def __init__(self, detector, keypoint_estimator, radius=3):
super(ProbabilisticKeypointPrediction, self).__init__()
# face detector
RGB2GRAY = pr.ConvertColorSpace(pr.RGB2GRAY)
self.detect = pr.Predict(detector, RGB2GRAY, pr.ToBoxes2D(['face']))
# creating pre-processing pipeline for keypoint estimator
preprocess = SequentialProcessor()
preprocess.add(pr.ResizeImage(keypoint_estimator.input_shape[1:3]))
preprocess.add(pr.ConvertColorSpace(pr.RGB2GRAY))
preprocess.add(pr.NormalizeImage())
preprocess.add(pr.ExpandDims(0))
preprocess.add(pr.ExpandDims(-1))
# creating post-processing pipeline for keypoint esimtator
# postprocess = SequentialProcessor()
# postprocess.add(ToNumpyArray())
# postprocess.add(pr.Squeeze(1))
# keypoint estimator predictions
self.estimate_keypoints = PredictMeanDistribution(
keypoint_estimator, preprocess)
# self.estimate_keypoints = pr.Predict(
# keypoint_estimator, preprocess, postprocess)
# used for drawing up keypoints in original image
self.change_coordinates = pr.ChangeKeypointsCoordinateSystem()
self.denormalize_keypoints = pr.DenormalizeKeypoints()
self.crop_boxes2D = pr.CropBoxes2D()
self.num_keypoints = len(keypoint_estimator.output_shape)
self.draw = pr.DrawKeypoints2D(self.num_keypoints, radius, False)
self.draw_boxes2D = pr.DrawBoxes2D(['face'], colors=[[0, 255, 0]])
self.wrap = pr.WrapOutput(['image', 'boxes2D'])
def __init__(self, radius=3):
super(DetectGMMKeypointNet2D, self).__init__()
self.detect = HaarCascadeFrontalFace(draw=False)
self.estimate_keypoints = GMMKeypointNet2D(draw=False)
self.num_keypoints = self.estimate_keypoints.num_keypoints
self.change_coordinates = pr.ChangeKeypointsCoordinateSystem()
self.denormalize_keypoints = pr.DenormalizeKeypoints()
self.crop = pr.CropBoxes2D()
self.compute_means = ComputeMeans()
self.draw_keypoints = pr.DrawKeypoints2D(self.num_keypoints, radius)
self.draw_probabilities = DrawProbabilities(self.num_keypoints)
self.draw_boxes2D = pr.DrawBoxes2D(['Face'], colors=[[0, 255, 0]])
outputs = ['image', 'boxes2D', 'keypoints2D', 'contours']
self.wrap = pr.WrapOutput(outputs)
def __init__(self, weights, measure, thresh, eigenfaces,
mean_face, offsets=[0, 0]):
super(DetectEigenFaces, self).__init__()
self.offsets = offsets
self.class_names = list(weights.keys()) + ['Face not found']
self.colors = lincolor(len(self.class_names))
self.croped_images = None
# detection
self.detect = HaarCascadeFrontalFace()
self.square = SequentialProcessor()
self.square.add(pr.SquareBoxes2D())
self.square.add(pr.OffsetBoxes2D(offsets))
self.clip = pr.ClipBoxes2D()
self.crop = pr.CropBoxes2D()
self.face_detector = EigenFaceDetector(weights, measure, thresh,
eigenfaces, mean_face)
# drawing and wrapping
self.draw = pr.DrawBoxes2D(self.class_names, self.colors,
weighted=True, with_score=False)
self.wrap = pr.WrapOutput(['image', 'boxes2D'])
def __init__(self, estimate_pose, offsets, valid_class_names, draw=True):
super(PIX2POSE_ROCK, self).__init__()
self.estimate_pose = estimate_pose
self.object_sizes = self.estimate_pose.object_sizes
self.postprocess_boxes = SequentialProcessor([
pr.FilterClassBoxes2D(valid_class_names),
pr.SquareBoxes2D(),
pr.OffsetBoxes2D(offsets)
])
self.clip = pr.ClipBoxes2D()
self.crop = pr.CropBoxes2D()
self.unwrap = pr.UnwrapDictionary(['pose6D', 'points2D', 'points3D'])
self.draw_boxes2D = pr.DrawBoxes2D(valid_class_names)
self.cube_points3D = build_cube_points3D(*self.object_sizes)
self.draw_pose6D = pr.DrawPose6D(self.cube_points3D,
self.estimate_pose.camera.intrinsics)
self.draw = draw
self.wrap = pr.WrapOutput(['image', 'poses6D'])
def __init__(self) -> None:
super(EmotionDetector, self).__init__()
self.detector = dlib.get_frontal_face_detector()
self.crop = pr.CropBoxes2D()
self.classify = MiniXceptionFER()
def __init__(self):
super(CropFrontalFace, self).__init__()
self.detect = HaarCascadeFrontalFace(draw=False)
self.crop = pr.CropBoxes2D()