Trying out 10 card model setup

Edmond Yoo

2018-09-15 c171a6ece870be48b07d5de93ee6301b1da0c7de

Trying out 10 card model setup

 data/test22.png

 fetch_data.py

@@ -4,8 +4,11 @@
import pandas as pd
import re
import os
import transform_data
import time

all_set_list = ['mrd', 'dst', '5dn', 'chk', 'bok', 'sok', 'rav', 'gpt', 'dis', 'csp', 'tsp', 'plc', 'fut',
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']

@@ -84,19 +87,18 @@


def main():
    '''
    for set_name in all_set_list:
        csv_name = 'data/csv/%s.csv' % set_name
        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=layout:normal+set:%s+lang:en+frame:2003'
            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)
        print(csv_name)
        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')
        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



 transform_data.py

@@ -26,10 +26,10 @@
    :param h_img: height of the entire image
    :return: <x> <y> <width> <height>
    """
    x1 = min([pt[0] for pt in key_pts])
    x2 = max([pt[0] for pt in key_pts])
    y1 = min([pt[1] for pt in key_pts])
    y2 = max([pt[1] for pt in key_pts])
    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
@@ -41,7 +41,7 @@
    """
    A template for generating a training image.
    """
    def __init__(self, img_bg, width, height, skew=None, cards=None):
    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
@@ -50,6 +50,7 @@
        :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
@@ -107,6 +108,12 @@

            # 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)
@@ -340,7 +347,8 @@
                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':
                    out_txt.write('0 %.6f %.6f %.6f %.6f\n' % obj_yolo_info)
                    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
@@ -491,10 +499,17 @@
    #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 = 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
@@ -504,7 +519,7 @@
        # 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(), 1440, 960, skew=skew)
        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'],
@@ -527,18 +542,20 @@
                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_skew/%s_%d'
                generator.export_training_data(visibility=0.0, out_name='%s/train/non_obstructive_custom/%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_skew/%s_%d'
                generator.export_training_data(visibility=0.0, out_name='%s/train/horizontal_span_custom/%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_skew/%s_%d'
                generator.export_training_data(visibility=0.0, out_name='%s/train/vertical_span_custom/%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))