.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/cluster/plot_face_compress.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` 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_face_compress.py: =========================== 矢量量化示例 =========================== 此示例展示了如何使用 :class:`~sklearn.preprocessing.KBinsDiscretizer` 对一组玩具图像（浣熊脸）进行矢量量化。 .. GENERATED FROM PYTHON SOURCE LINES 9-13 .. code-block:: Python # 作者：scikit-learn 开发者 # SPDX 许可证标识符：BSD-3-Clause .. GENERATED FROM PYTHON SOURCE LINES 14-20 原始图像 -------------- 我们首先从 SciPy 加载浣熊脸图像。我们还将检查一些关于图像的信息，例如图像的形状和用于存储图像的数据类型。 请注意，根据SciPy版本的不同，我们需要调整导入方式，因为返回图像的函数所在的模块不同。此外，SciPy >= 1.10 需要安装 `pooch` 包。 .. GENERATED FROM PYTHON SOURCE LINES 20-31 .. code-block:: Python try: # Scipy >= 1.10 from scipy.datasets import face except ImportError: from scipy.misc import face raccoon_face = face(gray=True) print(f"The dimension of the image is {raccoon_face.shape}") print(f"The data used to encode the image is of type {raccoon_face.dtype}") print(f"The number of bytes taken in RAM is {raccoon_face.nbytes}") .. rst-class:: sphx-glr-script-out .. code-block:: none The dimension of the image is (768, 1024) The data used to encode the image is of type uint8 The number of bytes taken in RAM is 786432 .. GENERATED FROM PYTHON SOURCE LINES 32-35 因此，该图像是一个高度为768像素、宽度为1024像素的二维数组。每个值是一个8位无符号整数，这意味着图像使用每像素8位进行编码。图像的总内存使用量为786千字节（1字节等于8位）。 使用8位无符号整数意味着图像最多使用256种不同的灰度级进行编码。我们可以检查这些值的分布。 .. GENERATED FROM PYTHON SOURCE LINES 35-48 .. code-block:: Python import matplotlib.pyplot as plt fig, ax = plt.subplots(ncols=2, figsize=(12, 4)) ax[0].imshow(raccoon_face, cmap=plt.cm.gray) ax[0].axis("off") ax[0].set_title("Rendering of the image") ax[1].hist(raccoon_face.ravel(), bins=256) ax[1].set_xlabel("Pixel value") ax[1].set_ylabel("Count of pixels") ax[1].set_title("Distribution of the pixel values") _ = fig.suptitle("Original image of a raccoon face") .. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_face_compress_001.png :alt: Original image of a raccoon face, Rendering of the image, Distribution of the pixel values :srcset: /auto_examples/cluster/images/sphx_glr_plot_face_compress_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 49-57 通过矢量量化进行压缩 --------------------------- 通过矢量量化进行压缩的思想是减少表示图像的灰度级数量。例如，我们可以使用8个值代替256个值。因此，这意味着我们可以有效地使用3位而不是8位来编码单个像素，从而将内存使用量减少大约2.5倍。我们稍后将讨论这种内存使用情况。 编码策略 可以使用 :class:`~sklearn.preprocessing.KBinsDiscretizer` 进行压缩。我们需要选择一种策略来定义 8 个灰度值进行子采样。最简单的策略是将它们等距定义，这对应于设置 `strategy="uniform"` 。从之前的直方图中，我们知道这种策略肯定不是最优的。 .. GENERATED FROM PYTHON SOURCE LINES 57-81 .. code-block:: Python from sklearn.preprocessing import KBinsDiscretizer n_bins = 8 encoder = KBinsDiscretizer( n_bins=n_bins, encode="ordinal", strategy="uniform", random_state=0, ) compressed_raccoon_uniform = encoder.fit_transform(raccoon_face.reshape(-1, 1)).reshape( raccoon_face.shape ) fig, ax = plt.subplots(ncols=2, figsize=(12, 4)) ax[0].imshow(compressed_raccoon_uniform, cmap=plt.cm.gray) ax[0].axis("off") ax[0].set_title("Rendering of the image") ax[1].hist(compressed_raccoon_uniform.ravel(), bins=256) ax[1].set_xlabel("Pixel value") ax[1].set_ylabel("Count of pixels") ax[1].set_title("Sub-sampled distribution of the pixel values") _ = fig.suptitle("Raccoon face compressed using 3 bits and a uniform strategy") .. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_face_compress_002.png :alt: Raccoon face compressed using 3 bits and a uniform strategy, Rendering of the image, Sub-sampled distribution of the pixel values :srcset: /auto_examples/cluster/images/sphx_glr_plot_face_compress_002.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 82-85 从定性上看，我们可以发现一些小区域在压缩后有影响（例如右下角的叶子）。但总的来说，生成的图像仍然看起来不错。 我们观察到像素值的分布已被映射到8个不同的值。我们可以检查这些值与原始像素值之间的对应关系。 .. GENERATED FROM PYTHON SOURCE LINES 85-90 .. code-block:: Python bin_edges = encoder.bin_edges_[0] bin_center = bin_edges[:-1] + (bin_edges[1:] - bin_edges[:-1]) / 2 bin_center .. rst-class:: sphx-glr-script-out .. code-block:: none array([ 15.625, 46.875, 78.125, 109.375, 140.625, 171.875, 203.125, 234.375]) .. GENERATED FROM PYTHON SOURCE LINES 91-98 .. code-block:: Python _, ax = plt.subplots() ax.hist(raccoon_face.ravel(), bins=256) color = "tab:orange" for center in bin_center: ax.axvline(center, color=color) ax.text(center - 10, ax.get_ybound()[1] + 100, f"{center:.1f}", color=color) .. