old mode 100644
new mode 100755
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| | | import pandas as pd |
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| | | from PIL import Image |
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| | | import time |
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| | | |
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| | | from multiprocessing import Pool |
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| | | from config import Config |
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| | | import fetch_data |
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| | | |
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| | |
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| | | https://github.com/hj3yoo/mtg_card_detector/tree/dea64611730c84a59c711c61f7f80948f82bcd31 |
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| | | """ |
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| | | |
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| | | |
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| | | def calc_image_hashes(card_pool, save_to=None, hash_size=None): |
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| | | """ |
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| | | Calculate perceptual hash (pHash) value for each cards in the database, then store them if needed |
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| | | :param card_pool: pandas dataframe containing all card information |
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| | | :param save_to: path for the pickle file to be saved |
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| | | :param hash_size: param for pHash algorithm |
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| | | :return: pandas dataframe |
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| | | """ |
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| | | if hash_size is None: |
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| | | hash_size = [16, 32] |
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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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| | | # Since some double-faced cards may result in two different cards, create a new dataframe to store the result |
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| | | def do_calc(args): |
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| | | card_pool = args[0] |
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| | | hash_size = args[1] |
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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['art_hash_%d' % hs] = np.NaN |
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| | | new_pool['card_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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| | | print('Calculating hashes: %dth card' % ind) |
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| | |
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| | | for card_name in card_names: |
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| | | # Fetch the image - name can be found based on the card's information |
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| | | card_info['name'] = card_name |
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| | | cname = card_name |
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| | | if cname == 'con': |
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| | | cname == 'con__' |
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| | | img_name = '%s/card_img/png/%s/%s_%s.png' % (Config.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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| | | fetch_data.get_valid_filename(cname)) |
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| | | card_img = cv2.imread(img_name) |
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| | | |
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| | | # If the image doesn't exist, download it from the URL |
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| | | if card_img is None: |
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| | | set_name = card_info['set'] |
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| | | if set_name == 'con': |
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| | | set_name = 'con__' |
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| | | fetch_data.fetch_card_image(card_info, |
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| | | out_dir='%s/card_img/png/%s' % (Config.data_dir, card_info['set'])) |
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| | | out_dir='%s/card_img/png/%s' % (Config.data_dir, set_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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| | | |
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| | | continue |
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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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| | | img_card = Image.fromarray(card_img) |
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| | | img_set = Image.fromarray(set_img) |
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| | | #cv2.imshow('Set' + card_names[0], 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.phash(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' % 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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| | | new_pool.loc[0 if new_pool.empty else new_pool.index.max() + 1] = card_info |
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| | | return new_pool |
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| | | |
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| | | def calc_image_hashes(card_pool, save_to=None, hash_size=None): |
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| | | """ |
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| | | Calculate perceptual hash (pHash) value for each cards in the database, then store them if needed |
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| | | :param card_pool: pandas dataframe containing all card information |
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| | | :param save_to: path for the pickle file to be saved |
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| | | :param hash_size: param for pHash algorithm |
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| | | :return: pandas dataframe |
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| | | """ |
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| | | if hash_size is None: |
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| | | hash_size = [16, 32] |
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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 = 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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| | | df_split = np.array_split(card_pool, num_partitions) |
