user_markings[s].append((x, y))
user_ips[s].append(ip)
except (KeyError, ValueError):
#classification["annotations"]
user_index += -1
if user_markings[5] == []:
print "skipping empty"
subject_index += -1
continue
clusters = {}
user_identified_penguins, clusters[5] = DivisiveDBSCAN(3).fit(
user_markings[5], user_ips[5], debug=True
) #,base_directory + "/Databases/penguins/images/"+object_id+".JPG")
penguins_at[5].append(len(user_identified_penguins))
print str(5) + " - " + str(len(user_identified_penguins))
user_identified_penguins, clusters[20] = DivisiveDBSCAN(6).fit(
user_markings[20], user_ips[20], debug=True
) #,base_directory + "/Databases/penguins/images/"+object_id+".JPG")
penguins_at[20].append(len(user_identified_penguins))
print str(20) + " - " + str(len(user_identified_penguins))
notFound = 0
total = 0
for clusterIndex in range(len(clusters[5])):
newCluster = []
total += 1
except (KeyError, ValueError):
#classification["annotations"]
user_index += -1
t = zip(complete_annotations, num_annotations)
print[c for n, c in t if n]
print[c for n, c in t if not (n)]
if user_markings[5] == []:
print "skipping empty"
subject_index += -1
continue
for s in steps:
user_identified_penguins = DivisiveDBSCAN(3).fit(
user_markings[s], user_ips[s]
) #,base_directory + "/Databases/penguins/images/"+object_id+".JPG")
penguins_at[s].append(len(user_identified_penguins))
print str(s) + " - " + str(len(user_identified_penguins))
# url = subject["location"]["standard"]
# object_id= str(subject["_id"])
# image_path = base_directory+"/Databases/penguins/images/"+object_id+".JPG"
# if not(os.path.isfile(image_path)):
# urllib.urlretrieve(url, image_path)
#
# image_file = cbook.get_sample_data(base_directory + "/Databases/penguins/images/"+object_id+".JPG")
# image = plt.imread(image_file)
# fig, ax = plt.subplots()
# im = ax.imshow(image)
# plt.show()