From 5a47c46b39475fc3581b9819f488b977ea1beca3 Mon Sep 17 00:00:00 2001
From: Edmond Yoo <hj3yoo@uwaterloo.ca>
Date: Sun, 16 Sep 2018 03:11:04 +0000
Subject: [PATCH] Moving files from MTGCardDetector
---
moving/test_files/test17.jpg | 0
moving/test_files/test25.jpg | 0
moving/test_files/test5.jpg | 0
moving/test_files/test18.jpg | 0
moving/test_files/test22.png | 0
moving/test_files/hand_of_card_ktk.png | 0
moving/test_files/test4.jpg | 0
moving/setup_train.py | 28 +
moving/test_files/test26.jpg | 0
moving/test_files/test10.jpg | 0
moving/test_files/test23.jpg | 0
moving/test_files/hand_of_card_tron.png | 0
moving/test_files/test1_yolo_out_py.jpg | 0
moving/test_files/image_orig.jpg | 0
moving/test_files/pro_tour_table.png | 0
moving/test_files/test1.jpg | 0
moving/test_files/rtr-174-jarad-golgari-lich-lord.jpg | 0
moving/test_files/test2.mp4 | 0
moving/test_files/test3.jpg | 0
moving/test_files/li38_handOfCards.jpg | 0
moving/test_files/C-12-26-2016-MTG-Klomparens-Article-Images-Hand-2.png | 0
moving/test_files/test19.jpg | 0
moving/test_files/frilly_0007.jpg | 0
moving/test_files/test1.mp4 | 0
moving/test_files/test24.jpg | 0
moving/test_files/hand_of_card_1.png | 0
moving/test_files/test11.jpg | 0
moving/test_files/test2.jpg | 0
moving/test_files/hand_of_card_new_frame_1.webp | 0
moving/test_files/test9.jpg | 0
moving/test_files/handOfCards.jpg | 0
moving/test_files/test21.jpg | 0
moving/test_files/tilted_card_2.jpg | 0
moving/test_files/pro_tour_side.png | 0
moving/test_files/mask.png | 0
moving/test_files/test12.jpg | 0
moving/card_detector.py | 124 ++++++
moving/test_files/c16-143-burgeoning.png | 0
moving/fetch_data.py | 107 +++++
moving/test_files/cn2-78-queen-marchesa.png | 0
moving/test_files/test8.jpg | 0
moving/generate_data.py | 229 ++++++++++++
moving/test_files/hand_of_card_green_2.jpeg | 0
moving/test_files/test13.jpg | 0
moving/test_files/tilted_card_1.jpg | 0
moving/test_files/hand_of_card_red.jpeg | 0
moving/test_files/card_in_plastic_case.jpg | 0
moving/test_files/test15.jpg | 0
moving/test_files/test7.jpg | 0
moving/test_files/hand_of_card_one_hand.jpg | 0
moving/test_files/test.jpg | 0
moving/test_files/s-l300.jpg | 0
moving/test_files/test14.jpg | 0
moving/test_files/test27.jpg | 0
moving/test_files/hand_of_card_easy.jpg | 0
moving/test_files/hand_of_card_green_1.jpg | 0
moving/test_files/test16.jpg | 0
moving/test_files/test20.jpg | 0
moving/test_files/test6.jpg | 0
moving/transform_data.py | 567 +++++++++++++++++++++++++++++++
60 files changed, 1,055 insertions(+), 0 deletions(-)
diff --git a/moving/card_detector.py b/moving/card_detector.py
new file mode 100644
index 0000000..aa8bd6a
--- /dev/null
+++ b/moving/card_detector.py
@@ -0,0 +1,124 @@
+import cv2
+import numpy as np
+import pandas as pd
+import math
+from screeninfo import get_monitors
+
+
+def detect_a_card(img, thresh_val=80, blur_radius=None, dilate_radius=None, min_hyst=80, max_hyst=200,
+ min_line_length=None, max_line_gap=None, debug=False):
+ dim_img = (len(img[0]), len(img)) # (width, height)
+ # Intermediate variables
+
+ # Default values
+ if blur_radius is None:
+ blur_radius = math.floor(min(dim_img) / 100 + 0.5) // 2 * 2 + 1 # Rounded to the nearest odd
+ if dilate_radius is None:
+ dilate_radius = math.floor(min(dim_img) / 67 + 0.5)
+ if min_line_length is None:
+ min_line_length = min(dim_img) / 10
+ if max_line_gap is None:
+ max_line_gap = min(dim_img) / 10
+
+ thresh_radius = math.floor(min(dim_img) / 20 + 0.5) // 2 * 2 + 1 # Rounded to the nearest odd
+
+ img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+ # Median blur better removes background textures than Gaussian blur
+ img_blur = cv2.medianBlur(img_gray, blur_radius)
+ # Truncate the bright area while detecting the border
+ img_thresh = cv2.adaptiveThreshold(img_blur, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
+ cv2.THRESH_BINARY_INV, thresh_radius, 20)
+ #_, img_thresh = cv2.threshold(img_blur, thresh_val, 255, cv2.THRESH_TRUNC)
+
+ # Dilate the image to emphasize thick borders around the card
+ kernel_dilate = np.ones((dilate_radius, dilate_radius), np.uint8)
+ #img_dilate = cv2.dilate(img_thresh, kernel_dilate, iterations=1)
+ img_dilate = cv2.erode(img_thresh, kernel_dilate, iterations=1)
+
+ img_contour = img_dilate.copy()
+ _, contours, _ = cv2.findContours(img_contour, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+ img_contour = cv2.cvtColor(img_contour, cv2.COLOR_GRAY2BGR)
+ img_contour = cv2.drawContours(img_contour, contours, -1, (128, 128, 128), 1)
+ card_found = contours is not None
+ print(len(contours))
+ print([len(contour) for contour in contours])
+
+ # find the biggest area
+ c = max(contours, key=cv2.contourArea)
+
+ x, y, w, h = cv2.boundingRect(c)
+ # draw the book contour (in green)
+ img_contour = cv2.drawContours(img_contour, [c], -1, (0, 255, 0), 1)
+
+ # Canny edge - low minimum hysteresis to detect glowed area,
+ # and high maximum hysteresis to compensate for high false positives.