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| | | new_pool = pd.concat(pool.map(do_calc, [(split, hash_size) for split in df_split])) |
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| | | pool.close() |
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| | | pool.join() |
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| | | # Since some double-faced cards may result in two different cards, create a new dataframe to store the result |
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| | | |
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| | | if save_to is not None: |
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| | | new_pool.to_pickle(save_to) |
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| | |
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| | | return corrected |
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| | | |
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| | | |
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| | | def find_card(img, thresh_c=5, kernel_size=(3, 3), size_thresh=10000): |
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| | | def find_card(img, thresh_c=5, kernel_size=(3, 3), size_thresh=10000, debug=False): |
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| | | """ |
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| | | Find contours of all cards in the image |
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| | | :param img: source image |
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| | |
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| | | # Typical pre-processing - grayscale, blurring, thresholding |
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| | | img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) |
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| | | img_blur = cv2.medianBlur(img_gray, 5) |
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| | | img_thresh = cv2.adaptiveThreshold(img_blur, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 5, thresh_c) |
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| | | |
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| | | img_thresh = cv2.adaptiveThreshold(img_blur, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 11, thresh_c) |
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| | | if debug: |
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| | | cv2.imshow('Thres', img_thresh) |
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| | | # Dilute the image, then erode them to remove minor noises |
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| | | kernel = np.ones(kernel_size, 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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| | | |
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| | | if debug: |
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| | | cv2.imshow('Eroded', img_erode) |
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| | | # Find the contour |
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| | | _, cnts, hier = cv2.findContours(img_erode, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) |
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| | | cnts, hier = cv2.findContours(img_erode, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) |
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| | | if len(cnts) == 0: |
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| | | #print('no contours') |
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| | | # print('no contours') |
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| | | return [] |
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| | | |
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| | | img_cont = cv2.cvtColor(img_erode, cv2.COLOR_GRAY2BGR) |
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| | | img_cont_base = img_cont.copy() |
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| | | cnts2 = sorted(cnts, key=cv2.contourArea, reverse=True) |
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| | | cnts2 = cnts2[:10] |
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| | | # for i in range(0, len(cnts2)): |
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| | | # print(i, len(cnts2[i])) |
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| | | if debug: |
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| | | cv2.drawContours(img_cont, cnts2, -1, (0, 255, 0), 3) |
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| | | cv2.imshow('Contours', img_cont) |
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| | | # The hierarchy from cv2.findContours() is similar to a tree: each node has an access to the parent, the first child |
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| | | # their previous and next node |
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| | | # Using recursive search, find the uppermost contour in the hierarchy that satisfies the condition |
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| | |
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| | | size = cv2.contourArea(cnt) |
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| | | peri = cv2.arcLength(cnt, True) |
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| | | approx = cv2.approxPolyDP(cnt, 0.04 * peri, True) |
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| | | #print('Base Size:', size) |
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| | | #print('Len Approx:', len(approx)) |
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| | | if size >= size_thresh and len(approx) == 4: |
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| | | cnts_rect.append(approx) |
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| | | # lets see if we got a contour very close in size as child |
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| | | if i_child != -1: |
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| | | img_ccont = img_cont_base.copy() |
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| | | # lets collect all children |
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| | | c_list = [cnts[i_child]] |
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| | | h_info = hier[0][i_child] |
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| | | while h_info[0] != -1: |
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| | | cld = cnts[h_info[0]] |
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| | | c_list.append(cld) |
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| | | h_info = hier[0][h_info[0]] |
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| | | # child with biggest area |
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| | | c_list.sort(key=cv2.contourArea, reverse=True) |
