def test_model(model, test_images, num_init):
face_detector = menpodetect.load_dlib_frontal_face_detector()
test_gt_shapes = util.get_gt_shapes(test_images)
test_boxes = util.get_bounding_boxes(test_images, test_gt_shapes, face_detector)
initial_errors = []
final_errors = []
initial_shapes = []
final_shapes = []
for k, (im, gt_shape, box) in enumerate(zip(test_images, test_gt_shapes, test_boxes)):
init_shapes, fin_shapes = model.apply(im, ([box], num_init, None))
init_shape = util.get_median_shape(init_shapes)
final_shape = fin_shapes[0]
initial_shapes.append(init_shape)
final_shapes.append(final_shape)
initial_errors.append(compute_error(init_shape, gt_shape))
final_errors.append(compute_error(final_shape, gt_shape))
print_dynamic('{}/{}'.format(k + 1, len(test_images)))
return initial_errors, final_errors, initial_shapes, final_shapes
def apply(self, image, extra):
boxes, init_num, initial_shapes = extra
if initial_shapes is None:
initial_shapes = np.array([fit_shape_to_box(self.mean_shape, box) for box in boxes])
shapes = deepcopy(initial_shapes)
for i, shape in enumerate(shapes):
init_shapes = util.perturb_init_shape(initial_shapes[i].copy(), init_num)
for j in xrange(init_num):
for r in self.weak_regressors:
offset = r.apply(image, init_shapes[j])
init_shapes[j].points += offset.points
shape.points[:] = util.get_median_shape(init_shapes).points
return initial_shapes, shapes
def apply(self, image, extra):
boxes, init_num, initial_shapes = extra
if initial_shapes is None:
initial_shapes = np.array(
[fit_shape_to_box(self.mean_shape, box) for box in boxes])
shapes = deepcopy(initial_shapes)
for i, shape in enumerate(shapes):
init_shapes = util.perturb_init_shape(initial_shapes[i].copy(),
init_num)
for j in xrange(init_num):
for r in self.weak_regressors:
offset = r.apply(image, init_shapes[j])
init_shapes[j].points += offset.points
shape.points[:] = util.get_median_shape(init_shapes).points
return initial_shapes, shapes
