From b95bf33cb5b296efb70a0c4b1c82c0f62286f52a Mon Sep 17 00:00:00 2001
From: Constantin Wenger <constantin.wenger@googlemail.com>
Date: Thu, 03 Feb 2022 20:18:17 +0000
Subject: [PATCH] added options to flip/rotate and specify different input resolutions also fixed displayed image to max 800x800 everything above will be scaled while keeping aspect ratio
---
opencv_dnn.py | 267 ++++++++++++++++++++++++++++++++++++++++++-----------
1 files changed, 211 insertions(+), 56 deletions(-)
diff --git a/opencv_dnn.py b/opencv_dnn.py
old mode 100644
new mode 100755
index 37ecaae..f525b0f
--- a/opencv_dnn.py
+++ b/opencv_dnn.py
@@ -12,6 +12,7 @@
from multiprocessing import Pool
from config import Config
import fetch_data
+import pytesseract
"""
@@ -28,7 +29,7 @@
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:
@@ -79,7 +80,7 @@
cnts2 = sorted(cnts, key=cv2.contourArea, reverse=True)
cnts2 = cnts2[:10]
if True:
- cv2.drawContours(img_cc, cnts2, -1, (0, 255, 0), 3)
+ cv2.rawContours(img_cc, cnts2, -1, (0, 255, 0), 3)
#cv2.imshow('Contours', card_img)
#cv2.waitKey(10000)
"""
@@ -89,11 +90,12 @@
#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.phash(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
@@ -113,8 +115,8 @@
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)
@@ -262,14 +264,14 @@
# 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]))
+# 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)
@@ -288,6 +290,8 @@
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:
@@ -304,28 +308,28 @@
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:
rect = cv2.minAreaRect(c_cnt)
box = cv2.boxPoints(rect)
box = np.intp(box)
- print(c_cnt)
- print(box)
+ #print(c_cnt)
+ #print(box)
- print('CSize:', c_size, '%:', c_size/size)
+ #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)
+ #print('CF:', (c_size/size))
+ #print('Size:', size)
cnts_rect.append(approx)
else:
#print('CF:', (c_size/size))
- print('Size:', size)
+ #print('Size:', size)
cnts_rect.append(approx)
else:
if i_child != -1:
@@ -333,7 +337,7 @@
return cnts_rect
-def draw_card_graph(exist_cards, card_pool, f_len):
+def draw_card_graph(exist_cards, card_pool, f_len, text_scale=0.8):
"""
Given the history of detected cards in the current and several previous frames, draw a simple graph
displaying the detected cards with its confidence level
@@ -349,7 +353,7 @@
gap_sm = 10 # Small offset
w_bar = 300 # Length of the confidence bar at 100%
h_bar = 12
- txt_scale = 0.8
+ txt_scale = text_scale
n_cards_p_col = 4 # Number of cards displayed per one column
w_img = gap + (w_card + gap + w_bar + gap) * 2 # Dimension of the entire graph (for 2 columns)
h_img = 480
@@ -395,7 +399,7 @@
def detect_frame(img, card_pool, hash_size=32, size_thresh=10000,
- out_path=None, display=True, debug=False):
+ out_path=None, display=True, debug=False, scale=1.0, tesseract=False):
"""
Identify all cards in the input frame, display or save the frame if needed
:param img: input frame
@@ -412,7 +416,9 @@
det_cards = []
# Detect contours of all cards in the image
cnts = find_card(img_result, size_thresh=size_thresh, debug=debug)
+ #print('Contours:', 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])
@@ -431,14 +437,63 @@
'''
img_card = Image.fromarray(img_warp.astype('uint8'), 'RGB')
img_card_size = img_warp.shape
- print(img_card_size)
+
+ # cut out the part of the image that has the set icon
+ #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)
+ #print(cut)
img_set_part = img_warp[cut[0]:cut[1], cut[2]:cut[3]]
- print(img_set_part.shape)
+ #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)
+ # tesseract takes a long time (200ms+), so if at all we should collect pictures
+ # and then if a card is detected successfully, add it to detected cards and run a background check with
+ # tesseract, if the identification with tesseract fails, mark somehow
+ # or only use tesseract in case of edition conflicts idk yet
+ # we will need to see what is needed
+ # also it is hard to detect with bad 500x600 px image
+ # maybe training it for the font would make it better or getting better resolution images
+ prefilter = True
+ if tesseract:
+ height, width, channels = img_warp.shape
+ blank_image = np.zeros((height, width, 3), np.uint8)
