| | |
|---|
| | | 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' % 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 |
|---|
| | | """ |
|---|
| | | 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_card = Image.fromarray(card_img) |
|---|
| | | 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=64) |
|---|
| | | card_info['card_hash_%d' % hs] = card_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 |
|---|
| | | new_pool.loc[0 if new_pool.empty else new_pool.index.max() + 1] = card_info |
|---|
| | |
|---|
| | | hash_size = [hash_size] |
|---|
| | | |
|---|
| | | num_cores = 15 |
|---|
| | | num_partitions = 60 |
|---|
| | | num_partitions = round(card_pool.shape[0]/100) |
|---|
| | | if num_partitions < min(num_cores, card_pool.shape[0]): |
|---|
| | | num_partitions = min(num_cores, card_pool.shape[0]) |
|---|
| | | pool = Pool(num_cores) |
|---|
| | | df_split = np.array_split(card_pool, num_partitions) |
|---|
| | | new_pool = pd.concat(pool.map(do_calc, [(split, hash_size) for split in df_split])) |
|---|
| | |
|---|
| | | return corrected |
|---|
| | | |
|---|
| | | |
|---|
| | | def find_card(img, thresh_c=5, kernel_size=(3, 3), size_thresh=10000): |
|---|
| | | def find_card(img, thresh_c=5, kernel_size=(3, 3), size_thresh=10000, debug=False): |
|---|
| | | """ |
|---|
| | | Find contours of all cards in the image |
|---|
| | | :param img: source image |
|---|
| | |
|---|
| | | # Typical pre-processing - grayscale, blurring, thresholding |
|---|
| | | img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) |
|---|
| | | img_blur = cv2.medianBlur(img_gray, 5) |
|---|
| | | img_thresh = cv2.adaptiveThreshold(img_blur, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 5, thresh_c) |
|---|
| | | |
|---|
| | | img_thresh = cv2.adaptiveThreshold(img_blur, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 11, thresh_c) |
|---|
| | | if debug: |
|---|
| | | cv2.imshow('Thres', img_thresh) |
|---|
| | | # Dilute the image, then erode them to remove minor noises |
|---|
| | | kernel = np.ones(kernel_size, np.uint8) |
|---|
| | | img_dilate = cv2.dilate(img_thresh, kernel, iterations=1) |
|---|
| | | img_erode = cv2.erode(img_dilate, kernel, iterations=1) |
|---|
| | | |
|---|
| | | if debug: |
|---|
| | | cv2.imshow('Eroded', img_erode) |
|---|
| | | # Find the contour |
|---|
| | | cnts, hier = cv2.findContours(img_erode, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) |
|---|
| | | if len(cnts) == 0: |
|---|
| | | #print('no contours') |
|---|
| | | return [] |
|---|
| | | |
|---|
| | | img_cont = cv2.cvtColor(img_erode, cv2.COLOR_GRAY2BGR) |
|---|
| | | img_cont_base = img_cont.copy() |
|---|
| | | cnts2 = sorted(cnts, key=cv2.contourArea, reverse=True) |
|---|
| | | cnts2 = cnts2[:10] |
|---|
| | | for i in range(0, len(cnts2)): |
|---|
| | | print(i, len(cnts2[i])) |
|---|
| | | if debug: |
|---|
| | | cv2.drawContours(img_cont, cnts2, -1, (0, 255, 0), 3) |
|---|
| | | cv2.imshow('Contours', img_cont) |
|---|
| | | # The hierarchy from cv2.findContours() is similar to a tree: each node has an access to the parent, the first child |
|---|
| | | # their previous and next node |
|---|
| | | # Using recursive search, find the uppermost contour in the hierarchy that satisfies the condition |
|---|
| | |
|---|
| | | peri = cv2.arcLength(cnt, True) |
|---|
| | | approx = cv2.approxPolyDP(cnt, 0.04 * peri, True) |
|---|
| | | if size >= size_thresh and len(approx) == 4: |
|---|
| | | cnts_rect.append(approx) |
|---|
| | | # lets see if we got a contour very close in size as child |
|---|
| | | if i_child != -1: |
|---|
| | | img_ccont = img_cont_base.copy() |
|---|
| | | # lets collect all children |
|---|
| | | c_list = [cnts[i_child]] |
|---|
| | | h_info = hier[0][i_child] |
|---|
| | | while h_info[0] != -1: |
|---|
| | | cld = cnts[h_info[0]] |
|---|
| | | c_list.append(cld) |
|---|
| | | h_info = hier[0][h_info[0]] |
