525 KiB
525 KiB
In [2]:
#%quickref
%qtconsole
In [3]:
# Load the files for scanning
from matplotlib.pyplot import imshow
import glob, os
target_path = os.path.join(os.getcwd(), "testimages")
print ("Scan for files in {}".format(target_path));
files = glob.glob(os.path.join(target_path, '*.jpg'));
print ("Found {} files".format(len(files)))
# .. for now we assume all are images
Scan for files in /home/ruben/Documents/Projecten/2017/ALLES WAT IK VOEL/testimages Found 14 files
In [4]:
# load as PIL Images
from PIL import Image
images = [Image.open(file) for file in files]
In [5]:
import numpy as np
arrays = [np.array(image) for image in images]
In [6]:
def getChannelImagesFromImage(arrImg):
"""
[(255,255,255)] => ( [(255,0,0)], [(0,255,0)], [(0,0,255)] )
Three separate images that show individual channels
"""
red = arrImg.copy()
red[:,:,1] = 0
red[:,:,2] = 0
green = arrImg.copy()
green[:,:,0] = 0
green[:,:,2] = 0
blue = arrImg.copy()
blue[:,:,0] = 0
blue[:,:,1] = 0
return (red, green, blue)
# (red,green,blue) = getChannelImagesFromImage(arrays[0])
# print (red)
# images[0].show()
# Image.fromarray(red).show()
# Image.fromarray(green).show()
# Image.fromarray(blue).show()
In [16]:
def getImageAsHSV(image):
hsv = np.array(image.convert('HSV'))
# If you want to get individual channels use
# hue = hsv[:,:,0]
# sat = hsv[:,:,1]
# val = hsv[:,:,2]
# hue = hsv.copy()
# hsv[:,:,0] = 255 #all hue to 100%
# hsv[:,:,1] = 255 #all saturation to 100%
# hsv[:,:,2] = 255 #all brightness to 100%
return hsv
# hue_img = Image.fromarray(getImageAsHSV(images[4]), mode="HSV")
# %matplotlib inline
# imshow(np.asarray(hue_img.convert('RGB')))
Out[16]:
<matplotlib.image.AxesImage at 0x7f0d928f2828>
In [10]:
# sorting pixels by hue
# http://stackoverflow.com/a/2828121
# Sort by column: a[a[:,1].argsort()]
hsv = np.array(images[4].convert('HSV'))
print (len(hsv), len(hsv[0]), len(hsv[0,0]))
height = len(hsv)
singleLine = np.concatenate(hsv)
sortedLine = np.array(sorted(singleLine, key=lambda pixel: pixel[0])) # change pixel[0] to 1 or 2 for saturation & brightness
sortedImage = sortedLine.reshape(int(len(sortedLine)/height), height, 3)
sortedImg = Image.fromarray(sortedImage, mode="HSV")
sortedImg.show()
%matplotlib inline
imshow(np.asarray(sortedImg.convert('RGB')))
452 640 3
Out[10]:
<matplotlib.image.AxesImage at 0x7f0d872a0e10>
In [63]:
hues = np.zeros(255)
# Get the spread of hue in an image
# for image in images:
image = images[4]
hsv = np.array(image.convert('HSV'))
hue = hsv[:,:,0]
print("Total %s" % len(hue.flatten()))
values, boxes = scipy.histogram(hue, 255, range=(0,255), density=True)
# print(values)
from pylab import *
plot(values)
Total 289280
Out[63]:
[<matplotlib.lines.Line2D at 0x7f0d7df97a58>]
In [137]:
import struct
import scipy
import scipy.misc
import scipy.cluster
import codecs
from IPython.display import Markdown, display, HTML
NUM_CLUSTERS = 64
def getColourAsHex(colour):
return '#' + ''.join(format(c, '02x') for c in colour.astype(int))
def getColoursForImageByClusters(image):
"""
Adapted on answers by
Peter Hansen (http://stackoverflow.com/a/3244061)
& Johan Mickos (http://stackoverflow.com/a/34140327)
"""
im = image.copy().resize((150, 150)) # optional, to reduce time
ar = scipy.misc.fromimage(im)
shape = ar.shape
