alleswatikvoel/ways_of_seeing_images.ipynb

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{
"cells": [
{
"cell_type": "code",
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"execution_count": 1,
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"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"#%quickref\n",
"%qtconsole"
]
},
{
"cell_type": "code",
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"execution_count": 9,
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"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Scan for files in /home/ruben/Documents/Projecten/2017/ALLES WAT IK VOEL/testimages\n",
"Found 14 files\n"
]
}
],
"source": [
"# Load the files for scanning\n",
"from matplotlib.pyplot import imshow\n",
"import glob, os\n",
"\n",
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"target_path = os.path.realpath(os.path.join(os.getcwd(), \"../testimages\"))\n",
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"\n",
"print (\"Scan for files in {}\".format(target_path));\n",
"\n",
"files = glob.glob(os.path.join(target_path, '*.jpg'));\n",
"print (\"Found {} files\".format(len(files)))\n",
"\n",
"# .. for now we assume all are images"
]
},
{
"cell_type": "code",
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"execution_count": 10,
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"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# load as PIL Images\n",
"from PIL import Image\n",
"images = [Image.open(file) for file in files]"
]
},
{
"cell_type": "code",
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"execution_count": 11,
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"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"arrays = [np.array(image) for image in images]"
]
},
{
"cell_type": "code",
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"execution_count": 12,
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"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def getChannelImagesFromImage(arrImg):\n",
" \"\"\"\n",
" [(255,255,255)] => ( [(255,0,0)], [(0,255,0)], [(0,0,255)] )\n",
" Three separate images that show individual channels\n",
" \"\"\"\n",
" red = arrImg.copy()\n",
" red[:,:,1] = 0\n",
" red[:,:,2] = 0\n",
"\n",
" green = arrImg.copy()\n",
" green[:,:,0] = 0\n",
" green[:,:,2] = 0\n",
"\n",
" blue = arrImg.copy()\n",
" blue[:,:,0] = 0\n",
" blue[:,:,1] = 0\n",
" return (red, green, blue)\n",
"\n",
"# (red,green,blue) = getChannelImagesFromImage(arrays[0])\n",
"# print (red)\n",
"# images[0].show()\n",
"# Image.fromarray(red).show()\n",
"# Image.fromarray(green).show()\n",
"# Image.fromarray(blue).show()"
]
},
{
"cell_type": "code",
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"execution_count": 13,
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"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
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"outputs": [],
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"source": [
"def getImageAsHSV(image):\n",
" hsv = np.array(image.convert('HSV'))\n",
"\n",
"# If you want to get individual channels use\n",
"# hue = hsv[:,:,0]\n",
"# sat = hsv[:,:,1]\n",
"# val = hsv[:,:,2]\n",
"\n",
"# hue = hsv.copy()\n",
"# hsv[:,:,0] = 255 #all hue to 100%\n",
"# hsv[:,:,1] = 255 #all saturation to 100%\n",
"# hsv[:,:,2] = 255 #all brightness to 100%\n",
" return hsv\n",
"\n",
"# hue_img = Image.fromarray(getImageAsHSV(images[4]), mode=\"HSV\")\n",
"\n",
"# %matplotlib inline\n",
"# imshow(np.asarray(hue_img.convert('RGB')))"
]
},
{
"cell_type": "code",
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"execution_count": 14,
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"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"452 640 3\n"
]
},
{
"data": {
"text/plain": [
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"<matplotlib.image.AxesImage at 0x7fdd92b11550>"
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]
},
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"execution_count": 14,
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"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAMUAAAD8CAYAAADHTWCVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEIQRmYSBE8Q1U0upmdO
