def get_masks(img, n_seg=250):
logger.debug('SLIC segmentation initialised')
segments = skimage.segmentation.slic(img, n_segments=n_seg, compactness=10, sigma=1)
logger.debug('SLIC segmentation complete')
logger.debug('contour extraction...')
masks = [[numpy.zeros((img.shape[0], img.shape[1]), dtype=numpy.uint8), None]]
for region in skimage.measure.regionprops(segments):
masks.append([masks[0][0].copy(), region.bbox])
x_min, y_min, x_max, y_max = region.bbox
masks[-1][0][x_min:x_max, y_min:y_max] = skimage.img_as_ubyte(region.convex_image)
logger.debug('contours extracted')
return masks[1:]
python类segmentation()的实例源码
def generate_markers(image):
#Creation of the internal Marker
marker_internal = image < -400
marker_internal = segmentation.clear_border(marker_internal)
marker_internal_labels = measure.label(marker_internal)
areas = [r.area for r in measure.regionprops(marker_internal_labels)]
areas.sort()
if len(areas) > 2:
for region in measure.regionprops(marker_internal_labels):
if region.area < areas[-2]:
for coordinates in region.coords:
marker_internal_labels[coordinates[0], coordinates[1]] = 0
marker_internal = marker_internal_labels > 0
#Creation of the external Marker
external_a = ndimage.binary_dilation(marker_internal, iterations=10)
external_b = ndimage.binary_dilation(marker_internal, iterations=55)
marker_external = external_b ^ external_a
#Creation of the Watershed Marker matrix
marker_watershed = np.zeros(image.shape, dtype=np.int)
marker_watershed += marker_internal * 255
marker_watershed += marker_external * 128
return marker_internal, marker_external, marker_watershed
def generate_markers(image):
#Creation of the internal Marker
marker_internal = image < -400
marker_internal = segmentation.clear_border(marker_internal)
marker_internal_labels = measure.label(marker_internal)
areas = [r.area for r in measure.regionprops(marker_internal_labels)]
areas.sort()
if len(areas) > 2:
for region in measure.regionprops(marker_internal_labels):
if region.area < areas[-2]:
for coordinates in region.coords:
marker_internal_labels[coordinates[0], coordinates[1]] = 0
marker_internal = marker_internal_labels > 0
#Creation of the external Marker
external_a = ndimage.binary_dilation(marker_internal, iterations=10)
external_b = ndimage.binary_dilation(marker_internal, iterations=55)
marker_external = external_b ^ external_a
#Creation of the Watershed Marker matrix
marker_watershed = np.zeros(image.shape, dtype=np.int)
marker_watershed += marker_internal * 255
marker_watershed += marker_external * 128
return marker_internal, marker_external, marker_watershed
def generate_markers(image):
#Creation of the internal Marker
marker_internal = image < -400
marker_internal = segmentation.clear_border(marker_internal)
marker_internal_labels = measure.label(marker_internal)
areas = [r.area for r in measure.regionprops(marker_internal_labels)]
areas.sort()
if len(areas) > 2:
for region in measure.regionprops(marker_internal_labels):
if region.area < areas[-2]:
for coordinates in region.coords:
marker_internal_labels[coordinates[0], coordinates[1]] = 0
marker_internal = marker_internal_labels > 0
#Creation of the external Marker
external_a = ndimage.binary_dilation(marker_internal, iterations=10)
external_b = ndimage.binary_dilation(marker_internal, iterations=55)
marker_external = external_b ^ external_a
#Creation of the Watershed Marker matrix
marker_watershed = np.zeros(image.shape, dtype=np.int)
marker_watershed += marker_internal * 255
marker_watershed += marker_external * 128
return marker_internal, marker_external, marker_watershed
