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| | | import cv2 |
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| | | import fetch_data |
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| | | from glob import glob |
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| | | import math |
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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 numpy as np |
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| | | import os |
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| | | import pandas as pd |
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| | | import pickle |
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| | | import random |
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| | | import transform_data |
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| | | |
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| | | from config import Config |
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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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| | | """ |
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| | | Container class for all background images for generator |
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| | | Referenced from geaxgx's playing-card-detection: https://github.com/geaxgx/playing-card-detection |
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| | | """ |
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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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| | | |
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| | | def load_dtd(dtd_dir='data/dtd/images', dump_it=True, dump_batch_size=1000): |
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| | | """ |
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| | | Load Describable Texture Dataset (DTD) from local |
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| | | :param dtd_dir: path of the DTD images folder |
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| | | :param dump_it: flag for pickling it |
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| | | :param dump_batch_size: # of images stored per pickle file |
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| | | :return: list of all DTD images |
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| | | """ |
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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('Warning: directory for DTD %s 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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| | | return bg_images |
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| | | |
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| | | def apply_bounding_box(img, card_info, display=False): |
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| | | """ |
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| | | Given a card image, extract specific features that can be used to train a model. |
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| | | Note: Mana & set symbols are deprecated from the feature list. Refer to previous commits for their implementation: |
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| | | https://github.com/hj3yoo/mtg_card_detector/tree/bb34d4e13da0f4753fbdefee837f54b16149d3ef |
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| | | :param img: image of the card |
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| | | :param card_info: characteristics of this card |
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| | | :param display: flag for displaying the extracted features |
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| | | :return: |
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| | | """ |
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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 = [transform_data.ExtractedObject('card', [(0, 0), (len(img[0]), 0), (len(img[0]), len(img)), |
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| | | (0, len(img))])] |
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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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| | | #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 Config.all_set_list: |
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| | | df = fetch_data.load_all_cards_text('%s/csv/%s.csv' % (Config.data_dir, 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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| | | card_pool = card_pool.append(df) |
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| | | ''' |
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| | | print(card_pool) |
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| | | mana_symbol_set = set() |
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| | | for _, card_info in card_pool.iterrows(): |
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| | | has_mana_cost = isinstance(card_info['mana_cost'], str) |
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| | | if has_mana_cost: |
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| | | mana_cost = re.findall('\{(.*?)\}', card_info['mana_cost']) |
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| | | for symbol in mana_cost: |
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| | | mana_symbol_set.add(symbol) |
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| | | |
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| | | print(mana_symbol_set) |
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| | | ''' |
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| | | |
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| | | for _, card_info in card_pool.iterrows(): |
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| | | img_name = '%s/card_img/png/%s/%s_%s.png' % (Config.data_dir, 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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| | | |
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| | | return |
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