From 0dab894a5be9f7d10d85e89dea91d02c71bae84d Mon Sep 17 00:00:00 2001
From: Edmond Yoo <hj3yoo@uwaterloo.ca>
Date: Sun, 16 Sep 2018 03:24:45 +0000
Subject: [PATCH] Moving files from MTGCardDetector repo
---
generate_data.py | 229 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++
1 files changed, 229 insertions(+), 0 deletions(-)
diff --git a/generate_data.py b/generate_data.py
new file mode 100644
index 0000000..7a2ce87
--- /dev/null
+++ b/generate_data.py
@@ -0,0 +1,229 @@
+from glob import glob
+import matplotlib.pyplot as plt
+import matplotlib.image as mpimage
+import pickle
+import math
+import random
+import os
+import re
+import cv2
+import fetch_data
+import sys
+import numpy as np
+import pandas as pd
+import transform_data
+
+# Referenced from geaxgx's playing-card-detection: https://github.com/geaxgx/playing-card-detection
+class Backgrounds:
+ def __init__(self, images=None, dumps_dir='data/dtd/images'):
+ if images is not None:
+ self._images = images
+ else: # load from pickle
+ if not os.path.exists(dumps_dir):
+ print('Warning: directory for dump %s doesn\'t exist' % dumps_dir)
+ return
+ self._images = []
+ for dump_name in glob(dumps_dir + '/*.pck'):
+ with open(dump_name, 'rb') as dump:
+ print('Loading ' + dump_name)
+ images = pickle.load(dump)
+ self._images += images
+ if len(self._images) == 0:
+ self._images = load_dtd()
+ print('# of images loaded: %d' % len(self._images))
+
+ def get_random(self, display=False):
+ bg = self._images[random.randint(0, len(self._images) - 1)]
+ if display:
+ plt.show(bg)
+ return bg
+
+
+def load_dtd(dtd_dir='data/dtd/images', dump_it=True, dump_batch_size=1000):
+ if not os.path.exists(dtd_dir):
+ print('Warning: directory for DTD 5s doesn\'t exist.' % dtd_dir)
+ print('You can download the dataset using this command:'
+ '!wget https://www.robots.ox.ac.uk/~vgg/data/dtd/download/dtd-r1.0.1.tar.gz')
+ return []
+ bg_images = []
+ # Search the directory for all images, and append them
+ for subdir in glob(dtd_dir + "/*"):
+ for f in glob(subdir + "/*.jpg"):
+ bg_images.append(mpimage.imread(f))
+ print("# of images loaded :", len(bg_images))
+
+ # Save them as a pickle if necessary
+ if dump_it:
+ for i in range(math.ceil(len(bg_images) / dump_batch_size)):
+ dump_name = '%s/dtd_dump_%d.pck' % (dtd_dir, i)
+ with open(dump_name, 'wb') as dump:
+ print('Dumping ' + dump_name)
+ pickle.dump(bg_images[i * dump_batch_size:(i + 1) * dump_batch_size], dump)
+
+ return bg_images
+
+
+def apply_bounding_box(img, card_info, display=False):
+ # List of detected objects to be fed into the neural net
+ # The first object is the entire card
+ detected_object_list = [transform_data.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
+ if has_mana_cost:
+ mana_cost = re.findall('\{(.*?)\}', card_info['mana_cost'])
+ x_anchor = 683
+ y_anchor = 65
+
+ # Cards with specific type or from old sets have their symbol at a different position
+ if card_info['set'] in ['8ed', 'mrd', 'dst', '5dn']:
+ y_anchor -= 2
+
+ for i in reversed(range(len(mana_cost))):
+ # Hybrid mana symbol are larger than a normal symbol
+ is_hybrid = '/' in mana_cost[i]
+ if is_hybrid:
+ x1 = x_anchor - 47
+ x2 = x_anchor + 2
+ y1 = y_anchor - 8
+ y2 = y_anchor + 43
+ x_anchor -= 45
+ else:
+ x1 = x_anchor - 39
+ x2 = x_anchor
+ y1 = y_anchor
+ y2 = y_anchor + 43
+ x_anchor -= 37
+ # Append them to the list of bounding box with the appropriate label
+ symbol_name = 'mana_symbol:' + mana_cost[i]
+ key_pts = [(x1, y1), (x2, y1), (x2, y2), (x1, y2)]
+ detected_object_list.append(transform_data.ExtractedObject(symbol_name, key_pts))
+
+ if display:
+ img_symbol = img[y1:y2, x1:x2]
+ cv2.imshow('symbol', img_symbol)
+ cv2.waitKey(0)
+
+ # Set symbol - located on the right side of the type box in the centre of the card, next to the card type
+ # Only one symbol exists, and its colour varies by rarity.
