def __init__(self):
self.project = Penguins()
# self.project.__migrate__()
# tkinter stuff
self.root = tkinter.Tk()
self.root.geometry('900x700')
self.root.title("Marmot")
self.root.resizable(False,False)
# ttk stuff - congrads if you understand the difference between tkinter and ttk
self.mainframe = ttk.Frame(self.root, padding="3 3 12 12")
self.mainframe.grid(column=0, row=0, sticky=(tkinter.N, tkinter.W, tkinter.E, tkinter.S))
self.mainframe.columnconfigure(0, weight=1)
self.mainframe.rowconfigure(0, weight=1)
self.root.option_add('*tearOff', False)
self.links = []
self.true_probabilities = {}
self.false_probabilities = {}
self.percentage_thresholds = {}
self.probability_threshold = {}
self.weightings = {}
# might have to truly random - but this way, we don't always download new images
random.seed(1)
# store all of the subjects in a random order
self.subjects = []
# self.subjects = self.project.__get_retired_subjects__(1,True)
#
# random.shuffle(self.subjects)
self.page_index = 0
self.step_size = 45
# see for deleting previous thumbnails when we go to a new page
self.thumbnails = []
self.true_positives = {}
self.false_positives = {}
self.unknown_positives = {}
self.run_mode = None
# used that we can store values when we go back to an image
self.matplotlib_points = {}
self.probabilities = {}
# by default no
self.require_gold_standard = False
#!/usr/bin/env python
__author__ = 'ggdhines'
from penguin import Penguins
import json
project = Penguins()
stmt = "select subject_id,aggregation from aggregations where workflow_id = -1"
cursor = project.postgres_session.cursor()
# cursor.execute(stmt)
# for i in cursor.fetchall():
# path = None
# aggregations = i[1]#json.loads(i[1])
#
# for cluster_index,cluster in aggregations["1"]["point clusters"].items():
# if cluster_index in ["param","all_users"]:
# continue
#
# # print cluster["existence"][0]
# if isinstance(cluster["existence"][0],dict):
# p = cluster["existence"][0]["1"]
# else:
#
# p = cluster["existence"][0][1]
#
# if p > 0.5:
# if path is None:
# subject = project.subject_collection.find_one({"zooniverse_id":i[0]})
# path = subject["metadata"]["path"][:-4]
# url = subject["location"]["standard"]
def __init__(self):
Penguins.__init__(self)
def __init__(self):
Penguins.__init__(self)
class Marmot:
def __init__(self):
self.project = Penguins()
# self.project.__migrate__()
# tkinter stuff
self.root = tkinter.Tk()
self.root.geometry('900x700')
self.root.title("Marmot")
self.root.resizable(False,False)
# ttk stuff - congrads if you understand the difference between tkinter and ttk
self.mainframe = ttk.Frame(self.root, padding="3 3 12 12")
self.mainframe.grid(column=0, row=0, sticky=(tkinter.N, tkinter.W, tkinter.E, tkinter.S))
self.mainframe.columnconfigure(0, weight=1)
self.mainframe.rowconfigure(0, weight=1)
self.root.option_add('*tearOff', False)
self.links = []
self.true_probabilities = {}
self.false_probabilities = {}
self.percentage_thresholds = {}
self.probability_threshold = {}
self.weightings = {}
# might have to truly random - but this way, we don't always download new images
random.seed(1)
# store all of the subjects in a random order
self.subjects = []
# self.subjects = self.project.__get_retired_subjects__(1,True)
#
# random.shuffle(self.subjects)
self.page_index = 0
self.step_size = 45
# see for deleting previous thumbnails when we go to a new page
self.thumbnails = []
self.true_positives = {}
self.false_positives = {}
self.unknown_positives = {}
self.run_mode = None
# used that we can store values when we go back to an image
self.matplotlib_points = {}
self.probabilities = {}
# by default no
self.require_gold_standard = False
def __create_thumb__(self,subject_id):
fname = self.project.__image_setup__(subject_id)
openImg = Image.open(fname)
openImg.thumbnail((250, 250))
openImg.save(DIR_THUMBS + subject_id+".jpg")
def __image_select__(self,require_gold_standard=False):
# setup for when the user wants to explore results that don't have a gold standard
return self.project.__get_retired_subjects__(1,False)
# random.shuffle(self.subjects)
def __run__(self):
# create the welcome window
run_type = None
t = tkinter.Toplevel(self.root)
t.resizable(False,False)
frame = ttk.Frame(t, padding="3 3 12 12")
frame.grid(column=0, row=0, sticky=(tkinter.N, tkinter.W, tkinter.E, tkinter.S))
frame.columnconfigure(0, weight=1)
frame.rowconfigure(0, weight=1)
ttk.Label(frame,text="Welcome to Marmot.").grid(column=1,row=1)
def setup(require_gold_standard):
# this will determine the whole run mode from here on in
self.require_gold_standard = require_gold_standard
self.subjects = self.__image_select__(require_gold_standard)