|---|
| | | # child with biggest area |
|---|
| | | c_list.sort(key=cv2.contourArea, reverse=True) |
|---|
| | | c_cnt = c_list[0] # the biggest child |
|---|
| | | if debug: |
|---|
| | | cv2.drawContours(img_ccont, c_list[:1], -1, (0, 255, 0), 1) |
|---|
| | | cv2.imshow('CCont %d' % i_cnt, img_ccont) |
|---|
| | | c_size = cv2.contourArea(c_cnt) |
|---|
| | | c_approx = cv2.approxPolyDP(c_cnt, 0.04 * peri, True) |
|---|
| | | if len(c_approx) == 4 and (c_size/size) > 0.85: |
|---|
| | | rect = cv2.minAreaRect(c_cnt) |
|---|
| | | box = cv2.boxPoints(rect) |
|---|
| | | box = np.intp(box) |
|---|
| | | print(c_cnt) |
|---|
| | | print(box) |
|---|
| | | |
|---|
| | | print('CSize:', c_size, '%:', c_size/size) |
|---|
| | | b2 = [] |
|---|
| | | for x in box: |
|---|
| | | b2.append([x]) |
|---|
| | | cnts_rect.append(np.array(b2)) |
|---|
| | | else: |
|---|
| | | print('CF:', (c_size/size)) |
|---|
| | | print('Size:', size) |
|---|
| | | cnts_rect.append(approx) |
|---|
| | | else: |
|---|
| | | #print('CF:', (c_size/size)) |
|---|
| | | print('Size:', size) |
|---|
| | | cnts_rect.append(approx) |
|---|
| | | else: |
|---|
| | | if i_child != -1: |
|---|
| | | stack.append((i_child, hier[0][i_child])) |
|---|
| | |
|---|
| | | img_result = img.copy() # For displaying and saving |
|---|
| | | det_cards = [] |
|---|
| | | # Detect contours of all cards in the image |
|---|
| | | cnts = find_card(img_result, size_thresh=size_thresh) |
|---|
| | | cnts = find_card(img_result, size_thresh=size_thresh, debug=debug) |
|---|
| | | for i in range(len(cnts)): |
|---|
| | | cnt = cnts[i] |
|---|
| | | # For the region of the image covered by the contour, transform them into a rectangular image |
|---|
| | |
|---|
| | | card_pool['hash_diff'] = card_pool['art_hash'].apply(lambda x: np.count_nonzero(x != art_hash)) |
|---|
| | | ''' |
|---|
| | | img_card = Image.fromarray(img_warp.astype('uint8'), 'RGB') |
|---|
| | | img_card_size = img_warp.shape |
|---|
| | | print(img_card_size) |
|---|
| | | cut = [round(img_card_size[0]*0.57),round(img_card_size[0]*0.615),round(img_card_size[1]*0.81),round(img_card_size[1]*0.940)] |
|---|
| | | print(cut) |
|---|
| | | img_set_part = img_warp[cut[0]:cut[1], cut[2]:cut[3]] |
|---|
| | | print(img_set_part.shape) |
|---|
| | | img_set = Image.fromarray(img_set_part.astype('uint8'), 'RGB') |
|---|
| | | if debug: |
|---|
| | | cv2.imshow("Set Img#%d" % i, img_set_part) |
|---|
| | | |
|---|
| | | # the stored values of hashes in the dataframe is pre-emptively flattened already to minimize computation time |
|---|
| | | card_hash = ih.phash(img_card, hash_size=hash_size).hash.flatten() |
|---|
| | | card_pool['hash_diff'] = card_pool['card_hash_%d' % hash_size] |
|---|
| | | card_pool['hash_diff'] = card_pool['hash_diff'].apply(lambda x: np.count_nonzero(x != card_hash)) |
|---|
| | | min_card = card_pool[card_pool['hash_diff'] == min(card_pool['hash_diff'])].iloc[0] |
|---|
| | | hash_diff = min_card['hash_diff'] |
|---|
| | | |
|---|
| | | top_matches = sorted(card_pool['hash_diff']) |
|---|
| | | card_one = card_pool[card_pool['hash_diff'] == top_matches[0]].iloc[0] |
|---|
| | | card_two = card_pool[card_pool['hash_diff'] == top_matches[1]].iloc[0] |
|---|
| | | |
|---|
| | | if card_one['name'] == card_two['name'] and card_one['set'] != card_two['set']: |
|---|
| | | set_img_hash = ih.whash(img_set, hash_size=hash_size).hash.flatten() |
|---|
| | | cd_data = pd.DataFrame(columns=list(card_pool.columns.values)) |
|---|
| | | print(list(card_pool.columns.values)) |
|---|
| | | candidates = [] |
|---|
| | | for ix in range(0, 2): |
|---|
| | | cd = card_pool[card_pool['hash_diff'] == top_matches[ix]].iloc[0] |
|---|
| | | cd_data.loc[0 if cd_data.empty else cd_data.index.max()+1] = cd |
|---|
| | | print('Idx:', ix, 'Name:', cd['name'], 'Set:', cd['set'], 'Diff:', top_matches[ix]) |
|---|
| | | |
