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,
prior_boxes,
split=pr.TRAIN,
num_classes=21,
size=300,
mean=pr.BGR_IMAGENET_MEAN,
IOU=.5,
variances=[0.1, 0.1, 0.2, 0.2]):
super(AugmentDetection, self).__init__()
# image processors
self.augment_image = AugmentImage()
self.augment_image.add(pr.ConvertColorSpace(pr.RGB2BGR))
self.preprocess_image = PreprocessImage((size, size), mean)
# box processors
self.augment_boxes = AugmentBoxes()
args = (num_classes, prior_boxes, IOU, variances)
self.preprocess_boxes = PreprocessBoxes(*args)
# pipeline
self.add(pr.UnpackDictionary(['image', 'boxes']))
self.add(pr.ControlMap(pr.LoadImage(), [0], [0]))
if split == pr.TRAIN:
self.add(pr.ControlMap(self.augment_image, [0], [0]))
self.add(pr.ControlMap(self.augment_boxes, [0, 1], [0, 1]))
self.add(pr.ControlMap(self.preprocess_image, [0], [0]))
self.add(pr.ControlMap(self.preprocess_boxes, [1], [1]))
self.add(
pr.SequenceWrapper({0: {
'image': [size, size, 3]
}}, {1: {
'boxes': [len(prior_boxes), 4 + num_classes]
}}))
def __init__(self, renderer, image_paths, num_occlusions, split=pr.TRAIN):
super(DomainRandomizationProcessor, self).__init__()
self.copy = pr.Copy()
self.render = pr.Render(renderer)
self.augment = RandomizeRenderedImage(image_paths, num_occlusions)
preprocessors = [pr.ConvertColorSpace(pr.RGB2BGR), pr.NormalizeImage()]
self.preprocess = SequentialProcessor(preprocessors)
self.split = split
def __init__(self, shape, bkg_paths, mean=pr.BGR_IMAGENET_MEAN):
super(AugmentImage, self).__init__()
# self.add(LoadImage(4))
self.add(pr.ResizeImage(shape))
self.add(pr.BlendRandomCroppedBackground(bkg_paths))
self.add(pr.RandomContrast())
self.add(pr.RandomBrightness())
self.add(pr.RandomSaturation(0.7))
self.add(pr.RandomHue())
self.add(pr.ConvertColorSpace(pr.RGB2BGR))
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_names):
super(HaarCascadeDetectors, self).__init__()
self.model_names = model_names
self.detectors = []
for class_arg, model_name in enumerate(self.model_names):
detector = pr.Predict(HaarCascadeDetector(model_name, class_arg),
pr.ConvertColorSpace(pr.RGB2GRAY),
pr.ToBoxes2D(args.models))
self.detectors.append(detector)
self.draw_boxes2D = pr.DrawBoxes2D(args.models)
self.wrap = pr.WrapOutput(['image', 'boxes2D'])
def __init__(self, model, draw=True):
super(GMMKeypoints, self).__init__()
self.num_keypoints = len(model.output_shape)
preprocess = SequentialProcessor()
preprocess.add(pr.ResizeImage(model.input_shape[1:3]))
preprocess.add(pr.ConvertColorSpace(pr.RGB2GRAY))
preprocess.add(pr.NormalizeImage())
preprocess.add(pr.ExpandDims(0))
preprocess.add(pr.ExpandDims(-1))
self.estimate_keypoints = PredictDistributions(model, preprocess)
self.to_grid = ToProbabilityGrid(GRID)
self.draw = draw
self.draw_probabilities = DrawProbabilities(self.num_keypoints)
self.wrap = pr.WrapOutput(['image', 'probabilities', 'distributions'])
def __init__(self, model, colors=None):
super(PostprocessSegmentation, self).__init__()
self.add(pr.UnpackDictionary(['image_path']))
self.add(pr.LoadImage())
self.add(pr.ResizeImage(model.input_shape[1:3]))
self.add(pr.ConvertColorSpace(pr.RGB2BGR))
self.add(pr.SubtractMeanImage(pr.BGR_IMAGENET_MEAN))
self.add(pr.ExpandDims(0))
self.add(pr.Predict(model))
self.add(pr.Squeeze(0))
self.add(Round())
self.add(MasksToColors(model.output_shape[-1], colors))
self.add(pr.DenormalizeImage())
self.add(pr.CastImage('uint8'))
self.add(pr.ShowImage())
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, mean=pr.BGR_IMAGENET_MEAN):
super(PreprocessImage, self).__init__()
self.add(pr.ConvertColorSpace(pr.RGB2BGR))
self.add(pr.SubtractMeanImage(mean))
def __init__(self):
super(PostProcessImage, self).__init__()
self.add(pr.AddMeanImage(pr.BGR_IMAGENET_MEAN))
self.add(pr.CastImage('uint8'))
self.add(pr.ConvertColorSpace(pr.BGR2RGB))