ar = ar.reshape(scipy.product(shape[:2]), shape[2])
# print( 'finding clusters')
codes, dist = scipy.cluster.vq.kmeans(ar.astype(float), NUM_CLUSTERS)
# print ('cluster centres:\n', codes)
vecs, dist = scipy.cluster.vq.vq(ar, codes) # assign codes
counts, bins = scipy.histogram(vecs, len(codes)) # count occurrences
# When only looking for single color:
# index_max = scipy.argmax(counts) # find most frequent
# peak = codes[index_max]
# colour = ''.join(chr(c) for c in peak).encode('hex')
# print( 'most frequent is %s (#%s)' % (peak, colour))
percentages = 100 * counts / sum(counts)
# print("Percentages", percentages)
# colours = [ in codes]
# print(colours)
return list(zip(codes, percentages))
def getColoursForImageByPxAvg(image):
im = image.copy().resize((8, 8))
pixels = np.concatenate(scipy.misc.fromimage(im))
# colours = ['#' + ''.join(format(c, '02x') for c in color.astype(int)) for color in pixels]
percentages = np.zeros(len(pixels)) + (100 / len(pixels))
return list(zip(pixels, percentages))
def getColoursAsHTML(colours):
return " ".join(['<span style="background:%s">%s - (%s %%)</span>' % (getColourAsHex(colour[0]), getColourAsHex(colour[0]), colour[1]) for colour in colours]);
# for image in images:
# display(image)
# print("Method 1: clustering (%s clusters)" % NUM_CLUSTERS)
# colours = getColoursForImageByClusters(image)
# display(HTML(getColoursAsHTML(colours)))
# print("Method 2: scaling")
# colours = getColoursForImageByPxAvg(image)
# display(HTML(getColoursAsHTML(colours)))
# break
In [142]:
print("Get colours for all images")
imgColours = []
for image in images:
# display(image)
colours = getColoursForImageByClusters(image)
imgColours.append(colours)
output = getColoursAsHTML(colours)
display(HTML(output))
Get colours for all images
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In [74]:
hues
for image in images:
img = image.copy().resize((150, 150)).convert('HSV') # optional scale, to reduce time
ar = np.array(img)
shape = ar.shape
ar = ar.reshape(scipy.product(shape[:2]), shape[2])
print(ar)
break
# Find a way to limit these per image without sacrificing the maximums in the detail
# Finding maximums, or _clustering_!
[[[233 3 166] [233 3 167] [233 3 167] ..., [198 4 173] [198 4 172] [198 4 172]] [[233 3 167] [233 3 167] [233 3 168] ..., [198 4 173] [198 4 172] [198 4 172]] [[233 3 168] [233 3 168] [233 3 169] ..., [198 4 173] [198 4 172] [198 4 172]] ..., [[ 7 17 178] [ 3 16 175] [ 3 15 177] ..., [205 8 174] [221 7 172] [221 7 171]] [[ 3 115 75] [ 3 109 79] [ 3 99 87] ..., [ 0 21 164] [ 2 24 155] [ 7 29 148]] [[ 16 186 111] [ 17 179 112] [ 17 184 109] ..., [ 13 172 105] [ 15 177 105] [ 15 177 105]]] [[233 3 166] [233 3 167] [233 3 167] ..., [ 13 172 105] [ 15 177 105] [ 15 177 105]]
In [153]:
#concatenate found colours of all images into one
allColours = sum(imgColours)
In [182]:
import colorsys
import math
# box 160, because center or circle = 100 => +/- 50 => + r of colour circle (max: 10) => 160
svg = '<svg viewBox="-160 -160 320 320" xmlns="http://www.w3.org/2000/svg">'
radius = 100
for colour in allColours:
rgb, percentage = colour
rgbNorm = rgb/255
hsv = colorsys.rgb_to_hsv(rgbNorm[0], rgbNorm[1], rgbNorm[2])
# find position on circle
radians = 2 * math.pi * hsv[0]
x = math.cos(radians)
