# choose the screenshots directory
amount = config.amount
path = data.getDataDir(amount=amount, cut=config.cut, big=config.big)
fileList = os.listdir(path)
fileExt = ".png"
# it is currently expected that the files to be histogrammed lie in the
# same directory as histogrammer.py
imgs = filter(lambda File: File[-4:] == fileExt, fileList)
imgs.sort()
print "Found %d %s images" % (len(imgs), fileExt)
histograms = []
for i in xrange(len(imgs)):
hist = image.imgToBinnedHistogram(path + imgs[i])
if np.count_nonzero(hist) < 3:
print hist
print "Removing", imgs[i]
os.remove(path + imgs[i])
else:
histograms.append(ml.normalizeData(np.array(hist)))
print "Each histogram has %d elements" % len(histograms[0])
to = path + "histograms.csv"
with open(to, "wb") as csvfile:
writer = csv.writer(csvfile, delimiter=",", quoting=csv.QUOTE_MINIMAL)
for i in xrange(len(histograms)):
j = imgs[i].find(".")
rank, name = int(imgs[i][:j]), imgs[i][j + 1 :]
writer.writerow([rank, name] + histograms[i].tolist())
# choose the screenshots directory
amount = config.amount
path = data.getDataDir(amount=amount, cut=config.cut, big=config.big)
fileList = os.listdir(path)
fileExt = ".png"
# it is currently expected that the files to be histogrammed lie in the
# same directory as histogrammer.py
imgs = filter(lambda File: File[-4:] == fileExt, fileList)
imgs.sort()
print "Found %d %s images" % (len(imgs), fileExt)
# we only grab the first 768 elements of each histogram, since
# we don't care about opacity
D = 768
histograms = [Image.open(path+img).histogram()[:D] for img in imgs]
assert(len(histograms) == len(imgs))
print "Each histogram has %d elements" % len(histograms[0])
print 'Normalizing them...'
assert(np.sum(ml.normalizeData(np.array(histograms[0]))) == 1.)
norm_histograms = [ml.normalizeData(np.array(h)) for h in histograms]
to = path + 'band-histograms.csv'
with open(to, 'wb') as csvfile:
writer = csv.writer(csvfile, delimiter=',', quoting=csv.QUOTE_MINIMAL)
for i in xrange(len(norm_histograms)):
j = imgs[i].find('.')
rank, name = int(imgs[i][:j]), imgs[i][j+1:]
writer.writerow([rank, name] + norm_histograms[i].tolist())