+ img_canny = cv2.Canny(img_dilate, min_hyst, max_hyst)
+ #img_canny = img_dilate
+ # Apply Hough transformation to detect the edges
+ detected_lines = cv2.HoughLinesP(img_dilate, 1, np.pi / 180, threshold=60,
+ minLineLength=min_line_length,
+ maxLineGap=max_line_gap)
+ card_found = detected_lines is not None
+ print(len(detected_lines))
+
+ if card_found:
+ if debug:
+ img_hough = cv2.cvtColor(img_dilate.copy(), cv2.COLOR_GRAY2BGR)
+ for line in detected_lines:
+ x1, y1, x2, y2 = line[0]
+ cv2.line(img_hough, (x1, y1), (x2, y2), (0, 0, 255), 1)
+ elif not debug:
+ print('Hough couldn\'t find any lines')
+
+ # Debug: display intermediate results from various steps
+ if debug:
+ img_blank = np.zeros((len(img), len(img[0]), 3), np.uint8)
+ img_thresh = cv2.cvtColor(img_thresh, cv2.COLOR_GRAY2BGR)
+ img_dilate = cv2.cvtColor(img_dilate, cv2.COLOR_GRAY2BGR)
+ #img_canny = cv2.cvtColor(img_canny, cv2.COLOR_GRAY2BGR)
+ if not card_found:
+ img_hough = img_blank
+
+ # Append all images together
+ img_row_1 = np.concatenate((img, img_thresh), axis=1)
+ img_row_2 = np.concatenate((img_contour, img_hough), axis=1)
+ img_result = np.concatenate((img_row_1, img_row_2), axis=0)
+
+ # Resize the final image to fit into the main monitor's resolution
+ screen_size = get_monitors()[0]
+ resize_ratio = max(len(img_result[0]) / screen_size.width, len(img_result) / screen_size.height, 1)
+ img_result = cv2.resize(img_result, (int(len(img_result[0]) // resize_ratio),
+ int(len(img_result) // resize_ratio)))
+ cv2.imshow('Result', img_result)
+ cv2.waitKey(0)
+
+ # TODO: output meaningful data
+ return card_found
+
+def main():
+ img_test = cv2.imread('data/li38_handOfCards.jpg')
+ card_found = detect_a_card(img_test,
+ #dilate_radius=5,
+ #thresh_val=100,
+ #min_hyst=40,
+ #max_hyst=160,
+ #min_line_length=50,
+ #max_line_gap=100,
+ debug=True)
+ if card_found:
+ return
+ return
+ for dilate_radius in range(1, 6):
+ for min_hyst in range(50, 91, 10):
+ for max_hyst in range(180, 119, -20):
+ print('dilate_radius=%d, min_hyst=%d, max_hyst=%d: ' % (dilate_radius, min_hyst, max_hyst),
+ end='', flush=True)
+ card_found = detect_a_card(img_test, dilate_radius=dilate_radius,
+ min_hyst=min_hyst, max_hyst=max_hyst, debug=True)
+ if card_found:
+ print('Card found')
+ else:
+ print('Not found')
+
+if __name__ == '__main__':
+ main()
diff --git a/moving/fetch_data.py b/moving/fetch_data.py
new file mode 100644
index 0000000..221e16c
--- /dev/null
+++ b/moving/fetch_data.py
@@ -0,0 +1,107 @@
+from urllib import request
+import ast
+import json
+import pandas as pd
+import re
+import os
+import transform_data
+import time
+
+all_set_list = ['cmd', 'bfz', 'all', 'ulg',
+ 'mrd', 'dst', '5dn', 'chk', 'bok', 'sok', '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']
+
+
+def fetch_all_cards_text(url='https://api.scryfall.com/cards/search?q=layout:normal+format:modern+lang:en+frame:2003',
+ csv_name=''):
+ has_more = True
+ cards = []
+ # get cards dataset as a json from the query
+ while has_more:
+ res_file_dir, http_message = request.urlretrieve(url)
+ with open(res_file_dir, 'r') as res_file:
+ res_json = json.loads(res_file.read())
+ cards += res_json['data']
+ has_more = res_json['has_more']
+ if has_more:
+ url = res_json['next_page']
+ print(len(cards))
+
+ # Convert them into a dataframe, and truncate unnecessary columns
+ df = pd.DataFrame.from_dict(cards)
+
+ if csv_name != '':
+ df = df[['artist', 'border_color', 'collector_number', 'color_identity', 'colors', 'flavor_text', 'image_uris',
+ 'mana_cost', 'legalities', 'name', 'oracle_text', 'rarity', 'type_line', 'set', 'set_name', 'power',
+ 'toughness']]
+ #df.to_json(csv_name)
+ df.to_csv(csv_name, sep=';') # Comma doesn't work, since some columns are saved as a dict
+
+ return df
+
+
+def load_all_cards_text(csv_name):
+ #with open(csv_name, 'r') as json_file:
+ # cards = json.loads(json_file.read())
+ #df = pd.DataFrame.from_dict(cards)
+ df = pd.read_csv(csv_name, sep=';')
+ return df
+
+
+# Pulled from Django framework (https://github.com/django/django/blob/master/django/utils/text.py)
+def get_valid_filename(s):
+ """
+ Return the given string converted to a string that can be used for a clean
