~speedprog/mtg/mtg_card_detector.git

			@@ -1,21 +1,19 @@
			import os
			import random
			import math
			import argparse
			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
			import imutils
			import math
			import numpy as np
			import os
			import pandas as pd
			import random
			from shapely import geometry

			card_mask = cv2.imread('data/mask.png')
			data_dir = os.path.abspath('/media/win10/data')
			darknet_dir = os.path.abspath('.')
			import fetch_data
			import generate_data
			from config import Config


			def key_pts_to_yolo(key_pts, w_img, h_img):
			@@ -39,7 +37,9 @@

			class ImageGenerator:
			"""
			A template for generating a training image.
			A template for generating a training image
			An ImageGenerator contains a background image, list of cards, and other environmental parameters to
			set up a training image for YOLO network
			"""
			def __init__(self, img_bg, class_ids, width, height, skew=None, cards=None):
			"""
			@@ -59,7 +59,7 @@
			else:
			self.cards = cards

			# Compute transform matrix for perspective transform
			# Compute transform matrix for perspective transform (used for skewing the final result)
			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)
			@@ -79,6 +79,7 @@
			:param scale: new scale for the card
			:return: none
			"""
			# If the position isn't given, push it out of the image so that it won't be visible during rendering
			if x is None:
			x = -len(card.img[0]) / 2
			if y is None:
			@@ -90,24 +91,28 @@
			card.scale = scale
			pass

			def render(self, visibility=0.5, display=False, debug=False, aug=None):
			def render(self, visibility=0.5, aug=None, display=False, debug=False):
			"""
			Display the current state of the generator
			Display the current state of the generator.
			:param visibility: portion of the card's image that must not be overlapped by other cards for the card to be
			considered as visible
			:param aug: image augmentator to apply during rendering
			:param display: flag for displaying the rendering result
			:param debug: flag for debug
			: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)
			card_mask = cv2.imread(Config.card_mask_path)

			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)))
			# Add a random glaring on individual card - it happens frequently in real life as MTG cards can reflect
			# the lights very well.
			if aug is not None:
			seq = iaa.Sequential([
			iaa.SimplexNoiseAlpha(first=iaa.Add(random.randrange(128)), size_px_max=[1, 3],
			@@ -147,15 +152,9 @@
			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)

			# Skew the cards if it's provided
			if self.M is not None:
			img_result = cv2.warpPerspective(img_result, self.M, (self.width, self.height))
			if debug:
			@@ -171,10 +170,11 @@
			img_bg = cv2.resize(self.img_bg, (self.width, self.height))
			img_result = np.where(img_result, img_result, img_bg)

			# Apply image augmentation
			if aug is not None:
			img_result = aug.augment_image(img_result)

			if display:
			if display or debug:
			cv2.imshow('Result', img_result)
			cv2.waitKey(0)

			@@ -184,6 +184,11 @@
			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
			:param gap: horizontal offset between each adjacent cards
			:param scale: scale of each cards in the generator
			:param theta: rotation of the entire span in radian
			:param shift: range of arbitrary offset for each card
			:param jitter: range of in-place rotation for each card in radian
			:return: True if successfully generated, otherwise False
			"""
			# Set scale of the cards, variance of shift & jitter to be applied if they're not given
			@@ -197,7 +202,8 @@
			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
			# Plus minus 10 degrees
			jitter = [-math.pi / 18, math.pi / 18]
			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
			@@ -221,6 +227,12 @@
			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
			:param gap: horizontal offset between each adjacent cards
			:param scale: scale of each cards in the generator
			:param theta: rotation of the entire span in radian
			:param shift: range of arbitrary offset for each card
			:param jitter: range of in-place rotation for each card in radian
			:return: True if successfully generated, otherwise False
			:return: True if successfully generated, otherwise False
			"""
			# Set scale of the cards, variance of shift & jitter to be applied if they're not given
			@@ -260,12 +272,14 @@
			Generating the third scenario where the cards are laid out in a fan shape
			:return: True if successfully generated, otherwise False
			"""
			# TODO
			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
			:param scale: scale of each cards in generator
			:return: True if successfully generated, otherwise False
			"""
			card_size = (len(self.cards[0].img[0]), len(self.cards[0].img))
			@@ -276,7 +290,6 @@
			# 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
			@@ -300,8 +313,8 @@

