'**SUMMARY**
Find Skintone blobs will look for continuous
regions of Skintone in a color image and return them as Blob features in a FeatureSet.
Parameters specify the binarize filter threshold value, and minimum and maximum size for
blobs. If a threshold value is -1, it will use an adaptive threshold.  See binarize() for
more information about thresholding.  The threshblocksize and threshconstant
parameters are only used for adaptive threshold.
**PARAMETERS**
* *minsize* - the minimum size of the blobs, in pixels, of the returned blobs. This helps to filter out noise.
* *maxsize* - the maximim size of the blobs, in pixels, of the returned blobs.
* *dilate_iter* - the number of times to run the dilation operation.
**RETURNS**
Returns a featureset (basically a list) of :py:class:`blob` features. If no blobs are found this method returns None.
**EXAMPLE**
>>> img = Image("lenna")
>>> fs = img.findSkintoneBlobs()
>>> if( fs is not None ):
>>>     fs.draw()
**NOTES**
It will be really awesome for making UI type stuff, where you want to track a hand or a face.
**SEE ALSO**
:py:meth:`threshold`
:py:meth:`binarize`
:py:meth:`invert`
:py:meth:`dilate`
:py:meth:`erode`
:py:meth:`findBlobsFromPalette`
:py:meth:`smartFindBlobs`'

def findSkintoneBlobs(self, minsize=10, maxsize=0, dilate_iter=1):

    if (maxsize == 0):
        maxsize = (self.width * self.height)
    mask = self.getSkintoneMask(dilate_iter)
    blobmaker = BlobMaker()
    blobs = blobmaker.extractFromBinary(mask, self, minsize=minsize, maxsize=maxsize)
    if (not len(blobs)):
        return None
    return FeatureSet(blobs).sortArea()
