def plotRecommend(removed, recommend, names, clusters, xFactor=10, yFactor=10, myDpi=96):
"""
removed: the color that we removed from the image
recommend: the color that was recommended from the modified image
names: the name of the image we were making a recommendation about
clusterNames: the names of the images in the cluster we assigned this image to
"""
imagePath = data.getDataDir(config.amount, cut=True, big=False)
fig = plt.figure(figsize=(1024 / myDpi, 1024 / myDpi), dpi=myDpi)
ax = fig.add_subplot(111)
plt.axis("off")
ctr = 0
for i in xrange(len(names)):
if ctr > SAMPLE_SIZE:
break
rr, rg, rb = image.binToRGB(removed[i])
cr, cg, cb = image.binToRGB(recommend[i])
rem = "#%02x%02x%02x" % (rr, rg, rb)
rec = "#%02x%02x%02x" % (cr, cg, cb)
# print rem
# print rec
SQ_SIZE = 50
SQ_OFFSET = 82
BASE_OFFSET = 80
ax.add_patch(patches.Rectangle((125, i * SQ_OFFSET), SQ_SIZE, SQ_SIZE, facecolor=rem))
ax.add_patch(patches.Rectangle((175, i * SQ_OFFSET), SQ_SIZE, SQ_SIZE, facecolor=rec))
try:
imager = mpimg.imread(imagePath + names[i])
fig.figimage(imager, 125, i * 50 + BASE_OFFSET)
if len(clusters) > 0:
plotCluster(clusters[i], 300, i * 50 + BASE_OFFSET, fig)
except IOError:
print "%s not found" % imagePath + names[i]
pass
ctr += 1
ax.set_ylim([0, 1024])
ax.set_xlim([0, 1024])
plt.show()
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import time
# Hack to import ml_util from the parent directory
import os, sys
sys.path.insert(1, os.path.join(sys.path[0], '..'))
from ml_util import ml
from data import data
# gets Screenshots directory as string
BIG = True
amount='top-15k'
path = data.getDataDir(amount=amount, cut=True, big=BIG)
# gets all files
fileList = os.listdir(path)
fileExt = ".png"
imgs = filter(lambda File: File[-4:] == fileExt, fileList)
imgs.sort()
print "Found %d %s images" % (len(imgs), fileExt)
def imgToArray(filepath):
try:
img = np.array(Image.open(path + imgs[i]))
except IOError as e:
print e
return img[:, :, :3].ravel()
def plotCluster(cluster, x, y, plotter):
imagePath = data.getDataDir(amount=config.amount, cut=True, big=False)
for i in xrange(len(cluster)):
imager = mpimg.imread(imagePath + cluster[i])
plotter.figimage(imager, i * 50 + x, y)
#from ml_util import ml from data import data from data import image import config import core from ml_util import ml from cluster_recommender import ClusterRecommender from duckling_recommender import DucklingRecommender from random_recommender import RandomRecommender import matplotlib.pyplot as plt import matplotlib.image as mpimg import matplotlib.patches as patches amount = config.amount path = data.getDataDir(config.amount, config.cut, config.big) # gets all files fileList = os.listdir(path) fileExt = ".png" imgs = filter(lambda File: File[-4:] == fileExt, fileList) imgs.sort() print "Found %d %s images" % (len(imgs), fileExt) ranks, names, histograms = data.getBinnedHistograms(amount, cut=True, big=False) X = np.array([image.imgToArray(path+imgs[i]) for i in xrange(len(imgs))]) print X.shape, histograms.shape website_names = imgs