| | |
|---|
| | | import pandas as pd |
|---|
| | | import imagehash as ih |
|---|
| | | import os |
|---|
| | | import ast |
|---|
| | | import queue |
|---|
| | | import sys |
|---|
| | | import math |
|---|
| | | import random |
|---|
| | |
|---|
| | | card_height = 440 |
|---|
| | | |
|---|
| | | |
|---|
| | | def calc_image_hashes(card_pool, save_to=None): |
|---|
| | | card_pool['art_hash'] = np.NaN |
|---|
| | | def calc_image_hashes(card_pool, save_to=None, hash_size=32, highfreq_factor=4): |
|---|
| | | new_pool = pd.DataFrame(columns=list(card_pool.columns.values)) |
|---|
| | | new_pool['card_hash'] = np.NaN |
|---|
| | | new_pool['art_hash'] = np.NaN |
|---|
| | | for ind, card_info in card_pool.iterrows(): |
|---|
| | | if ind % 100 == 0: |
|---|
| | | print(ind) |
|---|
| | | img_name = '%s/card_img/png/%s/%s_%s.png' % (transform_data.data_dir, card_info['set'], |
|---|
| | | card_info['collector_number'], |
|---|
| | | fetch_data.get_valid_filename(card_info['name'])) |
|---|
| | | card_img = cv2.imread(img_name) |
|---|
| | | if card_img is None: |
|---|
| | | fetch_data.fetch_card_image(card_info, |
|---|
| | | out_dir='%s/card_img/png/%s' % (transform_data.data_dir, card_info['set'])) |
|---|
| | | |
|---|
| | | card_names = [] |
|---|
| | | if card_info['layout'] in ['transform', 'double_faced_token']: |
|---|
| | | if isinstance(card_info['card_faces'], str): # For some reason, dict isn't being parsed in the previous step |
|---|
| | | card_faces = ast.literal_eval(card_info['card_faces']) |
|---|
| | | else: |
|---|
| | | card_faces = card_info['card_faces'] |
|---|
| | | for i in range(len(card_faces)): |
|---|
| | | card_names.append(card_faces[i]['name']) |
|---|
| | | else: # if card_info['layout'] == 'normal': |
|---|
| | | card_names.append(card_info['name']) |
|---|
| | | |
|---|
| | | for card_name in card_names: |
|---|
| | | card_info['name'] = card_name |
|---|
| | | img_name = '%s/card_img/png/%s/%s_%s.png' % (transform_data.data_dir, card_info['set'], |
|---|
| | | card_info['collector_number'], |
|---|
| | | fetch_data.get_valid_filename(card_info['name'])) |
|---|
| | | card_img = cv2.imread(img_name) |
|---|
| | | if card_img is None: |
|---|
| | | print('WARNING: card %s is not found!' % img_name) |
|---|
| | | img_art = Image.fromarray(card_img[121:580, 63:685]) # For 745*1040 size card image |
|---|
| | | art_hash = ih.phash(img_art, hash_size=32, highfreq_factor=4) |
|---|
| | | card_pool.at[ind, 'art_hash'] = art_hash |
|---|
| | | img_card = Image.fromarray(card_img) |
|---|
| | | card_hash = ih.phash(img_card, hash_size=32, highfreq_factor=4) |
|---|
| | | card_pool.at[ind, 'card_hash'] = card_hash |
|---|
| | | card_pool = card_pool[['artist', 'border_color', 'collector_number', 'color_identity', 'colors', 'flavor_text', |
|---|
| | | 'image_uris', 'mana_cost', 'legalities', 'name', 'oracle_text', 'rarity', 'type_line', |
|---|
| | | 'set', 'set_name', 'power', 'toughness', 'art_hash', 'card_hash']] |
|---|
| | | if card_img is None: |
|---|
| | | fetch_data.fetch_card_image(card_info, |
|---|
| | | out_dir='%s/card_img/png/%s' % (transform_data.data_dir, card_info['set'])) |
