def extractFromBinary(self,
binaryImg,
colorImg,
minsize=5,
maxsize=-1,
appx_level=3):
"""
This method performs blob extraction given a binary source image that is used
to get the blob images, and a color source image.
binarymg- The binary image with the blobs.
colorImg - The color image.
minSize - The minimum size of the blobs in pixels.
maxSize - The maximum blob size in pixels.
* *appx_level* - The blob approximation level - an integer for the maximum distance between the true edge and the approximation edge - lower numbers yield better approximation.
"""
#If you hit this recursion limit may god have mercy on your soul.
#If you really are having problems set the value higher, but this means
# you have over 10,000,000 blobs in your image.
sys.setrecursionlimit(5000)
#h_next moves to the next external contour
#v_next() moves to the next internal contour
if (maxsize <= 0):
maxsize = colorImg.width * colorImg.height
#binaryImg.show()
retVal = []
test = binaryImg.meanColor()
if (test[0] == 0.00 and test[1] == 0.00 and test[2] == 0.00):
return FeatureSet(retVal)
# There are a couple of weird corner cases with the opencv
# connect components libraries - when you try to find contours
# in an all black image, or an image with a single white pixel
# that sits on the edge of an image the whole thing explodes
# this check catches those bugs. -KAS
# Also I am submitting a bug report to Willow Garage - please bare with us.
ptest = (4 * 255.0) / (binaryImg.width * binaryImg.height
) # val if two pixels are white
if (test[0] <= ptest and test[1] <= ptest and test[2] <= ptest):
return retVal
contourImage = binaryImg.toGray().getGrayNumpy()
#print contourImage.shape, contourImage.dtype
contours, hierarchy = cv2.findContours(contourImage, cv2.RETR_CCOMP,
cv2.CHAIN_APPROX_SIMPLE)
self.contours = copy(contours)
self.hierarchy = copy(hierarchy)
if not contours:
warnings.warn("Unable to find Blobs. Retuning Empty FeatureSet.")
return FeatureSet([])
try:
# note to self
# http://code.activestate.com/recipes/474088-tail-call-optimization-decorator/
retVal = self._extractFromBinary(contours, hierarchy, colorImg,
minsize, maxsize, appx_level)
except RuntimeError, e:
logger.warning(
"You exceeded the recursion limit. This means you probably have too many blobs in your image. We suggest you do some morphological operations (erode/dilate) to reduce the number of blobs in your image. This function was designed to max out at about 5000 blobs per image."
)
def extractFromBinary(self, binaryImg, colorImg, minsize=5, maxsize=-1, appx_level=3):
"""
This method performs blob extraction given a binary source image that is used
to get the blob images, and a color source image.
binarymg- The binary image with the blobs.
colorImg - The color image.
minSize - The minimum size of the blobs in pixels.
maxSize - The maximum blob size in pixels.
* *appx_level* - The blob approximation level - an integer for the maximum distance between the true edge and the approximation edge - lower numbers yield better approximation.
"""
# If you hit this recursion limit may god have mercy on your soul.
# If you really are having problems set the value higher, but this means
# you have over 10,000,000 blobs in your image.
sys.setrecursionlimit(5000)
# h_next moves to the next external contour
# v_next() moves to the next internal contour
if maxsize <= 0:
maxsize = colorImg.width * colorImg.height
# binaryImg.show()
retVal = []
test = binaryImg.meanColor()
if test[0] == 0.00 and test[1] == 0.00 and test[2] == 0.00:
return FeatureSet(retVal)
# There are a couple of weird corner cases with the opencv
# connect components libraries - when you try to find contours
# in an all black image, or an image with a single white pixel
# that sits on the edge of an image the whole thing explodes
# this check catches those bugs. -KAS
# Also I am submitting a bug report to Willow Garage - please bare with us.
ptest = (4 * 255.0) / (binaryImg.width * binaryImg.height) # val if two pixels are white
if test[0] <= ptest and test[1] <= ptest and test[2] <= ptest:
return retVal
contourImage = binaryImg.toGray().getGrayNumpy()
# print contourImage.shape, contourImage.dtype
contours, hierarchy = cv2.findContours(contourImage, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
self.contours = copy(contours)
self.hierarchy = copy(hierarchy)
if not contours:
warnings.warn("Unable to find Blobs. Retuning Empty FeatureSet.")
return FeatureSet([])
try:
# note to self
# http://code.activestate.com/recipes/474088-tail-call-optimization-decorator/
retVal = self._extractFromBinary(contours, hierarchy, colorImg, minsize, maxsize, appx_level)
except RuntimeError, e:
logger.warning(
"You exceeded the recursion limit. This means you probably have too many blobs in your image. We suggest you do some morphological operations (erode/dilate) to reduce the number of blobs in your image. This function was designed to max out at about 5000 blobs per image."
)
def colorWithClusterMeans(self):
"""
**SUMMARY**
This function colors each superpixel with its mean color and
return an image.
**RETURNS**
Image with superpixles drawn in its mean color.
**EXAMPLE**
>>> image = Image("lenna")
>>> sp = image.segmentSuperpixels(300, 20)
>>> sp.colorWithClusterMeans().show()
"""
if type(self.clusterMeanImage) != type(None):
return self.clusterMeanImage
self.clusterMeanImage = Image(self.image.getEmpty(3))
_mLayers = []
for sp in self:
color = tuple(reversed(sp.meanColor()))
sp.draw(color=color, width=-1)
self.clusterMeanImage += sp.image.copy()
for layer in sp.image._mLayers:
_mLayers.append(layer)
self.clusterMeanImage._mLayers = copy(_mLayers)
return self.clusterMeanImage
def draw(self, color=Color.RED, width=2, alpha=255):
"""
**SUMMARY**
Draw all the superpixels, in the given color, to the appropriate layer
By default, this draws the superpixels boundary. If you
provide a width, an outline of the exterior and interior contours is drawn.
**PARAMETERS**
* *color* -The color to render the blob as a color tuple.
* *width* - The width of the drawn blob in pixels, if -1 then filled then the polygon is filled.
* *alpha* - The alpha value of the rendered blob 0=transparent 255=opaque.
**RETURNS**
Image with superpixels drawn on it.
**EXAMPLE**
>>> image = Image("lenna")
>>> sp = image.segmentSuperpixels(300, 20)
>>> sp.draw(color=(255, 0, 255), width=5, alpha=128).show()
"""
img = self.image.copy()
self._drawingImage = Image(self.image.getEmpty(3))
_mLayers = []
for sp in self:
sp.draw(color=color, width=width, alpha=alpha)
self._drawingImage += sp.image.copy()
for layer in sp.image._mLayers:
_mLayers.append(layer)
self._drawingImage._mLayers = copy(_mLayers)
return self._drawingImage.copy()
