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 test_preprocess_image():
preprocess_pipeline = SequentialProcessor(
[pr.NormalizeImage(),
pr.ResizeImage((320, 320)),
pr.ExpandDims(0)])
image = load_image('./sample.jpg')
processed_image = preprocess_pipeline(image)
assert len(processed_image.shape) == 4
assert processed_image.shape == (1, 320, 320, 3)
def test_segmentation_postprocess():
preprocess_pipeline = SequentialProcessor(
[pr.NormalizeImage(),
pr.ResizeImage((320, 320)),
pr.ExpandDims(0)])
image = load_image('./sample.jpg')
processed_image = preprocess_pipeline(image)
localization_pipeline = PostProcessSegmentation(HandSegNet)
localization_output = localization_pipeline(processed_image)
assert len(localization_output) == 5
assert localization_output[0].shape == (1, 256, 256, 3)
assert localization_output[1].shape == (1, 320, 320, 1)
assert localization_output[2].shape == (1, 2)
assert localization_output[3].shape == (1, 2, 2)
assert localization_output[4].shape == (1, 1)
def test_keypoints2D_process():
preprocess_pipeline = SequentialProcessor(
[pr.NormalizeImage(),
pr.ResizeImage((320, 320)),
pr.ExpandDims(0)])
image = load_image('./sample.jpg')
processed_image = preprocess_pipeline(image)
localization_pipeline = PostProcessSegmentation(HandSegNet)
localization_output = localization_pipeline(processed_image)
keypoints_pipeline = Process2DKeypoints(HandPoseNet)
score_maps_dict = keypoints_pipeline(
np.squeeze(localization_output[0], axis=0))
score_maps = score_maps_dict['score_maps']
assert score_maps.shape == (1, 32, 32, 21)
assert len(score_maps) == 1
from HandPoseEstimation import HandSegmentationNet, PosePriorNet, PoseNet
from HandPoseEstimation import ViewPointNet
from paz.backend.image.opencv_image import load_image, show_image, write_image
from pipelines import DetectHandKeypoints
use_pretrained = True
HandSegNet = HandSegmentationNet()
HandPoseNet = PoseNet()
HandPosePriorNet = PosePriorNet()
HandViewPointNet = ViewPointNet()
pipeline = DetectHandKeypoints(HandSegNet, HandPoseNet, HandPosePriorNet,
HandViewPointNet)
image = load_image('./sample.jpg')
detection = pipeline(image)
show_image(detection['image'].astype('uint8'))
write_image('./detection.jpg', detection['image'].astype('uint8'))