def main():
video = VideoCapture(video_sources.video_6)
work_area = WorkAreaView(video_sources.video_6_work_area_markers)
vc = VideoController(10, 'pause')
video_wnd, = Wnd.create('video')
# h_wnd, s_wnd, v_wnd = Wnd.create('H', 'S', 'V')
# L_wnd, a_wnd, b_wnd = Wnd.create('L', 'a', 'b')
frames_iter = work_area.skip_non_area(video.frames())
for frame, _ in frames_iter:
# hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
# h_wnd.imshow(hsv[:, :, 0])
# s_wnd.imshow(hsv[:, :, 1])
# v_wnd.imshow(hsv[:, :, 2])
# lab = cv2.cvtColor(frame, cv2.COLOR_BGR2Lab)
# L_wnd.imshow(lab[:, :, 0])
# a_wnd.imshow(lab[:, :, 1])
# b_wnd.imshow(lab[:, :, 2])
vis_img = utils.put_frame_pos(frame, video.frame_pos(), xy=(2, 55))
video_wnd.imshow(vis_img)
if vc.wait_key() == 27: break
video.release()
def main():
video = VideoCapture(video_sources.video_2)
frame = video.read()
backSubtractor = BackgroundSubtractorAVG(0.2, denoise(frame))
for frame in video.frames():
with utils.timeit_context():
frame = denoise(frame)
foreGround = backSubtractor.getForeground(frame)
# Apply thresholding on the background and display the resulting mask
ret, mask = cv2.threshold(foreGround, 15, 255, cv2.THRESH_BINARY)
cv2.imshow('input', frame)
cv2.imshow('foreground', foreGround)
# Note: The mask is displayed as a RGB image, you can
# display a grayscale image by converting 'foreGround' to
# a grayscale before applying the threshold.
cv2.imshow('mask', mask)
if cv2.waitKey(10) & 0xFF == 27:
break
video.release()
cv2.destroyAllWindows()
def main():
video = VideoCapture(video_sources.video_2)
workArea = WorkAreaView(video_sources.video_2_work_area_markers)
vc = VideoController(10, 'pause')
(video_wnd, bin_diff_wnd, gray_diff_wnd, colorDiffWnd,
learned_BG_wnd) = Wnd.create('video', 'binary diff', 'gray diff',
'color diff', 'Learned BG')
colorAbsDiffWnd = Wnd('color_abs_diff')
segmentedWnd = Wnd('segmented')
segmenter = Segmenter()
frames_iter = workArea.skip_non_area(video.frames())
motionDetector = MotionDetector(next(frames_iter)[0], 3)
backgroundModel = BackgroundModel(15)
prevBackground = None
for frame, _ in frames_iter:
motionDetector.detect(frame)
if motionDetector.motionEnded():
# calc fgMask
mask, gray_diff, color_diff, colorAbsDiff = calcForegroundMask(
prevBackground, frame)
# bin_diff_wnd.imshow(resize(mask, 0.5))
bin_diff_wnd.imshow(cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR))
# gray_diff_wnd.imshow(resize(gray_diff, .5))
# colorDiffWnd.imshow(resize(color_diff, .5))
# colorAbsDiffWnd.imshow(resize(colorAbsDiff, .5))
markers, objectsCount = segmenter.segment(mask)
segmentedWnd.imshow(
resize(Segmenter.markersToDisplayImage(markers, objectsCount),
.5))
backgroundModel = BackgroundModel(15)
if motionDetector.isSilence():
backgroundModel.learn(frame, foregroundMask=None)
learned_BG_wnd.imshow(resize(backgroundModel.learned, .5))
if motionDetector.motionStarted():
prevBackground = backgroundModel.learned
backgroundModel = None
learned_BG_wnd.imshow(resize(prevBackground, .5))
# VIS
vis_img = motionDetector.indicateCurrentState(frame.copy())
vis_img = utils.put_frame_pos(vis_img, video.frame_pos(), xy=(2, 55))
video_wnd.imshow(vis_img)
# bin_diff_wnd.imshow(resize(motionDetector.bin_diff, .5))
# gray_diff_wnd.imshow(resize(motionDetector.gray_diff, .5))
# VIS END
if vc.wait_key() == 27: break
video.release()
def main():
def roiSelectedEvent(roi, imageRoi, wnd, image):
if roi is None:
return
predictions = predict_on_image(model, None, imageRoi, 0.95)
cv2.imshow('roi', predictions)
model = getModel()
video = VideoCapture(video_sources.video_2)
vc = VideoController(10, 'pause')
wnd = Wnd('video', roiSelectedEvent=roiSelectedEvent)
for frame in video.frames():
cv2.destroyWindow('roi')
