Initial commit with project files

5_Bildanalyse/ü13/l_b.py

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+import numpy as np
+import cv2
+
+
+''' Load image and apply histogramm equalization '''
+img = cv2.imread("data/students_checklist.jpg").astype(np.float32)
+img[:, :, 0] = 255 * (img[:, :, 0] - np.min(img[:, :, 0])) / (np.max(img[:, :, 0]) - np.min(img[:, :, 0]))
+img[:, :, 1] = 255 * (img[:, :, 1] - np.min(img[:, :, 1])) / (np.max(img[:, :, 1]) - np.min(img[:, :, 1]))
+img[:, :, 2] = 255 * (img[:, :, 2] - np.min(img[:, :, 2])) / (np.max(img[:, :, 2]) - np.min(img[:, :, 2]))
+cv2.imshow("img", img.astype(np.uint8))
+
+''' Binary segmentation '''
+mask = (img[:, :, 0] > 100) * (img[:, :, 1] > 100) * (img[:, :, 2] > 100) *\
+       (img[:, :, 2] - img[:, :, 1] < 20) * (img[:, :, 2] - img[:, :, 0] < 20)
+mask = 1 - mask
+
+''' Morphologigcal operations '''
+kernel = np.ones((3, 3))
+mask = cv2.erode(mask.astype(np.uint8) * 255, kernel, iterations=15)
+mask = cv2.dilate(mask, kernel, iterations=15)
+
+cv2.imshow("mask", mask)
+
+''' Finding seed point '''
+seeds = cv2.erode(mask, kernel, iterations=70)
+cv2.imshow("seeds", seeds)
+
+''' Labeling seeds '''
+label_map = np.zeros_like(seeds)
+next_id = 1
+for i in range(img.shape[0]):
+    for j in range(img.shape[1]):
+        if seeds[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
+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
+
+''' Create distance labels for all labels '''
+mask = mask != 0
+
+while np.sum(mask > 0):
+    for l in np.unique(label_map):
+        if l == 0:
+            continue
+        kernel = np.ones((3, 3))
+        current_label = label_map == l
+        current_label = cv2.dilate(current_label.astype(np.uint8), kernel, iterations=1)
+        current_label = current_label * mask
+        mask[current_label != 0] = 0
+        label_map[current_label != 0] = l
+
+
+''' 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)