def assign_colors(photos, tags):
"""
Assigns photo color to most common tag
"""
used_colors = {}
cluster_info = defaultdict(list)
#assign color of photo to the color of the most related tag
for photo in photos:
min_count = len(photos) + 1
photo_tags = photo['tags'].split(' ')
#for photos with tag with search term, assign initially to first item
best_tag = photo_tags[0]
#find most common tag
for tag in photo_tags:
assoc_tag = tags[tag]
#found a common tag, not including one common to all photos
if assoc_tag['count'] < min_count:
min_count = assoc_tag['count']
best_tag = tag
#generate a color for tag if it does not have one
if tags[best_tag]['color'] is None:
tags[best_tag]['color'] = gen_hex_color(used_colors)
photo['iconcolor'] = tags[best_tag]['color']
cluster_info[best_tag].append(photo)
return cluster_info
def get_centroids(photos):
"""
Gets the centroids used in K-means computation
"""
max_int = len(photos) - 1
used_colors = {}
centroids = []
#get the random images
selections = random.sample(range(0, max_int), CLUSTER_COUNT)
#assign random unique colors
for selection in selections:
new_color = gen_hex_color(used_colors)
photo = photos[selection]
photo['iconcolor'] = new_color
centroids.append({'lat':float(photo['lat']), 'lon':photo['lon'], 'color':new_color, 'photos':[]})
return centroids
