def ruler_scale_factor(image, distance):
"""Returns the scale factor to convert from image coordinates to real world coordinates
Args:
image: BGR image of shape n x m x 3.
distance: The real world size of the smallest graduation spacing
Returns:
float: Unitless scale factor from image coordinates to real world coordinates.
"""
height, width = image.shape[:2]
image, mask = find_ruler(image)
binary_image = mask * threshold(image, mask)
if binary_image[mask].mean() > 0.5:
binary_image[mask] = ~binary_image[mask]
remove_large_components(binary_image, max(height, width))
edges = skeletonize(binary_image)
hspace, angles, distances = hough_transform(edges)
features = hspace_features(hspace, splits=16)
angle_index = best_angles(np.array(features))
max_graduation_size = int(max(image.shape))
line_separation_pixels = find_grid(hspace[:, angle_index], max_graduation_size)
logging.info('Line separation: {:.3f}'.format(line_separation_pixels))
return distance / line_separation_pixels
def crop_by_saliency(saliency_map, closing_size=11, border=50):
binary_image = threshold(saliency_map)
selem = np.ones((closing_size, closing_size))
binary_image = binary_closing(binary_image, selem)
labels = label(binary_image)
roi = max(regionprops(labels), key=attrgetter('filled_area'))
border = 50
return (slice(roi.bbox[0] - border, roi.bbox[2] + 2 * border),
slice(roi.bbox[1] - border, roi.bbox[3] + 2 * border))
def crop_by_saliency(saliency_map, closing_size=11, border=50):
binary_image = threshold(saliency_map)
selem = np.ones((closing_size, closing_size))
binary_image = binary_closing(binary_image, selem)
labels = label(binary_image)
roi = max(regionprops(labels), key=attrgetter('filled_area'))
border = 50
return (slice(roi.bbox[0] - border, roi.bbox[2] + 2 * border),
slice(roi.bbox[1] - border, roi.bbox[3] + 2 * border))
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)
labels = label(edges)
regions = regionprops(labels)
edge_lengths = np.zeros_like(labels)
for i, edge in enumerate(sorted(regions, key=attrgetter('filled_area'))):
edge_lengths[labels == edge.label] = edge.filled_area
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)
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)
# write_image('wing_edge.png', edges)
distance = scipy.ndimage.distance_transform_edt(~edges)