removed test files

SpeedProg

2020-01-02 8468f19b741475059bbd0bc4db310ee05ea7e6fb

 opencv_dnn.py

old mode 100644
new mode 100755
@@ -28,7 +28,7 @@
    new_pool = pd.DataFrame(columns=list(card_pool.columns.values))
    for hs in hash_size:
        new_pool['card_hash_%d' % hs] = np.NaN
        new_pool['set_hash_%d' % hs] = np.NaN
        new_pool['set_hash_%d' % 64] = np.NaN
        #new_pool['art_hash_%d' % hs] = np.NaN
    for ind, card_info in card_pool.iterrows():
        if ind % 100 == 0:
@@ -49,31 +49,52 @@
        for card_name in card_names:
            # Fetch the image - name can be found based on the card's information
            card_info['name'] = card_name
            cname = card_name
            if cname == 'con':
                cname == 'con__'
            img_name = '%s/card_img/png/%s/%s_%s.png' % (Config.data_dir, card_info['set'],
                                                         card_info['collector_number'],
                                                         fetch_data.get_valid_filename(card_info['name']))
                                                         fetch_data.get_valid_filename(cname))
            card_img = cv2.imread(img_name)

            # If the image doesn't exist, download it from the URL
            if card_img is None:
                set_name = card_info['set']
                if set_name == 'con':
                    set_name = 'con__'
                fetch_data.fetch_card_image(card_info,
                                            out_dir='%s/card_img/png/%s' % (Config.data_dir, card_info['set']))
                                            out_dir='%s/card_img/png/%s' % (Config.data_dir, set_name))
                card_img = cv2.imread(img_name)
            if card_img is None:
                print('WARNING: card %s is not found!' % img_name)
                continue

            set_img = card_img[575:638, 567:700]
            """
            img_cc = cv2.cvtColor(card_img, cv2.COLOR_BGR2GRAY)
            img_thresh = cv2.adaptiveThreshold(img_cc, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 11, 5)
            # Dilute the image, then erode them to remove minor noises
            kernel = np.ones((3, 3), np.uint8)
            img_dilate = cv2.dilate(img_thresh, kernel, iterations=1)
            img_erode = cv2.erode(img_dilate, kernel, iterations=1)
            cnts, hier = cv2.findContours(img_erode, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
            cnts2 = sorted(cnts, key=cv2.contourArea, reverse=True)
            cnts2 = cnts2[:10]
            if True:
                cv2.drawContours(img_cc, cnts2, -1, (0, 255, 0), 3)
                #cv2.imshow('Contours', card_img)
                #cv2.waitKey(10000)
            """
            set_img = card_img[595:635, 600:690]
            #cv2.imshow(card_info['name'], set_img)
            # Compute value of the card's perceptual hash, then store it to the database
            #img_art = Image.fromarray(card_img[121:580, 63:685])  # For 745*1040 size card image
            img_card = Image.fromarray(card_img)
            img_set = Image.fromarray(set_img)
            #cv2.imshow('Set' + card_names[0], set_img)
            for hs in hash_size:
                card_hash = ih.phash(img_card, hash_size=hs)
                set_hash = ih.whash(img_set, hash_size=hs)
                set_hash = ih.whash(img_set, hash_size=64)
                card_info['card_hash_%d' % hs] = card_hash
                card_info['set_hash_%d' % hs] = set_hash
                card_info['set_hash_%d' % 64] = set_hash
                #print('Setting set_hash_%d' % hs)
                #art_hash = ih.phash(img_art, hash_size=hs)
                #card_info['art_hash_%d' % hs] = art_hash
@@ -93,8 +114,8 @@
    elif isinstance(hash_size, int):
        hash_size = [hash_size]

