112 lines
3.7 KiB
Plaintext
112 lines
3.7 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from PIL import Image\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"\n",
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"class ImageFractalDimension:\n",
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" def __init__(self, image_name, SIZE):\n",
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" self.SIZE = SIZE\n",
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" image = Image.open(image_name)\n",
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"\n",
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" assert image.size[0] == image.size[1] and image.size[\n",
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" 0] == self.SIZE, \"Height and Width of the image must be equal.\"\n",
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"\n",
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" image = np.asarray(image)\n",
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" self.img_px_array = np.copy(image)\n",
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"\n",
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" self.convertImg()\n",
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" self.fractal_dim = self.calculate_fractal_dim()\n",
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"\n",
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" # Turn image data from colour into either 1 or zero for each colour\n",
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" def convertImg(self):\n",
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" for i in range(len(self.img_px_array)):\n",
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" for j in range(len(self.img_px_array[i])):\n",
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" for k in range(len(self.img_px_array[i][j])):\n",
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" if (self.img_px_array[i][j][k] == 255):\n",
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" self.img_px_array[i][j][k] = 0\n",
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" else:\n",
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" self.img_px_array[i][j][k] = 1\n",
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"\n",
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" def calculate_fractal_dim(self):\n",
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" self.dimensions = []\n",
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" self.filled_boxes = []\n",
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"\n",
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" self.img_px_array = self.img_px_array[:, :, 0]\n",
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"\n",
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" size = 1\n",
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" while size != self.SIZE:\n",
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" size *= 2\n",
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" filled_box = self.boxcount(size)\n",
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" self.filled_boxes.append(filled_box)\n",
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" self.dimensions.append(size / self.SIZE)\n",
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"\n",
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" return -np.polyfit(np.log(self.dimensions), np.log(self.filled_boxes), 1)[0]\n",
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"\n",
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" def blockshaped(self, square_size):\n",
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" h, w = self.img_px_array.shape\n",
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" assert h % square_size == 0, \"Array is not evenly divisible\".format(h, square_size)\n",
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" return (self.img_px_array.reshape(h // square_size, square_size, -1, square_size).swapaxes(1, 2).reshape(-1,\n",
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" square_size,\n",
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" square_size))\n",
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"\n",
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" def boxcount(self, size):\n",
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" blocked_arrays = self.blockshaped(size)\n",
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" counter = 0\n",
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"\n",
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" for i in range(len(blocked_arrays)):\n",
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" for j in range(len(blocked_arrays[i])):\n",
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" if (blocked_arrays[i][j].any()):\n",
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" counter += 1\n",
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" break\n",
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" return counter\n",
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"\n",
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" def graph(self):\n",
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" plt.plot(-np.log(self.dimensions), np.log(self.filled_boxes))\n",
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" plt.title(\"Fractal Dimension : \" + str(self.fractal_dim))\n",
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" plt.show()\n",
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" plt.clf()"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [],
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"metadata": {
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"collapsed": false
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}
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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