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"text/plain": [
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"<matplotlib.figure.Figure at 0x7fdd92fecf28>"
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]
},
"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",
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"execution_count": 16,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total 289280\n"
]
},
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fdd92a5d908>]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7fdd92bbd978>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import scipy\n",
"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": 39,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"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",
"import sklearn.cluster\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 getColoursForImageByClusters2(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",
" bandwidth = sklearn.cluster.estimate_bandwidth(ar.astype(float), quantile=0.1, n_samples=500)\n",
" ms = sklearn.cluster.MeanShift(bandwidth=bandwidth)\n",
" ms.fit(ar.astype(float))\n",
" labels = ms.labels_ # labels per point\n",
" cluster_centers = ms.cluster_centers_ # centers of found clusters\n",
"\n",
" labels_unique = np.unique(labels)\n",
" n_clusters_ = len(labels_unique)\n",
" \n",
" print(labels_unique, n_clusters_)\n",
" \n",
" print(cluster_centers)\n",
" \n",
" for k in labels_unique:\n",
" my_members = labels == k\n",
" \n",
" cluster_center = cluster_centers[k]\n",
" print(cluster_center, len(ar[my_members]))\n",
" display(HTML(\"<span style='background:%s'>%s</span>\" % (getColourAsHex(cluster_center),getColourAsHex(cluster_center))))\n",
" \n",
" \n",
" print(ar[my_members])\n",
" \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": 22,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Get colours for all images\n"
]
},
{
"data": {
"text/html": [
"<span style=\"background:#587496\">#587496 - (0.311111111111 %)</span> <span style=\"background:#884c50\">#884c50 - (0.951111111111 %)</span> <span style=\"background:#adacae\">#adacae - (6.04 %)</span> <span style=\"background:#2c272c\">#2c272c - (1.35555555556 %)</span> <span style=\"background:#bbaf82\">#bbaf82 - (0.573333333333 %)</span> <span style=\"background:#aaa9ab\">#aaa9ab - (5.81333333333 %)</span> <span style=\"background:#b0afaf\">#b0afaf - (4.65777777778 %)</span> <span style=\"background:#a6a4a6\">#a6a4a6 - (2.76 %)</span> <span style=\"background:#443646\">#443646 - (1.28 %)</span> <span style=\"background:#82787a\">#82787a - (0.72 %)</span> <span style=\"background:#bbb8ba\">#bbb8ba - (5.19555555556 %)</span> <span style=\"background:#53221f\">#53221f - (0.595555555556 %)</span> <span style=\"background:#a09151\">#a09151 - (0.764444444444 %)</span> <span style=\"background:#b5b19f\">#b5b19f - (0.884444444444 %)</span> <span style=\"background:#bab5c4\">#bab5c4 - (1.35111111111 %)</span> <span style=\"background:#338cb5\">#338cb5 - (0.715555555556 %)</span> <span style=\"background:#ba6771\">#ba6771 - (0.72 %)</span> <span style=\"background:#b4b2b6\">#b4b2b6 - (6.71111111111 %)</span> <span style=\"background:#94acc7\">#94acc7 - (0.315555555556 %)</span> <span style=\"background:#ba8185\">#ba8185 - (0.693333333333 %)</span> <span style=\"background:#daa9a9\">#daa9a9 - (0.595555555556 %)</span> <span style=\"background:#aeacb3\">#aeacb3 - (2.40444444444 %)</span> <span style=\"background:#b1b9b8\">#b1b9b8 - (1.64888888889 %)</span> <span style=\"background:#cf9599\">#cf9599 - (0.671111111111 %)</span> <span style=\"background:#c3a6aa\">#c3a6aa - (1.57777777778 %)</span> <span style=\"background:#b74e54\">#b74e54 - (0.586666666667 %)</span> <span style=\"background:#966266\">#966266 - (0.968888888889 %)</span> <span style=\"background:#b8abb0\">#b8abb0 - (2.96444444444 %)</span> <span style=\"background:#961422\">#961422 - (0.977777777778 %)</span> <span style=\"background:#a5b3ae\">#a5b3ae - (0.893333333333 %)</span> <span style=\"background:#cbafb6\">#cbafb6 - (1.12 %)</span> <span style=\"background:#7292ae\">#7292ae - (0.462222222222 %)</span> <span style=\"background:#b7b4b8\">#b7b4b8 - (5.36 %)</span> <span style=\"background:#b1bdd3\">#b1bdd3 - (0.471111111111 %)</span> <span