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_face_compress_003.png :alt: plot face compress :srcset: /auto_examples/cluster/images/sphx_glr_plot_face_compress_003.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 99-100 正如前面所述，均匀采样策略并不是最优的。请注意，例如映射到值7的像素将编码相当少量的信息，而映射到值3的像素将表示大量的计数。我们可以改用诸如k-means之类的聚类策略来找到更优的映射。 .. GENERATED FROM PYTHON SOURCE LINES 100-122 .. code-block:: Python encoder = KBinsDiscretizer( n_bins=n_bins, encode="ordinal", strategy="kmeans", random_state=0, ) compressed_raccoon_kmeans = encoder.fit_transform(raccoon_face.reshape(-1, 1)).reshape( raccoon_face.shape ) fig, ax = plt.subplots(ncols=2, figsize=(12, 4)) ax[0].imshow(compressed_raccoon_kmeans, cmap=plt.cm.gray) ax[0].axis("off") ax[0].set_title("Rendering of the image") ax[1].hist(compressed_raccoon_kmeans.ravel(), bins=256) ax[1].set_xlabel("Pixel value") ax[1].set_ylabel("Number of pixels") ax[1].set_title("Distribution of the pixel values") _ = fig.suptitle("Raccoon face compressed using 3 bits and a K-means strategy") .. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_face_compress_004.png :alt: Raccoon face compressed using 3 bits and a K-means strategy, Rendering of the image, Distribution of the pixel values :srcset: /auto_examples/cluster/images/sphx_glr_plot_face_compress_004.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 123-127 .. code-block:: Python bin_edges = encoder.bin_edges_[0] bin_center = bin_edges[:-1] + (bin_edges[1:] - bin_edges[:-1]) / 2 bin_center .. rst-class:: sphx-glr-script-out .. code-block:: none array([ 18.90885631, 53.34346583, 82.64447187, 109.28225276, 134.70763101, 159.78681467, 185.17226834, 224.02069427]) .. GENERATED FROM PYTHON SOURCE LINES 128-135 .. code-block:: Python _, ax = plt.subplots() ax.hist(raccoon_face.ravel(), bins=256) color = "tab:orange" for center in bin_center: ax.axvline(center, color=color) ax.text(center - 10, ax.get_ybound()[1] + 100, f"{center:.1f}", color=color) .. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_face_compress_005.png :alt: plot face compress :srcset: /auto_examples/cluster/images/sphx_glr_plot_face_compress_005.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 136-141 现在，各个箱中的计数更加均衡，并且它们的中心不再是等距的。请注意，我们可以通过使用 `strategy="quantile"` 而不是 `strategy="kmeans"` 来强制每个箱中的像素数量相同。 内存占用 我们之前说过，我们应该节省8倍的内存。让我们来验证一下。 .. GENERATED FROM PYTHON SOURCE LINES 141-145 .. code-block:: Python print(f"The number of bytes taken in RAM is {compressed_raccoon_kmeans.nbytes}") print(f"Compression ratio: {compressed_raccoon_kmeans.nbytes / raccoon_face.nbytes}") .. rst-class:: sphx-glr-script-out .. code-block:: none The number of bytes taken in RAM is 6291456 Compression ratio: 8.0 .. GENERATED FROM PYTHON SOURCE LINES 146-147 令人惊讶的是，我们的压缩图像占用了比原始图像多8倍的内存。这确实与我们的预期相反。其主要原因在于用于编码图像的数据类型。 .. GENERATED FROM PYTHON SOURCE LINES 147-151 .. code-block:: Python print(f"Type of the compressed image: {compressed_raccoon_kmeans.dtype}") .. rst-class:: sphx-glr-script-out .. code-block:: none Type of the compressed image: float64 .. GENERATED FROM PYTHON SOURCE LINES 152-155 确实，:class:`~sklearn.preprocessing.KBinsDiscretizer` 的输出是一个64位浮点数组。这意味着它占用了8倍的内存。然而，我们使用这种64位浮点表示来编码8个值。实际上，只有当我们将压缩图像转换为3位整数数组时，才能节省内存。我们可以使用 `numpy.ndarray.astype` 方法。然而，3位整数表示并不存在，并且为了编码这8个值，我们也需要使用8位无符号整数表示。 实际上，观察到内存增益需要原始图像是64位浮点表示。 .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.967 seconds) .. _sphx_glr_download_auto_examples_cluster_plot_face_compress.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_face_compress.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_face_compress.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_face_compress.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_face_compress.zip ` .. include:: plot_face_compress.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_