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)
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():
resultWnd, markers1Wnd, markers2Wnd = Wnd.create(
result=cv2.WINDOW_NORMAL,
markers1=cv2.WINDOW_NORMAL,
markers2=cv2.WINDOW_NORMAL)
# img = cv2.imread('water_coins.jpg')
# gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
thresh = realImage()
# thresh = testImage()
# noise removal
kernel = np.ones((3, 3), np.uint8)
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=2)
# sure background area
sure_bg = cv2.dilate(opening, kernel, iterations=3)
# Finding sure foreground area
dist_transform = cv2.distanceTransform(opening, cv2.DIST_L2, 5)
# dist_transform = cv2.distanceTransform(opening, cv2.DIST_L1, 3)
ret, sure_fg = cv2.threshold(dist_transform, 0.7 * dist_transform.max(),
255, 0)
# Finding unknown region
sure_fg = np.uint8(sure_fg)
unknown = cv2.subtract(sure_bg, sure_fg)
# Marker labelling
ret, markers = cv2.connectedComponents(sure_fg)
# Add one to all labels so that sure background is not 0, but 1
markers = markers + 1
# Now, mark the region of unknown with zero
markers[unknown == 255] = 0
markers1Wnd.imshow(markers / markers.max())
# markers = cv2.watershed(img, markers)
img = cv2.cvtColor(opening, cv2.COLOR_GRAY2BGR)
markers = cv2.watershed(img, markers)
markers2Wnd.imshow(markers / markers.max())
# [-1 1 2 3 4 5]
img[markers == 3] = [0, 255, 0]
img[markers == -1] = [255, 0, 0]
cv2.imshow('thr', thresh)
cv2.imshow('op', opening)
cv2.imshow('sure_bg', sure_bg)
cv2.imshow('sure_fg', sure_fg)
resultWnd.imshow(img)
cv2.waitKey()
def main():
img = testImage()
origWnd, dstWnd, centersWnd = CvNamedWindow.create('orig', 'dstTransform', 'centers')
dst = cv2.distanceTransform(img, cv2.DIST_L2, cv2.DIST_MASK_5)
dx = cv2.Sobel(dst, ddepth=cv2.CV_32F, dx=1, dy=0, ksize=5)
dy = cv2.Sobel(dst, ddepth=cv2.CV_32F, dx=0, dy=1, ksize=5)
grad = cv2.addWeighted(cv2.convertScaleAbs(dx), 1, cv2.convertScaleAbs(dy), 1, 0)
grad = cv2.convertScaleAbs(grad)
origWnd.imshow(img)
centersWnd.imshow(grad)
condition = (grad == 0) & (dst > 0)
print(np.where(condition))
cvWaitKeys()
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():
oringWnd, dstWnd, centersWnd = CvNamedWindow.create('orig', {'dstTransform': cv2.WINDOW_NORMAL}, 'centers')
img = testImage()
# img = realImage()
img = fillHoles(img)
centers = img.copy()
indexes, (distTransform, dx, dy) = findCentersIndexesSobel(img)
centers[indexes] = 127
oringWnd.imshow(img)
centersWnd.imshow(centers)
dstWnd.imshow(visDistTransformResult(distTransform))
def mouse_callback(evt, x, y, flags, _):
if evt == cv2.EVENT_LBUTTONDOWN:
print(x, y, distTransform[y, x], dx[y, x], dy[y, x])
dstWnd.mouse_callback = mouse_callback
cvWaitKeys()