924 lines
525 KiB
Text
924 lines
525 KiB
Text
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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"#%quickref\n",
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"%qtconsole"
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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": 3,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Scan for files in /home/ruben/Documents/Projecten/2017/ALLES WAT IK VOEL/testimages\n",
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"Found 14 files\n"
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]
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}
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],
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"source": [
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"# Load the files for scanning\n",
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"from matplotlib.pyplot import imshow\n",
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"import glob, os\n",
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"\n",
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"target_path = os.path.join(os.getcwd(), \"testimages\")\n",
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"\n",
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"print (\"Scan for files in {}\".format(target_path));\n",
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"\n",
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"files = glob.glob(os.path.join(target_path, '*.jpg'));\n",
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"print (\"Found {} files\".format(len(files)))\n",
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"\n",
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"# .. for now we assume all are images"
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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": 4,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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"# load as PIL Images\n",
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"from PIL import Image\n",
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"images = [Image.open(file) for file in files]"
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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": 5,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"\n",
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"arrays = [np.array(image) for image in images]"
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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": 6,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [],
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"source": [
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"def getChannelImagesFromImage(arrImg):\n",
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" \"\"\"\n",
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" [(255,255,255)] => ( [(255,0,0)], [(0,255,0)], [(0,0,255)] )\n",
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" Three separate images that show individual channels\n",
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" \"\"\"\n",
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" red = arrImg.copy()\n",
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" red[:,:,1] = 0\n",
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" red[:,:,2] = 0\n",
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"\n",
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" green = arrImg.copy()\n",
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" green[:,:,0] = 0\n",
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" green[:,:,2] = 0\n",
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"\n",
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" blue = arrImg.copy()\n",
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" blue[:,:,0] = 0\n",
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" blue[:,:,1] = 0\n",
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" return (red, green, blue)\n",
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"\n",
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"# (red,green,blue) = getChannelImagesFromImage(arrays[0])\n",
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"# print (red)\n",
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"# images[0].show()\n",
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"# Image.fromarray(red).show()\n",
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"# Image.fromarray(green).show()\n",
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"# Image.fromarray(blue).show()"
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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": 16,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<matplotlib.image.AxesImage at 0x7f0d928f2828>"
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]
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},
