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| | | import pandas as pd |
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| | | import imagehash as ih |
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| | | import os |
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| | | import ast |
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| | | import sys |
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| | | import math |
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| | | import random |
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| | | card_height = 440 |
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| | | def calc_image_hashes(card_pool, save_to=None): |
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| | | card_pool['art_hash'] = np.NaN |
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| | | def calc_image_hashes(card_pool, save_to=None, hash_size=32, highfreq_factor=4): |
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| | | new_pool = pd.DataFrame(columns=list(card_pool.columns.values)) |
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| | | new_pool['card_hash'] = np.NaN |
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| | | new_pool['art_hash'] = 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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| | | print(ind) |
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| | | |
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| | | card_names = [] |
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| | | if card_info['layout'] in ['transform', 'double_faced_token']: |
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| | | if isinstance(card_info['card_faces'], str): # For some reason, dict isn't being parsed in the previous step |
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| | | card_faces = ast.literal_eval(card_info['card_faces']) |
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| | | else: |
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| | | card_faces = card_info['card_faces'] |
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| | | for i in range(len(card_faces)): |
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| | | card_names.append(card_faces[i]['name']) |
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| | | else: # if card_info['layout'] == 'normal': |
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| | | card_names.append(card_info['name']) |
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| | | |
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| | | for card_name in card_names: |
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| | | card_info['name'] = card_name |
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| | | img_name = '%s/card_img/png/%s/%s_%s.png' % (transform_data.data_dir, card_info['set'], |
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| | | card_info['collector_number'], |
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| | | fetch_data.get_valid_filename(card_info['name'])) |
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| | | card_img = cv2.imread(img_name) |
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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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| | | img_art = Image.fromarray(card_img[121:580, 63:685]) # For 745*1040 size card image |
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| | | art_hash = ih.phash(img_art, hash_size=32, highfreq_factor=4) |
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| | | card_pool.at[ind, 'art_hash'] = art_hash |
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| | | #img_art = Image.fromarray(card_img[121:580, 63:685]) # For 745*1040 size card image |
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| | | #art_hash = ih.phash(img_art, hash_size=32, highfreq_factor=4) |
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| | | #card_pool.at[ind, 'art_hash'] = art_hash |
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| | | img_card = Image.fromarray(card_img) |
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| | | card_hash = ih.phash(img_card, hash_size=32, highfreq_factor=4) |
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| | | card_pool.at[ind, 'card_hash'] = card_hash |
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| | | card_pool = card_pool[['artist', 'border_color', 'collector_number', 'color_identity', 'colors', 'flavor_text', |
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| | | card_hash = ih.phash(img_card, hash_size=hash_size, highfreq_factor=highfreq_factor) |
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| | | #card_pool.at[ind, 'card_hash'] = card_hash |
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| | | card_info['card_hash'] = card_hash |
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| | | #print(new_pool.index.max()) |
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| | | new_pool.loc[0 if new_pool.empty else new_pool.index.max() + 1] = card_info |
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| | | |
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| | | new_pool = new_pool[['artist', 'border_color', 'collector_number', 'color_identity', 'colors', 'flavor_text', |
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| | | 'image_uris', 'mana_cost', 'legalities', 'name', 'oracle_text', 'rarity', 'type_line', |
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| | | 'set', 'set_name', 'power', 'toughness', 'art_hash', 'card_hash']] |
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| | | if save_to is not None: |
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| | | card_pool.to_pickle(save_to) |
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| | | return card_pool |
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| | | new_pool.to_pickle(save_to) |
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| | | return new_pool |
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| | | |
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| | | # www.pyimagesearch.com/2014/08/25/4-point-opencv-getperspective-transform-example/ |
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| | | def detect_frame(net, classes, img, card_pool, thresh_conf=0.5, thresh_nms=0.4, in_dim=(416, 416), out_path=None, display=True, |
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| | | debug=False): |
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| | | img_copy = img.copy() |
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| | | start_1 = time.time() |
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| | | elapsed = [] |
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| | | ''' |
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| | | # Create a 4D blob from a frame. |
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| | | blob = cv2.dnn.blobFromImage(img, 1 / 255, in_dim, [0, 0, 0], 1, crop=False) |
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| | | |
