def affine():
import menpo.io as mio
takeo = mio.import_builtin_asset.takeo_ppm()
# Use a bounding box of ground truth landmarks to create template
takeo.landmarks['bounding_box'] = takeo.landmarks['PTS'].lms.bounding_box()
template = takeo.crop_to_landmarks(group='bounding_box', boundary=10)
template.view()
from menpofit.lk import LucasKanadeFitter, InverseCompositional, SSD
from menpo.feature import no_op
fitter = LucasKanadeFitter(template,
group='bounding_box',
algorithm_cls=InverseCompositional,
residual_cls=SSD,
scales=(0.5, 1.0),
holistic_features=no_op)
print fitter
from menpofit.fitter import noisy_shape_from_bounding_box
gt_bb = takeo.landmarks['bounding_box'].lms
# generate perturbed bounding box
init_bb = noisy_shape_from_bounding_box(fitter.reference_shape,
gt_bb,
noise_percentage=0.1,
allow_alignment_rotation=True)
# fit image
result = fitter.fit_from_bb(takeo, init_bb, gt_shape=gt_bb, max_iters=80)
# result.view()
from menpowidgets import visualize_images
visualize_images(fitter.warped_images(result.image, result.shapes))
def test_AAM(fitter, images):
for image in images:
image = mio.import_image(image)
image = image.as_greyscale()
initial_bbox = image.landmarks['PTS'].bounding_box()
gt_shape = image.landmarks['PTS'].lms
initial_shape = noisy_shape_from_bounding_box(gt_shape,
gt_shape.bounding_box())
image.landmarks['boundingbox'] = initial_bbox
image.landmarks['init_shape'] = initial_shape
image.view_landmarks(group='boundingbox',
line_colour='red',
render_markers=False,
line_width=4)
image.view_landmarks(group='init_shape')
# fit image
result = fitter.fit_from_bb(image,
initial_bbox,
max_iters=[15, 5],
gt_shape=image.landmarks['PTS'].lms)
# print result
print(result)
# fit image
result1 = fitter.fit_from_shape(image,
initial_shape,
max_iters=[15, 5],
gt_shape=image.landmarks['PTS'].lms)
# print result
print(result1)
result.view(render_initial_shape=True)
def fitting_img(image, fitter):
# detect = load_dlib_frontal_face_detector()
# bboxes = detect(image)
# initial_bbox = bboxes[0]
# fitting_result = fitter.fit_from_bb(image, initial_bbox,
# #max_iters=[15, 5]
# )
# obtain groubnd truth (original) landmarks
gt_s = image.landmarks['PTS']
# generate initialization landmarks
initial_s = noisy_shape_from_bounding_box(gt_s, gt_s.bounding_box())
fitting_result = fitter.fit_from_shape(image, initial_s,max_iters=20, gt_shape=gt_s, return_costs=True)
return fitting_result
def get_noisy_init_from_bb(reference_shape, bb, noise_percentage=.02):
"""Roughly aligns a reference shape to a bounding box.
This adds some uniform noise for translation and scale to the
aligned shape.
Args:
reference_shape: a numpy array [num_landmarks, 2]
bb: bounding box, a numpy array [4, ]
noise_percentage: noise presentation to add.
Returns:
The aligned shape, as a numpy array [num_landmarks, 2]
"""
bb = PointCloud(bb)
reference_shape = PointCloud(reference_shape)
bb = noisy_shape_from_bounding_box(
reference_shape,
bb,
noise_percentage=[noise_percentage, 0,
noise_percentage]).bounding_box()
return align_shape_with_bounding_box(reference_shape, bb).points
def get_noisy_init_from_bb(reference_shape, bb, noise_percentage=.02):
"""Roughly aligns a reference shape to a bounding box.
This adds some uniform noise for translation and scale to the
aligned shape.
Args:
reference_shape: a numpy array [num_landmarks, 2]
bb: bounding box, a numpy array [4, ]
noise_percentage: noise presentation to add.
Returns:
The aligned shape, as a numpy array [num_landmarks, 2]
"""
bb = PointCloud(bb)
reference_shape = PointCloud(reference_shape)
bb = noisy_shape_from_bounding_box(
reference_shape, bb,
noise_percentage=[noise_percentage, 0, noise_percentage]
).bounding_box()
return align_shape_with_bounding_box(reference_shape, bb).points
def noisy_shape_from_bounding_box(self, bounding_box, noise_type='uniform',
noise_percentage=0.1, rotation=False):
return noisy_shape_from_bounding_box(
self.reference_shape, bounding_box, noise_type=noise_type,
noise_percentage=noise_percentage, rotation=rotation)
i = i.as_greyscale(mode='luminosity')
# append it to the list
images.append(i)
return images
# 测试集
image_path_test = "D:/电信研究院/人脸矫正/labelme-master/examples/transfer/test"
test_images = load_database(image_path_test, 0.5, max_images=5)
fitting_results = []
for i in test_images:
# obtain original landmarks
gt_s = i.landmarks['PTS'].lms
# generate perturbed landmarks
s = noisy_shape_from_bounding_box(gt_s, gt_s.bounding_box())
# fit image
fr = fitter.fit_from_shape(i, s, gt_shape=gt_s)
fitting_results.append(fr)
print('fr',type(fr),fr.final_shape.points,fr)
# from menpowidgets import visualize_fitting_result
# #预测
# image_path_pred = "D:/电信研究院/人脸矫正/labelme-master/examples/transfer/pred"
#
# pred_images = []
# # load landmarked images
# for i in mio.import_images(image_path_pred, max_images=None, verbose=True):
# # convert it to grayscale if needed
# if i.n_channels == 3:
# i = i.as_greyscale(mode='luminosity')
#