def segment(image,
spatial_radius,
range_radius,
min_density,
speedup_level=SPEEDUP_HIGH):
'''
Segment the input image (color or grayscale).
Keyword arguments:
image -- Input image (2-D or 3-D numpy array or compatible).
spatial_radius -- Spatial radius of the search window (integer).
range_radius -- Range radius of the search window (float).
min_density -- The minimum point density of a region in the segmented
image (integer).
speedup_level -- Filtering optimization level for fast execution
(default: high). See SpeedUpLevel.
Return value: tuple (segmented, labels, nb_regions)
segmented -- Image (Numpy array) where the color (or grayscale) of the
regions is the mean value of the pixels belonging to a region.
labels -- Image (2-D Numpy array, 32 unsigned bits per element) where a
pixel value correspond to the region number the pixel belongs to.
nb_regions -- The number of regions found by the mean shift algorithm.
NOTES: To avoid unnecessary image conversions when the function is called,
make sure the input image array is 8 unsigned bits per pixel and is
contiguous in memory.
'''
return _pymeanshift.segment(image, spatial_radius, range_radius,
min_density, speedup_level)
def __call__(self, image):
'''
Segment the input image (color or grayscale).
Keyword arguments:
image -- Input image (2-D or 3-D numpy array or compatible).
Return value: tuple (segmented, labels, nb_regions)
segmented -- Image (Numpy array) where the color (or grayscale) of the
regions is the mean value of the pixels belonging to a region.
labels -- Image (2-D Numpy array, 32 unsigned bits per element) where a
pixel value correspond to the region number the pixel belongs to.
nb_regions -- The number of regions found by the mean shift algorithm.
NOTES: To avoid unnecessary image conversions when the function is called,
make sure the input image array is 8 unsigned bits per pixel and is
contiguous in memory.
'''
if self._spatial_radius is None:
raise ValueError("Spatial radius has not been set")
if self._range_radius is None:
raise ValueError("Range radius has not been set")
if self._min_density is None:
raise ValueError("Minimum density has not been set")
return _pymeanshift.segment(image, self._spatial_radius,
self._range_radius, self._min_density,
self._speedup_level)
def __call__(self, image):
'''
Segment the input image (color or grayscale).
Keyword arguments:
image -- Input image (2-D or 3-D numpy array or compatible).
Return value: tuple (segmented, labels, nb_regions)
segmented -- Image (Numpy array) where the color (or grayscale) of the
regions is the mean value of the pixels belonging to a region.
labels -- Image (2-D Numpy array, 32 unsigned bits per element) where a
pixel value correspond to the region number the pixel belongs to.
nb_regions -- The number of regions found by the mean shift algorithm.
NOTES: To avoid unnecessary image conversions when the function is called,
make sure the input image array is 8 unsigned bits per pixel and is
contiguous in memory.
'''
if self._spatial_radius is None:
raise ValueError("Spatial radius has not been set")
if self._range_radius is None:
raise ValueError("Range radius has not been set")
if self._min_density is None:
raise ValueError("Minimum density has not been set")
return _pymeanshift.segment(image, self._spatial_radius, self._range_radius, self._min_density, self._speedup_level)
def segment(image, spatial_radius, range_radius, min_density, speedup_level=SPEEDUP_MEDIUM):
'''
Segment the input image (color or grayscale).
Keyword arguments:
image -- Input image (2-D or 3-D numpy array or compatible).
spatial_radius -- Spatial radius of the search window (integer).
range_radius -- Range radius of the search window (float).
min_density -- The minimum point density of a region in the segmented
image (integer).
speedup_level -- Filtering optimization level for fast execution
(default: high). See SpeedUpLevel.
Return value: tuple (segmented, labels, nb_regions)
segmented -- Image (Numpy array) where the color (or grayscale) of the
regions is the mean value of the pixels belonging to a region.
labels -- Image (2-D Numpy array, 32 unsigned bits per element) where a
pixel value correspond to the region number the pixel belongs to.
nb_regions -- The number of regions found by the mean shift algorithm.
NOTES: To avoid unnecessary image conversions when the function is called,
make sure the input image array is 8 unsigned bits per pixel and is
contiguous in memory.
'''
return _pymeanshift.segment(image, spatial_radius, range_radius, min_density, speedup_level)
def segment(img, spatial_radius, range_radius, min_density, return_colors=False) : ''' Segment the image into regions using the Mean Shift algorithm. An image of same shape is returned containing the region id of each pixel. ''' import _pymeanshift as _pms # @UnresolvedImport # Check if the image has the minimum size for the algorithm to run properly w, h, c = img.shape # @UnusedVariable if (w <= 2*range_radius) or (h <= 2*range_radius) : segmented_img = np.zeros((w,h), np.int) labels_img = np.zeros((w,h), np.int) nregions = 1 else: segmented_img, labels_img, nregions = _pms.segment(img, spatial_radius, range_radius, min_density) if return_colors : return segmented_img, labels_img, nregions else : return labels_img
def segment(image, spatial_radius, range_radius, min_density):
return _pymeanshift.segment(image, spatial_radius, range_radius,
min_density, _pymeanshift.SPEEDUP_HIGH)
def segment(image, spatial_radius, range_radius, min_density):
return _pymeanshift.segment(image, spatial_radius, range_radius, min_density, _pymeanshift.SPEEDUP_HIGH)