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"execution_count": 16,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAV8AAAD8CAYAAADQSqd1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nr/3Z44zGgS/95DvwlQH
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"text/plain": [
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"<matplotlib.figure.Figure at 0x7f0d929ad198>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"def getImageAsHSV(image):\n",
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" hsv = np.array(image.convert('HSV'))\n",
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"\n",
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"# If you want to get individual channels use\n",
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"# hue = hsv[:,:,0]\n",
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"# sat = hsv[:,:,1]\n",
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"# val = hsv[:,:,2]\n",
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"\n",
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"# hue = hsv.copy()\n",
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"# hsv[:,:,0] = 255 #all hue to 100%\n",
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"# hsv[:,:,1] = 255 #all saturation to 100%\n",
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"# hsv[:,:,2] = 255 #all brightness to 100%\n",
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" return hsv\n",
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"\n",
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"# hue_img = Image.fromarray(getImageAsHSV(images[4]), mode=\"HSV\")\n",
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"\n",
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"# %matplotlib inline\n",
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"# imshow(np.asarray(hue_img.convert('RGB')))"
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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": 10,
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"metadata": {
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"collapsed": false,
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"deletable": true,
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"editable": true
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"452 640 3\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"<matplotlib.image.AxesImage at 0x7f0d872a0e10>"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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|
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAMUAAAD8CAYAAADHTWCVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmI4S1gkeuoAo8EgdfxP
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f0d92b11f60>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# sorting pixels by hue\n",
|
||
|
|
"# http://stackoverflow.com/a/2828121\n",
|
||
|
|
"# Sort by column: a[a[:,1].argsort()]\n",
|
||
|
|
"hsv = np.array(images[4].convert('HSV'))\n",
|
||
|
|
"print (len(hsv), len(hsv[0]), len(hsv[0,0]))\n",
|
||
|
|
"height = len(hsv)\n",
|
||
|
|
"singleLine = np.concatenate(hsv)\n",
|
||
|
|
"sortedLine = np.array(sorted(singleLine, key=lambda pixel: pixel[0])) # change pixel[0] to 1 or 2 for saturation & brightness\n",
|
||
|
|
"sortedImage = sortedLine.reshape(int(len(sortedLine)/height), height, 3)\n",
|
||
|
|
"\n",
|
||
|
|
"sortedImg = Image.fromarray(sortedImage, mode=\"HSV\")\n",
|
||
|
|
"\n",
|
||
|
|
"sortedImg.show()\n",
|
||
|
|
"\n",
|
||
|
|
"%matplotlib inline\n",
|
||
|
|
"imshow(np.asarray(sortedImg.convert('RGB')))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 63,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Total 289280\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"[<matplotlib.lines.Line2D at 0x7f0d7df97a58>]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 63,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAD8CAYAAACb4nSYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4x3FN1T0fcZz+gFxyjY
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f0d84099f28>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"hues = np.zeros(255)\n",
|
||
|
|
"\n",
|
||
|
|
"# Get the spread of hue in an image\n",
|
||
|
|
"# for image in images:\n",
|
||
|
|
"image = images[4]\n",
|
||
|
|
"hsv = np.array(image.convert('HSV'))\n",
|
||
|
|
"hue = hsv[:,:,0]\n",
|
||
|
|
"print(\"Total %s\" % len(hue.flatten()))\n",
|
||
|
|
"values, boxes = scipy.histogram(hue, 255, range=(0,255), density=True)\n",
|
||
|
|
"# print(values)\n",
|
||
|
|
"\n",
|
||
|
|
"from pylab import *\n",
|
||
|
|
"plot(values)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 137,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"import struct\n",
|
||
|
|
"import scipy\n",
|
||
|
|
"import scipy.misc\n",
|
||
|
|
"import scipy.cluster\n",
|
||
|
|
"import codecs\n",
|
||
|
|
"from IPython.display import Markdown, display, HTML\n",
|
||
|
|
"\n",
|
||
|
|
"NUM_CLUSTERS = 64\n",
|
||
|
|
"\n",