+ filename. Remove leading and trailing spaces; convert other spaces to
+ underscores; and remove anything that is not an alphanumeric, dash,
+ underscore, or dot.
+ >>> get_valid_filename("john's portrait in 2004.jpg")
+ 'johns_portrait_in_2004.jpg'
+ """
+ s = str(s).strip().replace(' ', '_')
+ return re.sub(r'(?u)[^-\w.]', '', s)
+
+
+def fetch_all_cards_image(df, out_dir='', size='png'):
+ if isinstance(df, pd.Series):
+ fetch_card_image(df, out_dir, size)
+ else:
+ for ind, row in df.iterrows():
+ fetch_card_image(row, out_dir, size)
+
+
+def fetch_card_image(row, out_dir='', size='png'):
+ if isinstance(row['image_uris'], str): # For some reason, dict isn't being parsed in the previous step
+ png_url = ast.literal_eval(row['image_uris'])[size]
+ else:
+ png_url = row['image_uris'][size]
+ if out_dir == '':
+ out_dir = 'data/%s/%s' % (size, row['set'])
+ if not os.path.exists(out_dir):
+ os.makedirs(out_dir)
+ img_name = '%s/%s_%s.png' % (out_dir, row['collector_number'], get_valid_filename(row['name']))
+ if not os.path.isfile(img_name):
+ request.urlretrieve(png_url, filename=img_name)
+ print(img_name)
+
+
+def main():
+ for set_name in all_set_list:
+ csv_name = '%s/csv/%s.csv' % (transform_data.data_dir, set_name)
+ print(csv_name)
+ if not os.path.isfile(csv_name):
+ df = fetch_all_cards_text(url='https://api.scryfall.com/cards/search?q=set:%s+lang:en'
+ % set_name, csv_name=csv_name)
+ else:
+ df = load_all_cards_text(csv_name)
+ time.sleep(1)
+ #fetch_all_cards_image(df, out_dir='../usb/data/png/%s' % set_name)
+ #df = fetch_all_cards_text(url='https://api.scryfall.com/cards/search?q=layout:normal+lang:en+frame:2003',
+ # csv_name='data/csv/all.csv')
+ pass
+
+
+if __name__ == '__main__':
+ main()
+ pass
diff --git a/moving/generate_data.py b/moving/generate_data.py
new file mode 100644
index 0000000..7a2ce87
--- /dev/null
+++ b/moving/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()
diff --git a/moving/setup_train.py b/moving/setup_train.py
new file mode 100644
index 0000000..362be19
--- /dev/null
+++ b/moving/setup_train.py
@@ -0,0 +1,28 @@
+import os
+from glob import glob
+import random
+import transform_data
+
+
+def main():
+ random.seed()
+ data_list = []
+ for subdir in glob('%s/train/*_10' % transform_data.data_dir):
+ for data in glob(subdir + "/*.jpg"):
+ data_list.append(os.path.abspath(data))
+ random.shuffle(data_list)
+
+ test_ratio = 0.1
+ test_list = data_list[:int(test_ratio * len(data_list))]
+ train_list = data_list[int(test_ratio * len(data_list)):]
+ with open('%s/train_10.txt' % transform_data.darknet_dir, 'w') as train_txt:
+ for data in train_list:
+ train_txt.write(data + '\n')
+ with open('%s/test_10.txt' % transform_data.darknet_dir, 'w') as test_txt:
+ for data in test_list:
+ test_txt.write(data + '\n')
+ return
+
+
+if __name__ == '__main__':
+ main()
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diff --git a/moving/transform_data.py b/moving/transform_data.py
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@@ -0,0 +1,567 @@
+import os
+import random
+import math
+import cv2
+import numpy as np
+import imutils
+import pandas as pd
+import fetch_data
+import generate_data
+from shapely import geometry
+import pytesseract
+import imgaug as ia
+from imgaug import augmenters as iaa
+from imgaug import parameters as iap
+
+card_mask = cv2.imread('data/mask.png')
+data_dir = os.path.abspath('/media/win10/data')
+darknet_dir = os.path.abspath('darknet')
+
+
+def key_pts_to_yolo(key_pts, w_img, h_img):
+ """
+ Convert a list of keypoints into a yolo training format
+ :param key_pts: list of keypoints
+ :param w_img: width of the entire image
+ :param h_img: height of the entire image
+ :return: <x> <y> <width> <height>
+ """
+ x1 = max(0, min([pt[0] for pt in key_pts]))
+ x2 = min(w_img, max([pt[0] for pt in key_pts]))
+ y1 = max(0, min([pt[1] for pt in key_pts]))
+ y2 = min(h_img, max([pt[1] for pt in key_pts]))
+ x = (x2 + x1) / 2 / w_img
+ y = (y2 + y1) / 2 / h_img
+ width = (x2 - x1) / w_img
+ height = (y2 - y1) / h_img
+ return x, y, width, height
+
+
+class ImageGenerator:
+ """
+ A template for generating a training image.
