def __init__(self, size, image_size, crop_size, variance):
super(ExtractHandPose, self).__init__()
self.unwrap_inputs = pr.UnpackDictionary(
['image', 'segmentation_label', 'annotations'])
self.preprocess_image = pr.SequentialProcessor(
[pr.LoadImage(), pr.ResizeImage((size, size))])
self.preprocess_segmentation_map = pr.SequentialProcessor(
[pr.LoadImage(),
pr.ResizeImage((size, size)),
ExtractHandmask()])
self.extract_annotations = pr.UnpackDictionary(['xyz', 'uv_vis', 'K'])
self.extract_2D_keypoints = Extract2DKeypoints()
self.keypoints_to_palm = KeypointstoPalmFrame()
self.visibility_to_palm = TransformVisibilityMask()
self.extract_hand_side = ExtractHandsideandKeypoints()
self.to_one_hot = ToOneHot(num_classes=2)
self.normaliza_keypoints = NormalizeKeypoints()
self.to_relative_frame = TransformtoRelativeFrame()
self.canonical_transformations = GetCanonicalTransformation()
self.flip_right_hand = FlipRightHandToLeftHand()
self.get_matrix_inverse = CalculatePseudoInverse()
self.extract_hand_visibility = ExtractDominantHandVisibility()
self.extract_dominant_keypoints = ExtractDominantKeypoints2D()
self.crop_image_from_mask = CropImageFromMask()
self.create_scoremaps = CreateScoremaps(image_size=image_size,
crop_size=crop_size,
variance=variance)
self.wrap = pr.WrapOutput(
['score_maps', 'hand_side', 'keypoints3D', 'rotation_matrix'])
def __init__(self, image_shape, num_classes, input_name='input_1', dataset='CityScapes'):
super(PreprocessSegmentationIds, self).__init__()
self.add(pr.UnpackDictionary(['image_path', 'label_path']))
preprocess_image = pr.SequentialProcessor()
preprocess_image.add(pr.LoadImage())
preprocess_image.add(pr.ResizeImage(image_shape))
preprocess_image.add(pr.ConvertColorSpace(pr.RGB2BGR))
preprocess_image.add(pr.SubtractMeanImage(pr.BGR_IMAGENET_MEAN))
preprocess_label = pr.SequentialProcessor()
preprocess_label.add(pr.LoadImage())
preprocess_label.add(ResizeImageWithNearestNeighbors(image_shape))
preprocess_label.add(FromIdToMask(dataset))
self.add(pr.ControlMap(preprocess_image, [0], [0]))
self.add(pr.ControlMap(preprocess_label, [1], [1]))
H, W = image_shape[:2]
self.add(pr.SequenceWrapper({0: {input_name: [H, W, 3]}},
{1: {'masks': [H, W, num_classes]}}))
def __init__(self, base, mean_face, shape=(48, 48), with_crop=True):
super(CalculateFaceWeights, self).__init__()
self.base, self.mean_face = base, mean_face
self.preprocess = pr.SequentialProcessor()
self.convert_to_gray = pr.ConvertColorSpace(pr.RGB2GRAY)
if with_crop:
self.preprocess.add(pe.CropFrontalFace())
self.preprocess.add(pr.ResizeImage(shape))
self.preprocess.add(pr.ExpandDims(-1))
self.subtract = pe.SubtractMeanFace()
self.project = pe.ProjectToBase(self.base)
def __init__(self,
model,
flipped_keypoint_order,
with_flip,
data_with_center,
scale_output=True,
axes=[0, 3, 1, 2]):
super(GetHeatmapsAndTags, self).__init__()
self.with_flip = with_flip
self.predict = pr.SequentialProcessor(
[pr.Predict(model),
pr.TransposeOutput(axes),
pr.ScaleOutput(2)])
self.get_heatmaps = pr.GetHeatmaps(flipped_keypoint_order)
self.get_tags = pr.GetTags(flipped_keypoint_order)
self.postprocess = pr.SequentialProcessor()
if data_with_center:
self.postprocess.add(pr.RemoveLastElement())
if scale_output:
self.postprocess.add(pr.ScaleOutput(2, full_scaling=True))
def __init__(self, size=320):
super(ExtractHandSegmentation, self).__init__()
self.add(
pr.UnpackDictionary(['image', 'segmentation_label',
'annotations']))
preprocess_image = pr.SequentialProcessor(
[pr.LoadImage(), pr.ResizeImage((size, size))])
preprocess_segmentation_map = pr.SequentialProcessor(
[pr.LoadImage(),
pr.ResizeImage((size, size)),
ExtractHandmask()])
self.add(pr.ControlMap(preprocess_image, [0], [0]))
self.add(pr.ControlMap(preprocess_segmentation_map, [1], [1]))
self.add(
pr.SequenceWrapper({0: {
'image': [size, size, 3]
}}, {1: {
'hand_mask': [size, size]
}}))
def __init__(self,
right_hand=False,
input_config=MANOHandJoints,
output_config=MPIIHandJoints):
super(CalculateRelativeAngles, self).__init__()
output_labels = output_config.labels
origin_labels = input_config.labels
links_origin = input_config.links_origin
self.parents = input_config.parents
self.children = output_config.children
if right_hand:
links_origin = flip_along_x_axis(links_origin)
self.links_orientation = compute_orientation_vector(
links_origin, self.parents)
self.quaternions_to_rotations = pr.SequentialProcessor([
pr.ChangeLinkOrder(output_labels, origin_labels),
quaternions_to_rotation_matrices
])
self.calculate_relative_angle = pr.SequentialProcessor([
calculate_relative_angle,
pr.ChangeLinkOrder(origin_labels, output_labels)
])
def __init__(self, size, image_size, crop_size, variance):
super(ExtractHandPose2D, self).__init__()
self.unwrap_inputs = pr.UnpackDictionary(
['image', 'segmentation_label', 'annotations'])
self.preprocess_image = pr.SequentialProcessor(
[pr.LoadImage(), pr.ResizeImage((size, size))])
self.preprocess_segmentation_map = pr.SequentialProcessor(
[pr.LoadImage(),
pr.ResizeImage((size, size)),
ExtractHandmask()])
self.extract_annotations = pr.UnpackDictionary(['xyz', 'uv_vis', 'K'])
self.extract_2D_keypoints = Extract2DKeypoints()
self.keypoints_to_palm = KeypointstoPalmFrame()
self.visibility_to_palm = TransformVisibilityMask()
self.extract_hand_side = ExtractHandsideandKeypoints()
self.extract_visibility_dominant_hand = ExtractDominantHandVisibility()
self.create_scoremaps = CreateScoremaps(image_size, crop_size,
variance)
self.crop_image_from_mask = CropImageFromMask()
self.wrap = pr.WrapOutput(
['cropped_image', 'score_maps', 'keypoints_vis21'])
def __init__(self, links_origin=MPIIHandJoints.links_origin,
parents=MPIIHandJoints.parents, right_hand=False):
super(IKNetHandJointAngles, self).__init__()
self.calculate_orientation = pr.ComputeOrientationVector(parents)
self.links_origin = links_origin
self.right_hand = right_hand
if self.right_hand:
self.links_origin = flip_along_x_axis(self.links_origin)
self.links_delta = self.calculate_orientation(self.links_origin)
self.concatenate = pr.Concatenate(0)
self.compute_absolute_angles = pr.SequentialProcessor(
[pr.ExpandDims(0), IKNet(), pr.Squeeze(0)])
self.compute_relative_angles = pr.CalculateRelativeAngles()
self.wrap = pr.WrapOutput(['absolute_angles', 'relative_angles'])