|
||
|
|
"def getColourAsHex(colour):\n",
|
||
|
|
" return '#' + ''.join(format(c, '02x') for c in colour.astype(int))\n",
|
||
|
|
"\n",
|
||
|
|
"def getColoursForImageByClusters(image):\n",
|
||
|
|
" \"\"\"\n",
|
||
|
|
" Adapted on answers by\n",
|
||
|
|
" Peter Hansen (http://stackoverflow.com/a/3244061)\n",
|
||
|
|
" & Johan Mickos (http://stackoverflow.com/a/34140327)\n",
|
||
|
|
" \"\"\"\n",
|
||
|
|
" im = image.copy().resize((150, 150)) # optional, to reduce time\n",
|
||
|
|
" ar = scipy.misc.fromimage(im)\n",
|
||
|
|
" shape = ar.shape\n",
|
||
|
|
" ar = ar.reshape(scipy.product(shape[:2]), shape[2])\n",
|
||
|
|
"\n",
|
||
|
|
"# print( 'finding clusters')\n",
|
||
|
|
" codes, dist = scipy.cluster.vq.kmeans(ar.astype(float), NUM_CLUSTERS)\n",
|
||
|
|
"# print ('cluster centres:\\n', codes)\n",
|
||
|
|
" \n",
|
||
|
|
" vecs, dist = scipy.cluster.vq.vq(ar, codes) # assign codes\n",
|
||
|
|
" counts, bins = scipy.histogram(vecs, len(codes)) # count occurrences\n",
|
||
|
|
" \n",
|
||
|
|
"# When only looking for single color: \n",
|
||
|
|
"# index_max = scipy.argmax(counts) # find most frequent\n",
|
||
|
|
"# peak = codes[index_max]\n",
|
||
|
|
"# colour = ''.join(chr(c) for c in peak).encode('hex')\n",
|
||
|
|
"# print( 'most frequent is %s (#%s)' % (peak, colour))\n",
|
||
|
|
" \n",
|
||
|
|
" percentages = 100 * counts / sum(counts)\n",
|
||
|
|
"# print(\"Percentages\", percentages)\n",
|
||
|
|
"# colours = [ in codes]\n",
|
||
|
|
"# print(colours)\n",
|
||
|
|
" return list(zip(codes, percentages))\n",
|
||
|
|
"\n",
|
||
|
|
"def getColoursForImageByPxAvg(image):\n",
|
||
|
|
" im = image.copy().resize((8, 8))\n",
|
||
|
|
" pixels = np.concatenate(scipy.misc.fromimage(im))\n",
|
||
|
|
"# colours = ['#' + ''.join(format(c, '02x') for c in color.astype(int)) for color in pixels]\n",
|
||
|
|
" percentages = np.zeros(len(pixels)) + (100 / len(pixels))\n",
|
||
|
|
" return list(zip(pixels, percentages))\n",
|
||
|
|
"\n",
|
||
|
|
"def getColoursAsHTML(colours):\n",
|
||
|
|
" return \" \".join(['<span style=\"background:%s\">%s - (%s %%)</span>' % (getColourAsHex(colour[0]), getColourAsHex(colour[0]), colour[1]) for colour in colours]);\n",
|
||
|
|
"\n",
|
||
|
|
"# for image in images:\n",
|
||
|
|
"# display(image)\n",
|
||
|
|
"# print(\"Method 1: clustering (%s clusters)\" % NUM_CLUSTERS)\n",
|
||
|
|
"# colours = getColoursForImageByClusters(image)\n",
|
||
|
|
"# display(HTML(getColoursAsHTML(colours)))\n",
|
||
|
|
" \n",
|
||
|
|
"# print(\"Method 2: scaling\")\n",
|
||
|
|
"# colours = getColoursForImageByPxAvg(image)\n",
|
||
|
|
"# display(HTML(getColoursAsHTML(colours)))\n",
|
||
|
|
" \n",
|
||
|
|
"# break"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 142,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Get colours for all images\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
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"text/html": [
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|
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],
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|
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"text/plain": [
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|
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"<IPython.core.display.HTML object>"
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|
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]
|
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|
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},
|
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|
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"metadata": {},
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|
|
"output_type": "display_data"
|
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|
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},
|
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|
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{
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|
|
"data": {
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"text/html": [
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|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
"<IPython.core.display.HTML object>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
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||
|
|
"text/html": [
|
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|
|
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|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
"<IPython.core.display.HTML object>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
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|
|
{
|
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|
|
"data": {
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|
|
"text/html": [
|
