old mode 100644
new mode 100755
| | |
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| | | new_pool = pd.DataFrame(columns=list(card_pool.columns.values)) |
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| | | for hs in hash_size: |
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| | | new_pool['card_hash_%d' % hs] = np.NaN |
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| | | new_pool['set_hash_%d' % hs] = np.NaN |
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| | | new_pool['set_hash_%d' % 64] = np.NaN |
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| | | #new_pool['art_hash_%d' % hs] = np.NaN |
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| | | for ind, card_info in card_pool.iterrows(): |
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| | | if ind % 100 == 0: |
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| | |
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| | | if card_img is None: |
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| | | print('WARNING: card %s is not found!' % img_name) |
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| | | continue |
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| | | |
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| | | set_img = card_img[575:638, 567:700] |
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| | | """ |
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| | | img_cc = cv2.cvtColor(card_img, cv2.COLOR_BGR2GRAY) |
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| | | img_thresh = cv2.adaptiveThreshold(img_cc, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 11, 5) |
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| | | # Dilute the image, then erode them to remove minor noises |
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| | | kernel = np.ones((3, 3), np.uint8) |
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| | | img_dilate = cv2.dilate(img_thresh, kernel, iterations=1) |
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| | | img_erode = cv2.erode(img_dilate, kernel, iterations=1) |
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| | | cnts, hier = cv2.findContours(img_erode, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) |
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| | | cnts2 = sorted(cnts, key=cv2.contourArea, reverse=True) |
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| | | cnts2 = cnts2[:10] |
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| | | if True: |
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| | | cv2.drawContours(img_cc, cnts2, -1, (0, 255, 0), 3) |
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| | | #cv2.imshow('Contours', card_img) |
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| | | #cv2.waitKey(10000) |
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| | | """ |
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| | | set_img = card_img[595:635, 600:690] |
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| | | #cv2.imshow(card_info['name'], set_img) |
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| | | # Compute value of the card's perceptual hash, then store it to the database |
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| | | #img_art = Image.fromarray(card_img[121:580, 63:685]) # For 745*1040 size card image |
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| | |
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| | | img_set = Image.fromarray(set_img) |
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| | | for hs in hash_size: |
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| | | card_hash = ih.phash(img_card, hash_size=hs) |
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| | | set_hash = ih.whash(img_set, hash_size=hs) |
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| | | set_hash = ih.whash(img_set, hash_size=64) |
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| | | card_info['card_hash_%d' % hs] = card_hash |
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| | | card_info['set_hash_%d' % hs] = set_hash |
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| | | card_info['set_hash_%d' % 64] = set_hash |
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| | | #print('Setting set_hash_%d' % hs) |
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| | | #art_hash = ih.phash(img_art, hash_size=hs) |
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| | | #card_info['art_hash_%d' % hs] = art_hash |
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| | |
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| | | elif isinstance(hash_size, int): |
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| | | hash_size = [hash_size] |
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| | | |
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| | | num_cores = 15 |
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| | | num_partitions = round(card_pool.shape[0]/100) |
|---|
| | | num_cores = 16 |
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| | | num_partitions = round(card_pool.shape[0]/1000) |
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| | | if num_partitions < min(num_cores, card_pool.shape[0]): |
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| | | num_partitions = min(num_cores, card_pool.shape[0]) |
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| | | pool = Pool(num_cores) |
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| | |
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| | | print('Idx:', ix, 'Name:', cd['name'], 'Set:', cd['set'], 'Diff:', top_matches[ix]) |
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| | | |
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| | | |
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| | | cd_data['set_hash_diff'] = cd_data['set_hash_%d' % hash_size] |
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| | | cd_data['set_hash_diff'] = cd_data['set_hash_%d' % 64] |
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| | | cd_data['set_hash_diff'] = cd_data['set_hash_diff'].apply(lambda x: np.count_nonzero(x != set_img_hash)) |
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| | | conf = sorted(cd_data['set_hash_diff']) |
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| | | print('Confs:', conf) |
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| | |
|---|
| | | card_pool.drop('Unnamed: 0', axis=1, inplace=True, errors='ignore') |
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| | | card_pool = calc_image_hashes(card_pool, save_to=pck_path, hash_size=hash_sizes) |
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| | | ch_key = 'card_hash_%d' % args.hash_size |
|---|
| | | set_key = 'set_hash_%d' % args.hash_size |
|---|
| | | set_key = 'set_hash_%d' % 64 |
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| | | 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,)) |
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| | |
|---|
| | | if test_ext in ['jpg', 'jpeg', 'bmp', 'png', 'tiff']: |
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| | | # Test file is an image |
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| | | img = cv2.imread(args.in_path) |
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| | | if img is None: |
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| | | print('Could not read', args.in_path) |
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| | | detect_frame(img, card_pool, hash_size=args.hash_size, out_path=out_path, display=args.display, |
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| | | debug=args.debug) |
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| | | else: |
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