public-mirrors/NewsBlur
1
0
Fork 0
mirror of https://github.com/samuelclay/NewsBlur.git synced 2025-09-18 21:50:56 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

Commenting the icon importer for the Braindump Algorithms talk.

Browse source
This commit is contained in:
Samuel Clay 2011-04-13 20:05:34 -04:00
parent b7bd0c1186
commit 232af01b98
1 changed files with 19 additions and 8 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

27
apps/rss_feeds/icon_importer.py
View file

@ -204,14 +204,19 @@ class IconImporter(object):
def determine_dominant_color_in_image(self, image):
NUM_CLUSTERS = 5
# Convert image into array of values for each point.
ar = scipy.misc.fromimage(image)
shape = ar.shape
# Reshape array of values to merge color bands. [[R], [G], [B], [A]] => [R, G, B, A]
if len(shape) > 2:
ar = ar.reshape(scipy.product(shape[:2]), shape[2])
# Get NUM_CLUSTERS worth of centroids.
codes, _ = scipy.cluster.vq.kmeans(ar, NUM_CLUSTERS)
# print "Before: %s" % codes
# Pare centroids, removing blacks and whites and shades of really dark and really light.
original_codes = codes
for low, hi in [(60, 200), (35, 230), (10, 250)]:
codes = scipy.array([code for code in codes
@ -219,17 +224,23 @@ class IconImporter(object):
(code[0] > hi and code[1] > hi and code[2] > hi))])
if not len(codes): codes = original_codes
else: break
# print "After: %s" % codes
vecs, _ = scipy.cluster.vq.vq(ar, codes) # assign codes
counts, bins = scipy.histogram(vecs, len(codes)) # count occurrences
# Assign codes (vector quantization). Each vector is compared to the centroids
# and assigned the nearest one.
vecs, _ = scipy.cluster.vq.vq(ar, codes)
# Count occurences of each clustered vector.
counts, bins = scipy.histogram(vecs, len(codes))
# Show colors for each code in its hex value.
# colors = [''.join(chr(c) for c in code).encode('hex') for code in codes]
# total = scipy.sum(counts)
# print dict(zip(colors, [count/float(total) for count in counts]))
index_max = scipy.argmax(counts) # find most frequent
# Find the most frequent color, based on the counts.
index_max = scipy.argmax(counts)
peak = codes[index_max]
color = ''.join(chr(c) for c in peak).encode('hex')
# print 'most frequent is %s (#%s)' % (peak, color)
return color[:6]