def __call__(self, masks):
if masks.ndim == 3:
masks = masks.reshape(1, masks.shape[0], masks.shape[1], masks.shape[2])
class_params = self.class_params.items()
class_params.sort(key=lambda x: x[0])
outs = []
for mask in masks:
out = []
for class_num, params in class_params:
class_mask = np.ascontiguousarray(resize_mask(mask[:, :, class_num]))
for p in params['params']:
out.append(compute_mask_rect_area(class_mask, p['rect']) >= p['t'])
outs.append(out)
return image_search._bool_to_hash(np.array(outs))
def _postprocess(self, features):
features = self._condition_features(features, pad=True)
return image_search._bool_to_hash(features > self.median_feature)