From 6968d3d0574e346ae5d14b16d0a644ff1894659f Mon Sep 17 00:00:00 2001
From: SpeedProg <speedprog@googlemail.com>
Date: Fri, 06 Sep 2019 17:31:44 +0000
Subject: [PATCH] made set hash be 64 bit instead of same as full card hash
---
generate_data.py | 191 ++++++++++++++---------------------------------
1 files changed, 58 insertions(+), 133 deletions(-)
diff --git a/generate_data.py b/generate_data.py
index 54f4d4d..4131410 100644
--- a/generate_data.py
+++ b/generate_data.py
@@ -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 []
@@ -64,119 +75,19 @@
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
@@ -187,18 +98,31 @@
#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)
+ 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:
@@ -206,6 +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
--
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