def transform()

in sagemaker/src/handwriting_line_recognition.py [0:0]

• 20 lines of code
• 4 McCabe index (conditional complexity)

def transform(image, bbox, label):
    '''
    This function resizes the input image and converts so that it could be fed into the network.
    Furthermore, the label (text) is one-hot encoded.
    '''
    
    image = np.expand_dims(image, axis=0).astype(np.float32)
    if image[0, 0, 0] > 1:
        image = image/255.
    image = (image - 0.942532484060557) / 0.15926149044640417
    label_encoded = np.zeros(max_seq_len, dtype=np.float32)-1
    i = 0
    for word in label:
        word = word.replace("&quot", r'"')
        word = word.replace("&amp", r'&')
        word = word.replace('";', '\"')
        word = word.lower()
        for letter in word:
            label_encoded[i] = alphabet_dict[letter]
            i += 1
    return image, label_encoded