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"<span style=\"background:#ac98d7\">#ac98d7 - (0.413333333333 %)</span> <span style=\"background:#8a8fcb\">#8a8fcb - (0.622222222222 %)</span> <span style=\"background:#a6d1f4\">#a6d1f4 - (3.35111111111 %)</span> <span style=\"background:#44a7d9\">#44a7d9 - (0.4 %)</span> <span style=\"background:#92bcea\">#92bcea - (1.42222222222 %)</span> <span style=\"background:#7d7fbd\">#7d7fbd - (1.64 %)</span> <span style=\"background:#96c0e4\">#96c0e4 - (3.85333333333 %)</span> <span style=\"background:#98c4e7\">#98c4e7 - (3.52888888889 %)</span> <span style=\"background:#a7af9e\">#a7af9e - (0.622222222222 %)</span> <span style=\"background:#944d4c\">#944d4c - (0.44 %)</span> <span style=\"background:#de6dca\">#de6dca - (0.36 %)</span> <span style=\"background:#90c8f4\">#90c8f4 - (0.324444444444 %)</span> <span style=\"background:#416db2\">#416db2 - (1.26666666667 %)</span> <span style=\"background:#a2cdf0\">#a2cdf0 - (2.49333333333 %)</span> <span style=\"background:#86abd3\">#86abd3 - (3.66222222222 %)</span> <span style=\"background:#7cb0e3\">#7cb0e3 - (0.56 %)</span> <span style=\"background:#9d7594\">#9d7594 - (1.05333333333 %)</span> <span style=\"background:#6094d1\">#6094d1 - (0.906666666667 %)</span> <span style=\"background:#5d59a6\">#5d59a6 - (0.408888888889 %)</span> <span style=\"background:#5c82bd\">#5c82bd - (0.866666666667 %)</span> <span style=\"background:#376c84\">#376c84 - (0.591111111111 %)</span> <span style=\"background:#62b6e4\">#62b6e4 - (0.302222222222 %)</span> <span style=\"background:#49956c\">#49956c - (0.893333333333 %)</span> <span style=\"background:#7e9fc5\">#7e9fc5 - (2.32444444444 %)</span> <span style=\"background:#8eb7df\">#8eb7df - (3.55111111111 %)</span> <span style=\"background:#305da9\">#305da9 - (1.29333333333 %)</span> <span style=\"background:#a3cef1\">#a3cef1 - (3.09333333333 %)</span> <span style=\"background:#a0ccef\">#a0ccef - (3.06666666667 %)</span> <span style=\"background:#d155a9\">#d155a9 - (0.257777777778 %)</span> <span style=\"background:#81a4ca\">#81a4ca - (4.28888888889 %)</span> <span style=\"background:#6ea5a1\">#6ea5a1 - (0.493333333333 %)</span> <span style=\"background:#2c3e95\">#2c3e95 - (0.822222222222 %)</span> <span style=\"background:#b072c2\">#b072c2 - (0.4 %)</span> <span style=\"background:#97b6dc\">#97b6dc - (0.955555555556 %)</span> <span style=\"background:#9bc6eb\">#9bc6eb - (2.55111111111 %)</span> <span style=\"background:#b29f3f\">#b29f3f - (1.33333333333 %)</span> <span style=\"background:#bc6d48\">#bc6d48 - (0.853333333333 %)</span> <span style=\"background:#3075c6\">#3075c6 - (0.72 %)</span> <span style=\"background:#44525e\">#44525e - (0.404444444444 %)</span> <span style=\"background:#9396a6\">#9396a6 - (1.02666666667 %)</span> <span style=\"background:#b9b5e8\">#b9b5e8 - (0.44 %)</span> <span style=\"background:#766cb4\">#766cb4 - (1.26666666667 %)</span> <span style=\"background:#7796bf\">#7796bf - (1.41777777778 %)</span> <span style=\"background:#9ecaec\">#9ecaec - (2.84888888889 %)</span> <span style=\"background:#4e83a5\">#4e83a5 - (0.475555555556 %)</span> <span style=\"background:#a7d4f6\">#a7d4f6 - (3.48 %)</span> <span style=\"background:#89afd5\">#89afd5 - (4.21777777778 %)</span> <span style=\"background:#a4d0f3\">#a4d0f3 - (5.28 %)</span> <span style=\"background:#2ca5e1\">#2ca5e1 - (0.844444444444 %)</span> <span style=\"background:#4881c9\">#4881c9 - (0.875555555556 %)</span> <span style=\"background:#729ed6\">#729ed6 - (0.817777777778 %)</span> <span style=\"background:#669382\">#669382 - (0.737777777778 %)</span> <span style=\"background:#92bce2\">#92bce2 - (4.49333333333 %)</span> <span style=\"background:#8ab0db\">#8ab0db - (2.87555555556 %)</span> <span style=\"background:#968a37\">#968a37 - (0.92 %)</span> <span style=\"background:#aba36b\">#aba36b - (0.911111111111 %)</span> <span style=\"background:#85a8ce\">#85a8ce - (3.76444444444 %)</span> <span style=\"background:#cd91d9\">#cd91d9 - (0.582222222222 %)</span> <span style=\"background:#a5b3be\">#a5b3be - (0.928888888889 %)</span> <span style=\"background:#8eb4db\"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
"<IPython.core.display.HTML object>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/html": [
|
||
|
|
"<span style=\"background:#fefeff\">#fefeff - (66.7466666667 %)</span> <span style=\"background:#faeb6e\">#faeb6e - (1.53333333333 %)</span> <span style=\"background:#ecf6f8\">#ecf6f8 - (2.12444444444 %)</span> <span style=\"background:#c3c5c9\">#c3c5c9 - (0.36 %)</span> <span style=\"background:#56585d\">#56585d - (0.466666666667 %)</span> <span style=\"background:#818183\">#818183 - (0.431111111111 %)</span> <span style=\"background:#ee0c59\">#ee0c59 - (1.10666666667 %)</span> <span style=\"background:#efb0ec\">#efb0ec - (0.124444444444 %)</span> <span style=\"background:#a9a9a5\">#a9a9a5 - (0.391111111111 %)</span> <span style=\"background:#fcfdfe\">#fcfdfe - (2.30666666667 %)</span> <span style=\"background:#d9e0e2\">#d9e0e2 - (0.582222222222 %)</span> <span style=\"background:#e4eff1\">#e4eff1 - (1.20444444444 %)</span> <span