+ """
+ def __init__(self, img_bg, class_ids, width, height, skew=None, cards=None):
+ """
+ :param img_bg: background (textile) image
+ :param width: width of the training image
+ :param height: height of the training image
+ :param skew: 4 coordinates that indicates the corners (in normalized form) for perspective transform
+ :param cards: list of Card objects
+ """
+ self.img_bg = img_bg
+ self.class_ids = class_ids
+ self.img_result = None
+ self.width = width
+ self.height = height
+ if cards is None:
+ self.cards = []
+ else:
+ self.cards = cards
+
+ # Compute transform matrix for perspective transform
+ if skew is not None:
+ orig_corner = np.array([[0, 0], [0, height], [width, height], [width, 0]], dtype=np.float32)
+ new_corner = np.array([[width * s[0], height * s[1]] for s in skew], dtype=np.float32)
+ self.M = cv2.getPerspectiveTransform(orig_corner, new_corner)
+ pass
+ else:
+ self.M = None
+ pass
+
+ def add_card(self, card, x=None, y=None, theta=0.0, scale=1.0):
+ """
+ Add a card to this generator scenario.
+ :param card: card to be added
+ :param x: new X-coordinate for the centre of the card
+ :param y: new Y-coordinate for the centre of the card
+ :param theta: new angle for the card
+ :param scale: new scale for the card
+ :return: none
+ """
+ if x is None:
+ x = -len(card.img[0]) / 2
+ if y is None:
+ y = -len(card.img) / 2
+ self.cards.append(card)
+ card.x = x
+ card.y = y
+ card.theta = theta
+ card.scale = scale
+ pass
+
+ def render(self, visibility=0.5, display=False, debug=False, aug=None):
+ """
+ Display the current state of the generator
+ :return: none
+ """
+ self.check_visibility(visibility=visibility)
+ #img_result = cv2.resize(self.img_bg, (self.width, self.height))
+ img_result = np.zeros((self.height, self.width, 3), dtype=np.uint8)
+
+ for card in self.cards:
+ if card.x == 0.0 and card.y == 0.0 and card.theta == 0.0 and card.scale == 1.0:
+ continue
+ card_x = int(card.x + 0.5)
+ card_y = int(card.y + 0.5)
+ #print(card_x, card_y, card.theta, card.scale)
+
+ # Scale & rotate card image
+ img_card = cv2.resize(card.img, (int(len(card.img[0]) * card.scale), int(len(card.img) * card.scale)))
+ if aug is not None:
+ seq = iaa.Sequential([
+ iaa.SimplexNoiseAlpha(first=iaa.Add(random.randrange(128)), size_px_max=[1, 3],
+ upscale_method="cubic"), # Lighting
+ ])
+ img_card = seq.augment_image(img_card)
+ mask_scale = cv2.resize(card_mask, (int(len(card_mask[0]) * card.scale), int(len(card_mask) * card.scale)))
+ img_mask = cv2.bitwise_and(img_card, mask_scale)
+ img_rotate = imutils.rotate_bound(img_mask, card.theta / math.pi * 180)
+
+ # Calculate the position of the card image in relation to the background
+ # Crop the card image if it's out of boundary
+ card_w = len(img_rotate[0])
+ card_h = len(img_rotate)
+ card_crop_x1 = max(0, card_w // 2 - card_x)
+ card_crop_x2 = min(card_w, card_w // 2 + len(img_result[0]) - card_x)
+ card_crop_y1 = max(0, card_h // 2 - card_y)
+ card_crop_y2 = min(card_h, card_h // 2 + len(img_result) - card_y)
+ img_card_crop = img_rotate[card_crop_y1:card_crop_y2, card_crop_x1:card_crop_x2]
+
+ # Calculate the position of the corresponding area in the background
+ bg_crop_x1 = max(0, card_x - (card_w // 2))
+ bg_crop_x2 = min(len(img_result[0]), int(card_x + (card_w / 2) + 0.5))
+ bg_crop_y1 = max(0, card_y - (card_h // 2))
+ bg_crop_y2 = min(len(img_result), int(card_y + (card_h / 2) + 0.5))
+ img_result_crop = img_result[bg_crop_y1:bg_crop_y2, bg_crop_x1:bg_crop_x2]
+
+ # Override the background with the current card
+ img_result_crop = np.where(img_card_crop, img_card_crop, img_result_crop)
+ img_result[bg_crop_y1:bg_crop_y2, bg_crop_x1:bg_crop_x2] = img_result_crop
+
+ if debug:
+ for ext_obj in card.objects:
+ if ext_obj.visible:
+ for pt in ext_obj.key_pts:
+ cv2.circle(img_result, card.coordinate_in_generator(pt[0], pt[1]), 2, (1, 1, 255), 10)
+ bounding_box = card.bb_in_generator(ext_obj.key_pts)
+ cv2.rectangle(img_result, bounding_box[0], bounding_box[2], (1, 255, 1), 5)