			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)
			Check whether if extracted objects in a card is visible in the current scenario, and update their status
			:param cards: list of cards (in a correct Z-order). All cards in this Generator are checked by default.
			: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
			@@ -311,6 +324,8 @@
			cards = self.cards
			if i_check is None:
			i_check = range(len(cards))

			# Create a polygon of each card
			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)),
			@@ -324,22 +339,26 @@
			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
			# If there are other polygons with higher indices in the list, that card is overlapping this object
			# We assume that no objects from the same card is on top of each other
			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
			:param out_name: path of the output file (without extension)
			:param visibility: portion of the card's image that must not be overlapped by other cards for the card to be
			considered as visible
			:param aug: image augmentator to be applied
			:return: none
			"""
			self.render(visibility, aug=aug)
			cv2.imwrite(out_name + '.jpg', self.img_result)
			out_txt = open(out_name+ '.txt', 'w')
			out_txt = open(out_name + '.txt', 'w')
			for card in self.cards:
			for ext_obj in card.objects:
			if not ext_obj.visible:
			@@ -348,17 +367,9 @@
			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']]
			class_id = 0
			class_id = 0 # since only the entire card is used
			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:
			@@ -370,7 +381,6 @@
			: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
			@@ -389,7 +399,7 @@
			"""
			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.
			:param y: amount of Y-translation. If range is given, translate by a random amount within that range
			:return: none
			"""
			if isinstance(x, tuple) or (isinstance(x, list) and len(x) == 2):
			@@ -406,8 +416,8 @@
			"""
			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
			that range
			: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):
			@@ -467,18 +477,6 @@
			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)]


			@@ -492,68 +490,69 @@
			self.visible = False


			def main():
			def main(args):
			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')]
			bg_images = generate_data.load_dtd(dtd_dir='%s/dtd/images' % Config.data_dir, dump_it=False)
			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))
			for set_name in Config.all_set_list:
			df = fetch_data.load_all_cards_text('%s/csv/%s.csv' % (Config.data_dir, set_name))
			card_pool = card_pool.append(df)
			class_ids = {}
			with open('%s/obj.names' % data_dir) as names_file:
			with open('%s/obj.names' % Config.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

			num_gen = 60000
			num_iter = 1

			for i in range(num_gen):
			for i in range(args.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)
			generator = ImageGenerator(background.get_random(), class_ids, args.width, args.height, skew=skew)
			out_name = ''

			# Use 2 to 5 cards per generator
			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'],
			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']))
			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']))
			fetch_data.fetch_card_image(card_info, out_dir='%s/card_img/png/%s' % (Config.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):

			for j in range(args.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)
			iaa.Dropout(p=[0, 0.05], per_channel=0.1) # Dropout
			])

			if i % 3 == 0:
			generator.generate_non_obstructive()
			generator.export_training_data(visibility=0.0, out_name='%s/train/non_obstructive_update/%s%d'
			% (data_dir, out_name, j), aug=seq)
			% (Config.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_update/%s%d'
			% (data_dir, out_name, j), aug=seq)
			% (Config.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_update/%s%d'
			% (data_dir, out_name, j), aug=seq)
			% (Config.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)
			@@ -563,4 +562,12 @@


			if __name__ == '__main__':
			main()
			parser = argparse.ArgumentParser()
			parser.add_argument('-n', '--num_gen', dest='num_gen', help='Number of training images to generate',
			type=int, required=True)
			parser.add_argument('-ni', '--num_iter', dest='num_iter', help='Number of iterations to generate each config',
			type=int, default=1)
			parser.add_argument('-w', '--width', dest='width', help='Width of the training image', type=int, default=1440)
			parser.add_argument('-ht', '--height', dest='height', help='Height of the training image', type=int, default=960)
			args = parser.parse_args()
			main(args)