+ threshold = 70
+ athreshold = -30
+ athreshold = -cv2.getTrackbarPos("Threshold", "mainwindow")
+ cut = [round(img_card_size[0]*0.94),round(img_card_size[0]*0.98),round(img_card_size[1]*0.02),round(img_card_size[1]*0.3)]
+ blank_image = img_warp[cut[0]:cut[1], cut[2]:cut[3]]
+ cv2.imshow("Tesseract Image", blank_image)
+ if prefilter:
+ blank_image = cv2.cvtColor(blank_image, cv2.COLOR_BGR2GRAY)
+ blank_image = cv2.normalize(blank_image, None, 0, 255, cv2.NORM_MINMAX)
+ cv2.imshow("Normalized", blank_image)
+ result_image = cv2.adaptiveThreshold(blank_image, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 501, athreshold)
+ #_, result_image = cv2.threshold(blank_image, threshold, 255, cv2.THRESH_BINARY_INV)
+ cv2.imshow("TessImg", result_image)
+ tesseract_output = pytesseract.image_to_string(cv2.cvtColor(result_image, cv2.COLOR_GRAY2RGB))
+ else:
+ tesseract_output = pytesseract.image_to_string(cv2.cvtColor(blank_image, cv2.COLOR_BGR2RGB))
+ if "M20" in tesseract_output or 'm20' in tesseract_output:
+ tesseract_output = "M20"
+ print(tesseract_output)
+ else:
+ print(tesseract_output)
+ tesseract_output = "Set not detected"
+
+ #cv2.imshow("Tesseract Image", img_warp)
+ #img_gray = cv2.cvtColor(img_warp, cv2.COLOR_BGR2GRAY)
+ #img_blur = cv2.medianBlur(img_gray, 5)
+ #img_thresh = cv2.adaptiveThreshold(img_gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 11, 5)
+ #cv2.imshow('Thres', img_thresh)
+ #tesseract_output = pytesseract.image_to_string(cv2.cvtColor(img_thresh, cv2.COLOR_GRAY2RGB))
+ #if "M20" in tesseract_output or 'm20' in tesseract_output:
+ # tesseract_output = "M20"
+ # print(tesseract_output)
+ #else:
+ # print(tesseract_output)
+ # tesseract_output = "Set not detected"
# 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()
@@ -454,20 +509,20 @@
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))
+# 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])
+# 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)
+ #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'])
+ #print('Best Match', 'Name:', best_match['name'], 'Set:', best_match['set'])
min_card = best_match
card_name = min_card['name']
@@ -475,25 +530,32 @@
det_cards.append((card_name, card_set))
# Render the result, and display them if needed
+ image_header = card_name
+ if tesseract:
+ image_header += ' TS: ' + tesseract_output
cv2.drawContours(img_result, [cnt], -1, (0, 255, 0), 2)
- cv2.putText(img_result, card_name, (min(pts[0][0], pts[1][0]), min(pts[0][1], pts[1][1])),
- cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
+ cv2.putText(img_result, image_header, (int(min(pts[0][0], pts[1][0])), int(min(pts[0][1], pts[1][1]))),
+ cv2.FONT_HERSHEY_SIMPLEX, 0.5*scale+0.1, (255, 255, 255), 2)
if debug:
# cv2.rectangle(img_warp, (22, 47), (294, 249), (0, 255, 0), 2)
cv2.putText(img_warp, card_name + ':' + card_set + ', ' + str(hash_diff), (0, 20),
- cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 255, 255), 1)
+ cv2.FONT_HERSHEY_SIMPLEX, 0.4*scale+0.1, (255, 255, 255), 1)
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 trackbardummy(v):
+ pass
def detect_video(capture, card_pool, hash_size=32, size_thresh=10000,
- out_path=None, display=True, show_graph=True, debug=False, crop_x=0, crop_y=0):
+ out_path=None, display=True, show_graph=True, debug=False,
+ crop_x=0, crop_y=0, rotate=None, flip=None, tesseract=False):
"""
Identify all cards in the continuous video stream, display or save the result if needed
:param capture: input video stream
@@ -507,38 +569,75 @@
:return: list of detected card's name/set and resulting image
:return:
"""
+ if tesseract:
+ cv2.namedWindow('mainwindow')
+ cv2.createTrackbar("Threshold", "mainwindow", 30, 255, trackbardummy)
+ list_names_from = 0
+ # get some frame numers
+ f_width = 0
+ f_height = 0
+ f_scale = 1.0
+ if rotate is not None and (rotate == 0 or rotate == 2):
+ f_height = round(capture.get(cv2.CAP_PROP_FRAME_WIDTH)-2*crop_y)
+ f_width = round(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)-2*crop_x)
+ else:
+ f_width = round(capture.get(cv2.CAP_PROP_FRAME_WIDTH) - 2*crop_x)
+ f_height = round(capture.get(cv2.CAP_PROP_FRAME_HEIGHT) - 2*crop_y)
+
+ if f_width > 800 or f_height > 800:
+ f_max = max(f_width, f_height)
