def extract_digits(self, image):
"""
Extract digits from a binary image representing a sudoku
:param image: binary image/sudoku
:return: array of digits and their probabilities
"""
prob = np.zeros(4, dtype=np.float32)
digits = np.zeros((4, 9, 9), dtype=object)
for i in range(4):
labeled, features = label(image, structure=CROSS)
objs = find_objects(labeled)
for obj in objs:
roi = image[obj]
# center of bounding box
cy = (obj[0].stop + obj[0].start) / 2
cx = (obj[1].stop + obj[1].start) / 2
dists = cdist([[cy, cx]], CENTROIDS, 'euclidean')
pos = np.argmin(dists)
cy, cx = pos % 9, pos / 9
# 28x28 image, center relative to sudoku
prediction = self.classifier.classify(morph(roi))
if digits[i, cy, cx] is 0:
# Newly found digit
digits[i, cy, cx] = prediction
prob[i] += prediction[0, 0]
elif prediction[0, 0] > digits[i, cy, cx][0, 0]:
# Overlapping! (noise), choose the most probable prediction
prob[i] -= digits[i, cy, cx][0, 0]
digits[i, cy, cx] = prediction
prob[i] += prediction[0, 0]
image = np.rot90(image)
logging.info(prob)
return digits[np.argmax(prob)]
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