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@staticmethod
    def normalizeNot(image):
        return image

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#Use for testing ImageRecognizer Class
if __name__ == "__main__":
    ImageClassifier.createDatabase("images", "number_db")
    imageClassifier = ImageClassifier("number_db")
    imageClassifier.openDatabase()
    print(imageClassifier)
    imageClassifier.normalizeDatabase(ImageClassifier.normalizeBinary)
    for number in range(10):
        print(imageClassifier.classifyImage("test" + str(number) +".jpg", ImageClassifier.normalizeBinary))

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max_confidence = sorted(confidence_dict.values(), reverse=True)
    for key, value in confidence_dict.items():
        if value == max_confidence[0]:
            return key, min(round(((1.0/max_confidence[0])*max_confidence[1])*100,2), 100.00)

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for i in range(10):
        confidence_dict[i] = 0
    for number in range(len(self._database.keys())):
        for image in self._database[number]:
            for column in range(len(image)):
                for pixel in range(len(image[column])):
                    if image[column][pixel] == test_image[column][pixel] == 1:
                        confidence_dict[number] += 2
                    if image[column][pixel] == test_image[column][pixel] == 0:
                        continue

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def classifyImage(self, img, normFunction):
    test_image = Image.open(img)
    test_image = np.array(test_image).tolist()
    test_image = normFunction(test_image)

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def normalizeDatabase(self, normFunction):
    for number in range(len(self._database.keys())):
        for image in self._database[number]:
    image = normFunction(image)

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@staticmethod
def normalizeBinary(image):
    for column in image:
        for pixel in range(len(column)):
            total_color = 0
            for color in column[pixel]:
                total_color += color
            if total_color/3 < 255/2:
                column[pixel] = 0
            else:

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def openDatabase(self):
    with open(self._databaseName+".pkl", "rb") as db:
        self._database = pickle.load(db)

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if __name__ == "__main__":
    ImageClassifier.createDatabase("images", "number_db")

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with open(databaseName + ".pkl", "wb") as db:
    pickle.dump(number_db,db)