def _findLocalMax(self):
"""Find the centers of particles by thresholding and dilating."""
dilationKernel = im.makeCircularKernel(self._dilationRadius)
self._maxed = im.createImageArray(self, "morphMax",
dtype=np.bool,
shape = self._morphed[0].shape,
expectedrows=len(self._morphed))
for image in self._morphed:
# set pixels below morph thresh to 0
threshed = stats.threshold(image, self._morphThreshold, newval=0.0)
dilated = cv2.dilate(threshed, dilationKernel)
# expThreshold is so named because the original algorithm
# originally exponentiated and then thresholded, which is the same
# as flipping the sign and exponentiating the threshold.
binary = (dilated - threshed) >= self._expThreshold
self._maxed.append([binary])
self._maxed.flush()
def run(self):
images = self._import(im.RemoveBackground, "images")
self._seedImages = [] #debug
perFrame = self._param(EXPECTED_ELLIPSES_PER_FRAME)
self._table = self.context.createTable("ellipsesViaEdges", EllipseTable,
expectedrows=perFrame*images.nrows)
self._seedImages = im.createImageArray(self, "edgesSeedImages",
dtype=np.bool,
shape=images[0].shape,
expectedrows=len(images))
for i, image in enumerate(images):
particles = self._seedParticles(image)
particles = self._refineParticles(particles, image)
self._render(i, particles)
self._table.flush()
self._seedImages.flush()
