def play(filepath): global img global rects global video cv2.namedWindow(win_name) cv2.cv.SetMouseCallback(win_name, on_mouse, 0) clf = model.load_classifier() pos = 0 # frame position video = cv2.VideoCapture(filepath) frames = video.get(cv2.cv.CV_CAP_PROP_FRAME_COUNT) cv2.cv.CreateTrackbar('tracker', win_name, pos, int(frames), on_change) key_val = 0 while (video.isOpened()): ret, img = video.read() if not ret: break if key_val == 0: cv2.imshow(win_name, img) pos = pos + 1 c = cv2.waitKey(key_val) if c == ord('c'): key_val = 1 cv2.cv.SetMouseCallback(win_name, on_mouse_do_nothing, 0) elif c == ord('q'): break if key_val == 1: if op.has_score_board(img, target_imgs[0], rects[0]): show_recognize(clf, img, rects[1:]) cv2.imshow(win_name, img) video.release() cv2.destroyAllWindows()
def play(filepath): global img global rects global video cv2.namedWindow(win_name) cv2.cv.SetMouseCallback(win_name, on_mouse, 0) clf = model.load_classifier() pos = 0 # frame position video = cv2.VideoCapture(filepath) frames = video.get(cv2.cv.CV_CAP_PROP_FRAME_COUNT) cv2.cv.CreateTrackbar('tracker', win_name, pos, int(frames), on_change) key_val = 0 while(video.isOpened()): ret, img = video.read() if not ret: break if key_val == 0: cv2.imshow(win_name, img) pos = pos + 1 c = cv2.waitKey(key_val) if c == ord('c'): key_val = 1 cv2.cv.SetMouseCallback(win_name, on_mouse_do_nothing, 0) elif c == ord('q'): break if key_val==1: if op.has_score_board(img, target_imgs[0], rects[0]): show_recognize(clf, img, rects[1:]) cv2.imshow(win_name, img) video.release() cv2.destroyAllWindows()
def get_score_info(video): global step score_frames = [] twenty_four_frames = [] camera_change_frames = [] score_a, score_b = 0, 0 # score of both team last_sec = 0 # 24 second board # last_frame = None pos = 0 # frame position video.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, pos) clf = model.load_classifier() while (video.isOpened()): if pos % step == 0: ret, frame = video.red() else: ret = video.grab() if not ret: break # skip each step steps if pos % step != 0: pos += 1 continue # if camera change if pos != 0 and hist.camera_change(last_frame, frame): camera_change_frames.append(pos) print 'camera change', pos last_frame = frame if op.has_score_board(frame, target_imgs[0], rects[0]): # score a img = op.get_subimage(frame, rects[1][0], rects[1][1]) num = model.recognize_number(clf, img) # print 'a ', num if score_two_or_three(num, score_a): score_frames.append(pos) print 'a from', score_a, 'to', num, pos if not noise(num, score_a): score_a = num # score b img = op.get_subimage(frame, rects[2][0], rects[2][1]) num = model.recognize_number(clf, img) # print 'b ', num if score_two_or_three(num, score_b): score_frames.append(pos) print 'b from', score_b, 'to', num, pos if not noise(num, score_b): score_b = num # 24 second board img = op.get_subimage(frame, rects[3][0], rects[3][1]) sec = model.recognize_number(clf, img) if last_sec != 24 and sec == 24: print 'twenty four', pos twenty_four_frames.append(pos) last_sec = sec pos += 1 # print pos print score_frames, twenty_four_frames, camera_change_frames return score_frames, twenty_four_frames, camera_change_frames
def get_score_info(video): global step score_frames = [] twenty_four_frames = [] camera_change_frames = [] score_a, score_b = 0,0 # score of both team last_sec = 0 # 24 second board # last_frame = None pos = 0 # frame position video.set(cv2.cv.CV_CAP_PROP_POS_FRAMES, pos) clf = model.load_classifier() while(video.isOpened()): if pos % step == 0: ret, frame = video.red() else: ret = video.grab() if not ret: break # skip each step steps if pos % step != 0: pos += 1 continue # if camera change if pos != 0 and hist.camera_change(last_frame, frame): camera_change_frames.append(pos) print 'camera change', pos last_frame = frame if op.has_score_board(frame, target_imgs[0], rects[0]): # score a img = op.get_subimage(frame, rects[1][0], rects[1][1]) num = model.recognize_number(clf, img) # print 'a ', num if score_two_or_three(num,score_a): score_frames.append(pos) print 'a from',score_a,'to',num, pos if not noise(num, score_a): score_a = num # score b img = op.get_subimage(frame, rects[2][0], rects[2][1]) num = model.recognize_number(clf, img) # print 'b ', num if score_two_or_three(num,score_b): score_frames.append(pos) print 'b from',score_b,'to', num, pos if not noise(num, score_b): score_b = num # 24 second board img = op.get_subimage(frame, rects[3][0], rects[3][1]) sec = model.recognize_number(clf, img) if last_sec != 24 and sec == 24: print 'twenty four',pos twenty_four_frames.append(pos) last_sec = sec pos += 1 # print pos print score_frames, twenty_four_frames, camera_change_frames return score_frames,twenty_four_frames,camera_change_frames