def get_score(self, algo_result, gt, scene, with_visualization=False):
m_fattening = self.get_fattening(algo_result, gt, scene.get_fg_extrapolation())
mask = self.get_evaluation_mask(scene) * misc.get_mask_valid(m_fattening)
score = misc.percentage(np.sum(mask), np.sum(m_fattening * mask))
if not with_visualization:
return score
vis = np.ma.masked_array(plotting.adjust_binary_vis(m_fattening), mask=~mask)
return score, vis
def get_score(self, algo_result, gt, scene, with_visualization=False):
m_bad_pix = self.get_bad_pix(algo_result - gt)
mask = self.get_evaluation_mask(scene) * misc.get_mask_valid(m_bad_pix)
n_bad = np.sum(m_bad_pix[mask])
score = misc.percentage(np.sum(mask), n_bad)
if not with_visualization:
return score
m_bad_pix = plotting.adjust_binary_vis(m_bad_pix)
vis = np.ma.masked_array(m_bad_pix, mask=~mask)
return score, vis
def get_score(self, algo_result, gt, scene, with_visualization=False):
grid = scene.get_boxes()
dots_by_size = scene.get_dots_by_size()
bad_pix = BadPix(thresh=self.thresh)
vis = np.zeros(np.shape(algo_result), dtype=np.bool)
diffs = np.abs(gt - algo_result)
box_ids = sorted(list(np.unique(grid)))
box_ids.remove(0)
n_boxes = np.size(box_ids)
dot_labels = list(np.unique(dots_by_size))
# use only the nine biggest dots per box
dot_labels = [dl for dl in dot_labels if 0 < dl < 9]
n_dots = len(dot_labels)
total_dots = n_dots * n_boxes
detected_dots = 0
for box_id in box_ids:
m_box = (grid == box_id)
for idx_d in range(n_dots):
dot_mask = (dots_by_size == idx_d+1) * m_box
bad_pix_on_dot = bad_pix.get_score_from_diffs(diffs[dot_mask])
if bad_pix_on_dot < self.missed_dot_bad_pix:
detected_dots += 1
else:
vis[dot_mask] = 1
missed_dots = total_dots - detected_dots
score = misc.percentage(total_dots, missed_dots)
if not with_visualization:
return score
vis = plotting.adjust_binary_vis(vis)
return score, vis