# if not(os.path.isfile(base_directory+"/Databases/condors/images/"+object_id)): # urllib.urlretrieve (url, base_directory+"/Databases/condors/images/"+object_id) # # image_file = cbook.get_sample_data(base_directory+"/Databases/condors/images/"+object_id) # image = plt.imread(image_file) # # fig, ax = plt.subplots() # im = ax.imshow(image) a = datetime.datetime.now() user_identified, clusters, users = DivisiveKmeans(1).fit2( annotation_list, user_list, debug=True) b = datetime.datetime.now() print "==" print len(user_identified) #for (x,y) in user_identified: # plt.plot([x,],[y,],'.',color="red") c = datetime.datetime.now() user_identified = agglomerativeClustering( zip(annotation_list, user_list)) d = datetime.datetime.now() print len(user_identified) print b - a print d - c print "--" #for (x,y) in user_identified: # plt.plot([x-3,],[y-3,],'.',color="green") #plt.show() #plt.close()
x = scale*float(marking["x"]) y = scale*float(marking["y"]) condor_pts[zooniverse_id].add(((x,y),user_ip)) #condor_user_id[zooniverse_id].append(user_ip) condor_count += 1 except KeyError: continue condors_per_user[zooniverse_id].append(condor_count) #if (classification_count[zooniverse_id] == 5) and (condor_pts[zooniverse_id] != []): if (np.mean(condors_per_user[zooniverse_id]) > 2) and (len(condors_per_user[zooniverse_id]) > 4): if condor_pts[zooniverse_id] != set([]): object_id = str(r["subjects"][0]["id"]) url = r["subjects"][0]["location"]["standard"] cluster_center = agglomerativeClustering.agglomerativeClustering(condor_pts[zooniverse_id]) break if not(os.path.isfile("/home/greg/Databases/condors/images/"+object_id+".JPG")): urllib.urlretrieve (url, "/home/greg/Databases/condors/images/"+object_id+".JPG") image_file = cbook.get_sample_data("/home/greg/Databases/condors/images/"+object_id+".JPG") image = plt.imread(image_file) fig, ax = plt.subplots() im = ax.imshow(image) #plt.show() # if cluster_center != []: x,y = zip(*cluster_center) plt.plot(x,y,'.',color='blue')
# urllib.urlretrieve (url, base_directory+"/Databases/condors/images/"+object_id) # # image_file = cbook.get_sample_data(base_directory+"/Databases/condors/images/"+object_id) # image = plt.imread(image_file) # # fig, ax = plt.subplots() # im = ax.imshow(image) a = datetime.datetime.now() user_identified,clusters,users = DivisiveKmeans(1).fit2(annotation_list,user_list,debug=True) b = datetime.datetime.now() print "==" print len(user_identified) #for (x,y) in user_identified: # plt.plot([x,],[y,],'.',color="red") c = datetime.datetime.now() user_identified = agglomerativeClustering(zip(annotation_list,user_list)) d = datetime.datetime.now() print len(user_identified) print b-a print d-c print "--" #for (x,y) in user_identified: # plt.plot([x-3,],[y-3,],'.',color="green") #plt.show() #plt.close()
y = scale * float(marking["y"]) condor_pts[zooniverse_id].add(((x, y), user_ip)) #condor_user_id[zooniverse_id].append(user_ip) condor_count += 1 except KeyError: continue condors_per_user[zooniverse_id].append(condor_count) #if (classification_count[zooniverse_id] == 5) and (condor_pts[zooniverse_id] != []): if (np.mean(condors_per_user[zooniverse_id]) > 2) and (len( condors_per_user[zooniverse_id]) > 4): if condor_pts[zooniverse_id] != set([]): object_id = str(r["subjects"][0]["id"]) url = r["subjects"][0]["location"]["standard"] cluster_center = agglomerativeClustering.agglomerativeClustering( condor_pts[zooniverse_id]) break if not (os.path.isfile("/home/greg/Databases/condors/images/" + object_id + ".JPG")): urllib.urlretrieve( url, "/home/greg/Databases/condors/images/" + object_id + ".JPG") image_file = cbook.get_sample_data("/home/greg/Databases/condors/images/" + object_id + ".JPG") image = plt.imread(image_file) fig, ax = plt.subplots() im = ax.imshow(image) #plt.show() #