wnd.imshow(frame)
if vc.wait_key() == 27: break
def main():
video = VideoCapture(video_sources.video_6)
work_area = WorkAreaView(video_sources.video_6_work_area_markers)
vc = VideoController(10, 'pause')
(video_wnd, bin_diff_wnd, gray_diff_wnd, frame0_diff_wnd,
learned_BG_wnd) = Wnd.create('video', 'binary diff', 'gray diff',
'diff with frame0', 'Learned BG')
frames_iter = work_area.skip_non_area(video.frames())
motion_detector = MotionDetector(next(frames_iter)[0], 3)
background = BackgroundModel(motion_detector, 15)
for frame, _ in frames_iter:
motion_detector.detect(frame)
if not background.done:
background.learn()
else:
if motion_detector.motion_ended():
frame0_diff = cv2.absdiff(background.learned, frame)
gray_of_color_diff = Helper.to_gray(frame0_diff)
frame0_diff_wnd.imshow(
resize(
np.hstack(
(frame0_diff, Helper.to_bgr(gray_of_color_diff))),
.5))
_, binary = cv2.threshold(gray_of_color_diff, 35, 255,
cv2.THRESH_BINARY)
cv2.imshow('1 binary', resize(binary, .5))
# VIS
if background.done:
learned_BG_wnd.imshow(resize(background.learned, 1))
vis_img = motion_detector.put_current_state(frame.copy())
vis_img = utils.put_frame_pos(vis_img, video.frame_pos(), xy=(2, 55))
video_wnd.imshow(vis_img)
# bin_diff_wnd.imshow(resize(motion_detector.bin_diff, .5))
# gray_diff_wnd.imshow(resize(motion_detector.gray_diff, .5))
# VIS END
if vc.wait_key() == 27: break
video.release()
def main():
feature_params = dict(maxCorners=100, qualityLevel=0.3, minDistance=7, blockSize=7)
lk_params = dict(winSize=(15, 15), maxLevel=2, criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))
video = VideoCapture(video_sources.video_2)
wnd = CvNamedWindow('video')
vc = VideoController(delay=50)
prev_gray = None
po = None
tracking = False
for frame in video.frames():
if tracking:
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# calculate optical flow
p1, st, err = cv2.calcOpticalFlowPyrLK(prev_gray, frame_gray, p0, None, **lk_params)
p1 = p1[st == 1]
# draw the tracks
for pt in p1:
a, b = pt.ravel()
frame = cv2.circle(frame, (a, b), 5, (0, 255, 0), -1)
prev_gray = frame_gray
p0 = p1.reshape(-1, 1, 2)
wnd.imshow(frame)
key = vc.wait_key()
if key == 27:
break
elif not tracking and key == ord('r'): # init tracking
roi = cv2.selectROI('roi', frame)
cv2.destroyWindow('roi')
if roi is None or sum(roi) == 0:
continue
prev_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
p0 = cv2.goodFeaturesToTrack(prev_gray, mask=roi_mask(prev_gray, roi), **feature_params)
if p0 is not None and len(p0) > 0:
tracking = True
video.release()
def main():
video = VideoCapture(video_sources.video_6)
diff_wnd = CvNamedWindow('diff')
mask_wnd = CvNamedWindow('mask')
input_wnd = CvNamedWindow('input')
# prev_frame = denoise(video.read())
prev_frame = cv2.cvtColor(denoise(video.read()), cv2.COLOR_BGR2GRAY)
vm = VideoController(10)
diff = None
for frame in video.frames():
# with utils.timeit_context('frame processing'):
# frame = denoise(frame)
# diff = cv2.absdiff(prev_frame, frame, dst=diff)
# ret, mask = cv2.threshold(diff, 45, 255, cv2.THRESH_BINARY)
# binary_mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
# # cn = cv2.countNonZero(binary_mask)
# nonzero = cv2.findNonZero(binary_mask)
with utils.timeit_context('frame processing'):
frame = denoise(frame)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
diff = cv2.absdiff(prev_frame, gray, dst=diff)
ret, mask = cv2.threshold(diff, 45, 255, cv2.THRESH_BINARY)
# cn = cv2.countNonZero(binary_mask)
nonzero = cv2.findNonZero(mask)
diff_wnd.imshow(diff)
mask_wnd.imshow(mask)
input_wnd.imshow(utils.put_frame_pos(frame.copy(), video.frame_pos()))
prev_frame = gray
if not vm.wait_key():
break
video.release()
cv2.destroyAllWindows()