style=\"background:#8e682a\">#8e682a - (0.342222222222 %)</span> <span style=\"background:#ce994c\">#ce994c - (0.315555555556 %)</span> <span style=\"background:#f7c735\">#f7c735 - (0.728888888889 %)</span> <span style=\"background:#fdfdfc\">#fdfdfc - (7.35111111111 %)</span> <span style=\"background:#f9f4ad\">#f9f4ad - (0.382222222222 %)</span> <span style=\"background:#f8c6b1\">#f8c6b1 - (0.4 %)</span> <span style=\"background:#f9de55\">#f9de55 - (1.62666666667 %)</span> <span style=\"background:#f7fcfd\">#f7fcfd - (2.63111111111 %)</span> <span style=\"background:#f2f9fb\">#f2f9fb - (3.08 %)</span> <span style=\"background:#f3f1f1\">#f3f1f1 - (0.746666666667 %)</span> <span style=\"background:#f9f089\">#f9f089 - (1.01777777778 %)</span> <span style=\"background:#fcf9d3\">#fcf9d3 - (0.346666666667 %)</span> <span style=\"background:#f9f9f9\">#f9f9f9 - (2.01777777778 %)</span> <span style=\"background:#51221f\">#51221f - (0.413333333333 %)</span> <span style=\"background:#fdfdf0\">#fdfdf0 - (0.831111111111 %)</span> <span style=\"background:#eab18a\">#eab18a - (0.391111111111 %)</span>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
"<IPython.core.display.HTML object>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/html": [
|
||
|
|
"<span style=\"background:#b3b5b6\">#b3b5b6 - (1.60888888889 %)</span> <span style=\"background:#8d4b62\">#8d4b62 - (0.888888888889 %)</span> <span style=\"background:#a0a29b\">#a0a29b - (2.69333333333 %)</span> <span style=\"background:#985e94\">#985e94 - (0.662222222222 %)</span> <span style=\"background:#c17ba8\">#c17ba8 - (0.44 %)</span> <span style=\"background:#a8aaa6\">#a8aaa6 - (4.27555555556 %)</span> <span style=\"background:#b1b2b2\">#b1b2b2 - (2.64888888889 %)</span> <span style=\"background:#434d56\">#434d56 - (0.546666666667 %)</span> <span style=\"background:#ad929a\">#ad929a - (0.346666666667 %)</span> <span style=\"background:#965d78\">#965d78 - (1.67555555556 %)</span> <span style=\"background:#d3b88b\">#d3b88b - (0.168888888889 %)</span> <span style=\"background:#9fa49f\">#9fa49f - (2.25777777778 %)</span> <span style=\"background:#a7aaaa\">#a7aaaa - (3.33777777778 %)</span> <span style=\"background:#90908c\">#90908c - (1.28 %)</span> <span style=\"background:#5e8998\">#5e8998 - (0.244444444444 %)</span> <span style=\"background:#dfe0e2\">#dfe0e2 - (1.35555555556 %)</span> <span style=\"background:#d2d4d7\">#d2d4d7 - (1.52888888889 %)</span> <span style=\"background:#06739b\">#06739b - (0.346666666667 %)</span> <span style=\"background:#9da17b\">#9da17b - (0.462222222222 %)</span> <span style=\"background:#9ca29e\">#9ca29e - (2.63555555556 %)</span> <span style=\"background:#959692\">#959692 - (2.12444444444 %)</span> <span style=\"background:#809da9\">#809da9 - (0.32 %)</span> <span style=\"background:#1d2328\">#1d2328 - (0.884444444444 %)</span> <span style=\"background:#9d9e9c\">#9d9e9c - (2.15111111111 %)</span> <span style=\"background:#c55e4d\">#c55e4d - (0.426666666667 %)</span> <span style=\"background:#dadcde\">#dadcde - (1.84444444444 %)</span> <span style=\"background:#bbbcbd\">#bbbcbd - (1.68444444444 %)</span> <span style=\"background:#95b1ba\">#95b1ba - (0.24 %)</span> <span style=\"background:#c5c8c9\">#c5c8c9 - (1.42666666667 %)</span> <span style=\"background:#6e737e\">#6e737e - (0.768888888889 %)</span> <span style=\"background:#cacdce\">#cacdce - (1.43111111111 %)</span> <span style=\"background:#aaaca9\">#aaaca9 - (3.98222222222 %)</span> <span style=\"background:#9fa19f\">#9fa19f - (2.40444444444 %)</span> <span style=\"background:#a3a6a0\">#a3a6a0 - (2.75111111111 %)</span> <span style=\"background:#ad678c\">#ad678c - (1.19111111111 %)</span> <span style=\"background:#b6a0ab\">#b6a0ab - (0.4 %)</span> <span style=\"background:#cfd1d3\">#cfd1d3 - (1.79555555556 %)</span> <span style=\"background:#9a9d9b\">#9a9d9b - (2.75111111111 %)</span> <span style=\"background:#d798c3\">#d798c3 - (0.231111111111 %)</span> <span style=\"background:#aeb0ae\">#aeb0ae - (4.14666666667 %)</span> <span style=\"background:#879e54\">#879e54 - (0.853333333333 %)</span> <span style=\"background:#bec0c2\">#bec0c2 - (1.47111111111 %)</span> <span style=\"background:#a4a4a4\">#a4a4a4 - (1.95555555556 %)</span> <span style=\"background:#808286\">#808286 - (0.804444444444 %)</span> <span style=\"background:#a4a8a6\">#a4a8a6 - (3.12888888889 %)</span> <span style=\"background:#a47b92\">#a47b92 - (0.542222222222 %)</span> <span style=\"background:#a0a5a4\">#a0a5a4 - (2.30666666667 %)</span> <span style=\"background:#c2c4c5\">#c2c4c5 - (1.32 %)</span> <span style=\"background:#58626c\">#58626c - (0.577777777778 %)</span> <span style=\"background:#acaeab\">#acaeab - (3.47111111111 %)</span> <span style=\"background:#a64131\">#a64131 - (0.986666666667 %)</span> <span style=\"background:#a6a8a3\">#a6a8a3 - (3.08 %)</span> <span style=\"background:#aaacaf\">#aaacaf - (1.60888888889 %)</span> <span style=\"background:#045675\">#045675 - (1.63555555556 %)</span> <span style=\"background:#b6b8ba\">#b6b8ba - (1.37777777778 %)</span> <span style=\"background:#d6d8da\">#d6d8da - (1.70666666667 %)</span> <span style=\"background:#9c9f99\">#9c9f99 - (3.01777777778 %)</span> <span style=\"background:#a1a39f\">#a1a39f - (2.91555555556 %)</span> <span style=\"background:#316e85\">#316e85 - (0.293333333333 %)</span> <sp
