| | |
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| | | |
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| | | card_mask = cv2.imread('data/mask.png') |
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| | | data_dir = os.path.abspath('/media/win10/data') |
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| | | darknet_dir = os.path.abspath('darknet') |
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| | | darknet_dir = os.path.abspath('.') |
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| | | |
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| | | |
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| | | def key_pts_to_yolo(key_pts, w_img, h_img): |
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| | |
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| | | coords_in_gen = [card.coordinate_in_generator(key_pt[0], key_pt[1]) for key_pt in ext_obj.key_pts] |
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| | | obj_yolo_info = key_pts_to_yolo(coords_in_gen, self.width, self.height) |
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| | | if ext_obj.label == 'card': |
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| | | class_id = self.class_ids[card.info['name']] |
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| | | #class_id = self.class_ids[card.info['name']] |
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| | | class_id = 0 |
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| | | out_txt.write(str(class_id) + ' %.6f %.6f %.6f %.6f\n' % obj_yolo_info) |
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| | | pass |
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| | | elif ext_obj.label[:ext_obj.label.find[':']] == 'mana_symbol': |
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| | |
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| | | #bg_images = [cv2.imread('data/frilly_0007.jpg')] |
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| | | background = generate_data.Backgrounds(images=bg_images) |
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| | | |
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| | | #card_pool = pd.DataFrame() |
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| | | #for set_name in fetch_data.all_set_list: |
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| | | # df = fetch_data.load_all_cards_text('%s/csv/%s.csv' % (data_dir, set_name)) |
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| | | # card_pool = card_pool.append(df) |
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| | | card_pool = fetch_data.load_all_cards_text('%s/csv/custom.csv' % data_dir) |
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| | | card_pool = pd.DataFrame() |
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| | | for set_name in fetch_data.all_set_list: |
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| | | df = fetch_data.load_all_cards_text('%s/csv/%s.csv' % (data_dir, set_name)) |
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| | | card_pool = card_pool.append(df) |
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| | | class_ids = {} |
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| | | with open('%s/obj.names' % data_dir) as names_file: |
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| | | class_name_list = names_file.read().splitlines() |
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| | | for i in range(len(class_name_list)): |
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| | | class_ids[class_name_list[i]] = i |
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| | | print(class_ids) |
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| | | |
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| | | num_gen = 60000 |
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| | | num_iter = 1 |
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| | |
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| | | |
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| | | if i % 3 == 0: |
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| | | generator.generate_non_obstructive() |
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| | | generator.export_training_data(visibility=0.0, out_name='%s/train/non_obstructive_10/%s%d' |
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| | | generator.export_training_data(visibility=0.0, out_name='%s/train/non_obstructive_update/%s%d' |
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| | | % (data_dir, out_name, j), aug=seq) |
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| | | elif i % 3 == 1: |
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| | | generator.generate_horizontal_span(theta=random.uniform(-math.pi, math.pi)) |
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| | | generator.export_training_data(visibility=0.0, out_name='%s/train/horizontal_span_10/%s%d' |
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| | | generator.export_training_data(visibility=0.0, out_name='%s/train/horizontal_span_update/%s%d' |
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| | | % (data_dir, out_name, j), aug=seq) |
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| | | else: |
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| | | generator.generate_vertical_span(theta=random.uniform(-math.pi, math.pi)) |
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| | | generator.export_training_data(visibility=0.0, out_name='%s/train/vertical_span_10/%s%d' |
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| | | generator.export_training_data(visibility=0.0, out_name='%s/train/vertical_span_update/%s%d' |
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| | | % (data_dir, out_name, j), aug=seq) |
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| | | |
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| | | #generator.generate_horizontal_span(theta=random.uniform(-math.pi, math.pi)) |
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