Initial commit with project files

5_Bildanalyse/ü12/l_a.py

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+import numpy as np
+import cv2
+from matplotlib import pyplot as plt
+
+
+def n1(hist, s):
+    return np.sum(hist[0:s+1])
+
+
+def n2(hist, s):
+    return np.sum(hist[s+1:256])
+
+
+def mean(hist):
+    k = np.arange(0, 256)
+    return np.sum((hist * k)) / np.sum(hist)
+
+
+def mu1(hist, s, n):
+    k = np.arange(0, 256)
+    return (1 / n) * np.sum((hist * k)[0:s+1])
+
+
+def mu2(hist, s, n):
+    k = np.arange(0, 256)
+    return (1 / n) * np.sum((hist * k)[s+1:256])
+
+
+def sigma1(hist, s, n, mu):
+    k = np.arange(0, 256)
+    return np.sqrt((1 / n) * np.sum((hist * np.power(k - mu, 2))[0:s+1]))
+
+
+def sigma2(hist, s, n, mu):
+    k = np.arange(0, 256)
+    return np.sqrt((1 / n) * np.sum((hist * np.power(k - mu, 2))[s+1:256]))
+
+
+def Q(hist, s):
+    n_1, n_2 = n1(hist, s), n2(hist, s)
+    if n_1 == 0 or n_2 == 0:
+        return 0
+    mu_1, mu_2 = mu1(hist, s, n_1), mu2(hist, s, n_2)
+    sigma_1, sigma_2 = sigma1(hist, s, n_1, mu_1), sigma2(hist, s, n_2, mu_2)
+    _mean = mean(hist)
+
+    sigma_zw = n_1 * np.power(mu_1 - _mean, 2) + n_2 * np.power(mu_2 - _mean, 2)
+    sigma_in = n_1 * np.power(sigma_1, 2) + n_2 * np.power(sigma_2, 2)
+    q = np.power(sigma_zw, 2) / np.power(sigma_in, 2)
+    return q
+
+''' Load data '''
+# Load images and template
+img = cv2.imread("../../data/cameraman.png", cv2.IMREAD_GRAYSCALE)
+
+histr = cv2.calcHist([img], [0], None, [256], [0, 256])
+histr = histr[:, 0]
+plt.plot(histr)
+plt.xlim([0, 256])
+
+''' Iterate over all s '''
+q_list = list()
+
+for s in range(1, 255):
+    q_list.append(Q(histr, s))
+
+optimal_s = np.argmax(np.asarray(q_list))
+print("Threshold:", optimal_s)
+
+''' Visualize result '''
+mask = img >= optimal_s
+
+cv2.imshow("img", img)
+cv2.imshow("mask", mask.astype(np.uint8) * 255)
+
+plt.show()
+cv2.waitKey(0)