| | |
|---|
| | | new_pool = pd.DataFrame(columns=list(card_pool.columns.values)) |
|---|
| | | for hs in hash_size: |
|---|
| | | new_pool['card_hash_%d' % hs] = np.NaN |
|---|
| | | new_pool['set_hash_%d' % hs] = np.NaN |
|---|
| | | new_pool['set_hash_%d' % 64] = np.NaN |
|---|
| | | #new_pool['art_hash_%d' % hs] = np.NaN |
|---|
| | | for ind, card_info in card_pool.iterrows(): |
|---|
| | | if ind % 100 == 0: |
|---|
| | |
|---|
| | | for card_name in card_names: |
|---|
| | | # Fetch the image - name can be found based on the card's information |
|---|
| | | card_info['name'] = card_name |
|---|
| | | cname = card_name |
|---|
| | | if cname == 'con': |
|---|
| | | cname == 'con__' |
|---|
| | | 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'])) |
|---|
| | | fetch_data.get_valid_filename(cname)) |
|---|
| | | card_img = cv2.imread(img_name) |
|---|
| | | |
|---|
| | | # If the image doesn't exist, download it from the URL |
|---|
| | | if card_img is None: |
|---|
| | | set_name = card_info['set'] |
|---|
| | | if set_name == 'con': |
|---|
| | | set_name = 'con__' |
|---|
| | | fetch_data.fetch_card_image(card_info, |
|---|
| | | out_dir='%s/card_img/png/%s' % (Config.data_dir, card_info['set'])) |
|---|
| | | out_dir='%s/card_img/png/%s' % (Config.data_dir, set_name)) |
|---|
| | | card_img = cv2.imread(img_name) |
|---|
| | | if card_img is None: |
|---|
| | | print('WARNING: card %s is not found!' % img_name) |
|---|
| | | continue |
|---|
| | | |
|---|
| | | set_img = card_img[575:638, 567:700] |
|---|
| | | """ |
|---|
| | | img_cc = cv2.cvtColor(card_img, cv2.COLOR_BGR2GRAY) |
|---|
| | | img_thresh = cv2.adaptiveThreshold(img_cc, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 11, 5) |
|---|
| | | # Dilute the image, then erode them to remove minor noises |
|---|
| | | kernel = np.ones((3, 3), np.uint8) |
|---|
| | | img_dilate = cv2.dilate(img_thresh, kernel, iterations=1) |
|---|
| | | img_erode = cv2.erode(img_dilate, kernel, iterations=1) |
|---|
| | | cnts, hier = cv2.findContours(img_erode, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) |
|---|
| | | cnts2 = sorted(cnts, key=cv2.contourArea, reverse=True) |
|---|
| | | cnts2 = cnts2[:10] |
|---|
| | | if True: |
|---|
| | | cv2.drawContours(img_cc, cnts2, -1, (0, 255, 0), 3) |
|---|
| | | #cv2.imshow('Contours', card_img) |
|---|
| | | #cv2.waitKey(10000) |
|---|
| | | """ |
|---|
| | | set_img = card_img[595:635, 600:690] |
|---|
| | | #cv2.imshow(card_info['name'], set_img) |
|---|
| | | # Compute value of the card's perceptual hash, then store it to the database |
|---|
| | | #img_art = Image.fromarray(card_img[121:580, 63:685]) # For 745*1040 size card image |
|---|
| | |
|---|
| | | img_set = Image.fromarray(set_img) |
|---|
| | | for hs in hash_size: |
|---|
| | | card_hash = ih.phash(img_card, hash_size=hs) |
|---|
| | | set_hash = ih.whash(img_set, hash_size=hs) |
|---|
| | | set_hash = ih.whash(img_set, hash_size=64) |
|---|
| | | card_info['card_hash_%d' % hs] = card_hash |
|---|
| | | card_info['set_hash_%d' % hs] = set_hash |
|---|
| | | card_info['set_hash_%d' % 64] = set_hash |
|---|
| | | #print('Setting set_hash_%d' % hs) |
|---|
| | | #art_hash = ih.phash(img_art, hash_size=hs) |
|---|
| | | #card_info['art_hash_%d' % hs] = art_hash |
|---|
| | |
|---|
| | | return img_graph |
|---|
| | | |
|---|
| | | |
|---|
| | | def detect_frame(img, card_pool, hash_size=32, size_thresh=100000, |
|---|
| | | def detect_frame(img, card_pool, hash_size=32, size_thresh=10000, |
|---|
| | | out_path=None, display=True, debug=False): |
|---|
| | | """ |
|---|
| | | Identify all cards in the input frame, display or save the frame if needed |
|---|
| | |
|---|
| | | print('Idx:', ix, 'Name:', cd['name'], 'Set:', cd['set'], 'Diff:', top_matches[ix]) |
|---|
| | | |
|---|
| | | |
|---|
| | | cd_data['set_hash_diff'] = cd_data['set_hash_%d' % hash_size] |
|---|
| | | cd_data['set_hash_diff'] = cd_data['set_hash_%d' % 64] |
|---|
| | | cd_data['set_hash_diff'] = cd_data['set_hash_diff'].apply(lambda x: np.count_nonzero(x != set_img_hash)) |
|---|
| | | conf = sorted(cd_data['set_hash_diff']) |
|---|
| | | print('Confs:', conf) |
|---|
| | |
|---|
| | | # Merge database for all cards, then calculate pHash values of each, store them |
|---|
| | | df_list = [] |
|---|
| | | for set_name in Config.all_set_list: |
|---|
| | | if set_name == 'con': |
|---|
| | | set_name = 'con__' |
|---|
| | | csv_name = '%s/csv/%s.csv' % (Config.data_dir, set_name) |
|---|
| | | df = fetch_data.load_all_cards_text(csv_name) |
|---|
| | | df_list.append(df) |
|---|
| | |
|---|
| | | card_pool.drop('Unnamed: 0', axis=1, inplace=True, errors='ignore') |
|---|
| | | card_pool = calc_image_hashes(card_pool, save_to=pck_path, hash_size=hash_sizes) |
|---|
| | | ch_key = 'card_hash_%d' % args.hash_size |
|---|
| | | set_key = 'set_hash_%d' % args.hash_size |
|---|
| | | set_key = 'set_hash_%d' % 64 |
|---|
| | | if ch_key not in card_pool.columns: |
|---|
| | | # we did not generate this hash_size yet |
|---|
| | | print('We need to add hash_size=%d' % (args.hash_size,)) |
|---|