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| | | c_cnt = c_list[0] # the biggest child |
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| | | if debug: |
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| | | cv2.drawContours(img_ccont, c_list[:1], -1, (0, 255, 0), 1) |
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| | | cv2.imshow('CCont', img_ccont) |
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| | | c_size = cv2.contourArea(c_cnt) |
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| | | c_approx = cv2.approxPolyDP(c_cnt, 0.04 * peri, True) |
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| | | if len(c_approx) == 4 and (c_size/size) > 0.85: |
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| | | rect = cv2.minAreaRect(c_cnt) |
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| | | box = cv2.boxPoints(rect) |
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| | | box = np.intp(box) |
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| | | #print(c_cnt) |
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| | | #print(box) |
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| | | |
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| | | #print('CSize:', c_size, '%:', c_size/size) |
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| | | b2 = [] |
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| | | for x in box: |
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| | | b2.append([x]) |
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| | | cnts_rect.append(np.array(b2)) |
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| | | else: |
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| | | #print('CF:', (c_size/size)) |
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| | | #print('Size:', size) |
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| | | cnts_rect.append(approx) |
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| | | else: |
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| | | #print('CF:', (c_size/size)) |
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| | | #print('Size:', size) |
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| | | cnts_rect.append(approx) |
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| | | else: |
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| | | if i_child != -1: |
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| | | stack.append((i_child, hier[0][i_child])) |
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| | |
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| | | img_result = img.copy() # For displaying and saving |
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| | | det_cards = [] |
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| | | # Detect contours of all cards in the image |
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| | | cnts = find_card(img_result, size_thresh=size_thresh) |
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| | | cnts = find_card(img_result, size_thresh=size_thresh, debug=debug) |
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| | | #print('Contours:', len(cnts)) |
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| | | for i in range(len(cnts)): |
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| | | #print('Contour', i) |
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| | | cnt = cnts[i] |
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| | | # For the region of the image covered by the contour, transform them into a rectangular image |
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| | | pts = np.float32([p[0] for p in cnt]) |
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| | |
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| | | card_pool['hash_diff'] = card_pool['art_hash'].apply(lambda x: np.count_nonzero(x != art_hash)) |
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| | | ''' |
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| | | img_card = Image.fromarray(img_warp.astype('uint8'), 'RGB') |
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| | | img_card_size = img_warp.shape |
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| | | #print(img_card_size) |
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| | | 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)] |
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| | | #print(cut) |
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| | | img_set_part = img_warp[cut[0]:cut[1], cut[2]:cut[3]] |
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| | | #print(img_set_part.shape) |
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| | | img_set = Image.fromarray(img_set_part.astype('uint8'), 'RGB') |
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| | | #print('img set') |
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| | | if debug: |
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| | | cv2.imshow("Set Img#%d" % i, img_set_part) |
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| | | |
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| | | # the stored values of hashes in the dataframe is pre-emptively flattened already to minimize computation time |
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| | | card_hash = ih.phash(img_card, hash_size=hash_size).hash.flatten() |
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| | | card_pool['hash_diff'] = card_pool['card_hash_%d' % hash_size] |
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| | | card_pool['hash_diff'] = card_pool['hash_diff'].apply(lambda x: np.count_nonzero(x != card_hash)) |
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| | | min_card = card_pool[card_pool['hash_diff'] == min(card_pool['hash_diff'])].iloc[0] |
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| | | hash_diff = min_card['hash_diff'] |
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| | | |
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| | | top_matches = sorted(card_pool['hash_diff']) |
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| | | card_one = card_pool[card_pool['hash_diff'] == top_matches[0]].iloc[0] |
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| | | card_two = card_pool[card_pool['hash_diff'] == top_matches[1]].iloc[0] |
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| | | |
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| | | if card_one['name'] == card_two['name'] and card_one['set'] != card_two['set']: |
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| | | set_img_hash = ih.whash(img_set, hash_size=hash_size).hash.flatten() |
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| | | cd_data = pd.DataFrame(columns=list(card_pool.columns.values)) |
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| | | # print(list(card_pool.columns.values)) |
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| | | candidates = [] |
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| | | for ix in range(0, 2): |