|---|
| | | card_img = cv2.imread(img_name) |
|---|
| | | if card_img is None: |
|---|
| | | print('WARNING: card %s is not found!' % img_name) |
|---|
| | | #img_art = Image.fromarray(card_img[121:580, 63:685]) # For 745*1040 size card image |
|---|
| | | #art_hash = ih.phash(img_art, hash_size=32, highfreq_factor=4) |
|---|
| | | #card_pool.at[ind, 'art_hash'] = art_hash |
|---|
| | | img_card = Image.fromarray(card_img) |
|---|
| | | card_hash = ih.phash(img_card, hash_size=hash_size, highfreq_factor=highfreq_factor) |
|---|
| | | #card_pool.at[ind, 'card_hash'] = card_hash |
|---|
| | | card_info['card_hash'] = card_hash |
|---|
| | | #print(new_pool.index.max()) |
|---|
| | | new_pool.loc[0 if new_pool.empty else new_pool.index.max() + 1] = card_info |
|---|
| | | |
|---|
| | | new_pool = new_pool[['artist', 'border_color', 'collector_number', 'color_identity', 'colors', 'flavor_text', |
|---|
| | | 'image_uris', 'mana_cost', 'legalities', 'name', 'oracle_text', 'rarity', 'type_line', |
|---|
| | | 'set', 'set_name', 'power', 'toughness', 'art_hash', 'card_hash']] |
|---|
| | | if save_to is not None: |
|---|
| | | card_pool.to_pickle(save_to) |
|---|
| | | return card_pool |
|---|
| | | new_pool.to_pickle(save_to) |
|---|
| | | return new_pool |
|---|
| | | |
|---|
| | | |
|---|
| | | # www.pyimagesearch.com/2014/08/25/4-point-opencv-getperspective-transform-example/ |
|---|
| | |
|---|
| | | return corrected |
|---|
| | | |
|---|
| | | |
|---|
| | | def find_card(img, thresh_c=5, kernel_size=(3, 3), size_ratio=0.2): |
|---|
| | | def find_card(img, thresh_c=5, kernel_size=(3, 3), size_thresh=5000): |
|---|
| | | # Typical pre-processing - grayscale, blurring, thresholding |
|---|
| | | img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) |
|---|
| | | img_blur = cv2.medianBlur(img_gray, 5) |
|---|
| | |
|---|
| | | if len(cnts) == 0: |
|---|
| | | print('no contours') |
|---|
| | | return [] |
|---|
| | | cv2.drawContours(img, cnts, -1, (0, 0, 255), 1) |
|---|
| | | ''' |
|---|
| | | next = 0 |
|---|
| | | while next != -1: |
|---|
| | | img_copy = img.copy() |
|---|
| | | print(hier[0][next]) |
|---|
| | | cv2.drawContours(img_copy, cnts[hier[0][next][0]], -1, (0, 255, 0), 2) |
|---|
| | | cv2.imshow('hi', img_copy) |
|---|
| | | cv2.waitKey(0) |
|---|
| | | next = hier[0][next][0] |
|---|
| | | ''' |
|---|
| | | #img_contour = cv2.cvtColor(img_contour, cv2.COLOR_GRAY2BGR) |
|---|
| | | #img_contour = cv2.drawContours(img_contour, cnts, -1, (0, 255, 0), 1) |
|---|
| | | #cv2.imshow('test', img_contour) |
|---|
| | | |
|---|
| | | # For each contours detected, check if they are large enough and are rectangle |
|---|
| | | ''' |
|---|
| | | 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 (preorder) depth-first search, find the uppermost contour in the hierarchy that satisfies the condition |
|---|
| | | The candidate contour must be rectangle (has 4 points) and should be larger than a threshold |
|---|
| | | ''' |
|---|
| | | |
|---|
| | | cnts_rect = [] |
|---|
| | | stack = [(0, hier[0][0])] |
|---|
| | | while len(stack) > 0: |
|---|
| | | i_cnt, h = stack.pop() |
|---|
