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| | | import sys |
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| | | import numpy as np |
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| | | import pandas as pd |
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| | | from transform_data import ExtractedObject |
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| | | |
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| | | # Referenced from geaxgx's playing-card-detection: https://github.com/geaxgx/playing-card-detection |
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| | | class Backgrounds: |
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| | | def apply_bounding_box(img, card_info, display=False): |
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| | | # List of detected objects to be fed into the neural net |
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| | | # The first object is the entire card |
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| | | detected_object_list = [ExtractedObject('card', [(0, 0), (len(img[0]), 0), (len(img[0]), len(img)), (0, len(img))])] |
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| | | # Mana symbol - They are located on the top right side of the card, next to the name |
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| | | # Their position is stationary, and is right-aligned. |
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| | | has_mana_cost = isinstance(card_info['mana_cost'], str) # Cards with no mana cost will have nan |
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| | | if has_mana_cost: |
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| | | mana_cost = re.findall('\{(.*?)\}', card_info['mana_cost']) |
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| | | x2 = 683 |
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| | | y1 = 67 |
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| | | x_anchor = 683 |
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| | | y_anchor = 65 |
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| | | # Cards with specific type or from old sets have their symbol at a different position |
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| | | if card_info['set'] in ['8ed', 'mrd', 'dst', '5dn']: |
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| | | y1 -= 2 |
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| | | y_anchor -= 2 |
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| | | |
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| | | for i in reversed(range(len(mana_cost))): |
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| | | # Hybrid mana symbol are larger than a normal symbol |
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| | | is_hybrid = '/' in mana_cost[i] |
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| | | if is_hybrid: |
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| | | box = [(x2 - 47, y1 - 8), (x2 + 2, y1 + 43)] # (x1, y1), (x2, y2) |
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| | | x2 -= 45 |
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| | | x1 = x_anchor - 47 |
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| | | x2 = x_anchor + 2 |
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| | | y1 = y_anchor - 8 |
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| | | y2 = y_anchor + 43 |
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| | | x_anchor -= 45 |
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| | | else: |
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| | | box = [(x2 - 39, y1), (x2, y1 + 41)] # (x1, y1), (x2, y2) |
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| | | x2 -= 37 |
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| | | img_symbol = img[box[0][1]:box[1][1], box[0][0]:box[1][0]] |
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| | | #if display: |
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| | | # cv2.imshow('symbol', img_symbol) |
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| | | # cv2.waitKey(0) |
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| | | x1 = x_anchor - 39 |
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| | | x2 = x_anchor |
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| | | y1 = y_anchor |
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| | | y2 = y_anchor + 43 |
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| | | x_anchor -= 37 |
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| | | # Append them to the list of bounding box with the appropriate label |
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| | | symbol_name = 'mana_symbol:' + mana_cost[i] |
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| | | key_pts = [(x1, y1), (x2, y1), (x2, y2), (x1, y2)] |
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| | | detected_object_list.append(ExtractedObject(symbol_name, key_pts)) |
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| | | |
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| | | if display: |
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| | | img_symbol = img[y1:y2, x1:x2] |
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| | | cv2.imshow('symbol', img_symbol) |
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| | | cv2.waitKey(0) |
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| | | |
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| | | # Set symbol - located on the right side of the type box in the centre of the card, next to the card type |
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| | | # Only one symbol exists, and its colour varies by rarity. |
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| | | else: |
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| | | x1 = 630 |
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| | | x2 = 683 |
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| | | img_symbol = img[y1:y2, x1:x2] |
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| | | y1 = 589 |
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| | | y2 = 636 |
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| | | # Append them to the list of bounding box with the appropriate label |
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| | | symbol_name = 'set_symbol:' + card_info['set'] |
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| | | key_pts = [(x1, y1), (x2, y1), (x2, y2), (x1, y2)] |
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| | | detected_object_list.append(ExtractedObject(symbol_name, key_pts)) |
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| | | |
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| | | if display: |
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| | | img_symbol = img[y1:y2, x1:x2] |
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| | | cv2.imshow('symbol', img_symbol) |
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| | | cv2.waitKey(0) |
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| | | |
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| | | # Image box - the large image on the top half of the card |
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| | | # TODO |
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| | | return detected_object_list |
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| | | |
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| | | |
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| | | def main(): |
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| | | random.seed() |
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| | | #bg_images = load_dtd() |
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| | | #bg = Backgrounds() |
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| | | #bg.get_random(display=True) |
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| | | |
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| | | card_pool = pd.DataFrame() |
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| | | for set_name in ['8ed', 'mrd', 'dst', '5dn', 'chk', 'bok', 'sok', '9ed', 'rav', 'gpt', 'dis', 'csp', 'tsp', 'plc', |
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| | | 'fut', '10e', 'lrw', 'mor', 'shm', 'eve', 'ala', 'con', 'arb', 'm10', 'zen', 'wwk', 'roe', 'm11', |
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| | | 'som', 'mbs', 'nph', 'm12', 'isd', 'dka', 'avr', 'm13', 'rtr', 'gtc', 'dgm', 'm14', 'ths', 'bng', |
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| | | 'jou']: |
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| | | for set_name in fetch_data.all_set_list: |
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| | | df = fetch_data.load_all_cards_text('data/csv/%s.csv' % set_name) |
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| | | for _ in range(3): |
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| | | card_info = df.iloc[random.randint(0, df.shape[0] - 1)] |
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| | | if card_img is None: |
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| | | fetch_data.fetch_card_image(card_info, out_dir='../usb/data/png/%s' % card_info['set']) |
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| | | card_img = cv2.imread(img_name) |
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| | | apply_bounding_box(card_img, card_info, display=True) |
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| | | detected_object_list = apply_bounding_box(card_img, card_info, display=True) |
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| | | print(detected_object_list) |
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| | | return |
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