+
+ '''
+ try:
+ text = pytesseract.image_to_string(img_result, output_type=pytesseract.Output.DICT)
+ print(text)
+ except pytesseract.pytesseract.TesseractError:
+ pass
+ '''
+ img_result = cv2.GaussianBlur(img_result, (5, 5), 0)
+
+ if self.M is not None:
+ img_result = cv2.warpPerspective(img_result, self.M, (self.width, self.height))
+ if debug:
+ for card in self.cards:
+ for ext_obj in card.objects:
+ if ext_obj.visible:
+ new_pts = np.array([[list(card.coordinate_in_generator(pt[0], pt[1]))]
+ for pt in ext_obj.key_pts], dtype=np.float32)
+ new_pts = cv2.perspectiveTransform(new_pts, self.M)
+ for pt in new_pts:
+ cv2.circle(img_result, (pt[0][0], pt[0][1]), 2, (255, 1, 1), 10)
+
+ img_bg = cv2.resize(self.img_bg, (self.width, self.height))
+ img_result = np.where(img_result, img_result, img_bg)
+
+ if aug is not None:
+ img_result = aug.augment_image(img_result)
+
+ if display:
+ cv2.imshow('Result', img_result)
+ cv2.waitKey(0)
+
+ self.img_result = img_result
+ pass
+
+ def generate_horizontal_span(self, gap=None, scale=None, theta=0, shift=None, jitter=None):
+ """
+ Generating the first scenario where the cards are laid out in a straight horizontal line
+ :return: True if successfully generated, otherwise False
+ """
+ # Set scale of the cards, variance of shift & jitter to be applied if they're not given
+ card_size = (len(self.cards[0].img[0]), len(self.cards[0].img))
+ if scale is None:
+ # Scale the cards so that card takes about 50% of the image's height
+ coverage_ratio = 0.5
+ scale = self.height * coverage_ratio / card_size[1]
+ if shift is None:
+ # Plus minus 5% of the card's height
+ shift = [-card_size[1] * scale * 0.05, card_size[1] * scale * 0.05]
+ pass
+ if jitter is None:
+ jitter = [-math.pi / 18, math.pi / 18] # Plus minus 10 degrees
+ if gap is None:
+ # 25% of the card's width - set symbol and 1-2 mana symbols will be visible on each card
+ gap = card_size[0] * scale * 0.4
+
+ # Determine the location of the first card
+ # The cards will cover (width of a card + (# of cards - 1) * gap) pixels wide and (height of a card) pixels high
+ x_anchor = int(self.width / 2 + (len(self.cards) - 1) * gap / 2)
+ y_anchor = self.height // 2
+ for card in self.cards:
+ card.scale = scale
+ card.x = x_anchor
+ card.y = y_anchor
+ card.theta = 0
+ card.shift(shift, shift)
+ card.rotate(jitter)
+ card.rotate(theta, centre=(self.width // 2 - x_anchor, self.height // 2 - y_anchor))
+ x_anchor -= gap
+
+ return True
+
+ def generate_vertical_span(self, gap=None, scale=None, theta=0, shift=None, jitter=None):
+ """
+ Generating the second scenario where the cards are laid out in a straight vertical line
+ :return: True if successfully generated, otherwise False
+ """
+ # Set scale of the cards, variance of shift & jitter to be applied if they're not given
+ card_size = (len(self.cards[0].img[0]), len(self.cards[0].img))
+ if scale is None:
+ # Scale the cards so that card takes about 50% of the image's height
+ coverage_ratio = 0.5
+ scale = self.height * coverage_ratio / card_size[1]
+ if shift is None:
+ # Plus minus 5% of the card's height
+ shift = [-card_size[1] * scale * 0.05, card_size[1] * scale * 0.05]
+ pass
+ if jitter is None:
+ # Plus minus 5 degrees
+ jitter = [-math.pi / 36, math.pi / 36]
+ if gap is None:
+ # 15% of the card's height - the title bar (with mana symbols) will be visible
+ gap = card_size[1] * scale * 0.25
+
+ # Determine the location of the first card
+ # The cards will cover (width of a card) pixels wide and (height of a card + (# of cards - 1) * gap) pixels high
+ x_anchor = self.width // 2
+ y_anchor = int(self.height / 2 - (len(self.cards) - 1) * gap / 2)
+ for card in self.cards:
+ card.scale = scale
+ card.x = x_anchor
+ card.y = y_anchor
+ card.theta = 0
+ card.shift(shift, shift)
+ card.rotate(jitter)
+ card.rotate(theta, centre=(self.width // 2 - x_anchor, self.height // 2 - y_anchor))
+ y_anchor += gap
+ return True
+