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| | | cd = card_pool[card_pool['hash_diff'] == top_matches[ix]].iloc[0] |
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| | | cd_data.loc[0 if cd_data.empty else cd_data.index.max()+1] = cd |
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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' % 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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| | | best_match = cd_data[cd_data['set_hash_diff'] == min(cd_data['set_hash_diff'])].iloc[0] |
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| | | #print('Best Match', 'Name:', best_match['name'], 'Set:', best_match['set']) |
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| | | |
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| | | min_card = best_match |
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| | | card_name = min_card['name'] |
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| | | card_set = min_card['set'] |
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| | | det_cards.append((card_name, card_set)) |
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| | | hash_diff = min_card['hash_diff'] |
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| | | |
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| | | # Render the result, and display them if needed |
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| | | cv2.drawContours(img_result, [cnt], -1, (0, 255, 0), 2) |
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| | | cv2.putText(img_result, card_name, (min(pts[0][0], pts[1][0]), min(pts[0][1], pts[1][1])), |
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| | | cv2.putText(img_result, card_name, (int(min(pts[0][0], pts[1][0])), int(min(pts[0][1], pts[1][1]))), |
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| | | cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2) |
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| | | if debug: |
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| | | # cv2.rectangle(img_warp, (22, 47), (294, 249), (0, 255, 0), 2) |
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| | | cv2.putText(img_warp, card_name + ', ' + str(hash_diff), (0, 20), |
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| | | cv2.putText(img_warp, card_name + ':' + card_set + ', ' + str(hash_diff), (0, 20), |
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| | | cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 255, 255), 1) |
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| | | cv2.imshow('card#%d' % i, img_warp) |
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| | | if display: |
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| | | cv2.imshow('Result', img_result) |
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| | | cv2.waitKey(0) |
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| | | inp = cv2.waitKey(0) |
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| | | |
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| | | if out_path is not None: |
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| | | print(out_path) |
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| | | cv2.imwrite(out_path, img_result.astype(np.uint8)) |
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| | | return det_cards, img_result |
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| | | |
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| | | |
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| | | def detect_video(capture, card_pool, hash_size=32, size_thresh=10000, |
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| | | out_path=None, display=True, show_graph=True, debug=False): |
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| | | out_path=None, display=True, show_graph=True, debug=False, crop_x=0, crop_y=0): |
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| | | """ |
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| | | Identify all cards in the continuous video stream, display or save the result if needed |
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| | | :param capture: input video stream |
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| | |
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| | | :return: list of detected card's name/set and resulting image |
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| | | :return: |
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| | | """ |
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| | | list_names_from = 0 |
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| | | # Get the dimension of the output video, and set it up |
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| | | if show_graph: |
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| | | img_graph = draw_card_graph({}, pd.DataFrame(), -1) # Black image of the graph just to get the dimension |
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| | | width = round(capture.get(cv2.CAP_PROP_FRAME_WIDTH)) + img_graph.shape[1] |
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| | | height = max(round(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)), img_graph.shape[0]) |
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| | | width = round(capture.get(cv2.CAP_PROP_FRAME_WIDTH)) - 2*crop_x + img_graph.shape[1] |
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| | | height = max(round(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)) - 2*crop_y, img_graph.shape[0]) |
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| | | height += 200 # some space to display last detected cards |
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| | | else: |
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| | | width = round(capture.get(cv2.CAP_PROP_FRAME_WIDTH)) |
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| | | height = round(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)) |
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| | |
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| | | max_num_obj = 0 |
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| | | f_len = 10 # number of frames to consider to check for existing cards |
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| | | exist_cards = {} |
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| | | |
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| | | exist_card_single = {} |
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| | | written_out_cards = set() |
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| | | found_cards = [] |
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| | | try: |
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| | | while True: |
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| | | ret, frame = capture.read() |
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| | | if not ret: |
