quantized image ({n_colors} colors, K-Means)")
    plt.imshow(recreate_image(kmeans.cluster_centers_, labels, w, h))

    plt.figure(3)
    plt.clf()
    plt.axis("off")
    plt.title(f"Quantized image ({n_colors} colors, Random)")
    plt.imshow(recreate_image(codebook_random, labels_random, w, h))
    plt.show()


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.327 seconds)


.. _sphx_glr_download_auto_examples_cluster_plot_color_quantization.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/cluster/plot_color_quantization.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_color_quantization.ipynb <plot_color_quantization.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_color_quantization.py <plot_color_quantization.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_color_quantization.zip <plot_color_quantization.zip>`


.. include:: plot_color_quantization.recommendations


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_