Website on: cyrilzhang.github.io/livemau5
Put {stack, ROI} files in data/ with the following names
data/AMG#_exp#.{tif,zip}
Score on validation data:
import numpy as np
from test import *
def classify(data):
# in: image stack (time,height,width)
# out: ROI masks (nroi,height,width)
R = np.zeros((5, data.shape[0], data.shape[1]))
# ...?
return R
validation_data = ...
validation_labels = ...
print Score(classify, validation_data, validation_labels)
Score on test set (will not be possible on secret test set):
import numpy as np
from test import *
def classify(data):
# in: image stack (time,height,width)
# out: ROI masks (nroi,height,width)
R = np.zeros((5, data.shape[0], data.shape[1]))
# ...?
return R
T = TestSet()
print Score(classify, T.data, T.labels)
Score directly with predicted labels (will not be possible on secret test set):
import numpy as np
from test import *
def classify(data, alpha, ...):
# in: image stack (time,height,width)
# out: ROI masks (nroi,height,width)
R = np.zeros((5, data.shape[0], data.shape[1]))
# ...?
return R
validation_data = ...
vailidation_labels = ...
predicted_labels = classify(validation_data, 1, ...)
print Score(None, None, validation_labels, predicted_labels)
Score on function with multiple parameters:
import numpy as np
from test import *
def classify(data, alpha, ...):
# in: image stack (time,height,width)
# out: ROI masks (nroi,height,width)
R = np.zeros((alpha, data.shape[0], data.shape[1]))
# ...?
return R
validation_data = ...
validation_labels = ...
for alpha in range(1,5):
print Score(lambda data: classify(data, alpha, ...), validation_data, validation_labels)