lddmm_pytorch.py 文件源码

def level_curves(fname, npoints = 200, smoothing = 10, level = 0.5) :
    "Loads regularly sampled curves from a .PNG image."
    # Find the contour lines
    img = misc.imread(fname, flatten = True) # Grayscale
    img = (img.T[:, ::-1])  / 255.
    img = gaussian_filter(img, smoothing, mode='nearest')
    lines = find_contours(img, level)

    # Compute the sampling ratio for every contour line
    lengths = np.array( [arclength(line) for line in lines] )
    points_per_line = np.ceil( npoints * lengths / np.sum(lengths) )

    # Interpolate accordingly
    points = [] ; connec = [] ; index_offset = 0
    for ppl, line in zip(points_per_line, lines) :
        (p, c) = resample(line, ppl)
        points.append(p)
        connec.append(c + index_offset)
        index_offset += len(p)

    size   = np.maximum(img.shape[0], img.shape[1])
    points = np.vstack(points) / size
    connec = np.vstack(connec)
    return Curve(points, connec)
# Pyplot Output =================================================================================