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 test_image_cropping():
handsegnet = HandSegmentationNet()
preprocess_image = SequentialProcessor(
[pr.NormalizeImage(),
pr.ResizeImage((320, 320)),
pr.ExpandDims(0)])
postprocess_segmentation = PostProcessSegmentation(320, 320)
localize_hand = pr.Predict(handsegnet, preprocess_image,
postprocess_segmentation)
image = load_image('./sample.jpg')
hand_crop, segmentation_map, center, boxes, crop_sizes = localize_hand(
image)
box = boxes[0]
xmin, ymin, xmax, ymax = box
crop_size = crop_sizes[0]
assert len(hand_crop.shape) == 4
assert hand_crop.shape == (1, 256, 256, 3)
assert len(segmentation_map.shape) == 4
assert segmentation_map.shape == (1, 320, 320, 1)
assert center == [[191.5, 194.5]]
assert len(box) == 4
assert box == [114, 153, 269, 236]
assert xmax > xmin and ymin > ymax
assert round(crop_size[0], 2) == 1.32
def __init__(self, model, colors=None):
super(PostprocessSegmentation, self).__init__()
self.add(PreprocessImage())
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, 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,
handsegnet,
posenet,
posepriornet,
viewpointnet,
image_size=320,
crop_shape=(256, 256),
num_keypoints=21):
super(DetectHandKeypoints, self).__init__()
self.preprocess_image = SequentialProcessor([
pr.NormalizeImage(),
pr.ResizeImage((image_size, image_size)),
pr.ExpandDims(0)
])
postprocess_segmentation = PostProcessSegmentation(
image_size, crop_shape)
self.localize_hand = pr.Predict(handsegnet,
postprocess=postprocess_segmentation)
self.resize_scoremaps = ResizeScoreMaps(crop_shape)
self.merge_dictionaries = MergeDictionaries()
self.wrap_input = WrapToDictionary(['hand_side'])
self.predict_keypoints2D = pr.Predict(posenet)
self.predict_keypoints3D = pr.Predict(posepriornet)
self.predict_keypoints_angles = pr.Predict(viewpointnet)
self.postprocess_keypoints = PostProcessKeypoints()
self.resize = pr.ResizeImage(shape=crop_shape)
self.extract_2D_keypoints = ExtractKeypoints()
self.transform_keypoints = TransformKeypoints()
self.draw_keypoint = pr.DrawKeypoints2D(num_keypoints,
normalized=True,
radius=4)
self.denormalize = pr.DenormalizeImage()
self.wrap = pr.WrapOutput(['image', 'keypoints2D', 'keypoints3D'])
self.expand_dims = pr.ExpandDims(axis=0)
self.draw_boxes = pr.DrawBoxes2D(['hand'], [[0, 1, 0]])
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,
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, PoseNet):
super(Process2DKeypoints, self).__init__()
self.add(pr.ExpandDims(0))
self.add(pr.Predict(PoseNet))