import numpy as np from skimage.feature import canny from vision.segmentation.segment import crop_by_saliency, saliency_dragonfly from vision.tests import get_test_image from vision.measurements import subspace_shape src = np.zeros((11, 2)) src[:, 0] = np.arange(-5, 6) src[:, 1] = np.power(src[:, 0], 2) dst = np.zeros((11, 2)) dst[:, 0] = np.arange(-5, 6) + 3 dst[:, 1] = np.power(src[:, 0], 2) / 12 mu, phi, sigma2 = subspace_shape.learn((src, dst)) # plt.plot(src[:, 0], src[:, 1], 'r') # plt.plot(dst[:, 0], dst[:, 1], 'g') # avg = mu + phi @ (-0.75 * np.ones((1, 1))) # avg = avg.reshape(-1, 2) # plt.plot(avg[:, 0], avg[:, 1], 'b') # plt.show() image = np.zeros((100, 100), dtype=np.bool) j = np.arange(20, 80) i = np.power(j - 50, 2) / 10 image[90 - i.astype(np.int), j] = True subspace_shape.infer(image, mu, phi, sigma2)
def visualize_result(image, edge_image, shape, closest_points):
output_image = 0.5 * (image + edge_image[:, :, np.newaxis])
points = closest_points[:, [1, 0]]
perimeter = draw.polygon_perimeter(points[:, 0], points[:, 1])
draw.set_color(output_image, (perimeter[0].astype(np.int), perimeter[1].astype(np.int)), [0, 1, 0])
points = shape[:, [1, 0]]
perimeter = draw.polygon_perimeter(points[:, 0], points[:, 1])
draw.set_color(output_image, (perimeter[0].astype(np.int), perimeter[1].astype(np.int)), [0, 0, 1])
return output_image
shapes = [smoothed_shape(read_shape(i)) for i in range(4)]
aligned_shapes = procrustes.generalized_procrustes(shapes)
shape_model = subspace_shape.learn(aligned_shapes, K=8)
# wings_image = get_test_image('wing_area', 'cropped', 'unlabelled', '7.png')
wings_image = get_test_image('wing_area', 'pinned', '1.png')
edges = canny(wings_image[:, :, 1], 3)
saliency = saliency_dragonfly(wings_image)
thresh = threshold(saliency)
edges = skeletonize(edges)
gaps = scipy.ndimage.filters.convolve(1 * edges, np.ones((3, 3)), mode='constant', cval=False)
edges[(gaps == 2) & ~edges] = True
edges = skeletonize(edges)
distance = scipy.ndimage.distance_transform_edt(~edges)
def visualize_result(image, edge_image, shape, closest_points):
output_image = 0.5 * (image + edge_image[:, :, np.newaxis])
points = closest_points[:, [1, 0]]
perimeter = draw.polygon_perimeter(points[:, 0], points[:, 1])
draw.set_color(output_image, (perimeter[0].astype(np.int), perimeter[1].astype(np.int)), [0, 1, 0])
points = shape[:, [1, 0]]
perimeter = draw.polygon_perimeter(points[:, 0], points[:, 1])
draw.set_color(output_image, (perimeter[0].astype(np.int), perimeter[1].astype(np.int)), [0, 0, 1])
return output_image
shapes = [smoothed_shape(read_shape(i)) for i in range(4)]
aligned_shapes = procrustes.generalized_procrustes(shapes)
shape_model = subspace_shape.learn(aligned_shapes, K=8)
wings_image = get_test_image('wing_area', 'cropped', 'unlabelled', '7.png')
# write_image('wings.png', wings_image)
edges = canny(wings_image[:, :, 1], 3)
saliency = saliency_dragonfly(wings_image)
thresh = threshold(saliency)
background = threshold(scipy.ndimage.distance_transform_edt(~thresh))
contours = find_contours(thresh, level=0.5)
outline = max(contours, key=attrgetter('size')).astype(np.int)
outline_image = np.zeros_like(edges)
draw.set_color(outline_image, (outline[:, 0], outline[:, 1]), True)
