def test():
# print("Test not yet written, ending program")
import main, itktest
# import processing
testfish = "/scratch/images/J29-33/rec_J29-33_33D_PTAwt_16p2_30mm_w4Dd__x218_y232_z1013_orig_10um.nhdr"
hparts, npdata = main.unpacker(testfish, hdrstring=False)
npdata = npdata.reshape(hparts["dsize"][::-1])
#Canny Testing
npcanny = itktest.canny_edge_detector(npdata[85], returnNP=True)
#Hough Lines testing
#output = hough_lines(npcanny, d2_iscanny=True)
#print(output)
#Coordinate testing
boolean_data=npdata>18783.5193537
print(get_coordinates(boolean_data))
#Make sure we have the right type for ITK
if type(volume)==np.ndarray:
itk_volume=si.GetImageFromArray(np.float32(volume))
print(type(itk_volume))
#returnNP=True
else:
itk_volume=volume
#Do the edge detection
canny_edges_volume=si.CannyEdgeDetection(itk_volume, **kwargs)
if returnNP:
return np.asanyarray(si.GetArrayFromImage(canny_edges_volume),dtype="bool8")
return canny_edges_volume
#def Buffer<TImage>::GetArrayFromImage(TImage*) [with TImage = itk::Image<float, 2u>]
if __name__=='__main__':
import main
#import processing
testfish="/scratch/images/J29-33/rec_J29-33_33D_PTAwt_16p2_30mm_w4Dd__x218_y232_z1013_orig_10um.nhdr"
hparts,npdata=main.unpacker(testfish,hdrstring=False)
npdata=npdata.reshape(hparts["dsize"][::-1])
canny_edge_detector(npdata, returnNP=True)
canny_
#%load_ext cythonmagic import gc gc.enable() from scipy import ndimage ''' This is an experiment in the processing of the fish using some of the tools in scipy.ndimage. For the experimental purpose, see trevor's lab notebook page 17. ''' #Stage 1. Load Files #filename="/scratch/joinedImages/resized/rec_J29-33_33D_PTAwt_16p2_30mm_w4Dd_x292_y292_z1047_cubic-0-5_10.0um.nrrd" #filename="/scratch/joinedImages/uint16/rec_J29-33_33D_PTAwt_16p2_30mm_w4Dd_uint16_x586_y586_z2095_5.0um.nrrd" filename="/scratch/joinedImages/uint16/rec_J29-33_33D_PTAwt_16p2_30mm_w4Dd_uint16_x293_y293_z1047_10.0um.nrrd" header,npdata=main.unpacker(filename) npdata=npdata.reshape(header["dsize"][::-1]) #Automatic thresholding of the file thresh=processing.autothreshold(npdata) print(npdata.shape) mask=npdata>thresh #threshed=np.where(mask, npdata, npdata.min) #Do some binary morphology to get rid of excess stuff result=processing.binaryClosing(mask,structure=None, iterations=15) result2=processing.binaryOpening(result,kernel=None, iterations=1)