scenario generations (except fanout), training data export

Edmond Yoo

2018-09-03 d4d30efccd55c458ecbf0cda7c118d5493cdb4f4

 generate_data.py

@@ -67,6 +67,7 @@
    # List of detected objects to be fed into the neural net
    # The first object is the entire card
    detected_object_list = [ExtractedObject('card', [(0, 0), (len(img[0]), 0), (len(img[0]), len(img)), (0, len(img))])]
    '''
    # Mana symbol - They are located on the top right side of the card, next to the name
    # Their position is stationary, and is right-aligned.
    has_mana_cost = isinstance(card_info['mana_cost'], str)  # Cards with no mana cost will have nan
@@ -177,6 +178,7 @@

    # Image box - the large image on the top half of the card
    # TODO
    '''
    return detected_object_list


@@ -189,12 +191,25 @@
    card_pool = pd.DataFrame()
    for set_name in fetch_data.all_set_list:
        df = fetch_data.load_all_cards_text('data/csv/%s.csv' % set_name)
        for _ in range(3):
            card_info = df.iloc[random.randint(0, df.shape[0] - 1)]
            # Currently ignoring planeswalker cards due to their different card layout
            is_planeswalker = 'Planeswalker' in card_info['type_line']
            if not is_planeswalker:
                card_pool = card_pool.append(card_info)
        #for _ in range(3):
        #    card_info = df.iloc[random.randint(0, df.shape[0] - 1)]
        #    # Currently ignoring planeswalker cards due to their different card layout
        #    is_planeswalker = 'Planeswalker' in card_info['type_line']
        #    if not is_planeswalker:
        #        card_pool = card_pool.append(card_info)
        card_pool = card_pool.append(df)
    '''
    print(card_pool)
    mana_symbol_set = set()
    for _, card_info in card_pool.iterrows():
        has_mana_cost = isinstance(card_info['mana_cost'], str)
        if has_mana_cost:
            mana_cost = re.findall('\{(.*?)\}', card_info['mana_cost'])
            for symbol in mana_cost:
                mana_symbol_set.add(symbol)

    print(mana_symbol_set)
    '''

    for _, card_info in card_pool.iterrows():
        img_name = '../usb/data/png/%s/%s_%s.png' % (card_info['set'], card_info['collector_number'],
@@ -206,6 +221,7 @@
            card_img = cv2.imread(img_name)
        detected_object_list = apply_bounding_box(card_img, card_info, display=True)
        print(detected_object_list)

    return