+ def generate_fan_out(self, centre, theta_between_cards=None, scale=None, shift=None, jitter=None):
+ """
+ Generating the third scenario where the cards are laid out in a fan shape
+ :return: True if successfully generated, otherwise False
+ """
+ return False
+
+ def generate_non_obstructive(self, tolerance=0.90, scale=None):
+ """
+ Generating the fourth scenario where the cards are laid in arbitrary position that doesn't obstruct other cards
+ :param tolerance: minimum level of visibility for each cards
+ :return: True if successfully generated, otherwise False
+ """
+ card_size = (len(self.cards[0].img[0]), len(self.cards[0].img))
+ if scale is None:
+ # Total area of the cards should cover about 25-40% of the entire image, depending on the number of cards
+ scale = math.sqrt(self.width * self.height * min(0.25 + 0.02 * len(self.cards), 0.4)
+ / (card_size[0] * card_size[1] * len(self.cards)))
+ # Position each card at random location that doesn't obstruct other cards
+ i = 0
+ while i < len(self.cards):
+ #for i in range(len(self.cards)):
+ card = self.cards[i]
+ card.scale = scale
+ rep = 0
+ while True:
+ card.x = random.uniform(card_size[1] * scale / 2, self.width - card_size[1] * scale)
+ card.y = random.uniform(card_size[1] * scale / 2, self.height - card_size[1] * scale)
+ card.theta = random.uniform(-math.pi, math.pi)
+ self.check_visibility(self.cards[:i + 1], visibility=tolerance)
+ # This position is not obstructive if all of the cards are visible
+ is_visible = [other_card.objects[0].visible for other_card in self.cards[:i + 1]]
+ non_obstructive = all(is_visible)
+ if non_obstructive:
+ i += 1
+ break
+ rep += 1
+ if rep >= 1000:
+ # Reassign previous card's position
+ i -= 1
+ break
+ return True
+
+ def check_visibility(self, cards=None, i_check=None, visibility=0.5):
+ """
+ Check whether if extracted objects in each card are visible in the current scenario, and update their status
+ :param cards: list of cards (in a correct order)
+ :param i_check: indices of cards that needs to be checked. Cards that aren't in this list will only be used
+ to check visibility of other cards. All cards are checked by default.
+ :param visibility: minimum ratio of the object's area that aren't covered by another card to be visible
+ :return: none
+ """
+ if cards is None:
+ cards = self.cards
+ if i_check is None:
+ i_check = range(len(cards))
+ card_poly_list = [geometry.Polygon([card.coordinate_in_generator(0, 0),
+ card.coordinate_in_generator(0, len(card.img)),
+ card.coordinate_in_generator(len(card.img[0]), len(card.img)),
+ card.coordinate_in_generator(len(card.img[0]), 0)]) for card in self.cards]
+ template_poly = geometry.Polygon([(0, 0), (self.width, 0), (self.width, self.height), (0, self.height)])
+
+ # First card in the list is overlaid on the bottom of the card pile
+ for i in i_check:
+ card = cards[i]
+ for ext_obj in card.objects:
+ obj_poly = geometry.Polygon([card.coordinate_in_generator(pt[0], pt[1]) for pt in ext_obj.key_pts])
+ obj_area = obj_poly.area
+ # Check if the other cards are blocking this object or if it's out of the template
+ for card_poly in card_poly_list[i + 1:]:
+ obj_poly = obj_poly.difference(card_poly)
+ obj_poly = obj_poly.intersection(template_poly)
+ visible_area = obj_poly.area
+ #print(visible_area, obj_area, len(card.img[0]) * len(card.img) * card.scale * card.scale)
+ #print("%s: %.1f visible" % (ext_obj.label, visible_area / obj_area * 100))
+ ext_obj.visible = obj_area * visibility <= visible_area
+
+ def export_training_data(self, out_name, visibility=0.5, aug=None):
+ """
+ Export the generated training image along with the txt file for all bounding boxes
+ :return: none
+ """
+ self.render(visibility, aug=aug)
+ cv2.imwrite(out_name + '.jpg', self.img_result)
+ out_txt = open(out_name+ '.txt', 'w')
+ for card in self.cards:
+ for ext_obj in card.objects:
+ if not ext_obj.visible:
+ continue
+ coords_in_gen = [card.coordinate_in_generator(key_pt[0], key_pt[1]) for key_pt in ext_obj.key_pts]