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
"<IPython.core.display.HTML object>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(\"Get colours for all images\")\n",
|
||
|
|
"imgColours = []\n",
|
||
|
|
"for image in images:\n",
|
||
|
|
"# display(image)\n",
|
||
|
|
" colours = getColoursForImageByClusters(image)\n",
|
||
|
|
" imgColours.append(colours)\n",
|
||
|
|
" output = getColoursAsHTML(colours)\n",
|
||
|
|
" display(HTML(output))\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 74,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[[[233 3 166]\n",
|
||
|
|
" [233 3 167]\n",
|
||
|
|
" [233 3 167]\n",
|
||
|
|
" ..., \n",
|
||
|
|
" [198 4 173]\n",
|
||
|
|
" [198 4 172]\n",
|
||
|
|
" [198 4 172]]\n",
|
||
|
|
"\n",
|
||
|
|
" [[233 3 167]\n",
|
||
|
|
" [233 3 167]\n",
|
||
|
|
" [233 3 168]\n",
|
||
|
|
" ..., \n",
|
||
|
|
" [198 4 173]\n",
|
||
|
|
" [198 4 172]\n",
|
||
|
|
" [198 4 172]]\n",
|
||
|
|
"\n",
|
||
|
|
" [[233 3 168]\n",
|
||
|
|
" [233 3 168]\n",
|
||
|
|
" [233 3 169]\n",
|
||
|
|
" ..., \n",
|
||
|
|
" [198 4 173]\n",
|
||
|
|
" [198 4 172]\n",
|
||
|
|
" [198 4 172]]\n",
|
||
|
|
"\n",
|
||
|
|
" ..., \n",
|
||
|
|
" [[ 7 17 178]\n",
|
||
|
|
" [ 3 16 175]\n",
|
||
|
|
" [ 3 15 177]\n",
|
||
|
|
" ..., \n",
|
||
|
|
" [205 8 174]\n",
|
||
|
|
" [221 7 172]\n",
|
||
|
|
" [221 7 171]]\n",
|
||
|
|
"\n",
|
||
|
|
" [[ 3 115 75]\n",
|
||
|
|
" [ 3 109 79]\n",
|
||
|
|
" [ 3 99 87]\n",
|
||
|
|
" ..., \n",
|
||
|
|
" [ 0 21 164]\n",
|
||
|
|
" [ 2 24 155]\n",
|
||
|
|
" [ 7 29 148]]\n",
|
||
|
|
"\n",
|
||
|
|
" [[ 16 186 111]\n",
|
||
|
|
" [ 17 179 112]\n",
|
||
|
|
" [ 17 184 109]\n",
|
||
|
|
" ..., \n",
|
||
|
|
" [ 13 172 105]\n",
|
||
|
|
" [ 15 177 105]\n",
|
||
|
|
" [ 15 177 105]]]\n",
|
||
|
|
"[[233 3 166]\n",
|
||
|
|
" [233 3 167]\n",
|
||
|
|
" [233 3 167]\n",
|
||
|
|
" ..., \n",
|
||
|
|
" [ 13 172 105]\n",
|
||
|
|
" [ 15 177 105]\n",
|
||
|
|
" [ 15 177 105]]\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"hues\n",
|
||
|
|
"\n",
|
||
|
|
"for image in images:\n",
|
||
|
|
" img = image.copy().resize((150, 150)).convert('HSV') # optional scale, to reduce time\n",
|
||
|
|
" ar = np.array(img)\n",
|
||
|
|
" shape = ar.shape\n",
|
||
|
|
" ar = ar.reshape(scipy.product(shape[:2]), shape[2])\n",
|
||
|
|
" \n",
|
||
|
|
" print(ar)\n",
|
||
|
|
" break\n",
|
||
|
|
"# Find a way to limit these per image without sacrificing the maximums in the detail\n",
|
||
|
|
"# Finding maximums, or _clustering_!\n",
|
||
|
|
" "
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 153,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"#concatenate found colours of all images into one\n",
|
||
|
|
"allColours = sum(imgColours)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 182,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"<svg viewBox=\"-160 -160 320 320\" xmlns=\"http://www.w3.org/2000/svg\"><circle cx=\"-51.7001271219\" cy=\"38.030374836\" r=\"1.99288256228\" style=\"fill:#2d3530\" /><circle cx=\"-34.4049859725\" cy=\"-88.1541524636\" r=\"2.69480519481\" style=\"fill:#322d51\" /><circle cx=\"55.1844093851\" cy=\"-14.8880599213\" r=\"1\" style=\"fill:#9e9295\" /><circle cx=\"64.1409867305\" cy=\"-11.6760039413\" r=\"1\" style=\"fill:#947d81\" /><circle cx=\"-53.8799118995\" cy=\"-6.67364078148\" r=\"1\" style=\"fill:#aeb5b6\" /><circle cx=\"49.8265732445\" cy=\"63.0938334354\" r=\"1\" style=\"fill:#b9b281\" /><circle cx=\"85.6164653701\" cy=\"-5.97282016149\" r=\"1\" style=\"fill:#a2686c\" /><circle cx=\"-67.7806952611\" cy=\"-57.1290473968\" r=\"1\" style=\"fill:#56688c\" /><circle cx=\"-94.2199518224\" cy=\"-85.5727574281\" r=\"3.38235294118\" style=\"fill:#1a3472\" /><circle cx=\"97.9360437597\" cy=\"99.0421921816\" r=\"2.64705882353\" style=\"fill:#a58111\" /><circle cx=\"136.682664482\" cy=\"30.1582894959\" r=\"1\" style=\"fill:#a32e10\" /><circle cx=\"-72.9827896955\" cy=\"-67.1111153809\" r=\"1\" style=\"fill:#3c4d76\" /><circle cx=\"55.5473329927\" cy=\"-17.6261594244\" r=\"5.62256809339\" style=\"fill:#b5a6aa\" /><circle cx=\"9.28543688052\" cy=\"-50.927773954\" r=\"8.07197943445\" style=\"fill:#b9b7ba\" /><circle cx=\"75.866149531\" cy=\"-7.01833824768\" r=\"3.28358208955\" style=\"fill:#120d0d\" /><circle cx=\"30.490340243\" cy=\"-40.4128749116\" r=\"8.04347826087\" style=\"fill:#aeadae\" /><circle cx=\"-50.0459313815\" cy=\"-42.8840080302\" r=\"1\" style=\"fill:#b2bdd4\" /><circle cx=\"97.0848112192\" cy=\"8.76996019077\" r=\"1.17647058824\" style=\"fill:#78443f\" /><circle cx=\"-26.9794619317\" cy=\"-45.6533383804\" r=\"4.89795918367\" style=\"fill:#acacb1\" /><circle