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, class_names, prior_boxes, variances=[.1, .2]):
super(ShowBoxes, self).__init__()
self.deprocess_boxes = SequentialProcessor([
pr.DecodeBoxes(prior_boxes, variances),
pr.ToBoxes2D(class_names, True),
pr.FilterClassBoxes2D(class_names[1:])
])
self.denormalize_boxes2D = pr.DenormalizeBoxes2D()
self.draw_boxes2D = pr.DrawBoxes2D(class_names)
self.show_image = 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, 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,
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]])
class AugmentBoxes(SequentialProcessor):
def __init__(self, mean=pr.BGR_IMAGENET_MEAN):
super(AugmentBoxes, self).__init__()
self.add(pr.ToImageBoxCoordinates())
self.add(pr.Expand(mean=mean))
self.add(pr.RandomSampleCrop())
self.add(pr.RandomFlipBoxesLeftRight())
self.add(pr.ToNormalizedBoxCoordinates())
# We now visualize our current box augmentation
# For that we build a quick pipeline for drawing our boxes
draw_boxes = SequentialProcessor([
pr.ControlMap(pr.ToBoxes2D(class_names, False), [1], [1]),
pr.ControlMap(pr.DenormalizeBoxes2D(), [0, 1], [1], {0: 0}),
pr.DrawBoxes2D(class_names),
pr.ShowImage()
])
# Let's test it our box data augmentation pipeline
augment_boxes = AugmentBoxes()
print('Box augmentation examples')
for _ in range(10):
image = P.image.load_image(image_fullpath)
image, boxes = augment_boxes(image, box_data.copy())
draw_boxes(P.image.resize_image(image, (300, 300)), boxes)
# There is also some box-preprocessing that is required.
# Mostly we must match our boxes to a set of default (prior) boxes.
# Then we must encode them and expand the class label to a one-hot vector.
# of **top-left** bounding-box corner.
H, W = load_image(image_fullpath).shape[:2]
# The x_max, y_max are the **normalized** coordinates
class_names = ['background', 'human', 'horse']
box_data = np.array([[200 / W, 60 / H, 300 / W, 200 / H, 1],
[100 / W, 90 / H, 400 / W, 300 / H, 2]])
# Let's visualize our boxes!
# first we transform our numpy array into our built-in ``Box2D`` messages
to_boxes2D = pr.ToBoxes2D(class_names)
denormalize = pr.DenormalizeBoxes2D()
boxes2D = to_boxes2D(box_data)
image = load_image(image_fullpath)
boxes2D = denormalize(image, boxes2D)
draw_boxes2D = pr.DrawBoxes2D(class_names)
show_image(draw_boxes2D(image, boxes2D))
# As you can see, we were not able to put everything as a
# ``SequentialProcessor``. This is because we are dealing with 2 inputs:
# ``box_data`` and ``image``. We can join them into a single processor
# using ``pr.ControlMap`` wrap. ``pr.ControlMap`` allows you to select which
# arguments (``intro_indices``) are passed to your processor, and also where
# you should put the output of your processor (``outro_indices``).
draw_boxes = SequentialProcessor()
draw_boxes.add(pr.ControlMap(to_boxes2D, intro_indices=[1], outro_indices=[1]))
draw_boxes.add(pr.ControlMap(pr.LoadImage(), [0], [0]))
draw_boxes.add(pr.ControlMap(denormalize, [0, 1], [1], keep={0: 0}))
draw_boxes.add(pr.DrawBoxes2D(class_names))
draw_boxes.add(pr.ShowImage())
def __init__(self, class_names, preprocess=None, colors=None):
super(DrawBoxData2D, self).__init__()
self.class_names, self.colors = class_names, colors
self.to_boxes2D = pr.ToBoxes2D(self.class_names)
self.draw_boxes2D = pr.DrawBoxes2D(self.class_names, self.colors)
self.preprocess = preprocess
def test_DrawBoxes2D_with_invalid_class_names_type():
with pytest.raises(TypeError):
class_names = 'Face'
colors = [[255, 0, 0]]
pr.DrawBoxes2D(class_names, colors)