Initial commit with project files

5_Bildanalyse/ü13/l_a.py

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+import numpy as np
+import cv2
+
+
+''' Load image '''
+img = cv2.imread("data/bild.png", cv2.IMREAD_GRAYSCALE)
+cv2.imshow("img", img)
+
+''' Iterate over rows / columns'''
+label_map = np.zeros_like(img)
+next_id = 1
+for i in range(img.shape[0]):
+    for j in range(img.shape[1]):
+        if img[i, j] != 0:
+            upper_label = label_map[i - 1, j] if i > 0 else 0
+            left_label = label_map[i, j - 1] if j > 0 else 0
+            if upper_label == 0 and left_label == 0:
+                label_map[i, j] = next_id
+                next_id += 1
+            elif upper_label == 0 and left_label != 0:
+                label_map[i, j] = left_label
+            elif upper_label != 0 and left_label == 0:
+                label_map[i, j] = upper_label
+            elif upper_label != 0 and left_label != 0:
+                if upper_label == left_label:
+                    label_map[i, j] = upper_label
+                else:
+                    new_label = min(upper_label, left_label)
+                    old_label = max(upper_label, left_label)
+                    label_map[label_map == old_label] = new_label
+                    label_map[i, j] = new_label
+
+''' Rename labels (not necessary, but for visualisation) '''
+labels = sorted(np.unique(label_map))
+next_id = 1
+for l in labels:
+    if l == 0:
+        continue
+    label_map[label_map == l] = next_id
+    next_id += 1
+
+''' Visualize label map '''
+color_map = {
+    1: [255, 0, 0],
+    2: [255, 255, 0],
+    3: [255, 255, 255],
+    4: [0, 255, 0],
+    5: [0, 255, 255],
+    6: [0, 0, 255],
+    7: [100, 100, 100],
+    8: [50, 200, 80],
+    9: [200, 140, 88],
+    10: [120, 0, 190],
+}
+
+colored_image = np.zeros((img.shape[0], img.shape[1], 3))
+for c, value in color_map.items():
+    colored_image[label_map == c] = value
+
+cv2.imshow("colored_image", colored_image.astype(np.uint8))
+cv2.waitKey(0)