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 fit_all(model_builder, train_images, test_images, num_init):
face_detector = menpodetect.load_dlib_frontal_face_detector()
train_gt_shapes = util.get_gt_shapes(train_images)
train_boxes = util.get_bounding_boxes(train_images, train_gt_shapes, face_detector)
model = model_builder.build(train_images, train_gt_shapes, train_boxes)
initial_errors, final_errors, initial_shapes, final_shapes = test_model(model, test_images, num_init)
return initial_errors, final_errors, initial_shapes, final_shapes, model