style=\"background:#0e0a0b\">#0e0a0b - (1.03555555556 %)</span> <span style=\"background:#b9bbaa\">#b9bbaa - (1.12 %)</span> <span style=\"background:#a18e37\">#a18e37 - (0.826666666667 %)</span> <span style=\"background:#b89a9f\">#b89a9f - (0.791111111111 %)</span> <span style=\"background:#a52f0f\">#a52f0f - (0.964444444444 %)</span> <span style=\"background:#884322\">#884322 - (1.16 %)</span> <span style=\"background:#bdafb6\">#bdafb6 - (3.15111111111 %)</span> <span style=\"background:#aeb3c2\">#aeb3c2 - (1.34666666667 %)</span> <span style=\"background:#c2bc97\">#c2bc97 - (0.604444444444 %)</span> <span style=\"background:#b1b0b4\">#b1b0b4 - (5.86666666667 %)</span> <span style=\"background:#c6b8ad\">#c6b8ad - (0.875555555556 %)</span> <span style=\"background:#a3787f\">#a3787f - (0.804444444444 %)</span> <span style=\"background:#a48210\">#a48210 - (1.41333333333 %)</span> <span style=\"background:#1a3578\">#1a3578 - (1.4 %)</span> <span style=\"background:#b1a367\">#b1a367 - (0.484444444444 %)</span> <span style=\"background:#6b6066\">#6b6066 - (0.693333333333 %)</span> <span style=\"background:#1f1a19\">#1f1a19 - (1.77333333333 %)</span> <span style=\"background:#c6bec2\">#c6bec2 - (0.822222222222 %)</span> <span style=\"background:#b18e95\">#b18e95 - (0.8 %)</span> <span style=\"background:#272d57\">#272d57 - (1.08444444444 %)</span> <span style=\"background:#274530\">#274530 - (0.573333333333 %)</span> <span style=\"background:#b3a6aa\">#b3a6aa - (2.01777777778 %)</span> <span style=\"background:#4c5353\">#4c5353 - (0.804444444444 %)</span> <span style=\"background:#989192\">#989192 - (0.782222222222 %)</span> <span style=\"background:#d17783\">#d17783 - (0.373333333333 %)</span> <span style=\"background:#3c
],
"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",
" break\n"
]
},
{
"cell_type": "code",
"execution_count": 40,
2017-03-06 21:11:00 +01:00
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
2017-03-11 11:49:47 +01:00
"[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13] 14\n",
"[[ 179.86118132 176.15218837 178.5385127 ]\n",
" [ 32.34522111 27.34236805 27.59058488]\n",
" [ 130.90238095 62.86190476 47.66666667]\n",
" [ 145.05755396 105.72182254 111.21822542]\n",
" [ 31.74009901 53.10891089 113.23514851]\n",
" [ 158.92119565 32.95652174 30.49456522]\n",
" [ 132.81481481 73.65242165 76.33333333]\n",
" [ 165.18910256 133.26923077 18.25 ]\n",
" [ 161.29411765 146.98366013 70.25490196]\n",
" [ 50.89864865 140.47972973 182.16891892]\n",
" [ 89.60747664 141.86915888 174.3271028 ]\n",
" [ 124.50485437 146.61165049 172.50485437]\n",
" [ 83.28571429 148. 119.71428571]\n",
" [ 92.33333333 101.66666667 20.33333333]]\n",
"[ 179.86118132 176.15218837 178.5385127 ] 16223\n"
]
},
{
"data": {
"text/html": [
"<span style='background:#b3b0b2'>#b3b0b2</span>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[166 164 165]\n",
" [167 165 166]\n",
" [167 165 166]\n",
" ..., \n",
" [188 177 183]\n",
" [178 174 163]\n",
" [158 170 184]]\n",
"[ 32.34522111 27.34236805 27.59058488] 1217\n"
]
},
{
"data": {
"text/html": [
"<span style='background:#201b1b'>#201b1b</span>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[42 42 42]\n",
" [43 34 35]\n",
" [ 9 14 10]\n",
" ..., \n",
" [16 31 62]\n",
" [19 43 55]\n",
" [77 43 42]]\n",
"[ 130.90238095 62.86190476 47.66666667] 496\n"
]
},
{
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"[0 1 2 3 4] 5\n",
"[[ 175.60767146 78.82968522 87.88888889]\n",
" [ 185.5705579 202.28926395 217.18096578]\n",
" [ 152.08324742 123.72036082 146.01237113]\n",
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" [ 52.04996669 134.16522318 197.23917388]]\n",
"[ 175.60767146 78.82968522 87.88888889] 7888\n"
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},
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{
"name": "stdout",
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"text": [
"[[ 17 37 61]\n",
" [179 108 116]\n",
" [127 96 104]\n",
" ..., \n",
" [ 67 21 32]\n",
" [100 34 44]\n",
" [195 91 102]]\n",
"[ 185.5705579 202.28926395 217.18096578] 5119\n"
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},
{
"data": {
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{
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"text": [