+ obj_yolo_info = key_pts_to_yolo(coords_in_gen, self.width, self.height)
+ if ext_obj.label == 'card':
+ class_id = self.class_ids[card.info['name']]
+ out_txt.write(str(class_id) + ' %.6f %.6f %.6f %.6f\n' % obj_yolo_info)
+ pass
+ elif ext_obj.label[:ext_obj.label.find[':']] == 'mana_symbol':
+ # TODO
+ pass
+ elif ext_obj.label[:ext_obj.label.find[':']] == 'set_symbol':
+ # TODO
+ pass
+ out_txt.close()
+ pass
+
+
+class Card:
+ """
+ A class for storing required information about a card in relation to the ImageGenerator
+ """
+ def __init__(self, img, card_info, objects, x=None, y=None, theta=None, scale=None):
+ """
+ :param img: image of the card
+ :param card_info: details like name, mana cost, type, set, etc
+ :param objects: list of ExtractedObjects like mana & set symbol, etc
+ :param generator: ImageGenerator object that the card is bound to
+ :param x: X-coordinate of the card's centre in relation to the generator
+ :param y: Y-coordinate of the card's centre in relation to the generator
+ :param theta: angle of rotation of the card in relation to the generator
+ :param scale: scale of the card in the generator in relation to the original image
+ """
+ self.img = img
+ self.info = card_info
+ self.objects = objects
+ self.x = x
+ self.y = y
+ self.theta = theta
+ self.scale = scale
+ pass
+
+ def shift(self, x, y):
+ """
+ Apply a X/Y translation on this image
+ :param x: amount of X-translation. If range is given, translate by a random amount within that range
+ :param y: amount of Y-translation. Refer to x when a range is given.
+ :return: none
+ """
+ if isinstance(x, tuple) or (isinstance(x, list) and len(x) == 2):
+ self.x += random.uniform(x[0], x[1])
+ else:
+ self.x += x
+ if isinstance(y, tuple) or (isinstance(y, list) and len(y) == 2):
+ self.y += random.uniform(y[0], y[1])
+ else:
+ self.y += y
+ pass
+
+ def rotate(self, theta, centre=(0, 0)):
+ """
+ Apply a rotation on this image with a centre
+ :param theta: amount of rotation in radian (clockwise). If a range is given, rotate by a random amount within
+ :param centre: coordinate of the centre of the rotation in relation to the centre of this card
+ that range
+ :return: none
+ """
+ if isinstance(theta, tuple) or (isinstance(theta, list) and len(theta) == 2):
+ theta = random.uniform(theta[0], theta[1])
+
+ # If the centre given is the centre of this card, the whole math simplifies a bit
+ # (This still works without the if statement, but let's not do useless trigs if we know the answer already)
+ if centre is not (0, 0):
+ # Rotation math
+ self.x -= -centre[1] * math.sin(theta) + centre[0] * math.cos(theta)
+ self.y -= centre[1] * math.cos(theta) + centre[0] * math.sin(theta)
+
+ # Offset for the coordinate translation
+ self.x += centre[0]
+ self.y += centre[1]
+
+ self.theta += theta
+ pass
+
+ def coordinate_in_generator(self, x, y):
+ """
+ Converting coordinate within the card into the coordinate in the generator it is associated with
+ :param x: x coordinate within the card
+ :param y: y coordinate within the card
+ :return: (x, y) coordinate in the generator
+ """
+ # Relative distance in X & Y axis, if the centre of the card is at the origin (0, 0)
+ rel_x = x - len(self.img[0]) // 2
+ rel_y = y - len(self.img) // 2
+
+ # Scaling
+ rel_x *= self.scale
+ rel_y *= self.scale
+
+ # Rotation
+ rot_x = rel_x - rel_y * math.sin(self.theta) + rel_x * math.cos(self.theta)
+ rot_y = rel_y + rel_y * math.cos(self.theta) + rel_x * math.sin(self.theta)
+
+ # Negate offset
+ rot_x -= rel_x
+ rot_y -= rel_y
+
+ # Shift
+ gen_x = rot_x + self.x
+ gen_y = rot_y + self.y
+
+ return int(gen_x), int(gen_y)
+
+ def bb_in_generator(self, key_pts):
+ """
+ Convert a keypoints of bounding box in card into the coordinate in the generator
+ :param key_pts: keypoints of the bounding box
+ :return: bounding box represented by 4 points in the generator
+ """
+ coords_in_gen = [self.coordinate_in_generator(key_pt[0], key_pt[1]) for key_pt in key_pts]
