[Scipy-svn] r3765 - trunk/scipy/ndimage/tests

scipy-svn@scip... scipy-svn@scip...
Wed Jan 2 15:31:52 CST 2008


Author: jarrod.millman
Date: 2008-01-02 15:31:45 -0600 (Wed, 02 Jan 2008)
New Revision: 3765

Removed:
   trunk/scipy/ndimage/tests/test_segment.py
Log:
removing redundant test


Deleted: trunk/scipy/ndimage/tests/test_segment.py
===================================================================
--- trunk/scipy/ndimage/tests/test_segment.py	2008-01-02 07:54:58 UTC (rev 3764)
+++ trunk/scipy/ndimage/tests/test_segment.py	2008-01-02 21:31:45 UTC (rev 3765)
@@ -1,142 +0,0 @@
-
-import numpy as N
-from numpy.testing import *
-import scipy.ndimage.segment as S
-
-inputname = 'slice112.raw'
-
-import os
-filename = os.path.join(os.path.split(__file__)[0],inputname)
-
-
-def shen_castan(image, IIRFilter=0.8, scLow=0.3, window=7, lowThreshold=220+2048, highThreshold=600+2048, dust=16):
-    labeledEdges, numberObjects = S.shen_castan_edges(scLow, IIRFilter, window, lowThreshold, highThreshold, image)
-    # allocated struct array for edge object measures. for now just the rect bounding box
-    ROIList = N.zeros(numberObjects, dtype=S.objstruct)
-    # return the bounding box for each connected edge
-    S.get_object_stats(labeledEdges, ROIList)
-    return labeledEdges, ROIList[ROIList['Area']>dust]
-
-def sobel(image, sLow=0.3, tMode=1, lowThreshold=220+2048, highThreshold=600+2048, BPHigh=10.0, apearture=21, dust=16):
-    # get sobel edge points. return edges that are labeled (1..numberObjects)
-    labeledEdges, numberObjects = S.sobel_edges(sLow, tMode, lowThreshold, highThreshold, BPHigh, apearture, image)
-    # allocated struct array for edge object measures. for now just the rect bounding box
-    ROIList = N.zeros(numberObjects, dtype=S.objstruct)
-    # return the bounding box for each connected edge
-    S.get_object_stats(labeledEdges, ROIList)
-    # thin (medial axis transform) of the sobel edges as the sobel produces a 'band edge'
-    S.morpho_thin_filt(labeledEdges, ROIList)
-    return labeledEdges, ROIList[ROIList['Area']>dust]
-
-def canny(image, cSigma=1.0, cLow=0.5, cHigh=0.8, tMode=1, lowThreshold=220+2048, highThreshold=600+2048,
-          BPHigh=10.0, apearture=21, dust=16):
-    # get canny edge points. return edges that are labeled (1..numberObjects)
-    labeledEdges, numberObjects = S.canny_edges(cSigma, cLow, cHigh, tMode, lowThreshold, highThreshold, 
-                                               BPHigh, apearture, image)
-    # allocated struct array for edge object measures. for now just the rect bounding box
-    ROIList = N.zeros(numberObjects, dtype=S.objstruct)
-    # return the bounding box for each connected edge
-    S.get_object_stats(labeledEdges, ROIList)
-    return labeledEdges, ROIList[ROIList['Area']>dust]
-
-def get_shape_mask(labeledEdges, ROIList):
-    # pass in Sobel morph-thinned labeled edge image (LEI) and ROIList
-    # GetShapeMask will augment the ROI list
-    # labeledEdges is the original edge image and overwritten as mask image
-    # maskImage is the mask that is used for blob texture / pixel features
-    S.build_boundary(labeledEdges, ROIList)
-    return 
-
-def get_voxel_measures(rawImage, labeledEdges, ROIList):
-    #
-    # pass raw image, labeled mask and the partially filled ROIList
-    # VoxelMeasures will fill the voxel features in the list
-    #
-    S.voxel_measures(rawImage, labeledEdges, ROIList)
-    return 
-
-def get_texture_measures(rawImage, labeledEdges, ROIList):
-    #
-    # pass raw image, labeled mask and the partially filled ROIList
-    # VoxelMeasures will fill the texture (Law's, co-occurence, Gabor) features in the list
-    #
-    S.texture_measures(rawImage, labeledEdges, ROIList)
-    return 
-
-def segment_regions():
-    # get slice from the CT volume
-    image = get_slice(filename)
-    # need a copy of original image as filtering will occur on the extracted slice
-    sourceImage = image.copy()
-    # Sobel is the first level segmenter. Sobel magnitude and MAT (medial axis transform)
-    # followed by connected component analysis. What is returned is labeled edges and the object list
-    labeledMask, ROIList = sobel(image)
-    # From the labeled edges and the object list get the labeled mask for each blob object
-    get_shape_mask(labeledMask, ROIList)
-    # Use the labeled mask and source image (raw) to get voxel features 
-    get_voxel_measures(sourceImage, labeledMask, ROIList)
-    # Use the labeled mask and source image (raw) to get texture features 
-    get_texture_measures(sourceImage, labeledMask, ROIList)
-    return sourceImage, labeledMask, ROIList
-
-def grow_regions():
-    # get slice from the CT volume
-    image = get_slice(filename)
-    regionMask, numberRegions = region_grow(image)
-    return regionMask, numberRegions 
-
-
-def region_grow(image, lowThreshold=220+2048, highThreshold=600+2048, open=7, close=7):
-    # morphology filters need to be clipped to 11 max and be odd
-    regionMask, numberRegions = S.region_grow(lowThreshold, highThreshold, close, open, image)
-    return regionMask, numberRegions
-      
-
-def get_slice(imageName='junk.raw', bytes=2, rows=512, columns=512):
-    # get a slice alrady extracted from the CT volume
-    #image = open(imageName, 'rb')
-    #slice = image.read(rows*columns*bytes)
-    #values = struct.unpack('h'*rows*columns, slice)
-    #ImageSlice = N.array(values, dtype=float).reshape(rows, columns)
-
-    ImageSlice = N.fromfile(imageName, dtype=N.uint16).reshape(rows, columns);
-
-    # clip the ends for this test CT image file as the spine runs off the end of the image
-    ImageSlice[505:512, :] = 0
-    return (ImageSlice).astype(float)
-
-def get_slice2(image_name='junk.raw', bytes=2, shape=(512,512)):
-    import mmap
-    file = open(image_name, 'rb')
-    mm = mmap.mmap(file.fileno(), 0, access=mmap.ACCESS_READ)
-    slice = N.frombuffer(mm, dtype='u%d' % bytes).reshape(shape) 
-    slice = slice.astype(float)
-    slice[505:512,:] = 0
-    return slice
-
-def save_slice(mySlice, filename='junk.raw', bytes=4):
-    # just save the slice to a fixed file
-    slice = mySlice.astype('u%d' % bytes)
-    slice.tofile(filename)
-
-
-class TestSegment(NumpyTestCase):
-    def test1(self):
-        image = get_slice(filename)
-        sourceImage = image.copy()
-        edges, objects = sobel(image)
-        get_shape_mask(edges, objects)
-        get_voxel_measures(sourceImage, edges, objects)
-        get_texture_measures(sourceImage, edges, objects)
-
-    def test2(self):
-        sourceImage, labeledMask, ROIList = segment_regions()
-
-    def test3(self):
-        regionMask, numberRegions = grow_regions()
-        print regionMask.max()
-        #save_slice(regionMask, 'regionMask.raw')
-
-    
-if __name__ == "__main__":
-    NumpyTest().run()



More information about the Scipy-svn mailing list