from glob import glob
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import matplotlib.pyplot as plt
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import matplotlib.image as mpimage
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import pickle
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import math
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import random
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import os
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import re
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import cv2
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import fetch_data
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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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# 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 __init__(self, images=None, dumps_dir='data/dtd/images'):
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if images is not None:
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self._images = images
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else: # load from pickle
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if not os.path.exists(dumps_dir):
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print('Warning: directory for dump %s doesn\'t exist' % dumps_dir)
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return
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self._images = []
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for dump_name in glob(dumps_dir + '/*.pck'):
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with open(dump_name, 'rb') as dump:
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print('Loading ' + dump_name)
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images = pickle.load(dump)
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self._images += images
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if len(self._images) == 0:
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self._images = load_dtd()
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print('# of images loaded: %d' % len(self._images))
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def get_random(self, display=False):
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bg = self._images[random.randint(0, len(self._images) - 1)]
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if display:
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plt.show(bg)
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return bg
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def load_dtd(dtd_dir='data/dtd/images', dump_it=True, dump_batch_size=1000):
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if not os.path.exists(dtd_dir):
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print('Warning: directory for DTD 5s doesn\'t exist.' % dtd_dir)
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print('You can download the dataset using this command:'
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'!wget https://www.robots.ox.ac.uk/~vgg/data/dtd/download/dtd-r1.0.1.tar.gz')
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return []
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bg_images = []
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# Search the directory for all images, and append them
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for subdir in glob(dtd_dir + "/*"):
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for f in glob(subdir + "/*.jpg"):
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bg_images.append(mpimage.imread(f))
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print("# of images loaded :", len(bg_images))
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# Save them as a pickle if necessary
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if dump_it:
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for i in range(math.ceil(len(bg_images) / dump_batch_size)):
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dump_name = '%s/dtd_dump_%d.pck' % (dtd_dir, i)
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with open(dump_name, 'wb') as dump:
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print('Dumping ' + dump_name)
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pickle.dump(bg_images[i * dump_batch_size:(i + 1) * dump_batch_size], dump)
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return bg_images
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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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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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y_anchor -= 2
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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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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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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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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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# 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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if card_info['set'] in ['8ed']:
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x1 = 622
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x2 = 670
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elif card_info['set'] in ['mrd', 'm10', 'm11', 'm12', 'm13', 'm14']:
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x1 = 602
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x2 = 684
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elif card_info['set'] in ['dst']:
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x1 = 636
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x2 = 673
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elif card_info['set'] in ['5dn']:
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x1 = 630
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x2 = 675
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elif card_info['set'] in ['bok', 'rtr']:
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x1 = 633
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x2 = 683
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elif card_info['set'] in ['sok', 'mbs']:
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x1 = 638
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x2 = 683
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elif card_info['set'] in ['rav']:
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x1 = 640
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x2 = 678
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elif card_info['set'] in ['csp']:
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x1 = 650
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x2 = 683
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elif card_info['set'] in ['tsp', 'lrw', 'zen', 'wwk', 'ths']:
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x1 = 640
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x2 = 683
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elif card_info['set'] in ['plc', 'fut', 'shm', 'eve']:
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x1 = 625
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x2 = 685
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elif card_info['set'] in ['10e']:
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x1 = 623
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x2 = 680
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elif card_info['set'] in ['mor', 'roe', 'bng']:
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x1 = 637
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x2 = 687
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elif card_info['set'] in ['ala', 'arb']:
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x1 = 635
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x2 = 680
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elif card_info['set'] in ['nph']:
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x1 = 642
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x2 = 678
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elif card_info['set'] in ['gtc']:
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x1 = 610
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x2 = 683
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elif card_info['set'] in ['dgm']:
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x1 = 618
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x2 = 678
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else:
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x1 = 630
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x2 = 683
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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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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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# Name box - The long bar on the top with card name and mana symbols
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# TODO
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# Type box - The long bar on the middle with card type and set symbols
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# TODO
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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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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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card_pool = pd.DataFrame()
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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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# Currently ignoring planeswalker cards due to their different card layout
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is_planeswalker = 'Planeswalker' in card_info['type_line']
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if not is_planeswalker:
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card_pool = card_pool.append(card_info)
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for _, card_info in card_pool.iterrows():
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img_name = '../usb/data/png/%s/%s_%s.png' % (card_info['set'], card_info['collector_number'],
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fetch_data.get_valid_filename(card_info['name']))
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print(img_name)
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card_img = cv2.imread(img_name)
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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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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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if __name__ == '__main__':
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main()
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