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| | | import sys |
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| | | import numpy as np |
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| | | import pandas as pd |
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| | | from transform_data import ExtractedObject |
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| | | # Referenced from geaxgx's playing-card-detection: https://github.com/geaxgx/playing-card-detection |
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| | | class Backgrounds: |
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| | | def apply_bounding_box(img, card_info, display=False): |
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| | | # List of (object class, bounding box pts) pair of each objects |
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| | | object_info_list = [] |
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| | | # List of detected objects to be fed into the neural net |
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| | | # The first object is the entire card |
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| | | detected_object_list = [ExtractedObject('card', [(0, 0), (len(img[0]), 0), (len(img[0]), len(img)), (0, len(img))])] |
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| | | # Mana symbol - They are located on the top right side of the card, next to the name |
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| | | # Their position is stationary, and is right-aligned. |
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| | | has_mana_cost = isinstance(card_info['mana_cost'], str) # Cards with no mana cost will have nan |
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| | | # Append them to the list of bounding box with the appropriate label |
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| | | symbol_name = 'mana_symbol:' + mana_cost[i] |
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| | | key_pts = [(x1, y1), (x2, y1), (x2, y2), (x1, y2)] |
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| | | object_info_list.append((symbol_name, key_pts)) |
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| | | detected_object_list.append(ExtractedObject(symbol_name, key_pts)) |
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| | | if display: |
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| | | img_symbol = img[y1:y2, x1:x2] |
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| | | # Append them to the list of bounding box with the appropriate label |
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| | | symbol_name = 'set_symbol:' + card_info['set'] |
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| | | key_pts = [(x1, y1), (x2, y1), (x2, y2), (x1, y2)] |
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| | | object_info_list.append((symbol_name, key_pts)) |
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| | | detected_object_list.append(ExtractedObject(symbol_name, key_pts)) |
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| | | if display: |
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| | | img_symbol = img[y1:y2, x1:x2] |
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| | | # Image box - the large image on the top half of the card |
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| | | # TODO |
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| | | return object_info_list |
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| | | return detected_object_list |
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| | | def main(): |
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| | | if card_img is None: |
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| | | fetch_data.fetch_card_image(card_info, out_dir='../usb/data/png/%s' % card_info['set']) |
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| | | card_img = cv2.imread(img_name) |
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| | | object_list_info = apply_bounding_box(card_img, card_info, display=True) |
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| | | print(object_list_info) |
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| | | detected_object_list = apply_bounding_box(card_img, card_info, display=True) |
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| | | print(detected_object_list) |
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| | | return |
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