|---|
| | | |
|---|
| | | 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) |
|---|
| | | best_match = cd_data[cd_data['set_hash_diff'] == min(cd_data['set_hash_diff'])].iloc[0] |
|---|
| | | print('Best Match', 'Name:', best_match['name'], 'Set:', best_match['set']) |
|---|
| | | |
|---|
| | | min_card = best_match |
|---|
| | | card_name = min_card['name'] |
|---|
| | | card_set = min_card['set'] |
|---|
| | | det_cards.append((card_name, card_set)) |
|---|
| | | hash_diff = min_card['hash_diff'] |
|---|
| | | |
|---|
| | | # Render the result, and display them if needed |
|---|
| | | cv2.drawContours(img_result, [cnt], -1, (0, 255, 0), 2) |
|---|
| | |
|---|
| | | |
|---|
| | | |
|---|
| | | def detect_video(capture, card_pool, hash_size=32, size_thresh=10000, |
|---|
| | | out_path=None, display=True, show_graph=True, debug=False): |
|---|
| | | out_path=None, display=True, show_graph=True, debug=False, crop_x=0, crop_y=0): |
|---|
| | | """ |
|---|
| | | Identify all cards in the continuous video stream, display or save the result if needed |
|---|
| | | :param capture: input video stream |
|---|
| | |
|---|
| | | # Get the dimension of the output video, and set it up |
|---|
| | | if show_graph: |
|---|
| | | img_graph = draw_card_graph({}, pd.DataFrame(), -1) # Black image of the graph just to get the dimension |
|---|
| | | width = round(capture.get(cv2.CAP_PROP_FRAME_WIDTH)) + img_graph.shape[1] |
|---|
| | | height = max(round(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)), img_graph.shape[0]) |
|---|
| | | width = round(capture.get(cv2.CAP_PROP_FRAME_WIDTH)) - 2*crop_x + img_graph.shape[1] |
|---|
| | | height = max(round(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)) - 2*crop_y, img_graph.shape[0]) |
|---|
| | | height += 200 # some space to display last detected cards |
|---|
| | | else: |
|---|
| | | width = round(capture.get(cv2.CAP_PROP_FRAME_WIDTH)) |
|---|
| | | height = round(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)) |
|---|
| | |
|---|
| | | max_num_obj = 0 |
|---|
| | | f_len = 10 # number of frames to consider to check for existing cards |
|---|
| | | exist_cards = {} |
|---|
| | | |
|---|
| | | exist_card_single = {} |
|---|
| | | written_out_cards = set() |
|---|
| | | found_cards = [] |
|---|
| | | try: |
|---|
| | | while True: |
|---|
| | | ret, frame = capture.read() |
|---|
| | | croped_img = frame[crop_y:-crop_y, crop_x:-crop_x] |
|---|
| | | fimg = cv2.flip(croped_img, -1) |
|---|
| | | start_time = time.time() |
|---|
| | | if not ret: |
|---|
| | | # End of video |
|---|
| | |
|---|
| | | cv2.waitKey(0) |
|---|
| | | break |
|---|
| | | # Detect all cards from the current frame |
|---|
| | | det_cards, img_result = detect_frame(frame, card_pool, hash_size=hash_size, size_thresh=size_thresh, |
|---|
| | | det_cards, img_result = detect_frame(fimg, card_pool, hash_size=hash_size, size_thresh=size_thresh, |
|---|
| | | out_path=None, display=False, debug=debug) |
|---|
| | | if show_graph: |
|---|
| | | # If the card was already detected in the previous frame, append 1 to the list |
|---|
| | |
|---|
| | | else: |
|---|
| | | exist_cards[key] = exist_cards[key][1 - f_len:] + [0] |
|---|
| | | if len(val) == f_len and sum(val) == 0: |
|---|
| | | gone.append(key) |
|---|
| | | gone.append(key) # not there anymore |
|---|
| | | |
|---|
| | | det_card_map = {} |
|---|
| | | gone_single = [] |
|---|
| | | for card_name, card_set in det_cards: |
|---|
| | | skey = '%s (%s)' % (card_name, card_set) |
|---|
| | | det_card_map[skey] = (card_name, card_set) |
|---|
| | | |
|---|
| | | for key, val in exist_card_single.items(): |
|---|
| | | if key in det_card_map: |
|---|
| | | exist_card_single[key] = exist_card_single[key][1 - f_len:] + [1] |
|---|
| | | else: |
|---|
| | | exist_card_single[key] = exist_card_single[key][1 - f_len:] + [0] |
|---|
| | | |
|---|
| | | if len(val) == f_len and sum(val) == 0: |
