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from __future__ import division |
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import cv2 |
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from .pupil import Pupil |
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class Calibration(object): |
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""" |
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This class calibrates the pupil detection algorithm by finding the |
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best binarization threshold value for the person and the webcam. |
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""" |
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def __init__(self): |
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self.nb_frames = 20 |
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self.thresholds_left = [] |
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self.thresholds_right = [] |
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def is_complete(self): |
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"""Returns true if the calibration is completed""" |
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return len(self.thresholds_left) >= self.nb_frames and len(self.thresholds_right) >= self.nb_frames |
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def threshold(self, side): |
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"""Returns the threshold value for the given eye. |
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Argument: |
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side: Indicates whether it's the left eye (0) or the right eye (1) |
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""" |
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if side == 0: |
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return int(sum(self.thresholds_left) / len(self.thresholds_left)) |
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elif side == 1: |
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return int(sum(self.thresholds_right) / len(self.thresholds_right)) |
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@staticmethod |
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def iris_size(frame): |
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"""Returns the percentage of space that the iris takes up on |
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the surface of the eye. |
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Argument: |
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frame (numpy.ndarray): Binarized iris frame |
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""" |
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frame = frame[5:-5, 5:-5] |
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height, width = frame.shape[:2] |
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nb_pixels = height * width |
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nb_blacks = nb_pixels - cv2.countNonZero(frame) |
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return nb_blacks / nb_pixels |
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@staticmethod |
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def find_best_threshold(eye_frame): |
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"""Calculates the optimal threshold to binarize the |
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frame for the given eye. |
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Argument: |
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eye_frame (numpy.ndarray): Frame of the eye to be analyzed |
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""" |
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average_iris_size = 0.48 |
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trials = {} |
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for threshold in range(5, 100, 5): |
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iris_frame = Pupil.image_processing(eye_frame, threshold) |
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trials[threshold] = Calibration.iris_size(iris_frame) |
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best_threshold, iris_size = min(trials.items(), key=(lambda p: abs(p[1] - average_iris_size))) |
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return best_threshold |
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def evaluate(self, eye_frame, side): |
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"""Improves calibration by taking into consideration the |
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given image. |
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Arguments: |
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eye_frame (numpy.ndarray): Frame of the eye |
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side: Indicates whether it's the left eye (0) or the right eye (1) |
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""" |
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threshold = self.find_best_threshold(eye_frame) |
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if side == 0: |
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self.thresholds_left.append(threshold) |
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elif side == 1: |
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self.thresholds_right.append(threshold) |
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