# if r == "a":
# self.subjects = self.project.__get_retired_subjects__(1,True)
# self.run_mode = "a"
# else:
# # when we want to explore subjects which don't have gold standard
# # basically creating some as we go
# # False => read in all subjects, not just those with gold standard annotations
# # todo - takes a while in read in all subjects. Better way?
# self.subjects = self.project.__get_retired_subjects__(1,False)
# self.run_mode = "b"
random.shuffle(self.subjects)
self.__thumbnail_display__()
self.__add_buttons__()
t.destroy()
ttk.Button(frame, text="Explore results using existing expert annotations", command = lambda : setup(True)).grid(column=1, row=2)
ttk.Button(frame, text="Explore and create gold standard on the fly", command = lambda : setup(False)).grid(column=1, row=3)
t.lift(self.root)
# self.outputButtons()
self.root.mainloop()
def __calculate__(self):
if self.percentage_thresholds != {}:
plt.close()
subject_ids = self.probability_threshold.keys()
X = [self.probability_threshold[s] for s in subject_ids]
Y = [self.weightings[s] for s in subject_ids]
plt.plot(X,Y,'.')
plt.xlim((-0.02,1.02))
plt.show()
def __thumbnail_display__(self):
# destroy any previously existing thumbnails - for when we're flipping through pages
for thumb_index in range(len(self.thumbnails)-1,-1,-1):
old_thumb = self.thumbnails.pop(thumb_index)
old_thumb.destroy()
for ii,subject_id in enumerate(self.subjects[9*self.page_index:9+9*self.page_index]):
# do we already have a thumb for this file?
thumb_path = DIR_THUMBS+str(subject_id)+".jpg"
if not os.path.exists(thumb_path):
self.__create_thumb__(subject_id)
render_image = ImageTk.PhotoImage(file=thumb_path)
but = ttk.Button(self.root, image=render_image)
but.grid(column=ii/3+1, row=(1+ii)%3,sticky=tkinter.W)
# the interaction with the subject will depend on whether we have gold standard data for it or not
# if not, the user will need to create some
if self.require_gold_standard:
assert False
# but.bind('<Button-1>', lambda event,t=thumb_path: self.(t) if self.run_mode == "a" else self.__create_gold_standard__(t))
else:
but.bind('<Button-1>', lambda event,t=thumb_path: self.__create_gold_standard__(t))
self.thumbnails.append(but)
# sigh - I hate having to do this
# MUST keep - otherwise garbage collection in Python will remove it
self.links.append(render_image)
# todo - this window is not actually popping up
# determine which of the subjects we are interested in have actually been processed
# we may need to do some additional aggregation
aggregated_subjects = self.project.__get_aggregated_subjects__(-1)
not_aggregated = [s for s in self.subjects[:self.step_size] if s not in aggregated_subjects]
# print not_aggregated
if not_aggregated != []:
self.project.__aggregate__([-1],self.subjects[:self.step_size])
def __increment__(self):
self.page_index += 1
self.__thumbnail_display__()
def __decrement__(self):
self.page_index -= 1
self.__thumbnail_display__()
def __add_buttons__(self):
# for ii,thumbfile in enumerate(thumbfiles[:3]):
ttk.Button(self.root, text="<--", command=self.__decrement__).grid(column=2, row=4)
ttk.Button(self.root, text="-->", command=self.__increment__).grid(column=2, row=5)
# ttk.Button(self.root, text="Threshold Plot", command=self.__calculate__).grid(column=1, row=5)
# ttk.Button(self.root, text="Re-aggregate", command=self.__reaggregate__).grid(column=1, row=6)
ttk.Button(self.root, text="ROC estimate", command=self.__roc_estimate__).grid(column=1, row=6)
def __roc_estimate__(self):
plt.close()
true_positives = []
false_positives = []
unknowns = []
for subject_id in self.true_positives:
for pt in self.true_positives[subject_id]:
true_positives.append(self.probabilities[subject_id][pt])
for pt in self.false_positives[subject_id]:
false_positives.append(self.probabilities[subject_id][pt])
for pt in self.unknown_positives[subject_id]:
unknowns.append(self.probabilities[subject_id][pt])
overall_probabilities = true_positives[:]
overall_probabilities.extend(false_positives)
