def run(self, ips, snap, img, para=None):
edge = sobel(snap)
img[:] = 0
img[snap > para['thr2']] = 2
img[snap < para['thr1']] = 1
ips.lut = self.buflut
mark = watershed(edge, img.astype(np.uint16))
return (mark == 2) * 255
def run(self, ips, imgs, para=None):
dist = -ndimg.distance_transform_edt(imgs)
pts = find_maximum(dist, para['tor'], False)
buf = np.zeros(imgs.shape, dtype=np.uint16)
buf[pts[:, 0], pts[:, 1], pts[:, 2]] = 1
markers, n = ndimg.label(buf, np.ones((3, 3, 3)))
line = watershed(dist, markers, line=True, conn=para['con'] + 1)
imgs[line == 0] = 0
def run(self, ips, snap, img, para=None):
img[:] = snap
dist = -ndimg.distance_transform_edt(snap)
pts = find_maximum(dist, para['tor'], False)
buf = np.zeros(ips.size, dtype=np.uint16)
buf[pts[:, 0], pts[:, 1]] = 1
markers, n = ndimg.label(buf, np.ones((3, 3)))
line = watershed(dist, markers)
img[line == 0] = 0
def run(self, ips, snap, img, para=None):
img[:] = snap > 0
dist = -ndimg.distance_transform_edt(snap)
pts = find_maximum(dist, para['tor'], False)
buf = np.zeros(ips.size, dtype=np.uint32)
buf[pts[:, 0], pts[:, 1]] = img[pts[:, 0], pts[:, 1]] = 2
markers, n = ndimg.label(buf, np.ones((3, 3)))
line = watershed(dist, markers, line=True, conn=para['con'] + 1)
msk = apply_hysteresis_threshold(img, 0, 1)
img[:] = snap * ~((line == 0) & msk)
def run(self, ips, snap, img, para=None):
dist = ndimg.distance_transform_edt(snap)
markers, n = ndimg.label(snap == 0, np.ones((3, 3)))
line = watershed(dist, markers, line=True)
if para['type'] == 'segment with ori':
img[:] = np.where(line == 0, 0, snap)
if para['type'] == 'segment only':
img[:] = (line > 0) * 255
if para['type'] == 'white line':
img[:] = (line == 0) * 255
if para['type'] == 'gray line':
img[:] = np.where(line == 0, dist, 0)
def run(self, ips, snap, img, para=None):
edge = sobel(snap)
img[:] = 0
img[snap > para['thr2']] = 2
img[snap < para['thr1']] = 1
ips.lut = self.buflut
mark = watershed(edge, img, line=True)
if para['type'] == 'line':
img[mark == 0] = ips.range[1]
elif para['type'] == 'up area':
img[mark != 1] = ips.range[1]
elif para['type'] == 'down area':
img[mark != 2] = ips.range[1]
def run(self, ips, imgs, para=None):
imgs[:] = imgs > 0
dist = -ndimg.distance_transform_edt(imgs)
pts = find_maximum(dist, para['tor'], False)
buf = np.zeros(imgs.shape, dtype=np.uint32)
buf[pts[:, 0], pts[:, 1], pts[:, 2]] = 2
imgs[pts[:, 0], pts[:, 1], pts[:, 2]] = 2
markers, n = ndimg.label(buf, np.ones((3, 3, 3)))
line = watershed(dist, markers, line=True, conn=para['con'] + 1)
msk = apply_hysteresis_threshold(imgs, 0, 1)
imgs[:] = imgs > 0
imgs *= 255
imgs *= ~((line == 0) & msk)
def run(self, ips, snap, img, para=None):
self.ips.lut[:] = self.buflut
ndimg.gaussian_filter(snap, para['sigma'], output=img)
mark = img < para['thr'] if para['ud'] else img > para['thr']
markers, n = ndimg.label(mark, np.ones((3, 3)), output=np.uint16)
if not para['ud']: img[:] = 255 - img
mark = watershed(img, markers, line=True, conn=para['con'] + 1)
mark = np.multiply((mark == 0), 255, dtype=np.uint8)
if para['type'] == 'white line':
img[:] = mark
if para['type'] == 'gray line':
np.minimum(snap, mark, out=img)
if para['type'] == 'white line on ori':
#img //=2
np.maximum(snap, mark, out=img)
def run(self, ips, snap, img, para=None):
#denoised = rank.median(img, disk(para['sigma']))
#gradient = rank.gradient(denoised, disk(para['gdt']))
ndimg.gaussian_filter(snap, para['sigma'], output=img)
markers, n = ndimg.label(ips.mask(), np.ones((3, 3)), output=np.uint32)
if not para['ud']: img[:] = 255 - img
mark = watershed(img, markers, line=True, conn=para['con'] + 1)
mark = np.multiply((mark == 0), 255, dtype=np.uint8)
if para['type'] == 'white line':
img[:] = mark
if para['type'] == 'gray line':
np.minimum(snap, mark, out=img)
if para['type'] == 'white line on ori':
np.maximum(snap, mark, out=img)
def run(self, ips, snap, img, para=None):
imgs = ips.imgs
gradient = np.zeros(imgs.shape, dtype=np.float32)
gradient += ndimg.sobel(imgs, axis=0, output=np.float32)**2
gradient += ndimg.sobel(imgs, axis=1, output=np.float32)**2
gradient += ndimg.sobel(imgs, axis=2, output=np.float32)**2
gradient **= 0.5
msk = np.zeros(imgs.shape, dtype=np.uint8)
msk[imgs > para['thr2']] = 1
msk[imgs < para['thr1']] = 2
#rst = watershed(gradient, msk)
rst = watershed(gradient, msk.astype(np.uint16))
imgs[:] = (rst == 1) * 255
ips.lut = self.buflut
def run(self, ips, snap, img, para=None):
markers = img * 0
markers[[0, -1]] = [[1], [2]]
mark = watershed(img, markers, line=True, conn=1)
img[:] = (mark == 0) * 255