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| | | continue |
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| | | y_max_index = -crop_y |
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| | | if crop_y == 0: |
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| | | y_max_index = frame.shape[0] |
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| | | x_max_index = -crop_x |
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| | | if crop_x == 0: |
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| | | x_max_index = frame.shape[1] |
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| | | |
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| | | croped_img = frame[crop_y:y_max_index, crop_x:x_max_index] |
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| | | fimg = cv2.flip(croped_img, -1) |
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| | | start_time = time.time() |
|---|
| | | if not ret: |
|---|
| | | # End of video |
|---|
| | | print("End of video. Press any key to exit") |
|---|
| | | cv2.waitKey(0) |
|---|
| | | break |
|---|
| | | if fimg is None: |
|---|
| | | print("flipped image is none") |
|---|
| | | 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]) |
|---|
| | | list_names_from += 1 |
|---|
| | | |
|---|
| | | 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 |
|---|
| | | start_at = max(0,list_names_from-10) |
|---|
| | | end_at = min(len(found_cards), list_names_from) |
|---|
| | | for c, card in enumerate(reversed(found_cards[start_at:end_at]), 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 |
|---|
| | | |
|---|
| | |
|---|
| | | print('Elapsed time: %.2f ms' % elapsed_ms) |
|---|
| | | if out_path is not None: |
|---|
| | | vid_writer.write(img_save.astype(np.uint8)) |
|---|
| | | cv2.waitKey(1) |
|---|
| | | if debug: |
|---|
| | | print("Waiting for keypress to continue") |
|---|
| | | inp = cv2.waitKey(0) |
|---|
| | | else: |
|---|
| | | inp = cv2.waitKey(1) |
|---|
| | | if 'u' == chr(inp & 255): |
|---|
| | | if len(found_cards) > 0: |
|---|
| | | del found_cards[list_names_from-1] |
|---|
| | | list_names_from = min(len(found_cards), max(0, list_names_from)) |
|---|
| | | |
|---|
| | | #os.sleep(1000) |
|---|
| | | elif 'p' == chr(inp & 255): |
|---|
| | | list_names_from = max(1, list_names_from - 1) |
|---|
| | | elif 'o' == chr(inp & 255): |
|---|
| | | list_names_from = min(len(found_cards),list_names_from + 1) |
|---|
| | | elif 'q' == chr(inp & 255): |
|---|
| | | break |
|---|
| | | except KeyboardInterrupt: |
|---|
| | | print("KeyboardInterrupt happened") |
|---|
| | | finally: |
|---|
| | | write_found_cards(found_cards) |
|---|
| | | capture.release() |
|---|
| | | if out_path is not None: |
|---|
| | | vid_writer.release() |
|---|
| | | cv2.destroyAllWindows() |
|---|
| | | |
|---|
| | | def write_found_cards(found_cards): |
|---|
| | | 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 |
|---|
| | | |
|---|
| | | hash_sizes = {16, 32} |
|---|
| | | hash_sizes.add(args.hash_size) |
|---|
| | | pck_path = os.path.abspath('card_pool.pck') |
|---|
| | | if os.path.isfile(pck_path): |
|---|
| | | card_pool = pd.read_pickle(pck_path) |
|---|
| | |
|---|
| | | # 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 = pd.concat(df_list, sort=True) |
|---|
| | | card_pool.reset_index(drop=True, inplace=True) |
|---|
| | | card_pool.drop('Unnamed: 0', axis=1, inplace=True, errors='ignore') |
|---|
| | | calc_image_hashes(card_pool, save_to=pck_path) |
|---|
| | | card_pool = calc_image_hashes(card_pool, save_to=pck_path, hash_size=hash_sizes) |
|---|
| | | ch_key = 'card_hash_%d' % args.hash_size |
|---|
| | | card_pool = card_pool[['name', 'set', 'collector_number', ch_key]] |
|---|
| | | 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, 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 |
|---|
| | | card_pool = card_pool[card_pool['set'].isin(Config.set_2003_list)] |
|---|
| | | #card_pool = card_pool[card_pool['set'].isin(Config.set_2003_list)] |
|---|
| | | |
|---|
| | | # 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()) |
|---|
| | | print("Hash-Database setup done") |
|---|
| | | # If the test file isn't given, use webcam to capture video |
|---|
| | | if args.in_path is None: |
|---|
| | | capture = cv2.VideoCapture(0) |
|---|
| | | 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) |
|---|
| | | if args.stream_url is None: |
|---|
| | | print("Using webcam") |
|---|
| | | capture = cv2.VideoCapture(0) |
|---|
| | | 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) |
|---|
| | | else: |
|---|
| | | print(f"Using streami {args.stream_url}") |
|---|
| | | capture = cv2.VideoCapture(args.stream_url) |
|---|
| | | |
|---|
| | | thres = int((1920-2*args.crop_x)*(1080-2*args.crop_y)*(float(args.threshold_percent)/100)) |
|---|
| | | print('Threshold:', thres) |
|---|
| | | if args.out_path is None: |
|---|
| | | out_path = None |
|---|
| | | else: |
|---|
| | | out_path = '%s/result.avi' % args.out_path |
|---|
| | | detect_video(capture, card_pool, hash_size=args.hash_size, out_path=out_path, |
|---|
| | | display=args.display, show_graph=args.show_graph, debug=args.debug, crop_x=args.crop_x, crop_y=args.crop_y, size_thresh=thres) |
|---|
| | | capture.release() |
|---|
| | | else: |
|---|
| | | print(f"Using image or video {args.in_path}") |
|---|
| | | # Save the detection result if args.out_path is provided |
|---|
| | | if args.out_path is None: |
|---|
| | | out_path = None |
|---|
| | |
|---|
| | | if test_ext in ['jpg', 'jpeg', 'bmp', 'png', 'tiff']: |
|---|
| | | # Test file is an image |
|---|
| | | img = cv2.imread(args.in_path) |
|---|
| | | if img is None: |
|---|
| | | print('Could not read', args.in_path) |
|---|
| | | detect_frame(img, card_pool, hash_size=args.hash_size, out_path=out_path, display=args.display, |
|---|
| | | debug=args.debug) |
|---|
| | | else: |
|---|
| | |
|---|
| | | parser.add_argument('-dbg', '--debug', dest='debug', help='Enable debug mode', action='store_true', default=False) |
|---|
| | | parser.add_argument('-gph', '--show_graph', dest='show_graph', help='Display the graph for video output', |
|---|
| | | action='store_true', default=False) |
|---|
| | | parser.add_argument('-s', '--stream', dest='stream_url', type=str) |
|---|
| | | parser.add_argument('-cx', '--crop-x', dest='crop_x', help='crop x amount of pixel on each side in x-axis', type=int, default=0) |
|---|
| | | parser.add_argument('-cy', '--crop-y', dest='crop_y', help='crop x amount of pixel on each side in y-axis', type=int, default=0) |
|---|
| | | parser.add_argument('-tp', '--threshold-percent', dest='threshold_percent', help='percentage amount that the card image needs to take up to be detected',type=int, default=5) |
|---|
| | | args = parser.parse_args() |
|---|
| | | if not args.display and args.out_path is None: |
|---|
| | | # Then why the heck are you running this thing in the first place? |
|---|