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| | | |
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| | | # Runs the forward pass to get output of the output layers |
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| | | outs = net.forward(get_outputs_names(net)) |
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| | | elapsed.append((time.time() - start_1) * 1000) |
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| | | |
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| | | start_2 = time.time() |
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| | | img_result = img.copy() |
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| | | |
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| | | # Remove the bounding boxes with low confidence |
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| | |
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| | | class_id, confidence, box = obj |
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| | | left, top, width, height = box |
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| | | draw_pred(img_result, class_id, classes, confidence, left, top, left + width, top + height) |
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| | | |
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| | | elapsed.append((time.time() - start_2) * 1000) |
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| | | ''' |
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| | | img_result = img.copy() |
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| | | obj_list = [] |
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| | | # Put efficiency information. The function getPerfProfile returns the |
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| | | # overall time for inference(t) and the timings for each of the layers(in layersTimes) |
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| | | #if display: |
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| | |
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| | | ''' |
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| | | det_cards = [] |
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| | | for i in range(len(obj_list)): |
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| | | start_3 = time.time() |
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| | | _, _, box = obj_list[i] |
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| | | left, top, width, height = box |
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| | | # Just in case the bounding box trimmed the edge of the cards, give it a bit of offset around the edge |
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| | |
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| | | y2 = min(img.shape[0], int(top + (1 + offset_ratio) * height)) |
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| | | img_snip = img[y1:y2, x1:x2] |
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| | | cnts = find_card(img_snip) |
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| | | elapsed.append((time.time() - start_3) * 1000) |
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| | | if len(cnts) > 0: |
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| | | start_4 = time.time() |
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| | | cnt = cnts[0] # The largest (rectangular) contour |
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| | | pts = np.float32([p[0] for p in cnt]) |
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| | | img_warp = four_point_transform(img_snip, pts) |
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| | | img_warp = cv2.resize(img_warp, (card_width, card_height)) |
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| | | elapsed.append((time.time() - start_4) * 1000) |
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| | | ''' |
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| | | img_art = img_warp[47:249, 22:294] |
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| | | img_art = Image.fromarray(img_art.astype('uint8'), 'RGB') |
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| | |
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| | | min_cards = card_pool[card_pool['hash_diff'] == min(card_pool['hash_diff'])] |
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| | | card_name = min_cards.iloc[0]['name'] |
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| | | ''' |
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| | | start_5 = time.time() |
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| | | img_card = Image.fromarray(img_warp.astype('uint8'), 'RGB') |
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| | | card_hash = ih.phash(img_card, hash_size=32, highfreq_factor=4) |
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| | | card_pool['hash_diff'] = card_pool['card_hash'] - card_hash |
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| | | card_hash = ih.phash(img_card, hash_size=32, highfreq_factor=4).hash.flatten() |
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| | | card_pool['hash_diff'] = card_pool['card_hash'].apply(lambda x: np.count_nonzero(x != card_hash)) |
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| | | min_cards = card_pool[card_pool['hash_diff'] == min(card_pool['hash_diff'])] |
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| | | card_name = min_cards.iloc[0]['name'] |
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| | | card_set = min_cards.iloc[0]['set'] |
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| | | det_cards.append((card_name, card_set)) |
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| | | hash_diff = min_cards.iloc[0]['hash_diff'] |
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| | | elapsed.append((time.time() - start_5) * 1000) |
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| | | |
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| | | # Display the result |
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| | | if debug: |
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| | | |
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| | | if out_path is not None: |
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| | | cv2.imwrite(out_path, img_result.astype(np.uint8)) |
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| | | |
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| | | elapsed = [(time.time() - start_1) * 1000] + elapsed |
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| | | #print(', '.join(['%.2f' % t for t in elapsed])) |
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| | | return obj_list, det_cards, img_result |
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| | | |
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| | | |
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| | | cv2.waitKey(0) |
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| | | break |
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| | | # Use the YOLO model to identify each cards annonymously |
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| | | start_yolo = time.time() |
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| | | obj_list, det_cards, img_result = detect_frame(net, classes, frame, card_pool, thresh_conf=thresh_conf, |
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| | | thresh_nms=thresh_nms, in_dim=in_dim, out_path=None, |
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| | | display=display, debug=debug) |
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| | | |
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| | | elapsed_yolo = (time.time() - start_yolo) * 1000 |
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| | | # If the card was already detected in the previous frame, append 1 to the list |
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| | | # If the card previously detected was not found in this trame, append 0 to the list |
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| | | # If the card wasn't previously detected, make a new list and add 1 to it |
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| | |
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| | | exist_cards[key] = [1] |
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| | | for key in gone: |
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| | | exist_cards.pop(key) |
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| | | start_graph = time.time() |
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| | | img_graph = draw_card_graph(exist_cards, card_pool, f_len) |
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| | | |
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| | | elapsed_graph = (time.time() - start_graph) * 1000 |
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| | | if debug: |
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| | | max_num_obj = max(max_num_obj, len(obj_list)) |
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| | | for i in range(len(obj_list), max_num_obj): |
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| | | cv2.imshow('card#%d' % i, np.zeros((1, 1), dtype=np.uint8)) |
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| | | |
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| | | start_display = time.time() |
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| | | img_save = np.zeros((height, width, 3), dtype=np.uint8) |
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| | | img_save[0:img_result.shape[0], 0:img_result.shape[1]] = img_result |
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| | | img_save[0:img_graph.shape[0], img_result.shape[1]:img_result.shape[1] + img_graph.shape[1]] = img_graph |
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| | | if display: |
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| | | cv2.imshow('result', img_save) |
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| | | elapsed_display = (time.time() - start_display) * 1000 |
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| | | |
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| | | elapsed_ms = (time.time() - start_time) * 1000 |
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| | | print('Elapsed time: %.2f ms' % elapsed_ms) |
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| | | #print('Elapsed time: %.2f ms, %.2f, %.2f, %.2f' % (elapsed_ms, elapsed_yolo, elapsed_graph, elapsed_display)) |
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| | | if out_path is not None: |
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| | | vid_writer.write(img_save.astype(np.uint8)) |
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| | | cv2.waitKey(1) |
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| | |
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| | | df = fetch_data.load_all_cards_text(csv_name) |
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| | | df_list.append(df) |
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| | | #print(df) |
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| | | card_pool = pd.concat(df_list) |
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| | | card_pool = pd.concat(df_list, sort=True) |
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| | | card_pool.reset_index(drop=True, inplace=True) |
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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='card_pool.pck') |
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| | | for hash_size in [8, 16, 32, 64]: |
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| | | for highfreq_factor in [4, 8, 16, 32]: |
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| | | pck_name = 'card_pool_%d_%d.pck' % (hash_size, highfreq_factor) |
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| | | if not os.path.exists(pck_name): |
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| | | print(pck_name) |
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| | | calc_image_hashes(card_pool, save_to=pck_name, hash_size=hash_size, highfreq_factor=highfreq_factor) |
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| | | ''' |
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| | | # csv_name = '%s/csv/%s.csv' % (transform_data.data_dir, 'rtr') |
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| | | # card_pool = fetch_data.load_all_cards_text(csv_name) |
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| | | # card_pool = calc_image_hashes(card_pool) |
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| | | card_pool = pd.read_pickle('card_pool.pck') |
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| | | card_pool = card_pool[(card_pool['set'] == 'rtr') | (card_pool['set'] == 'isd')] |
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| | | #card_pool = calc_image_hashes(card_pool, save_to='card_pool.pck') |
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| | | #return |
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| | | card_pool = pd.read_pickle('card_pool_32_4.pck') |
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| | | #card_pool = card_pool[(card_pool['set'] == 'rtr') | (card_pool['set'] == 'isd')] |
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| | | card_pool = card_pool[['name', 'set', 'collector_number', 'card_hash']] |
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| | | |
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| | | # ImageHash is basically just one numpy.ndarray with (hash_size)^2 number of bits. pre-emptively flattening it |
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| | | # significantly increases speed for subtracting hashes in the future. |
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| | | card_pool['card_hash'] = card_pool['card_hash'].apply(lambda x: x.hash.flatten()) |
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| | | |
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| | | thresh_conf = 0.01 |
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| | | thresh_nms = 0.8 |
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