From b95bf33cb5b296efb70a0c4b1c82c0f62286f52a Mon Sep 17 00:00:00 2001
From: Constantin Wenger <constantin.wenger@googlemail.com>
Date: Thu, 03 Feb 2022 20:18:17 +0000
Subject: [PATCH] added options to flip/rotate and specify different input resolutions also fixed displayed image to max 800x800 everything above will be scaled while keeping aspect ratio
---
generate_data.py | 84 ++++++++++++++++++++++++++++++++++++++---
1 files changed, 77 insertions(+), 7 deletions(-)
diff --git a/generate_data.py b/generate_data.py
index a6f1f26..4131410 100644
--- a/generate_data.py
+++ b/generate_data.py
@@ -1,14 +1,24 @@
+import cv2
+import fetch_data
from glob import glob
+import math
import matplotlib.pyplot as plt
import matplotlib.image as mpimage
-import pickle
-import math
-import random
+import numpy as np
import os
+import pandas as pd
+import pickle
+import random
+import transform_data
+
+from config import Config
-# Referenced from geaxgx's playing-card-detection: https://github.com/geaxgx/playing-card-detection
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
@@ -34,8 +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 []
@@ -57,10 +74,63 @@
return bg_images
+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 = [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)
+ #bg = Backgrounds()
+ #bg.get_random(display=True)
+
+ card_pool = pd.DataFrame()
+ 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 = '%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:
+ 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
--
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