|---|
| | | gone_single.append(key) |
|---|
| | | if key in written_out_cards: |
|---|
| | | written_out_cards.remove(key) |
|---|
| | | if len(val) == f_len and sum(val) == f_len: |
|---|
| | | if key not in written_out_cards and key in det_card_map: |
|---|
| | | written_out_cards.add(key) |
|---|
| | | found_cards.append(det_card_map[key]) |
|---|
| | | |
|---|
| | | for key in det_card_map: |
|---|
| | | if key not in exist_card_single.keys(): |
|---|
| | | exist_card_single[key] = [1] |
|---|
| | | for key in gone_single: |
|---|
| | | exist_card_single.pop(key) |
|---|
| | | |
|---|
| | | |
|---|
| | | for key in det_cards_list: |
|---|
| | | if key not in exist_cards.keys(): |
|---|
| | | exist_cards[key] = [1] |
|---|
| | | for key in gone: |
|---|
| | | exist_cards.pop(key) |
|---|
| | | |
|---|
| | | |
|---|
| | | # Draw the graph based on the history of detected cards, then concatenate it with the result image |
|---|
| | | img_graph = draw_card_graph(exist_cards, card_pool, f_len) |
|---|
| | | img_save = np.zeros((height, width, 3), dtype=np.uint8) |
|---|
| | | img_save[0:img_result.shape[0], 0:img_result.shape[1]] = img_result |
|---|
| | | img_save[0:img_graph.shape[0], img_result.shape[1]:img_result.shape[1] + img_graph.shape[1]] = img_graph |
|---|
| | | for c, card in enumerate(reversed(found_cards[-10:]), 1): |
|---|
| | | cv2.putText(img_save, f'{card[0]} ({card[1].upper()})',(0, height-200+18*c), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0)) |
|---|
| | | else: |
|---|
| | | img_save = img_result |
|---|
| | | |
|---|
| | |
|---|
| | | vid_writer.release() |
|---|
| | | cv2.destroyAllWindows() |
|---|
| | | |
|---|
| | | with open('detect.txt', 'w') as of: |
|---|
| | | counter = collections.Counter(found_cards) |
|---|
| | | for key in counter: |
|---|
| | | of.write(f'{counter[key]} [{key[1].upper()}] {key[0]}\n') |
|---|
| | | |
|---|
| | | |
|---|
| | | |
|---|
| | | def main(args): |
|---|
| | | # Specify paths for all necessary files |
|---|
| | |
|---|
| | | # 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' % 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,)) |
|---|
| | | card_pool = calc_image_hashes(card_pool, save_to=pck_path, hash_size=[args.hash_size]) |
|---|
| | | |
|---|
| | | card_pool = card_pool[['name', 'set', 'collector_number', ch_key]] |
|---|
| | | card_pool = card_pool[['name', 'set', 'collector_number', ch_key, set_key]] |
|---|
| | | |
|---|
| | | # Processing time is almost linear to the size of the database |
|---|
| | | # Program can be much faster if the search scope for the card can be reduced |
|---|
| | |
|---|
| | | # ImageHash is basically just one numpy.ndarray with (hash_size)^2 number of bits. pre-emptively flattening it |
|---|
| | | # significantly increases speed for subtracting hashes in the future. |
|---|
| | | card_pool[ch_key] = card_pool[ch_key].apply(lambda x: x.hash.flatten()) |
|---|
| | | card_pool[set_key] = card_pool[set_key].apply(lambda x: x.hash.flatten()) |
|---|
| | | |
|---|
| | | # If the test file isn't given, use webcam to capture video |
|---|
| | | if args.in_path is None: |
|---|
| | | capture = cv2.VideoCapture(0) |
|---|
| | | capture = cv2.VideoCapture(0, cv2.CAP_V4L) |
|---|
| | | capture.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*"MJPG")) |
|---|
| | | capture.set(cv2.CAP_PROP_FRAME_WIDTH, 1920) |
|---|
| | | capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 1080) |
|---|
| | | detect_video(capture, card_pool, hash_size=args.hash_size, out_path='%s/result.avi' % args.out_path, |
|---|
| | | display=args.display, show_graph=args.show_graph, debug=args.debug) |
|---|
| | | display=args.display, show_graph=args.show_graph, debug=args.debug, crop_x=500, crop_y=200) |
|---|
| | | capture.release() |
|---|
| | | else: |
|---|
| | | # Save the detection result if args.out_path is provided |
|---|
