# 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()
Example #2
0
                    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()

Example #4
0
                    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()
#