141cb21fec5b5557a800cb2131e463b92913985a,niftynet/utilities/misc_common.py,,otsu_threshold,#Any#,206

Before Change

    threshold = 0
    sum_im = 0
    for i in range(0, 256):
        mean_ip = np.mean(img[img > bin_edges[i]])
        mean_im = np.mean(img[img < bin_edges[i]])
        sum_im = sum_im + hist[i]
        sum_ip = 1 - sum_im
        target = sum_ip*sum_im*np.square(mean_ip-mean_im)

After Change

def otsu_threshold(img):
    """ Implementation of otsu thresholding"""
    hist, bin_edges = np.histogram(img.ravel(), bins=256, density=True)
    hist = hist.astype(float)
    target_max = 0
    threshold = bin_edges[0]
    sum_im = 0

    mean_ip = np.cumsum(hist)
    mean_im = np.cumsum(hist[::-1])[::-1]
    for i in range(0, 256):
        sum_im = sum_im + hist[i]
        sum_ip = 1 - sum_im
        target = sum_ip * sum_im * np.square(mean_ip[i] - mean_im[i])
        if target > target_max:
            target_max, threshold = target, bin_edges[i]
    return threshold

Instances