# remove duplicates and sort
overall_probabilities = sorted(list(set(overall_probabilities)),reverse=True)
X = []
Y = []
for p in overall_probabilities:
num_true = sum([1 for p1 in true_positives if p1 >= p])
num_false = sum([1 for p1 in false_positives if p1 >= p])
# treat them as positives
num_true += sum([1 for p1 in unknowns if p1 >= p])
Y.append(num_true)
X.append(num_false)
X = [x/float(max(X)) for x in X]
Y = [y/float(max(Y)) for y in Y]
plt.plot(X,Y,"o-")
X = []
Y = []
for p in overall_probabilities:
num_true = sum([1 for p1 in true_positives if p1 >= p])
num_false = sum([1 for p1 in false_positives if p1 >= p])
# treat them as negatives
num_false += sum([1 for p1 in unknowns if p1 >= p])
Y.append(num_true)
X.append(num_false)
X = [x/float(max(X)) for x in X]
Y = [y/float(max(Y)) for y in Y]
plt.plot(X,Y,"o-")
plt.show()
def __reaggregate__(self):
"""
just rerun aggregation stuff for the current selection of subjects
:return:
"""
# todo - just rid of the hard coded 45
assert (self.true_positives != {}) or (self.false_positives != {})
self.project.__aggregate__([-1],self.subjects[:45],(self.true_positives,self.false_positives))
def __update_threshold__(self,new_percentile_threshold,matplotlib_objects):
"""
update whether we think objects are true positives or not - without using gold standard data
so this is for creating gold standard
returns a tuple of all TP probabilities and the FP ones too, according to the given threshold
:param new_percentile_threshold:
:return:
"""
assert isinstance(matplotlib_objects,dict)
# TP = []
# FP = []
# if self.probabilities == []:
# return None,([],[],[],[])
objects,individual_probabilities = zip(*matplotlib_objects.values())
prob_threshold = numpy.percentile(individual_probabilities,(1-new_percentile_threshold)*100)
print new_percentile_threshold
print individual_probabilities
print prob_threshold
print
# clusters we have corrected identified as true positivies
green_pts = []
# clusters we have incorrectly identified as true positives
red_pts = []
# clusters have incorrectly idenfitied as false positivies
yellow_pts = []
# etc.
blue_pts = []
for center,(obj,prob_existence) in matplotlib_objects.items():
# x,y = matplotlib_pt.get_data()
# x = x[0]
# y = y[0]
# if correct_pts is not None:
# if prob_existence >= prob_threshold:
# # based on the threshold - we think this point exists
# if center in correct_pts:
# # woot - we were right
# matplotlib_pt.set_color("green")
# # green_pts.append(prob_existence)
# else:
# # boo - we were wrong
# matplotlib_pt.set_color("red")
# # green_pts.append(prob_existence)
# else:
# # we think this point is a false positive
# if center in correct_pts:
# matplotlib_pt.set_color("yellow")
# # green_pts.append(prob_existence)
# else:
# matplotlib_pt.set_color("blue")
# # green_pts.append(prob_existence)
# else:
# in this case, with no expert data, we are assuming that all points accepted
# are correctly accepted and making no judgement about rejected points
# do not change any points which have been assigned to be a false positive
if prob_existence >= prob_threshold:
if obj.get_color() != "red":
obj.set_color("green")
green_pts.append(prob_existence)
else:
if obj.get_color() != "red":
obj.set_color("yellow")
print "yellow"
yellow_pts.append(prob_existence)
print (1-new_percentile_threshold)*100
print prob_threshold
print green_pts
print yellow_pts
print
return prob_threshold
def __create_gold_standard__(self,thumb_path):
print "hello world"
slash_index = thumb_path.rindex("/")
subject_id = thumb_path[slash_index+1:-4]
# get the clusters
# close any previously open graph
plt.close()
fig = plt.figure()
axes = fig.add_subplot(1, 1, 1)
# get the cluster markings from the aggregation api
self.probabilities[subject_id] = self.project.__get_cluster_markings__(-1,subject_id,1,"point")
# if this is the first time we are seeing this particular image
# by default, accept the 50% in terms of probability threshold
if subject_id not in self.percentage_thresholds:
self.percentage_thresholds[subject_id] = 0.5