+ x1 = min([pt[0] for pt in coords_in_gen])
+ x2 = max([pt[0] for pt in coords_in_gen])
+ y1 = min([pt[1] for pt in coords_in_gen])
+ y2 = max([pt[1] for pt in coords_in_gen])
+ '''
+ x1 = -math.inf
+ x2 = math.inf
+ y1 = -math.inf
+ y2 = math.inf
+ for key_pt in key_pts:
+ coord_in_gen = self.coordinate_in_generator(key_pt[0], key_pt[1])
+ x1 = max(x1, coord_in_gen[0])
+ x2 = min(x2, coord_in_gen[0])
+ y1 = max(y1, coord_in_gen[1])
+ y2 = min(y2, coord_in_gen[1])
+ '''
+ return [(x1, y1), (x2, y1), (x2, y2), (x1, y2)]
+
+
+class ExtractedObject:
+ """
+ Simple struct to hold information about an extracted object
+ """
+ def __init__(self, label, key_pts):
+ self.label = label
+ self.key_pts = key_pts
+ self.visible = False
+
+
+def main():
+ random.seed()
+ ia.seed(random.randrange(10000))
+
+ bg_images = generate_data.load_dtd(dtd_dir='%s/dtd/images' % data_dir, dump_it=False)
+ #bg_images = [cv2.imread('data/frilly_0007.jpg')]
+ background = generate_data.Backgrounds(images=bg_images)
+
+ #card_pool = pd.DataFrame()
+ #for set_name in fetch_data.all_set_list:
+ # df = fetch_data.load_all_cards_text('%s/csv/%s.csv' % (data_dir, set_name))
+ # card_pool = card_pool.append(df)
+ card_pool = fetch_data.load_all_cards_text('%s/csv/custom.csv' % data_dir)
+ class_ids = {}
+ with open('%s/obj.names' % data_dir) as names_file:
+ class_name_list = names_file.read().splitlines()
+ for i in range(len(class_name_list)):
+ class_ids[class_name_list[i]] = i
+ print(class_ids)
+
+ num_gen = 60000
+ num_iter = 1
+
+ for i in range(num_gen):
+ # Arbitrarily select top left and right corners for perspective transformation
+ # Since the training image are generated with random rotation, don't need to skew all four sides
+ skew = [[random.uniform(0, 0.25), 0], [0, 1], [1, 1],
+ [random.uniform(0.75, 1), 0]]
+ generator = ImageGenerator(background.get_random(), class_ids, 1440, 960, skew=skew)
+ out_name = ''
+ for _, card_info in card_pool.sample(random.randint(2, 5)).iterrows():
+ img_name = '%s/card_img/png/%s/%s_%s.png' % (data_dir, card_info['set'], card_info['collector_number'],
+ fetch_data.get_valid_filename(card_info['name']))
+ out_name += '%s%s_' % (card_info['set'], card_info['collector_number'])
+ card_img = cv2.imread(img_name)
+ if card_img is None:
+ fetch_data.fetch_card_image(card_info, out_dir='%s/card_img/png/%s' % (data_dir, card_info['set']))
+ card_img = cv2.imread(img_name)
+ if card_img is None:
+ print('WARNING: card %s is not found!' % img_name)
+ detected_object_list = generate_data.apply_bounding_box(card_img, card_info)
+ card = Card(card_img, card_info, detected_object_list)
+ generator.add_card(card)
+ for j in range(num_iter):
+ seq = iaa.Sequential([
+ iaa.Multiply((0.8, 1.2)), # darken / brighten the whole image
+ iaa.SimplexNoiseAlpha(first=iaa.Add(random.randrange(64)), per_channel=0.1, size_px_max=[3, 6],
+ upscale_method="cubic"), # Lighting
+ iaa.AdditiveGaussianNoise(scale=random.uniform(0, 0.05) * 255, per_channel=0.1), # Noises
+ iaa.Dropout(p=[0, 0.05], per_channel=0.1)
+ ])
+
+ if i % 3 == 0:
+ generator.generate_non_obstructive()
+ generator.export_training_data(visibility=0.0, out_name='%s/train/non_obstructive_10/%s%d'
+ % (data_dir, out_name, j), aug=seq)
+ elif i % 3 == 1:
+ generator.generate_horizontal_span(theta=random.uniform(-math.pi, math.pi))
+ generator.export_training_data(visibility=0.0, out_name='%s/train/horizontal_span_10/%s%d'
+ % (data_dir, out_name, j), aug=seq)
+ else:
+ generator.generate_vertical_span(theta=random.uniform(-math.pi, math.pi))
+ generator.export_training_data(visibility=0.0, out_name='%s/train/vertical_span_10/%s%d'
+ % (data_dir, out_name, j), aug=seq)
+
+ #generator.generate_horizontal_span(theta=random.uniform(-math.pi, math.pi))
+ #generator.render(display=True, aug=seq, debug=True)
+ print('Generated %s%d' % (out_name, j))
+ generator.img_bg = background.get_random()
+ pass
+
+
+if __name__ == '__main__':
+ main()
--
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