| | | i_next, i_prev, i_child, i_parent = h |
|---|
| | | if i_next != -1: |
|---|
| | | stack.append((i_next, hier[0][i_next])) |
|---|
| | | cnt = cnts[i_cnt] |
|---|
| | | size = cv2.contourArea(cnt) |
|---|
| | | peri = cv2.arcLength(cnt, True) |
|---|
| | | approx = cv2.approxPolyDP(cnt, 0.04 * peri, True) |
|---|
| | | if size >= size_thresh: |
|---|
| | | cv2.drawContours(img, [cnt], -1, (255, 0, 0), 1) |
|---|
| | | #print(size) |
|---|
| | | if len(approx) == 4: |
|---|
| | | cnts_rect.append(approx) |
|---|
| | | else: |
|---|
| | | if i_child != -1: |
|---|
| | | stack.append((i_child, hier[0][i_child])) |
|---|
| | | |
|---|
| | | |
|---|
| | | ''' |
|---|
| | | # For each contours detected, check if they are large enough and are rectangle |
|---|
| | | ind_sort = sorted(range(len(cnts)), key=lambda i: cv2.contourArea(cnts[i]), reverse=True) |
|---|
| | | for i in range(min(len(cnts), 5)): # The card should be within top 5 largest contour |
|---|
| | | size = cv2.contourArea(cnts[ind_sort[i]]) |
|---|
| | | for i in range(len(cnts)): |
|---|
| | | peri = cv2.arcLength(cnts[ind_sort[i]], True) |
|---|
| | | approx = cv2.approxPolyDP(cnts[ind_sort[i]], 0.04 * peri, True) |
|---|
| | | if size > img.shape[0] * img.shape[1] * size_ratio and len(approx) == 4: |
|---|
| | | if len(approx) == 4: |
|---|
| | | cnts_rect.append(approx) |
|---|
| | | ''' |
|---|
| | | |
|---|
| | | return cnts_rect |
|---|
| | | |
|---|
| | |
|---|
| | | return img_graph |
|---|
| | | |
|---|
| | | |
|---|
| | | 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, |
|---|
| | | debug=False): |
|---|
| | | img_copy = img.copy() |
|---|
| | | def detect_frame(net, classes, img, card_pool, thresh_conf=0.5, thresh_nms=0.4, in_dim=(416, 416), card_size=1000, |
|---|
| | | out_path=None, display=True, debug=False): |
|---|
| | | start_1 = time.time() |
|---|
| | | elapsed = [] |
|---|
| | | ''' |
|---|
| | | # Create a 4D blob from a frame. |
|---|
| | | blob = cv2.dnn.blobFromImage(img, 1 / 255, in_dim, [0, 0, 0], 1, crop=False) |
|---|
| | | |
|---|
| | |
|---|
| | | |
|---|
| | | # Runs the forward pass to get output of the output layers |
|---|
| | | outs = net.forward(get_outputs_names(net)) |
|---|
| | | elapsed.append((time.time() - start_1) * 1000) |
|---|
| | | |
|---|
| | | start_2 = time.time() |
|---|
| | | img_result = img.copy() |
|---|
| | | |
|---|
| | | # Remove the bounding boxes with low confidence |
|---|
| | |
|---|
| | | class_id, confidence, box = obj |
|---|
| | | left, top, width, height = box |
|---|
| | | draw_pred(img_result, class_id, classes, confidence, left, top, left + width, top + height) |
|---|
| | | |
|---|
| | | elapsed.append((time.time() - start_2) * 1000) |
|---|
| | | ''' |
|---|
| | | img_result = img.copy() |
|---|
| | | # Put efficiency information. The function getPerfProfile returns the |
|---|
| | | # overall time for inference(t) and the timings for each of the layers(in layersTimes) |
|---|
| | | #if display: |
|---|
| | |
|---|
| | | comparing the perceptual hashing of the image with the other cards' image from the database. |
|---|
| | | ''' |
|---|
| | | det_cards = [] |
|---|
| | | for i in range(len(obj_list)): |
|---|
| | | _, _, box = obj_list[i] |
|---|
| | | left, top, width, height = box |
|---|
| | | # Just in case the bounding box trimmed the edge of the cards, give it a bit of offset around the edge |
|---|
| | | offset_ratio = 0.1 |
|---|
| | | x1 = max(0, int(left - offset_ratio * width)) |
|---|
| | | x2 = min(img.shape[1], int(left + (1 + offset_ratio) * width)) |
|---|
| | | y1 = max(0, int(top - offset_ratio * height)) |
|---|
| | | y2 = min(img.shape[0], int(top + (1 + offset_ratio) * height)) |
|---|
| | | img_snip = img[y1:y2, x1:x2] |
|---|
| | | cnts = find_card(img_snip) |
|---|
| | | if len(cnts) > 0: |
|---|
| | | cnt = cnts[0] # The largest (rectangular) contour |
|---|
| | | pts = np.float32([p[0] for p in cnt]) |
|---|
| | | img_warp = four_point_transform(img_snip, pts) |
|---|
| | | img_warp = cv2.resize(img_warp, (card_width, card_height)) |
|---|
| | | ''' |
|---|
| | | img_art = img_warp[47:249, 22:294] |
|---|
| | | img_art = Image.fromarray(img_art.astype('uint8'), 'RGB') |
|---|
| | | art_hash = ih.phash(img_art, hash_size=32, highfreq_factor=4) |
|---|
| | | card_pool['hash_diff'] = card_pool['art_hash'] - art_hash |
|---|
| | | min_cards = card_pool[card_pool['hash_diff'] == min(card_pool['hash_diff'])] |
|---|
| | | card_name = min_cards.iloc[0]['name'] |
|---|
| | | ''' |
|---|
| | | img_card = Image.fromarray(img_warp.astype('uint8'), 'RGB') |
|---|
| | | card_hash = ih.phash(img_card, hash_size=32, highfreq_factor=4) |
|---|
| | | card_pool['hash_diff'] = card_pool['card_hash'] - card_hash |
|---|
| | | min_cards = card_pool[card_pool['hash_diff'] == min(card_pool['hash_diff'])] |
|---|
| | | card_name = min_cards.iloc[0]['name'] |
|---|
| | | card_set = min_cards.iloc[0]['set'] |
|---|
| | | det_cards.append((card_name, card_set)) |
|---|
| | | hash_diff = min_cards.iloc[0]['hash_diff'] |
|---|
| | | start_3 = time.time() |
|---|
| | | cnts = find_card(img_result) |
|---|
| | | for i in range(len(cnts)): |
|---|
| | | cnt = cnts[i] |
|---|
| | | # ignore any contours smaller than threshold |
|---|
| | | elapsed.append((time.time() - start_3) * 1000) |
|---|
| | | start_4 = time.time() |
|---|
| | | pts = np.float32([p[0] for p in cnt]) |
|---|
| | | img_warp = four_point_transform(img, pts) |
|---|
| | | img_warp = cv2.resize(img_warp, (card_width, card_height)) |
|---|
| | | elapsed.append((time.time() - start_4) * 1000) |
|---|
| | | ''' |
|---|
| | | img_art = img_warp[47:249, 22:294] |
|---|
| | | img_art = Image.fromarray(img_art.astype('uint8'), 'RGB') |
|---|
| | | art_hash = ih.phash(img_art, hash_size=32, highfreq_factor=4) |
|---|
| | | card_pool['hash_diff'] = card_pool['art_hash'] - art_hash |
|---|
| | | min_cards = card_pool[card_pool['hash_diff'] == min(card_pool['hash_diff'])] |
|---|
| | | card_name = min_cards.iloc[0]['name'] |
|---|
| | | ''' |
|---|
| | | start_5 = time.time() |
|---|
| | | img_card = Image.fromarray(img_warp.astype('uint8'), 'RGB') |
|---|
| | | card_hash = ih.phash(img_card, hash_size=32, highfreq_factor=4).hash.flatten() |
|---|
| | | card_pool['hash_diff'] = card_pool['card_hash'].apply(lambda x: np.count_nonzero(x != card_hash)) |
|---|
| | | min_cards = card_pool[card_pool['hash_diff'] == min(card_pool['hash_diff'])] |
|---|
| | | card_name = min_cards.iloc[0]['name'] |
|---|
| | | card_set = min_cards.iloc[0]['set'] |
|---|
| | | det_cards.append((card_name, card_set)) |
|---|
| | | hash_diff = min_cards.iloc[0]['hash_diff'] |
|---|
| | | elapsed.append((time.time() - start_5) * 1000) |
|---|
| | | |
|---|
| | | # Display the result |
|---|
| | | if debug: |
|---|
| | | # cv2.rectangle(img_warp, (22, 47), (294, 249), (0, 255, 0), 2) |
|---|
| | | cv2.putText(img_warp, card_name + ', ' + str(hash_diff), (0, 50), |
|---|
| | | cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2) |
|---|
| | | cv2.putText(img_result, card_name, (x1, y1), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2) |
|---|
| | | if debug: |
|---|
| | | cv2.imshow('card#%d' % i, img_warp) |
|---|
| | | elif debug: |
|---|
| | | cv2.imshow('card#%d' % i, np.zeros((1, 1), dtype=np.uint8)) |
|---|
| | | # Display the result |
|---|
| | | if debug: |
|---|
| | | # cv2.rectangle(img_warp, (22, 47), (294, 249), (0, 255, 0), 2) |
|---|
| | | cv2.putText(img_warp, card_name + ', ' + str(hash_diff), (0, 50), |
|---|
| | | cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2) |
|---|
| | | cv2.drawContours(img_result, [cnt], -1, (0, 255, 0), 1) |
|---|
| | | cv2.putText(img_result, card_name, (pts[0][0], pts[0][1]), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2) |
|---|
| | | if debug: |
|---|
| | | cv2.imshow('card#%d' % i, img_warp) |
|---|
| | | #if debug: |
|---|
| | | # cv2.imshow('card#%d' % i, np.zeros((1, 1), dtype=np.uint8)) |
|---|
| | | |
|---|
| | | if out_path is not None: |
|---|
| | | cv2.imwrite(out_path, img_result.astype(np.uint8)) |
|---|
| | | |
|---|
| | | return obj_list, det_cards, img_result |
|---|
| | | elapsed = [(time.time() - start_1) * 1000] + elapsed |
|---|
| | | #print(', '.join(['%.2f' % t for t in elapsed])) |
|---|
| | | return det_cards, img_result |
|---|
| | | |
|---|
| | | |
|---|
| | | def detect_video(net, classes, capture, card_pool, thresh_conf=0.5, thresh_nms=0.4, in_dim=(416, 416), out_path=None, |
|---|
| | |
|---|
| | | cv2.waitKey(0) |
|---|
| | | break |
|---|
| | | # Use the YOLO model to identify each cards annonymously |
|---|
| | | obj_list, det_cards, img_result = detect_frame(net, classes, frame, card_pool, thresh_conf=thresh_conf, |
|---|
| | | thresh_nms=thresh_nms, in_dim=in_dim, out_path=None, |
|---|
| | | display=display, debug=debug) |
|---|
| | | |
|---|
| | | start_yolo = time.time() |
|---|
| | | det_cards, img_result = detect_frame(net, classes, frame, card_pool, thresh_conf=thresh_conf, |
|---|
| | | thresh_nms=thresh_nms, in_dim=in_dim, out_path=None, display=display, |
|---|
| | | debug=debug) |
|---|
| | | elapsed_yolo = (time.time() - start_yolo) * 1000 |
|---|
| | | # If the card was already detected in the previous frame, append 1 to the list |
|---|
| | | # If the card previously detected was not found in this trame, append 0 to the list |
|---|
| | | # If the card wasn't previously detected, make a new list and add 1 to it |
|---|
| | |
|---|
| | | exist_cards[key] = [1] |
|---|
| | | for key in gone: |
|---|
| | | exist_cards.pop(key) |
|---|
| | | start_graph = time.time() |
|---|
| | | img_graph = draw_card_graph(exist_cards, card_pool, f_len) |
|---|
| | | elapsed_graph = (time.time() - start_graph) * 1000 |
|---|
| | | #if debug: |
|---|
| | | # max_num_obj = max(max_num_obj, len(obj_list)) |
|---|
| | | # for i in range(len(obj_list), max_num_obj): |
|---|
| | | # cv2.imshow('card#%d' % i, np.zeros((1, 1), dtype=np.uint8)) |
|---|
| | | |
|---|
| | | if debug: |
|---|
| | | max_num_obj = max(max_num_obj, len(obj_list)) |
|---|
| | | for i in range(len(obj_list), max_num_obj): |
|---|
| | | cv2.imshow('card#%d' % i, np.zeros((1, 1), dtype=np.uint8)) |
|---|
| | | |
|---|
| | | start_display = time.time() |
|---|
| | | 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 |
|---|
| | | if display: |
|---|
| | | cv2.imshow('result', img_save) |
|---|
| | | elapsed_display = (time.time() - start_display) * 1000 |
|---|
| | | |
|---|
| | | elapsed_ms = (time.time() - start_time) * 1000 |
|---|
| | | print('Elapsed time: %.2f ms' % elapsed_ms) |
|---|
| | | print('Elapsed time: %.2f ms, %.2f, %.2f, %.2f' % (elapsed_ms, elapsed_yolo, elapsed_graph, elapsed_display)) |
|---|
| | | if out_path is not None: |
|---|
| | | vid_writer.write(img_save.astype(np.uint8)) |
|---|
| | | cv2.waitKey(1) |
|---|
| | |
|---|
| | | df = fetch_data.load_all_cards_text(csv_name) |
|---|
| | | df_list.append(df) |
|---|
| | | #print(df) |
|---|
| | | card_pool = pd.concat(df_list) |
|---|
| | | 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') |
|---|
| | | card_pool = calc_image_hashes(card_pool, save_to='card_pool.pck') |
|---|
| | | for hash_size in [8, 16, 32, 64]: |
|---|
| | | for highfreq_factor in [4, 8, 16, 32]: |
|---|
| | | pck_name = 'card_pool_%d_%d.pck' % (hash_size, highfreq_factor) |
|---|
| | | if not os.path.exists(pck_name): |
|---|
| | | print(pck_name) |
|---|
| | | calc_image_hashes(card_pool, save_to=pck_name, hash_size=hash_size, highfreq_factor=highfreq_factor) |
|---|
| | | ''' |
|---|
| | | # csv_name = '%s/csv/%s.csv' % (transform_data.data_dir, 'rtr') |
|---|
| | | # card_pool = fetch_data.load_all_cards_text(csv_name) |
|---|
| | | # card_pool = calc_image_hashes(card_pool) |
|---|
| | | card_pool = pd.read_pickle('card_pool.pck') |
|---|
| | | card_pool = card_pool[(card_pool['set'] == 'rtr') | (card_pool['set'] == 'isd')] |
|---|
| | | #csv_name = '%s/csv/%s.csv' % (transform_data.data_dir, 'rtr') |
|---|
| | | #card_pool = fetch_data.load_all_cards_text(csv_name) |
|---|
| | | #card_pool = calc_image_hashes(card_pool, save_to='card_pool.pck') |
|---|
| | | #return |
|---|
| | | card_pool = pd.read_pickle('card_pool_32_4.pck') |
|---|
| | | #card_pool = card_pool[(card_pool['set'] == 'rtr') | (card_pool['set'] == 'isd')] |
|---|
| | | card_pool = card_pool[['name', 'set', 'collector_number', 'card_hash']] |
|---|
| | | |
|---|
| | | # 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['card_hash'] = card_pool['card_hash'].apply(lambda x: x.hash.flatten()) |
|---|
| | | |
|---|
| | | thresh_conf = 0.01 |
|---|
| | | thresh_nms = 0.8 |
|---|
| | | |
|---|