# plot centers
matplotlib_objects = self.__plot_cluster_markings__(self.probabilities[subject_id],"point",axes,self.percentage_thresholds[subject_id])
# if this is the first time we've seen this image
# accept all points above the threshold
# any point below the threshold is just unknown - NOT a false positive
if subject_id not in self.true_positives:
# ,"point",axes,self.percentage_thresholds[subject_id]
# use the colour to determine if a point has been labelled as true positive
self.true_positives[subject_id] = [pt for pt,(obj,prob) in matplotlib_objects.items() if obj.get_color() == "green"]
# by default any point above the threshold is just unknown - NOT a false positive
self.false_positives[subject_id] = []
self.unknown_positives[subject_id] = [pt for pt in matplotlib_objects if pt not in self.true_positives[subject_id]]
else:
# immediately reload previous colours
for pt,(obj,prob) in matplotlib_objects.items():
if pt in self.true_positives[subject_id]:
obj.set_color("green")
elif pt in self.false_positives[subject_id]:
obj.set_color("red")
else:
obj.set_color("yellow")
fig.canvas.draw_idle()
dimensions = self.project.__plot_image__(subject_id,axes)
plt.subplots_adjust(bottom=0.2)
# axcolor = 'lightgoldenrodyellow'
# todo - 1.92 is a hack for penguin watch
# this rescaling must happen before we add the slider axes - because why not
if dimensions is not None:
plt.axis((0,dimensions["width"]/1.92,dimensions["height"]/1.92,0))
# plt.xlim((dimensions["height"],0))
# plt.ylim((0,dimensions["width"]))
axfreq = plt.axes([0.25, 0.1, 0.65, 0.03])
# todo - has got to be a better to initalize this
# self.weightings[subject_id] = len(matplotlib_points)
threshold_silder = Slider(axfreq, 'Percentage', 0., 1., valinit=self.percentage_thresholds[subject_id])
# self.project.__get_expert_annotations__(-1,subject_id)
# needs to be an inner function - grrrr
def update(val):
new_threshold = threshold_silder.val
# store this new threshold for future use i.e. when the image is reopened
self.percentage_thresholds[subject_id] = new_threshold
self.probability_threshold[subject_id] = self.__update_threshold__(new_threshold,matplotlib_objects)
# if a point is not a true positive - we make no claim about what it is
self.true_positives[subject_id] = [pt for pt,(obj,prob) in matplotlib_objects.items() if obj.get_color() == "green"]
self.unknown_positives[subject_id] = [pt for pt,(obj,prob) in matplotlib_objects.items() if obj.get_color() == "yellow"]
# false positives should not change
fig.canvas.draw_idle()
def onpick3(event):
x = event.xdata
y = event.ydata
if (x is None) or (y is None):
return
# pt = event.xdata,event.ydata
nearest_pt = None
closest_dist = float("inf")
for x2,y2 in matplotlib_objects.keys():
dist = math.sqrt((x-x2)**2+(y-y2)**2)
if dist < closest_dist:
closest_dist = dist
nearest_pt = (x2,y2)
if closest_dist < 20:
obj,prob = matplotlib_objects[nearest_pt]
# new_color = color_list[(color_list.index(color)+1)%3]
# matplotlib_objects[nearest_pt] = pt,prob,new_color
# pt.set_color(new_color)
old_colour = obj.get_color()
# first is going from red to green
if old_colour == "red":
# gone from a false positive to a true positive
self.false_positives[subject_id].remove(nearest_pt)
self.true_positives[subject_id].append(nearest_pt)
obj.set_color("green")
elif old_colour == "green":
# gone from true positive to don't know
self.true_positives[subject_id].remove(nearest_pt)
self.unknown_positives[subject_id].append(nearest_pt)
obj.set_color("yellow")
else:
# gone from don't know to false positive
self.unknown_positives[subject_id].remove(nearest_pt)
self.false_positives[subject_id].append(nearest_pt)
obj.set_color("red")
fig.canvas.draw_idle()
threshold_silder.on_changed(update)
fig.canvas.mpl_connect('button_press_event', onpick3)
plt.show()
def __compare_aggregations__(self,agg1,agg2):
X = []
Y = []
plt.close()
for subject_id in agg1:
if subject_id == "param":
continue
print agg1[subject_id]
assert subject_id in agg2
for task_id in agg1[subject_id]:
if task_id == "param":
continue
print agg1[subject_id][task_id]
assert task_id in agg2[subject_id]
for shape in agg1[subject_id][task_id]:
if shape == "param":
continue
print agg1[subject_id][task_id][shape]
assert shape in agg2[subject_id][task_id]
for cluster_index in agg1[subject_id][task_id][shape]:
if (cluster_index == "param") or (cluster_index == "all_users"):
continue
assert cluster_index in agg2[subject_id][task_id][shape]
print cluster_index
X.append(agg1[subject_id][task_id][shape][cluster_index]["existence"][0][1])
Y.append(agg2[subject_id][task_id][shape][cluster_index]["existence"][0][1])
plt.close()
plt.plot(X,Y,"o")
plt.show()
def __plot_cluster_markings__(self,cluster_list,shape,axes,percentile_threshold=None,correct_pts=None,incorrect_pts=None):
"""
take a listing of cluster centers and mark them on the image - cluster list is a dictionary with
the keywords the centers and values the probabilities of existence. With these, we can figure out which
colour to make each image
"""
# main thing we are returning so that objects can be updated
matplotlib_objects = {}
# convert from a percentile threshold to a probability threshold
if percentile_threshold is not None:
prob_threshold = numpy.percentile(cluster_list.values(),(1-percentile_threshold)*100)
marker = '.'
else:
prob_threshold = None
marker = '^'
for center,prob_existence in cluster_list.items():
if shape == "point":
# with whatever alg we used, what do we think the probability is that
# this cluster actually exists?
# if we have gold standard to compare to - use that to determine the colour
if correct_pts is not None:
# if is equal to None - just compared directly against gold standard with out threshold
if prob_threshold is not None:
# we have both a threshold and gold standard - gives us four options
if prob_existence >= prob_threshold:
# based on the threshold - we think this point exists
if center in correct_pts:
# woot - we were right
color = "green"
else:
# boo - we were wrong
color = "red"
else:
# we think this point is a false positive
if center in correct_pts:
# boo - we were wrong
color = "yellow"
else:
# woot
color = "blue"
else:
# we have just the gold standard - so we are purely reviewing the expert results
if center in correct_pts:
color = "green"
else:
color = "red"
matplotlib_objects[center] = axes.plot(center[0],center[1],marker=marker,color=color)[0],prob_existence
else:
# we have nothing to compare against - so we are not showing correctness so much
# as just showing which points would be rejected/accepted with the default understanding
# that points will be correctly accepted - points that are rejected - we make no statement about
# they will not be included in the gold standard
if prob_existence >= prob_threshold:
color = "green"
# matplotlib_cluster[center] = axes.plot(center[0],center[1],".",color="green"),prob_existence
else:
# we think this is a false positive
color = "yellow"
# matplotlib_cluster[center] = axes.plot(center[0],center[1],".",color="red"),prob_existence
matplotlib_objects[center] = axes.plot(center[0],center[1],marker=marker,color=color)[0],prob_existence
return matplotlib_objects
__author__ = 'ggdhines'
from penguin import Penguins
import numpy
import math
import scipy
project = Penguins()
subjects = project.__get_retired_subjects__(1, False)
jj = 0
for zooniverse_id in subjects:
subject = project.subject_collection.find_one(
{"zooniverse_id": zooniverse_id})
count = subject["classification_count"]
raw_classifications, raw_markings = project.__sort_annotations__(
-1, [zooniverse_id], False)
# clusters = project.__get_users_per_cluster__(-1,zooniverse_id,1,"point")
clustering_aggregations = project.__cluster__(raw_markings)
print subject
# print raw_markings
centers = []
for cluster_index, cluster in clustering_aggregations[zooniverse_id][1][
"point clusters"].items():
if not (cluster_index in ["param", "all_users"]):
# print cluster_index
# print cluster
centers.append(
(cluster["center"][0], cluster["center"][1], cluster["users"]))