+ if card_info['set'] in ['8ed']:
+ x1 = 622
+ x2 = 670
+ elif card_info['set'] in ['mrd', 'm10', 'm11', 'm12', 'm13', 'm14']:
+ x1 = 602
+ x2 = 684
+ elif card_info['set'] in ['dst']:
+ x1 = 636
+ x2 = 673
+ elif card_info['set'] in ['5dn']:
+ x1 = 630
+ x2 = 675
+ elif card_info['set'] in ['bok', 'rtr']:
+ x1 = 633
+ x2 = 683
+ elif card_info['set'] in ['sok', 'mbs']:
+ x1 = 638
+ x2 = 683
+ elif card_info['set'] in ['rav']:
+ x1 = 640
+ x2 = 678
+ elif card_info['set'] in ['csp']:
+ x1 = 650
+ x2 = 683
+ elif card_info['set'] in ['tsp', 'lrw', 'zen', 'wwk', 'ths']:
+ x1 = 640
+ x2 = 683
+ elif card_info['set'] in ['plc', 'fut', 'shm', 'eve']:
+ x1 = 625
+ x2 = 685
+ elif card_info['set'] in ['10e']:
+ x1 = 623
+ x2 = 680
+ elif card_info['set'] in ['mor', 'roe', 'bng']:
+ x1 = 637
+ x2 = 687
+ elif card_info['set'] in ['ala', 'arb']:
+ x1 = 635
+ x2 = 680
+ elif card_info['set'] in ['nph']:
+ x1 = 642
+ x2 = 678
+ elif card_info['set'] in ['gtc']:
+ x1 = 610
+ x2 = 683
+ elif card_info['set'] in ['dgm']:
+ x1 = 618
+ x2 = 678
+ else:
+ x1 = 630
+ x2 = 683
+ y1 = 589
+ y2 = 636
+ # Append them to the list of bounding box with the appropriate label
+ symbol_name = 'set_symbol:' + card_info['set']
+ key_pts = [(x1, y1), (x2, y1), (x2, y2), (x1, y2)]
+ detected_object_list.append(transform_data.ExtractedObject(symbol_name, key_pts))
+
+ if display:
+ img_symbol = img[y1:y2, x1:x2]
+ cv2.imshow('symbol', img_symbol)
+ cv2.waitKey(0)
+
+ # Name box - The long bar on the top with card name and mana symbols
+ # TODO
+
+ # Type box - The long bar on the middle with card type and set symbols
+ # TODO
+
+ # Image box - the large image on the top half of the card
+ # TODO
+ '''
+ return detected_object_list
+
+
+def main():
+ random.seed()
+ #bg_images = load_dtd()
+ #bg = Backgrounds()
+ #bg.get_random(display=True)
+
+ 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)
+ 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'],
+ fetch_data.get_valid_filename(card_info['name']))
+ print(img_name)
+ card_img = cv2.imread(img_name)
+ if card_img is None:
+ fetch_data.fetch_card_image(card_info, out_dir='../usb/data/png/%s' % card_info['set'])
+ card_img = cv2.imread(img_name)
+ detected_object_list = apply_bounding_box(card_img, card_info, display=True)
+ print(detected_object_list)
+
+ return
+
+
+if __name__ == '__main__':
+ main()
--
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