"[[122 171 230]\n",
" [142 197 251]\n",
" [147 197 250]\n",
" ..., \n",
" [208 223 242]\n",
" [172 173 193]\n",
" [211 209 233]]\n",
"[ 152.08324742 123.72036082 146.01237113] 4518\n"
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},
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"text": [
"[[155 167 179]\n",
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" ..., \n",
" [152 102 155]\n",
" [152 94 135]\n",
" [111 89 110]]\n",
"[ 198.70037453 168.11771001 29.27661851] 2389\n"
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},
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"text": [
"[[220 215 95]\n",
" [227 213 55]\n",
" [226 211 60]\n",
" ..., \n",
" [195 163 0]\n",
" [200 167 2]\n",
" [197 180 76]]\n",
"[ 52.04996669 134.16522318 197.23917388] 2586\n"
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},
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"text": [
"[[ 33 67 79]\n",
" [ 40 73 92]\n",
" [ 67 104 133]\n",
" ..., \n",
" [ 55 107 165]\n",
" [ 39 144 212]\n",
" [ 86 90 153]]\n"
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"[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24\n",
" 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48] 49\n",
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" [ 212.03333333 120.95 125.83333333]\n",
" [ 120.45283019 196.1509434 232.96226415]\n",
" [ 167.5 224.26923077 249.80769231]\n",
" [ 250.69230769 248.09615385 66.86538462]\n",
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" [ 114.33333333 67.33333333 55. ]\n",
" [ 147. 159.5 113.5 ]\n",
" [ 102. 44.5 34.5 ]\n",
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"[ 250.27535693 251.91863489 253.23029563] 19257\n"
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"text/plain": [
"<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x452 at 0x7FDD92FDC128>"
]
},
"metadata": {},
"output_type": "display_data"
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}
],
"source": [
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"i = 0\n",
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"for image in images:\n",
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" colours = getColoursForImageByClusters2(image)\n",
" display(image)\n",
" i+=1\n",
" if(i>=5):\n",
" break"
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]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
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"collapsed": false,
"deletable": true,
"editable": true
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},
"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": {
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"collapsed": false,
"deletable": true,
"editable": true
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},
"outputs": [],
"source": [
"#concatenate found colours of all images into one\n",
"allColours = sum(imgColours)"
]
},
{
"cell_type": "code",
"execution_count": 182,
"metadata": {
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"collapsed": false,
"deletable": true,
"editable": true
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},
"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": {
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"collapsed": false,
"deletable": true,
"editable": true
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},
"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": {
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"collapsed": false,
"deletable": true,
"editable": true
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},
"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": {
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"collapsed": false,
"deletable": true,
"editable": true
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},
"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",
"u 54\n",
"f 55\n",
"u 56\n",
"f 57\n",
"u 58\n",
"m 59\n",
"u 510\n",
"f 511\n",
"u 512\n",
"m 513\n",
"u 514\n",
"u 515\n",
"u 516\n",
"u 517\n",
"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')"
]
},
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2017-03-11 11:49:47 +01:00
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2017-03-06 21:11:00 +01:00
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