def test_synthetic_data_segmentation(self): """ Function uses organ_segmentation for synthetic box object segmentation. """ data3d, segm, voxelsize_mm, slab = self.synthetic_data() # @TODO je tam bug, prohlížeč neumí korektně pracovat s doubly # app = QApplication(sys.argv) # #pyed = QTSeedEditor(noise ) # pyed = QTSeedEditor(data3d) # pyed.exec_() # #img3d = np.zeros([256,256,80], dtype=np.int16) # pyed = sed3.sed3(data3d) # pyed.show() outputTmp = segmentation.vesselSegmentation( data3d, # .astype(np.uint8), segmentation=(segm == slab['liver']), # .astype(np.uint8), # segmentation = oseg.orig_scale_segmentation, voxelsize_mm=voxelsize_mm, threshold=180, inputSigma=0.15, dilationIterations=2, nObj=1, interactivity=False, biggestObjects=True, binaryClosingIterations=5, binaryOpeningIterations=1) # ověření výsledku # pyed = sed3.sed3(outputTmp, contour=segm==slab['porta']) # pyed.show() # @TODO opravit chybu v vesselSegmentation outputTmp = (outputTmp == 2) errim = np.abs( outputTmp.astype(np.int) - (segm == slab['porta']).astype(np.int) ) # ověření výsledku # pyed = sed3.sed3(errim, contour=segm==slab['porta']) # pyed.show() # evaluation sum_of_wrong_voxels = np.sum(errim) sum_of_voxels = np.prod(segm.shape) # print "wrong ", sum_of_wrong_voxels # print "voxels", sum_of_voxels errorrate = sum_of_wrong_voxels/sum_of_voxels # import pdb; pdb.set_trace() self.assertLess(errorrate, 0.1)
def test_synthetic_data_segmentation(self): """ Function uses organ_segmentation for synthetic box object segmentation. """ data3d, segm, voxelsize_mm, slab = self.synthetic_data() # @TODO je tam bug, prohlížeč neumí korektně pracovat s doubly # app = QApplication(sys.argv) # #pyed = QTSeedEditor(noise ) # pyed = QTSeedEditor(data3d) # pyed.exec_() # #img3d = np.zeros([256,256,80], dtype=np.int16) # pyed = sed3.sed3(data3d) # pyed.show() outputTmp = segmentation.vesselSegmentation( data3d, # .astype(np.uint8), segmentation=(segm == slab['liver']), # .astype(np.uint8), # segmentation = oseg.orig_scale_segmentation, voxelsize_mm=voxelsize_mm, threshold=180, inputSigma=0.15, dilationIterations=2, nObj=1, interactivity=False, biggestObjects=True, binaryClosingIterations=5, binaryOpeningIterations=1) # ověření výsledku # pyed = sed3.sed3(outputTmp, contour=segm==slab['porta']) # pyed.show() # @TODO opravit chybu v vesselSegmentation outputTmp = (outputTmp == 2) errim = np.abs( outputTmp.astype(np.int) - (segm == slab['porta']).astype(np.int) ) # ověření výsledku # pyed = sed3.sed3(errim, contour=segm==slab['porta']) # pyed.show() # evaluation sum_of_wrong_voxels = np.sum(errim) sum_of_voxels = np.prod(segm.shape) # print "wrong ", sum_of_wrong_voxels # print "voxels", sum_of_voxels errorrate = sum_of_wrong_voxels/sum_of_voxels # import pdb; pdb.set_trace() self.assertLess(errorrate, 0.1)
def test_ui_threshold_same_params_as_portal_segmentation(self): params = { 'threshold': -1, 'inputSigma': 0.15, 'dilationIterations': 10, 'nObj': 1, 'biggestObjects': False, 'useSeedsOfCompactObjects': True, 'interactivity': True, 'binaryClosingIterations': 2, 'binaryOpeningIterations': 0 } import imtools import imtools.sample_data datap = imtools.sample_data.generate() app = QApplication(sys.argv) outputTmp = segmentation.vesselSegmentation( datap['data3d'], datap['segmentation'], # datap['voxelsize_mm'], **params)
def test_ui_threshold_same_params_as_portal_segmentation(self): params = { 'threshold': -1, 'inputSigma': 0.15, 'dilationIterations': 10, 'nObj': 1, 'biggestObjects': False, 'useSeedsOfCompactObjects': True, 'interactivity': True, 'binaryClosingIterations': 2, 'binaryOpeningIterations': 0 } import imtools import imtools.sample_data datap = imtools.sample_data.generate() app = QApplication(sys.argv) outputTmp = segmentation.vesselSegmentation( datap['data3d'], datap['segmentation'], # datap['voxelsize_mm'], **params)
def do_segmentation_with_connected_threshold(self, image: np.ndarray, resolution: np.ndarray, time_resolution: float, qapp=None): from imtools import segmentation as imsegmentation params = { # 'threshold': threshold, 'inputSigma': 0.15, 'aoi_dilation_iterations': 0, 'nObj': 1, 'biggestObjects': False, 'useSeedsOfCompactObjects': True, 'interactivity': True, 'binaryClosingIterations': 2, 'binaryOpeningIterations': 0, # 'seeds': seeds, } # params.update(inparams) # logger.debug("ogran_label ", organ_label) # target_segmentation = (self.segmentation == self.nlabels(organ_label)).astype(np.int8) import imma.image_manipulation as ima # target_segmentation = ima.select_labels( # self.segmentation, organ_label, self.slab # ) vxsz_mm = np.array([1.0, resolution[0] * 1000, resolution[1] * 1000]) outputSegmentation = imsegmentation.vesselSegmentation( image, voxelsize_mm=vxsz_mm, # target_segmentation, segmentation=np.ones(image.shape, dtype=np.uint8), aoi_label=1, # aoi_label=organ_label, # forbidden_label=forbidden_label, # slab=self.slab, # debug=self.debug_mode, **params)