def main():
"""Load image, calculate optimal threshold, binarize, plot."""
# load image
img = data.coins()
height, width = img.shape
nb_pixels = height * width
# precalculate some values for speedup
# average pixel value
g_avg = np.average(img)
# P(pixel-value), i.e. #pixels-with-value / #all-pixels
p_g = [0] * 256
for g in range(0, 256):
p_g[g] = np.sum(img == g) / nb_pixels
# Otsu method
# calculations are based on standard formulas
q_best = None
threshold_best = None
img_bin_best = None
# iterate over all possible thresholds
for t in range(1, 255):
img_bin = np.zeros(img.shape)
img_bin[img >= t] = 1
p1 = np.sum(img_bin) / nb_pixels
p0 = 1 - p1
g0 = np.average(img[img_bin == 0]) if np.sum(img[img_bin == 0]) > 0 else 0
g1 = np.average(img[img_bin == 1]) if np.sum(img[img_bin == 1]) > 0 else 0
var0 = sum([(g-g0)**2 * p_g[g] for g in range(0, t+1)])
var1 = sum([(g-g1)**2 * p_g[g] for g in range(t+1, 256)])
var_between = p0 * (g0 - g_avg)**2 + p1 * (g1 - g_avg)**2
var_inner = p0 * var0**2 + p1 * var1**2
# q is the relation of variance between classes and variance within classes
q = var_between / var_inner if var_inner > 0 else 0
print(t, p0, p1, g0, g1, g_avg, var_between, var_inner, q)
if q_best is None or q_best < q:
q_best = q
threshold_best = t
img_bin_best = img <= t
# ground truth, based on scikit-image
gt_tresh = skifilters.threshold_otsu(img)
ground_truth = img <= gt_tresh
# plot
util.plot_images_grayscale(
[img, img_bin_best, ground_truth],
["Image", "Otsu", "Otsu (Ground Truth)"]
)
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