y = math.sin(radians)
# based on saturation, we move inwards/outwards
# min = 0.5, max = 1.5 (dus + 0.5)
pos = np.array([x,y]) * (0.5 + hsv[1]) * radius
# Posibilitiy: determine position based on avg(saturation, value) => dark & grey inside, shiney and colourful outside
# pos = np.array([x,y]) * (0.5 + (hsv[1]+hsv[2])/2) * radius
r = max(1,-10/percentage+10) # as r, we converge to maximum radius 10, but don't want to get smaller radi then 1
c = '<circle cx="%s" cy="%s" r="%s" style="fill:%s" />' % (pos[0], pos[1], r, getColourAsHex(rgb))
svg += c
svg += "</svg>"
print (svg)
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In [228]:
import json
def coloursToJson(colours):
colours2 = [(list(colour[0]), colour[1]) for colour in colours]
return json.dumps(colours2)
def jsonToColours(string):
data = json.loads(string)
return [(np.array(d[0]), d[1]) for d in data]
In [229]:
from peewee import *
from playhouse.sqlite_ext import SqliteExtDatabase
import datetime
class ColoursField(TextField):
# db_field = 'colour'
def db_value(self, value):
return coloursToJson(value)
def python_value(self, value):
return jsonToColours(value) # convert str to UUID
db = SqliteExtDatabase('images.db')
class BaseModel(Model):
class Meta:
database = db
class Emotion(BaseModel):
name = CharField(unique=True)
class Group(BaseModel):
name = CharField(unique=True)
class Artwork(BaseModel):
author = CharField()
age = SmallIntegerField(index=True)
gender = FixedCharField(max_length=1) # we should not really use this one
group = ForeignKeyField(Group, related_name='artworks', index=True)
emotion = ForeignKeyField(Emotion, related_name='artworks', index=True)
created_date = DateTimeField(default=datetime.datetime.now)
filename = CharField()
colours = ColoursField() # serialised colours + percentages: [([r,g,b], percentage), ...]
db.connect()
db.create_tables([Emotion, Group, Artwork])
In [230]:
import random
from PIL import Image
emos = ["anger","contempt","disgust","fear","joy","sadness","surprise"]
emotions = []
for emo in emos:
emotion = Emotion(name=emo)
emotion.save()
emotions.append(emotion)
# # Generate some random data:
for i in range(4,8):
group = Group.create(name='Groep %s' % i)
group.save()
# some images:
for j in range(20):
genders = ['m','f','u']
img = Artwork()
img.gender = random.choice(genders)
img.author = "%s %s%s" % (img.gender, i,j)
img.age = i + 4 + random.choice([-1,0,0,0,0,1,1,2])
img.group = group
img.emotion = random.choice(emotions)
img.filename = random.choice(files)
img.colours = getColoursForImageByClusters(Image.open(img.filename))
img.save()
print(img.author)
# # No need to set `is_published` or `created_date` since they
# # will just use the default values we specified.
# Tweet.create(user=charlie, message='My first tweet')
m 40 u 41 f 42 f 43 u 44 u 45 f 46 f 47 m 48 f 49 m 410 m 411 f 412 m 413 m 414 f 415 f 416 u 417 f 418 m 419 f 50 f 51 m 52 f 53 u 54 f 55 u 56 f 57 u 58 m 59 u 510 f 511 u 512 m 513 u 514 u 515 u 516 u 517 f 518 m 519 m 60 f 61 f 62 f 63 m 64 m 65 f 66 u 67 f 68 u 69 u 610 m 611 m 612 f 613 m 614 f 615 u 616 u 617 f 618 m 619 u 70 u 71 f 72 f 73 m 74 u 75 m 76 f 77 f 78 u 79 f 710 f 711 u 712 f 713 f 714 u 715 f 716 m 717 m 718 u 719
In [211]:
Out[211]:
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