def get_closest_label(self, sp):
"""
find in label maps the label whose centroid is the closest to
"""
loc_compare = self.locs.loc[self.locs['frame'] == sp['frame']]
if (loc_compare.shape[0] == 0):
return None
dists = [
np.linalg.norm(np.array((sp['x'], sp['y'])) - np.array(r_compare))
for r_compare in [(
x, y) for x, y in zip(loc_compare['x'], loc_compare['y'])]
]
loc_min = loc_compare.iloc[np.argmin(dists)]
i_min, j_min = csv.coord2Pixel(loc_min['x'], loc_min['y'],
self.shape[1], self.shape[0])
return self.labels[sp['frame'], i_min, j_min]
def is_entrance(self, loc_2d, idx=None):
# idx is row index of gaze file, and also entrance masks
i_gaze, j_gaze = csv.coord2Pixel(loc_2d['x'], loc_2d['y'],
self.shape[1], self.shape[0])
if (self.mask_path is not None):
return self.masks[idx].astype(bool)[i_gaze, j_gaze]
else:
mask = np.zeros(self.shape, dtype=bool)
rr, cc = circle(i_gaze,
j_gaze,
self.shape[0] * self.entrance_radius,
shape=self.shape)
mask[rr, cc] = True
return mask[i_gaze, j_gaze]
def get_i_j(self, loc):
i, j = csv.coord2Pixel(loc['x'], loc['y'], self.shape[1],
self.shape[0])
return i, j