machinelearning_notebook/3_kmeans/2-kmeans-color-vq.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 用Ｋ-means进行颜色量化\n",
    "\n",
    "对给定的图像进行**像素矢量量化(VQ)**，将显示图像所需的颜色从16777216（$2^{24}$)种减少到64种，同时保持整体外观质量。\n",
    "\n",
    "\n",
    "在本例中，像素在3D空间中表示，使用K-means找到64个颜色簇。在图像处理文献中，由K-means(聚类中心)得到的码本称为调色板。使用单个字节，最多可以寻址256种颜色，而RGB编码需要每个像素3个字节。例如，GIF文件格式就使用了这样一个调色板。\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import pairwise_distances_argmin\n",
    "from sklearn.datasets import load_sample_image\n",
    "from sklearn.utils import shuffle\n",
    "from time import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting model on a small sub-sample of the data\n",
      "    done in 44.144s.\n",
      "Predicting color indices on the full image (k-means)\n",
      "    done in 0.036s.\n",
      "Predicting color indices on the full image (random)\n",
      "(273280,)\n",
      "    done in 0.283s.\n"
     ]
    }
   ],
   "source": [
    "n_colors = 64\n",
    "\n",
    "# 加载图像\n",
    "img = load_sample_image(\"china.jpg\")\n",
    "\n",
    "# 转化为浮点数而不是默认的８位整数编码。\n",
    "# 除以255是重要的这样plt.imshow在浮点数(需要在[0-1]的范围)上的表现会很好 \n",
    "\n",
    "img = np.array(img, dtype=np.float64) / 255\n",
    "\n",
    "# 加载图像并转化成2D的numpy数组。\n",
    "w, h, d = original_shape = tuple(img.shape)\n",
    "assert d == 3\n",
    "image_array = np.reshape(img, (w * h, d))\n",
    "\n",
    "print(\"Fitting model on a small sub-sample of the data\")\n",
    "t0 = time()\n",
    "#image_array_sample = shuffle(image_array, \n",
    "#                            random_state=0)[:1000]\n",
    "image_array_sample = image_array\n",
    "kmeans = KMeans(n_clusters=n_colors, random_state=0).fit(\n",
    "    image_array_sample)\n",
    "print(\"    done in %0.3fs.\" % (time() - t0))\n",
    "\n",
    "# 获得所有点的标签\n",
    "print(\"Predicting color indices on the full image (k-means)\")\n",
    "t0 = time()\n",
    "labels = kmeans.predict(image_array)\n",
    "print(\"    done in %0.3fs.\" % (time() - t0))\n",
    "\n",
    "\n",
    "codebook_random = shuffle(image_array, \n",
    "                          random_state=0)[:n_colors + 1]\n",
    "print(\"Predicting color indices on the full image (random)\")\n",
    "t0 = time()\n",
    "# FIXME: 需要确认这个函数的具体操作，和手册的不一致\n",
    "labels_random = pairwise_distances_argmin(codebook_random,\n",
    "                                          image_array,\n",
    "                                          axis=0)\n",
    "print(labels_random.shape)\n",
    "print(\"    done in %0.3fs.\" % (time() - t0))\n",
    "\n",
    "\n",
    "def recreate_image(codebook, labels, w, h):\n",
    "    \"\"\"Recreate the (compressed) image from the code book & labels\"\"\"\n",
    "    d = codebook.shape[1]\n",
    "    image = np.zeros((w, h, d))\n",
    "    label_idx = 0\n",
    "    for i in range(w):\n",
    "        for j in range(h):\n",
    "            image[i][j] = codebook[labels[label_idx]]\n",
    "            label_idx += 1\n",
    "    return image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f3bfc3e2750>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {