def distance_transform(bitmap):
"""
@type bitmap: numpy array
@param bitmap: image for which distance transform will be calculated
@return: numpy array holding distance transform of provided bitmap
Calculates distance transtofm of provided bitmap. This function is wrapper
for actual distance transform.
"""
return dtransform(bitmap)
def distance_transform(bitmap):
"""
@type bitmap: np array
@param bitmap: image for which distance transform will be calculated
@return: np array holding distance transform of provided bitmap
Calculates distance transtofm of provided bitmap. This function is wrapper
for actual distance transform.
"""
return dtransform(bitmap)
def getBestLabelLocation(ImageForTracing):
"""
@type ImageForTracing: PIL image
@param ImageForTracing: Image to calculate distance transform
@return: tuple of two integers (x,y): coordinates of best-placed label
Calculates coordinates corresponding to visual-center of given area defined
by black pixels. "visual-center" means: 'looks like is is in the center of
the structure". Coordinates of central points are calculated basing on
maximum of distance transform: Central point coordinate = location of
distance transform's maximum.
Input bitmap: object: black, background: white
Used to dtransform: obj. white, bg - black
"""
ImageForTracing = ImageOps.invert(ImageForTracing)
Imbbox = ImageForTracing.getbbox()
# We need to provide frame with background color ensure proper results
Imbbox = tuple(np.array(Imbbox) + np.array([-1, -1, 1, 1]))
# Crop input image so distance transform is not calculated only in essential
# part of the image. Plain backgroud area is skipped.
CroppedImTracing = ImageOps.invert(
ImageForTracing.crop(Imbbox).convert("L"))
CroppedImTracing = ImageOps.invert(CroppedImTracing)
# Cacluate distance transform and extract its maximum.
distanceTransform = dtransform(tonpArray(CroppedImTracing))
(y,x) = ( distanceTransform.argmax() // distanceTransform.shape[1] ,\
( distanceTransform.argmax() % distanceTransform.shape[1] ))
# Take into account that initial image was cropped -> return to original
# image coordinates
x, y = x + Imbbox[0], y + Imbbox[1]
return (x, y)
def getBestLabelLocation(ImageForTracing):
"""
@type ImageForTracing: PIL image
@param ImageForTracing: Image to calculate distance transform
@return: tuple of two integers (x,y): coordinates of best-placed label
Calculates coordinates corresponding to visual-center of given area defined
by black pixels. "visual-center" means: 'looks like is is in the center of
the structure". Coordinates of central points are calculated basing on
maximum of distance transform: Central point coordinate = location of
distance transform's maximum.
Input bitmap: object: black, background: white
Used to dtransform: obj. white, bg - black
"""
ImageForTracing = ImageOps.invert(ImageForTracing)
Imbbox = ImageForTracing.getbbox()
# We need to provide frame with background color ensure proper results
Imbbox = tuple(numpy.array(Imbbox) + numpy.array([-1, -1, 1, 1]))
# Crop input image so distance transform is not calculated only in essential
# part of the image. Plain backgroud area is skipped.
CroppedImTracing = ImageOps.invert(ImageForTracing.crop(Imbbox).convert("L") )
CroppedImTracing = ImageOps.invert(CroppedImTracing)
# Cacluate distance transform and extract its maximum.
distanceTransform = dtransform(toNumpyArray(CroppedImTracing))
(y,x) = ( distanceTransform.argmax() // distanceTransform.shape[1] ,\
( distanceTransform.argmax() % distanceTransform.shape[1] ))
# Take into account that initial image was cropped -> return to original
# image coordinates
x,y = x+Imbbox[0],y+Imbbox[1]
return (x,y)
