25 Lights First Embed part

25lights_image_gen.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import math\n",
    "import numpy as np\n",
    "import random as rnd\n",
    "from numpy.random import random, permutation\n",
    "from scipy import misc, ndimage\n",
    "from scipy.ndimage.interpolation import zoom\n",
    "from matplotlib.pyplot import imshow\n",
    "from PIL import Image, ImageDraw\n",
    "\n",
    "def makeRandomBitArray(numberOfBits=25):\n",
    "    \n",
    "    bits = rnd.getrandbits(numberOfBits)\n",
    "    bitArray = np.zeros(numberOfBits)\n",
    "    for i in range(numberOfBits):\n",
    "        bitArray[i] = (bits & 0b1)        \n",
    "        bits = bits >> 1\n",
    "    return bitArray\n",
    "    #return np.ones(numberOfBits)\n",
    "        \n",
    "def drawLEDImage(imageSize=300, dotsPerSide=4):\n",
    "    \n",
    "    boxSize = imageSize/math.sqrt(2)\n",
    "    spaces = (dotsPerSide+1)\n",
    "    dotSize = (boxSize * 0.6) / dotsPerSide\n",
    "    spacerSize = (boxSize * 0.4) / spaces\n",
    "    stride = (boxSize - 2 * spacerSize) / dotsPerSide \n",
    "    \n",
    "    bitArray = makeRandomBitArray(dotsPerSide*dotsPerSide)\n",
    "    img = Image.new('P', (imageSize, imageSize), color=255)\n",
    "    draw = ImageDraw.Draw(img)\n",
    "    box = (spacerSize, spacerSize, imageSize - (2*spacerSize), imageSize - (2*spacerSize))\n",
    "    draw.ellipse((1,1,imageSize-2,imageSize-2), fill=0)\n",
    "    \n",
    "    offset = ((spacerSize+dotSize+imageSize-boxSize)/2, (spacerSize+dotSize+imageSize-boxSize)/2)\n",
    "    for y in range(dotsPerSide):\n",
    "        for x in range(dotsPerSide):\n",
    "            box = (offset[0] + x*stride, offset[1] + y*stride, offset[0] + x*stride+dotSize, offset[1] + y*stride+dotSize)\n",
    "            value = bitArray[x + y * dotsPerSide]\n",
    "            if value:\n",
    "                draw.ellipse(box, fill=0xFFFFFF)\n",
    "    del draw\n",
    "   \n",
    "    return (img)\n",
    "        \n",
    "\n",
    "def img_to_float_array(img):\n",
    "    img_array = np.asarray(img, dtype='float32')\n",
    "    img_array = img_array.astype('float32') / 255.\n",
    "    img_array = img_array.reshape((img_array.shape[0], img_array.shape[1], 1))\n",
    "    return img_array\n",
    "\n",
    "    \n",
    "def imageSet(batchSize=32, imageSize=300, dotsPerSide=4):\n",
    "    images = np.empty([batchSize, imageSize, imageSize, 1], dtype='float32')\n",
    "    for i in range(0, batchSize):\n",
    "        image = drawLEDImage(imageSize, dotsPerSide)\n",
    "        images[i] = img_to_float_array(image)\n",
    "    \n",
    "    return images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8c14142f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use Matplotlib (don't ask)\n",
    "import matplotlib.pyplot as plt\n",
    "n = 4\n",
    "imageSize=128\n",
    "training_images = imageSet(batchSize=n, imageSize=imageSize, dotsPerSide=4)\n",
    "training_images = training_images.reshape((len(training_images), np.prod(training_images.shape[1:])))\n",
    "\n",
    "plt.figure(figsize=(20, 4))\n",
    "for i in range(n):\n",
    "    ax = plt.subplot(2, n, i + 1)\n",
    "    plt.imshow(training_images[i].reshape(imageSize, imageSize))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "plt.show()"
   ]
  }
 ],
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