def train(self, train_generator):
x, y = [], []
for i, (image, mask) in enumerate(train_generator):
point_generator = PointGeneratorBalanced(mask, self.__patch_count,
balance=self.__training_balance)
feature_extractor = FeatureExtractor(image, self.__patch_size, point_generator)
pixel_extractor = PixelExtractor(mask, point_generator)
x.extend(feature_extractor.extract())
y.extend(pixel_extractor.extract())
stdout.write("\rTraining features: %.1f%%" % (100.0 * (i + 1) / train_generator.size))
stdout.flush()
stdout.write("\nTrain...")
stdout.flush()
self.__model.fit(x, y)
stdout.write("\rTraining done\n")
def _inner_classify(self, input_image):
point_generator = PointGeneratorAll(input_image.shape)
feature_extractor = FeatureExtractor(input_image, self.__patch_size, point_generator)
return self.__model.predict([*feature_extractor.extract()]).reshape(input_image.shape)