cx=\"78.7620605233\" cy=\"85.5689892158\" r=\"1\" style=\"fill:#a08a36\" /><circle cx=\"133.940755495\" cy=\"-15.2433807178\" r=\"1\" style=\"fill:#941624\" /><circle cx=\"46.7748268561\" cy=\"-25.4478295459\" r=\"1\" style=\"fill:#c7c0c3\" /><circle cx=\"52.8332126997\" cy=\"-13.3162231796\" r=\"1\" style=\"fill:#5f5b5c\" /><circle cx=\"-12.8003685331\" cy=\"-52.2678380534\" r=\"4.0\" style=\"fill:#bbb9c1\" /><circle cx=\"87.4905512737\" cy=\"-14.472087817\" r=\"1\" style=\"fill:#cf7f8b\" /><circle cx=\"94.9606169688\" cy=\"-11.1487826946\" r=\"1\" style=\"fill:#c26973\" /><circle cx=\"57.6922160979\" cy=\"-7.25209869362\" r=\"1\" style=\"fill:#7c7273\" /><circle cx=\"45.6502875839\" cy=\"45.6681154007\" r=\"1\" style=\"fill:#b6b09c\" /><circle cx=\"115.412863494\" cy=\"39.1679139787\" r=\"1\" style=\"fill:#834225\" /><circle cx=\"44.3086458001\" cy=\"56.2202345028\" r=\"1\" style=\"fill:#c2bc98\" /><circle cx=\"67.2749135969\" cy=\"-12.5873143773\" r=\"1\" style=\"fill:#b49399\" /><circle cx=\"28.1362545267\" cy=\"50.1329439513\" r=\"1\" style=\"fill:#b9b9ab\" /><circle cx=\"54.4339086644\" cy=\"33.8934639346\" r=\"1\" style=\"fill:#c5b8a9\" /><circle cx=\"109.972627162\" cy=\"4.55373971686\" r=\"1\" style=\"fill:#5b2624\" /><circle cx=\"52.5400812811\" cy=\"16.7267532572\" r=\"5.64796905222\" style=\"fill:#b6afac\" /><circle cx=\"-64.3428140012\" cy=\"-39.674543242\" r=\"1\" style=\"fill:#96aec9\" /><circle cx=\"62.2980819326\" cy=\"-10.2184932594\" r=\"3.93530997305\" style=\"fill:#c3a9ad\" /><circle cx=\"43.3706982573\" cy=\"32.0961359182\" r=\"3.26347305389\" style=\"fill:#bdbab6\" /><circle cx=\"105.828347973\" cy=\"-7.77916082381\" r=\"1\" style=\"fill:#b44f56\" /><circle cx=\"-46.9739182207\" cy=\"-39.7942245784\" r=\"1\" style=\"fill:#adb4c3\" /><circle cx=\"48.2207685172\" cy=\"-28.401383484\" r=\"1.81818181818\" style=\"fill:#494446\" /><circle cx=\"66.7268629437\" cy=\"-4.88165722648\" r=\"1\" style=\"fill:#d4b0b2\" /><circle cx=\"76.9108399682\" cy=\"28.9602929706\" r=\"1\" style=\"fill:#ca9f89\" /><circle cx=\"56.4745064464\" cy=\"66.8975379657\" r=\"1\" style=\"fill:#aca16b\" /><circle cx=\"38.7823679089\" cy=\"-33.8980359211\" r=\"7.22906403941\" style=\"fill:#a7a5a7\" /><circle cx=\"12.8832189792\" cy=\"-50.1252439187\" r=\"7.930082796
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"import colorsys\n",
|
||
|
|
"import math\n",
|
||
|
|
"\n",
|
||
|
|
"# box 160, because center or circle = 100 => +/- 50 => + r of colour circle (max: 10) => 160\n",
|
||
|
|
"svg = '<svg viewBox=\"-160 -160 320 320\" xmlns=\"http://www.w3.org/2000/svg\">'\n",
|
||
|
|
"\n",
|
||
|
|
"radius = 100\n",
|
||
|
|
"\n",
|
||
|
|
"for colour in allColours:\n",
|
||
|
|
" rgb, percentage = colour\n",
|
||
|
|
" rgbNorm = rgb/255\n",
|
||
|
|
" hsv = colorsys.rgb_to_hsv(rgbNorm[0], rgbNorm[1], rgbNorm[2])\n",
|
||
|
|
" # find position on circle\n",
|
||
|
|
" radians = 2 * math.pi * hsv[0]\n",
|
||
|
|
" x = math.cos(radians)\n",
|
||
|
|
" y = math.sin(radians)\n",
|
||
|
|
" \n",
|
||
|
|
" # based on saturation, we move inwards/outwards\n",
|
||
|
|
" # min = 0.5, max = 1.5 (dus + 0.5)\n",
|
||
|
|
" pos = np.array([x,y]) * (0.5 + hsv[1]) * radius\n",
|
||
|
|
" # Posibilitiy: determine position based on avg(saturation, value) => dark & grey inside, shiney and colourful outside \n",
|
||
|
|
" # pos = np.array([x,y]) * (0.5 + (hsv[1]+hsv[2])/2) * radius\n",
|
||
|
|
" r = max(1,-10/percentage+10) # as r, we converge to maximum radius 10, but don't want to get smaller radi then 1\n",
|
||
|
|
" c = '<circle cx=\"%s\" cy=\"%s\" r=\"%s\" style=\"fill:%s\" />' % (pos[0], pos[1], r, getColourAsHex(rgb))\n",
|
||
|
|
" svg += c\n",
|
||
|
|
"\n",
|
||
|
|
"svg += \"</svg>\"\n",
|
||
|
|
"\n",
|
||
|
|
"print (svg)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 228,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"import json\n",
|
||
|
|
"\n",
|
||
|
|
"def coloursToJson(colours):\n",
|
||
|
|
" colours2 = [(list(colour[0]), colour[1]) for colour in colours]\n",
|
||
|
|
" return json.dumps(colours2)\n",
|
||
|
|
"\n",
|
||
|
|
"def jsonToColours(string):\n",
|
||
|
|
" data = json.loads(string)\n",
|
||
|
|
" return [(np.array(d[0]), d[1]) for d in data]\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 229,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"from peewee import *\n",
|
||
|
|
"from playhouse.sqlite_ext import SqliteExtDatabase\n",
|
||
|
|
"import datetime\n",
|
||
|
|
"\n",
|
||
|
|
"class ColoursField(TextField):\n",
|
||
|
|
"# db_field = 'colour'\n",
|
||
|
|
"\n",
|
||
|
|
" def db_value(self, value):\n",
|
||
|
|
" return coloursToJson(value)\n",
|
||
|
|
"\n",
|
||
|
|
" def python_value(self, value):\n",
|
||
|
|
" return jsonToColours(value) # convert str to UUID\n",
|
||
|
|
"\n",
|
||
|
|
"db = SqliteExtDatabase('images.db')\n",
|
||
|
|
"\n",
|
||
|
|
"class BaseModel(Model):\n",
|
||
|
|
" class Meta:\n",
|
||
|
|
" database = db\n",
|
||
|
|
" \n",
|
||
|
|
"class Emotion(BaseModel):\n",
