alphabet

alphabet/main.py

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+import matplotlib.pyplot as plt
+import numpy as np
+from skimage.filters import threshold_otsu
+from skimage.measure import label, regionprops
+from collections import defaultdict
+from pathlib import Path
+
+
+def filling_factor(arr):
+    return np.sum(arr) / arr.size
+
+
+def count_holes(arr):
+    labeled = label(np.logical_not(arr))
+    regions = regionprops(labeled)
+    holes = 0
+    for region in regions:
+        coords = np.transpose(region.coords, (1,0))
+        ymin = np.min(coords[1])
+        ymax = np.max(coords[1])
+        xmin = np.min(coords[0])
+        xmax = np.max(coords[0])
+        if ymin == 0 or ymax == arr.shape[1]-1 or xmin == 0 or xmax == arr.shape[0]-1: continue
+        holes += 1
+    return holes
+
+
+def count_holes_rame(arr):
+    labeled = label(np.logical_not(arr))
+    return np.max(labeled)
+
+
+def count_vline(arr):
+    return np.sum(arr.mean(0) == 1)
+
+
+def recognize(region):
+    if filling_factor(region.image) == 1.0:
+        return '-'
+    else:
+        holes = count_holes(region.image)
+        if holes == 2: # B or 8
+            if count_vline(region.image) >= 3:
+                return 'B'
+            else: return '8'
+        elif holes == 1: #A or 0
+            if count_vline(region.image) >= 2:
+                if abs((region.local_centroid[0] / region.image.shape[0]) - (region.local_centroid[1] / region.image.shape[1])) > 0.035:
+                    return 'D'
+                else: return 'P'
+            elif abs((region.local_centroid[0] / region.image.shape[0]) - (region.local_centroid[1] / region.image.shape[1])) > 0.02:
+                return 'A'
+            else: return '0'
+        else:
+            if count_vline(region.image) >= 1:
+                return '1'
+            else:
+                if region.eccentricity < 0.4:
+                    return '*'
+                else:
+                    match count_holes_rame(region.image):
+                        case 2: return '/'
+                        case 4: return 'X'
+                        case 5: return 'W'
+    return '_'
+
+img = plt.imread('/home/jezv/Projects/Volovikov_CV/alphabet/symbols.png')
+
+img = np.mean(img, axis=2)
+thrash = threshold_otsu(img)
+img[img > 0] = 1
+
+regions = regionprops(label(img))
+
+result = defaultdict(lambda: 0)
+
+path = Path('.') / 'alphabet' /'result'
+path.mkdir(exist_ok=True)
+
+for i,region in enumerate(regions):
+    symbol = recognize(region)
+    result[symbol] += 1
+    if symbol in 'PD':
+        plt.clf()
+        plt.title(f"{symbol}")
+        plt.imshow(region.image)
+        plt.savefig(path / f"{i}")
+
+print(result, sum(result.values()))