~speedprog/mtg/mtg_card_detector.git

			@@ -1,20 +1,24 @@
			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
			from glob import glob
			import math
			import matplotlib.pyplot as plt
			import matplotlib.image as mpimage
			import numpy as np
			import os
			import pandas as pd
			from transform_data import ExtractedObject
			import pickle
			import random
			import transform_data

			# Referenced from geaxgx's playing-card-detection: https://github.com/geaxgx/playing-card-detection
			from config import Config


			class Backgrounds:
			"""
			Container class for all background images for generator
			Referenced from geaxgx's playing-card-detection: https://github.com/geaxgx/playing-card-detection
			"""
			def __init__(self, images=None, dumps_dir='data/dtd/images'):
			if images is not None:
			self._images = images
			@@ -40,8 +44,15 @@


			def load_dtd(dtd_dir='data/dtd/images', dump_it=True, dump_batch_size=1000):
			"""
			Load Describable Texture Dataset (DTD) from local
			:param dtd_dir: path of the DTD images folder
			:param dump_it: flag for pickling it
			:param dump_batch_size: # of images stored per pickle file
			:return: list of all DTD images
			"""
			if not os.path.exists(dtd_dir):
			print('Warning: directory for DTD 5s doesn\'t exist.' % dtd_dir)
			print('Warning: directory for DTD %s 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 []
			@@ -64,143 +75,54 @@


			def apply_bounding_box(img, card_info, display=False):
			"""
			Given a card image, extract specific features that can be used to train a model.
			Note: Mana & set symbols are deprecated from the feature list. Refer to previous commits for their implementation:
			https://github.com/hj3yoo/mtg_card_detector/tree/bb34d4e13da0f4753fbdefee837f54b16149d3ef
			:param img: image of the card
			:param card_info: characteristics of this card
			:param display: flag for displaying the extracted features
			:return:
			"""
			# List of detected objects to be fed into the neural net
			# The first object is the entire card
			detected_object_list = [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(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(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
			detected_object_list = [transform_data.ExtractedObject('card', [(0, 0), (len(img[0]), 0), (len(img[0]), len(img)),
			(0, len(img))])]
			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 ['8ed', 'mrd', 'dst', '5dn', 'chk', 'bok', 'sok', '9ed', 'rav', 'gpt', 'dis', 'csp', 'tsp', 'plc',
			'fut', '10e', 'lrw', 'mor', 'shm', 'eve', 'ala', 'con', 'arb', 'm10', 'zen', 'wwk', 'roe', 'm11',
			'som', 'mbs', 'nph', 'm12', 'isd', 'dka', 'avr', 'm13', 'rtr', 'gtc', 'dgm', 'm14', 'ths', 'bng',
			'jou']:
			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)
			for set_name in Config.all_set_list:
			df = fetch_data.load_all_cards_text('%s/csv/%s.csv' % (Config.data_dir, 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']))
			img_name = '%s/card_img/png/%s/%s_%s.png' % (Config.data_dir, 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:
			@@ -208,6 +130,7 @@
			card_img = cv2.imread(img_name)
			detected_object_list = apply_bounding_box(card_img, card_info, display=True)
			print(detected_object_list)

			return