def _doMorph(self):
"""Apply a gaussian blur and then a tophat."""
tophatKernel = im.makeCircularKernel(self._featureRadius)
morphed = []
for image in self._images:
scaled = im.forceRange(image, 0.0, 1.0) # scale pixel values to 0-1
blurred = cv2.GaussianBlur(scaled, (0, 0), self._blurSigma)
tophat = cv2.morphologyEx(blurred, cv2.MORPH_TOPHAT, tophatKernel)
# maximize contrast by forcing the range to be 0-1 again
morphed.append(im.forceRange(tophat, 0.0, 1.0))
self._morphed = morphed
def _findLocalMax(self):
"""Find the centers of particles by thresholding and dilating."""
dilationKernel = im.makeCircularKernel(self._dilationRadius)
maxed = []
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
maxed.append(binary)
self._maxed = maxed
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()