    num_cores = 15
    num_partitions = round(card_pool.shape[0]/100)
    num_cores = 16
    num_partitions = round(card_pool.shape[0]/1000)
    if num_partitions < min(num_cores, card_pool.shape[0]):
        num_partitions = min(num_cores, card_pool.shape[0])
    pool = Pool(num_cores)
@@ -242,7 +263,7 @@
    # Find the contour
    cnts, hier = cv2.findContours(img_erode, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    if len(cnts) == 0:
        #print('no contours')
        print('no contours')
        return []
    img_cont = cv2.cvtColor(img_erode, cv2.COLOR_GRAY2BGR)
    img_cont_base = img_cont.copy()
@@ -268,6 +289,8 @@
        size = cv2.contourArea(cnt)
        peri = cv2.arcLength(cnt, True)
        approx = cv2.approxPolyDP(cnt, 0.04 * peri, True)
        print('Base Size:', size)
        print('Len Approx:', len(approx))
        if size >= size_thresh and len(approx) == 4:
            # lets see if we got a contour very close in size as child
            if i_child != -1:
@@ -284,7 +307,7 @@
                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)
                    cv2.imshow('CCont', 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:
@@ -374,7 +397,7 @@
    return img_graph


def detect_frame(img, card_pool, hash_size=32, size_thresh=100000,
def detect_frame(img, card_pool, hash_size=32, size_thresh=10000,
                 out_path=None, display=True, debug=False):
    """
    Identify all cards in the input frame, display or save the frame if needed
@@ -392,7 +415,9 @@
    det_cards = []
    # Detect contours of all cards in the image
    cnts = find_card(img_result, size_thresh=size_thresh, debug=debug)
    print('Countours:', len(cnts))
    for i in range(len(cnts)):
        print('Contour', i)
        cnt = cnts[i]
        # For the region of the image covered by the contour, transform them into a rectangular image
        pts = np.float32([p[0] for p in cnt])
@@ -417,6 +442,7 @@
        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')
        print('img set')
        if debug:
            cv2.imshow("Set Img#%d" % i, img_set_part)

@@ -442,7 +468,7 @@
                print('Idx:', ix, 'Name:', cd['name'], 'Set:', cd['set'], 'Diff:', top_matches[ix])


            cd_data['set_hash_diff'] = cd_data['set_hash_%d' % hash_size]
            cd_data['set_hash_diff'] = cd_data['set_hash_%d' % 64]
            cd_data['set_hash_diff'] = cd_data['set_hash_diff'].apply(lambda x: np.count_nonzero(x != set_img_hash))
            conf = sorted(cd_data['set_hash_diff'])
            print('Confs:', conf)
@@ -465,9 +491,10 @@
            cv2.imshow('card#%d' % i, img_warp)
    if display:
        cv2.imshow('Result', img_result)
        cv2.waitKey(0)
        inp = cv2.waitKey(0)

    if out_path is not None:
        print(out_path)
        cv2.imwrite(out_path, img_result.astype(np.uint8))
    return det_cards, img_result

@@ -598,7 +625,9 @@
            print('Elapsed time: %.2f ms' % elapsed_ms)
            if out_path is not None:
                vid_writer.write(img_save.astype(np.uint8))
            cv2.waitKey(1)
            inp = cv2.waitKey(0)
            if 'q' == chr(inp & 255):
                break
    except KeyboardInterrupt:
        capture.release()
        if out_path is not None:
@@ -624,6 +653,8 @@
        # 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)
@@ -632,7 +663,7 @@
        card_pool.drop('Unnamed: 0', axis=1, inplace=True, errors='ignore')
        card_pool = calc_image_hashes(card_pool, save_to=pck_path, hash_size=hash_sizes)
    ch_key = 'card_hash_%d' % args.hash_size
    set_key = 'set_hash_%d' % args.hash_size
    set_key = 'set_hash_%d' % 64
    if ch_key not in card_pool.columns:
        # we did not generate this hash_size yet
        print('We need to add hash_size=%d' % (args.hash_size,))
@@ -655,8 +686,11 @@
        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)

        thres = int(((1920-2*500)*(1080-2*200)*0.3))
        print('Threshold:', thres)
        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, crop_x=500, crop_y=200)
                     display=args.display, show_graph=args.show_graph, debug=args.debug, crop_x=500, crop_y=200, size_thresh=thres)
        capture.release()
    else:
        # Save the detection result if args.out_path is provided
@@ -674,6 +708,8 @@
        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: