~speedprog/mtg/mtg_card_detector.git

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 +114,10 @@
			elif isinstance(hash_size, int):
			hash_size = [hash_size]

			num_cores = 15
			num_partitions = 60
			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)
			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]))
			@@ -216,7 +239,7 @@
			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
			@@ -229,24 +252,28 @@
			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, 11, thresh_c)
			cv2.imshow('Thres', img_thresh)
			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)
			cv2.imshow('Eroded', img_erode)
			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')
			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]))
			cv2.drawContours(img_cont, cnts2, -1, (0, 255, 0), 3)
			cv2.imshow('Contours', img_cont)
			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
			@@ -262,9 +289,47 @@
			size = cv2.contourArea(cnt)
			peri = cv2.arcLength(cnt, True)
			approx = cv2.approxPolyDP(cnt, 0.04 * peri, True)
			if size >= size_thresh and len(approx) < 6:
			print('Size:', size)
			cnts_rect.append(approx)
			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:
			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', 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]))
			@@ -332,7 +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
			@@ -349,8 +414,10 @@
			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)
			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])
			@@ -375,7 +442,9 @@
			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')
			cv2.imshow("Set Img#%d" % i, img_set_part)
			print('img set')
			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()
			@@ -385,24 +454,28 @@
			hash_diff = min_card['hash_diff']

			top_matches = sorted(card_pool['hash_diff'])
			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):
			cdr = 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] = cdr
			cd = cdr
			print('Idx:', ix, 'Name:', cd['name'], 'Set:', cd['set'], 'Diff:', top_matches[ix])
			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]

			cd_data['set_hash_diff'] = cd_data['set_hash_%d' % hash_size]
			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'])
			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])

			min_card = best_match

			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))
			@@ -418,15 +491,16 @@
			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


			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
			@@ -443,8 +517,9 @@
			# 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))
			@@ -453,9 +528,15 @@
			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
			@@ -463,7 +544,7 @@
			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
			@@ -485,18 +566,50 @@
			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

			@@ -512,13 +625,21 @@
			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:
			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
			@@ -532,6 +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)
			@@ -540,7 +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,))
			@@ -559,9 +682,15 @@

			# 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)

			thres = int(((1920-2500)(1080-2200)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)
			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
			@@ -579,6 +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: