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| | | from PIL import Image |
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| | | import time |
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| | | |
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| | | from config import Config |
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| | | import fetch_data |
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| | | import transform_data |
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| | | |
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| | | |
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| | | """ |
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| | | As of the current version, the YOLO network has been removed from this code during optimization. |
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| | | It was found out that YOLO was adding too much processing delay, and the benefits from using it couldn't justify |
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| | | such heavy cost. |
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| | | If you're interested to see the implementation using YOLO, please check out the previous commit: |
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| | | https://github.com/hj3yoo/mtg_card_detector/tree/dea64611730c84a59c711c61f7f80948f82bcd31 |
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| | | """ |
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| | | |
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| | | |
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| | | def calc_image_hashes(card_pool, save_to=None, hash_size=32, highfreq_factor=4): |
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| | |
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| | | for card_name in card_names: |
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| | | # Fetch the image - name can be found based on the card's information |
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| | | card_info['name'] = card_name |
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| | | img_name = '%s/card_img/png/%s/%s_%s.png' % (transform_data.data_dir, card_info['set'], |
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| | | img_name = '%s/card_img/png/%s/%s_%s.png' % (Config.data_dir, card_info['set'], |
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| | | card_info['collector_number'], |
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| | | fetch_data.get_valid_filename(card_info['name'])) |
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| | | card_img = cv2.imread(img_name) |
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| | |
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| | | # If the image doesn't exist, download it from the URL |
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| | | if card_img is None: |
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| | | fetch_data.fetch_card_image(card_info, |
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| | | out_dir='%s/card_img/png/%s' % (transform_data.data_dir, card_info['set'])) |
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| | | out_dir='%s/card_img/png/%s' % (Config.data_dir, card_info['set'])) |
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| | | card_img = cv2.imread(img_name) |
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| | | if card_img is None: |
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| | | print('WARNING: card %s is not found!' % img_name) |
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| | |
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| | | # Find the contour |
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| | | _, cnts, hier = cv2.findContours(img_erode, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) |
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| | | if len(cnts) == 0: |
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| | | print('no contours') |
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| | | #print('no contours') |
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| | | return [] |
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| | | |
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| | | # The hierarchy from cv2.findContours() is similar to a tree: each node has an access to the parent, the first child |
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| | |
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| | | card_set = key[key.find('(') + 1:key.find(')')] |
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| | | confidence = sum(val) / f_len |
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| | | card_info = card_pool[(card_pool['name'] == card_name) & (card_pool['set'] == card_set)].iloc[0] |
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| | | img_name = '%s/card_img/tiny/%s/%s_%s.png' % (transform_data.data_dir, card_info['set'], |
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| | | img_name = '%s/card_img/tiny/%s/%s_%s.png' % (Config.data_dir, card_info['set'], |
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| | | card_info['collector_number'], |
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| | | fetch_data.get_valid_filename(card_info['name'])) |
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| | | # If the card image is not found, just leave it blank |
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| | |
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| | | else: |
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| | | # Merge database for all cards, then calculate pHash values of each, store them |
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| | | df_list = [] |
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| | | for set_name in fetch_data.all_set_list: |
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| | | csv_name = '%s/csv/%s.csv' % (transform_data.data_dir, set_name) |
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| | | for set_name in Config.all_set_list: |
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| | | csv_name = '%s/csv/%s.csv' % (Config.data_dir, set_name) |
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| | | df = fetch_data.load_all_cards_text(csv_name) |
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| | | df_list.append(df) |
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| | | card_pool = pd.concat(df_list, sort=True) |
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