yield contour
contour = contour.h_next()
cv.Zero(markers)
comp_count = 0
for c in contour_iterator(contours):
cv.DrawContours(markers,
c,
cv.ScalarAll(comp_count + 1),
cv.ScalarAll(comp_count + 1),
-1,
-1,
8)
comp_count += 1
cv.Watershed(img0, markers)
cv.Set(wshed, cv.ScalarAll(255))
# paint the watershed image
color_tab = [(cv.RandInt(rng) % 180 + 50, cv.RandInt(rng) % 180 + 50, cv.RandInt(rng) % 180 + 50) for i in range(comp_count)]
for j in range(markers.height):
for i in range(markers.width):
idx = markers[j, i]
if idx != -1:
wshed[j, i] = color_tab[int(idx - 1)]
cv.AddWeighted(wshed, 0.5, img_gray, 0.5, 0, wshed)
cv.ShowImage("watershed transform", wshed)
cv.DestroyAllWindows()
def contour_iterator(contour):
while contour:
yield contour
contour = contour.h_next()
cv.Zero(markers)
comp_count = 0
for c in contour_iterator(contours):
cv.DrawContours(markers, c, cv.ScalarAll(comp_count + 1),
cv.ScalarAll(comp_count + 1), -1, -1, 8)
comp_count += 1
cv.Watershed(img0, markers)
cv.Set(wshed, cv.ScalarAll(255))
# paint the watershed image
color_tab = [
(cv.RandInt(rng) % 180 + 50, cv.RandInt(rng) % 180 + 50,
cv.RandInt(rng) % 180 + 50) for i in range(comp_count)
]
for j in range(markers.height):
for i in range(markers.width):
idx = markers[j, i]
if idx != -1:
wshed[j, i] = color_tab[int(idx - 1)]
cv.AddWeighted(wshed, 0.5, img_gray, 0.5, 0, wshed)
cv.ShowImage("watershed transform", wshed)
cv.CV_RGB(255, 255, 0)
]
img = cv.CreateImage((500, 500), 8, 3)
rng = cv.RNG(-1)
cv.NamedWindow("clusters", 1)
while True:
cluster_count = randint(2, MAX_CLUSTERS)
sample_count = randint(1, 1000)
points = cv.CreateMat(sample_count, 1, cv.CV_32FC2)
clusters = cv.CreateMat(sample_count, 1, cv.CV_32SC1)
# generate random sample from multigaussian distribution
for k in range(cluster_count):
center = (cv.RandInt(rng) % img.width,
cv.RandInt(rng) % img.height)
first = k * sample_count / cluster_count
last = sample_count
if k != cluster_count:
last = (k + 1) * sample_count / cluster_count
point_chunk = cv.GetRows(points, first, last)
cv.RandArr(rng, point_chunk, cv.CV_RAND_NORMAL,
cv.Scalar(center[0], center[1], 0, 0),
cv.Scalar(img.width * 0.1, img.height * 0.1, 0, 0))
# shuffle samples
cv.RandShuffle(points, rng)