def find_best_threshold(eye_frame):
average_iris_size = 0.48
trials = {}
for threshold in range(5, 100, 5):
iris_frame = Pupil.image_processing(eye_frame, threshold)
trials[threshold] = Calibration.iris_size(iris_frame)
best_threshold, iris_size = min(
trials.items(), key=(lambda p: abs(p[1] - average_iris_size)))
return best_threshold
def find_best_threshold(eye_frame):
"""Calculates the optimal threshold to binarize the
frame for the given eye.
Argument:
eye_frame (numpy.ndarray): Frame of the eye to be analyzed
"""
average_iris_size = 0.48
trials = {}
for threshold in range(5, 100, 5):
iris_frame = Pupil.image_processing(eye_frame, threshold)
trials[threshold] = Calibration.iris_size(iris_frame)
best_threshold, iris_size = min(trials.items(), key=(lambda p: abs(p[1] - average_iris_size)))
return best_threshold