|
||
|
|
" name = CharField(unique=True)\n",
|
||
|
|
" \n",
|
||
|
|
"class Group(BaseModel):\n",
|
||
|
|
" name = CharField(unique=True)\n",
|
||
|
|
"\n",
|
||
|
|
"class Artwork(BaseModel):\n",
|
||
|
|
" author = CharField()\n",
|
||
|
|
" age = SmallIntegerField(index=True)\n",
|
||
|
|
" gender = FixedCharField(max_length=1) # we should not really use this one\n",
|
||
|
|
" group = ForeignKeyField(Group, related_name='artworks', index=True)\n",
|
||
|
|
" emotion = ForeignKeyField(Emotion, related_name='artworks', index=True)\n",
|
||
|
|
" created_date = DateTimeField(default=datetime.datetime.now)\n",
|
||
|
|
" filename = CharField()\n",
|
||
|
|
" colours = ColoursField() # serialised colours + percentages: [([r,g,b], percentage), ...]\n",
|
||
|
|
"\n",
|
||
|
|
"db.connect()\n",
|
||
|
|
"db.create_tables([Emotion, Group, Artwork])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 230,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"m 40\n",
|
||
|
|
"u 41\n",
|
||
|
|
"f 42\n",
|
||
|
|
"f 43\n",
|
||
|
|
"u 44\n",
|
||
|
|
"u 45\n",
|
||
|
|
"f 46\n",
|
||
|
|
"f 47\n",
|
||
|
|
"m 48\n",
|
||
|
|
"f 49\n",
|
||
|
|
"m 410\n",
|
||
|
|
"m 411\n",
|
||
|
|
"f 412\n",
|
||
|
|
"m 413\n",
|
||
|
|
"m 414\n",
|
||
|
|
"f 415\n",
|
||
|
|
"f 416\n",
|
||
|
|
"u 417\n",
|
||
|
|
"f 418\n",
|
||
|
|
"m 419\n",
|
||
|
|
"f 50\n",
|
||
|
|
"f 51\n",
|
||
|
|
"m 52\n",
|
||
|
|
"f 53\n",
|
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|
|
"u 54\n",
|
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|
|
"f 55\n",
|
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|
|
"u 56\n",
|
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|
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"f 57\n",
|
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|
|
"u 58\n",
|
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|
|
"m 59\n",
|
||
|
|
"u 510\n",
|
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|
|
"f 511\n",
|
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|
|
"u 512\n",
|
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|
|
"m 513\n",
|
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"u 514\n",
|
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|
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"u 515\n",
|
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|
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"u 516\n",
|
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|
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"u 517\n",
|
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|
|
"f 518\n",
|
||
|
|
"m 519\n",
|
||
|
|
"m 60\n",
|
||
|
|
"f 61\n",
|
||
|
|
"f 62\n",
|
||
|
|
"f 63\n",
|
||
|
|
"m 64\n",
|
||
|
|
"m 65\n",
|
||
|
|
"f 66\n",
|
||
|
|
"u 67\n",
|
||
|
|
"f 68\n",
|
||
|
|
"u 69\n",
|
||
|
|
"u 610\n",
|
||
|
|
"m 611\n",
|
||
|
|
"m 612\n",
|
||
|
|
"f 613\n",
|
||
|
|
"m 614\n",
|
||
|
|
"f 615\n",
|
||
|
|
"u 616\n",
|
||
|
|
"u 617\n",
|
||
|
|
"f 618\n",
|
||
|
|
"m 619\n",
|
||
|
|
"u 70\n",
|
||
|
|
"u 71\n",
|
||
|
|
"f 72\n",
|
||
|
|
"f 73\n",
|
||
|
|
"m 74\n",
|
||
|
|
"u 75\n",
|
||
|
|
"m 76\n",
|
||
|
|
"f 77\n",
|
||
|
|
"f 78\n",
|
||
|
|
"u 79\n",
|
||
|
|
"f 710\n",
|
||
|
|
"f 711\n",
|
||
|
|
"u 712\n",
|
||
|
|
"f 713\n",
|
||
|
|
"f 714\n",
|
||
|
|
"u 715\n",
|
||
|
|
"f 716\n",
|
||
|
|
"m 717\n",
|
||
|
|
"m 718\n",
|
||
|
|
"u 719\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"import random\n",
|
||
|
|
"from PIL import Image\n",
|
||
|
|
"\n",
|
||
|
|
"emos = [\"anger\",\"contempt\",\"disgust\",\"fear\",\"joy\",\"sadness\",\"surprise\"]\n",
|
||
|
|
"emotions = []\n",
|
||
|
|
"for emo in emos:\n",
|
||
|
|
" emotion = Emotion(name=emo)\n",
|
||
|
|
" emotion.save()\n",
|
||
|
|
" emotions.append(emotion)\n",
|
||
|
|
"\n",
|
||
|
|
"# # Generate some random data:\n",
|
||
|
|
"for i in range(4,8):\n",
|
||
|
|
" group = Group.create(name='Groep %s' % i)\n",
|
||
|
|
" group.save()\n",
|
||
|
|
" \n",
|
||
|
|
"# some images:\n",
|
||
|
|
" for j in range(20):\n",
|
||
|
|
" genders = ['m','f','u']\n",
|
||
|
|
" img = Artwork()\n",
|
||
|
|
" img.gender = random.choice(genders)\n",
|
||
|
|
" img.author = \"%s %s%s\" % (img.gender, i,j)\n",
|
||
|
|
" img.age = i + 4 + random.choice([-1,0,0,0,0,1,1,2])\n",
|
||
|
|
" img.group = group\n",
|
||
|
|
" img.emotion = random.choice(emotions)\n",
|
||
|
|
" img.filename = random.choice(files)\n",
|
||
|
|
" img.colours = getColoursForImageByClusters(Image.open(img.filename))\n",
|
||
|
|
" img.save()\n",
|
||
|
|
" print(img.author)\n",
|
||
|
|
" \n",
|
||
|
|
"\n",
|
||
|
|
"# # No need to set `is_published` or `created_date` since they\n",
|
||
|
|
"# # will just use the default values we specified.\n",
|
||
|
|
"# Tweet.create(user=charlie, message='My first tweet')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 211,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
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