+ f_scale = (800.0/float(f_max))
+
# 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)) - 2*crop_x + img_graph.shape[1]
- height = max(round(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)) - 2*crop_y, img_graph.shape[0])
+ width = int(f_width * f_scale) + img_graph.shape[1]
+ height = max(int(f_height * f_scale), 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))
+ width = int(f_width * f_scale)
+ height = int(f_height * f_scale)
if out_path is not None:
vid_writer = cv2.VideoWriter(out_path, cv2.VideoWriter_fourcc(*'MJPG'), 10.0, (width, height))
max_num_obj = 0
f_len = 10 # number of frames to consider to check for existing cards
exist_cards = {}
-
+ #print(f"fw{f_width} fh{f_height} w{width} h{height} fs{f_scale}")
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)
+ if not ret:
+ continue
+
+ if flip is not None:
+ frame = cv2.flip(frame, flip)
+ if rotate is not None:
+ frame = cv2.rotate(frame, rotate)
+
+ y_max_index = -crop_y
+ if crop_y == 0:
+ y_max_index = frame.shape[0]
+ x_max_index = -crop_x
+ if crop_x == 0:
+ x_max_index = frame.shape[1]
+
+ croped_img = frame[crop_y:y_max_index, crop_x:x_max_index]
+ fimg = croped_img
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(fimg, card_pool, hash_size=hash_size, size_thresh=size_thresh,
- out_path=None, display=False, debug=debug)
+ out_path=None, display=False, debug=debug, scale=1.0/f_scale, tesseract=tesseract)
if show_graph:
# 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
@@ -581,6 +680,7 @@
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():
@@ -599,9 +699,16 @@
# 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)
+ # resize result to out predefined area
+ if f_scale != 1.0:
+ img_result = cv2.resize(img_result, (min(800, int(img_result.shape[1]*f_scale)), min(800, int(img_result.shape[0] * f_scale))), interpolation=cv2.INTER_LINEAR)
+ #print(f'ri_w{img_result.shape[1]} ri_h{img_result.shape[0]}')
+ #print(f"gi_w{img_graph.shape[1]} gi_h{img_graph.shape[0]}")
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):
+ 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
@@ -618,17 +725,37 @@
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()
- 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 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[0]} [{key[1].upper()}]\n')
@@ -654,7 +781,7 @@
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,))
@@ -664,23 +791,38 @@
# 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, 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, crop_x=500, crop_y=200)
+ 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, args.rx)
+ capture.set(cv2.CAP_PROP_FRAME_HEIGHT, args.ry)
+ else:
+ print(f"Using stream {args.stream_url}")
+ capture = cv2.VideoCapture(args.stream_url)
+
+ thres = int((args.rx-2*args.crop_x)*(args.ry-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,
+ rotate=args.rotate, flip=args.flip, tesseract=args.tesseract)
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
@@ -696,13 +838,17 @@
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:
# Test file is a video
capture = cv2.VideoCapture(args.in_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)
+ show_graph=args.show_graph, debug=args.debug,
+ rotate=args.rotate, flip=args.flip, tesseract=args.tesseract)
+
capture.release()
pass
@@ -720,6 +866,15 @@
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)
+ parser.add_argument('-r', '--rotate', dest='rotate', help='Rotate image before usage 0 90_CLOCK, 1 180, 2 90 COUNTER_CLOCK', type=int, default=None)
+ parser.add_argument('-f', '--flip', dest='flip', help='flip image before using, this is done before rotation -1(both axis), 0(x-axis), 1(y-axis)', type=int, default=None)
+ parser.add_argument('-rx', '--resolution-x', dest='rx', help='X-Resolution of the source, defaults to 1920', type=int, default=1920)
+ parser.add_argument('-ry', '--resulution-y', dest='ry', help="Y-Resolution of the source, defaults to 1080", type=int, default=1080)
+ parser.add_argument('-t', '--tesseract', dest='tesseract', help='enable tesseract edition detection (not used only displayed)', action='store_true', default=False)
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?
--
Gitblit v1.10.0