Python SimpleITK.ReadImage方法代码示例

# 需要导入模块: import SimpleITK [as 别名]
# 或者: from SimpleITK import ReadImage [as 别名]
def __GetFeatureValuesEachModality(self, data_path, roi_path, key_name):
        if self.__is_ignore_tolerence:
            image = sitk.ReadImage(data_path)
            roi_image = sitk.ReadImage(roi_path)
            roi_image.CopyInformation(image)
            result = self.extractor.execute(image, roi_image)
        else:
            result = self.extractor.execute(data_path, roi_path)

        feature_names = []
        feature_values = []
        for feature_name, feature_value in zip(list(result.keys()), list(result.values())):
            if self.__is_ignore_diagnostic and 'diagnostics' in feature_name:
                continue
            feature_names.append(key_name + '_' + feature_name)
            feature_values.append(feature_value)
        return feature_names, feature_values 

# 需要导入模块: import SimpleITK [as 别名]
# 或者: from SimpleITK import ReadImage [as 别名]
def load_itk_image(filename):
    with open(filename) as f:
        contents = f.readlines()
        line = [k for k in contents if k.startswith('TransformMatrix')][0]
        transformM = np.array(line.split(' = ')[1].split(' ')).astype('float')
        transformM = np.round(transformM)
        if np.any( transformM!=np.array([1,0,0, 0, 1, 0, 0, 0, 1])):
            isflip = True
        else:
            isflip = False

    itkimage = sitk.ReadImage(filename)
    numpyImage = sitk.GetArrayFromImage(itkimage)
     
    numpyOrigin = np.array(list(reversed(itkimage.GetOrigin())))
    numpySpacing = np.array(list(reversed(itkimage.GetSpacing())))
     
    return numpyImage, numpyOrigin, numpySpacing,isflip 

# 需要导入模块: import SimpleITK [as 别名]
# 或者: from SimpleITK import ReadImage [as 别名]
def eyesize_basic_test():

  downloader = imagedownloader.ImageDownloader()
  estimator = eyesize.EyeSize()

  image_name = "TralitusSaltrator.jpg"

  downloader.set_figshare_id("1066744")
  downloader.set_image_name(image_name)
  downloader.download()

  input_image = sitk.ReadImage(image_name)

  estimator.set_image(input_image)
  estimator.set_seed_point([204,400])

  eyes_segmented,radius_estimate = estimator.estimate()

  sitk.WriteImage(eyes_segmented,'SegmentedEye.png')

  assert radius_estimate == 85 

# 需要导入模块: import SimpleITK [as 别名]
# 或者: from SimpleITK import ReadImage [as 别名]
def eyesize_noisy_test():

  downloader = imagedownloader.ImageDownloader()
  estimator = eyesize.EyeSize()

  image_name = "TralitusSaltrator.jpg"

  downloader.set_figshare_id("1066744")
  downloader.set_image_name(image_name)
  downloader.download()

  input_image = sitk.ReadImage(image_name)

  estimator.set_image(input_image)
  estimator.set_seed_point([204,400])

  eyes_segmented,radius_estimate = estimator.estimate()

  sitk.WriteImage(eyes_segmented,'SegmentedEye.png')

  expected_value = 85
  assert radius_estimate == expected_value, \
    "Problem with estimating radius: current: %s, expected: %s" % (radius_estimate, expected_value) 

# 需要导入模块: import SimpleITK [as 别名]
# 或者: from SimpleITK import ReadImage [as 别名]
def N4BiasFieldCorrection(src_path, dst_path):
        '''
        This function carry out BiasFieldCorrection for the files in a specific directory
        :param src_path: path of the source file
        :param dst_path: path of the target file
        :return:
        '''
        print("N4 bias correction runs.")
        inputImage = sitk.ReadImage(src_path)

        maskImage = sitk.OtsuThreshold(inputImage, 0, 1, 200)
        sitk.WriteImage(maskImage, dst_path)

        inputImage = sitk.Cast(inputImage, sitk.sitkFloat32)

        corrector = sitk.N4BiasFieldCorrectionImageFilter()

        # corrector.SetMaximumNumberOfIterations(10)

        output = corrector.Execute(inputImage, maskImage)
        sitk.WriteImage(output, dst_path)
        print("Finished N4 Bias Field Correction.....")

    # normalize the data(zero mean and unit variance) 

# 需要导入模块: import SimpleITK [as 别名]
# 或者: from SimpleITK import ReadImage [as 别名]
def load_nifty_volume_as_4d_array(filename):
    """Read a nifty image and return a dictionay storing data array, spacing and direction
    output['data_array'] 4d array with shape [C, D, H, W]
    output['spacing']    a list of spacing in z, y, x axis 
    output['direction']  a 3x3 matrix for direction
    """
    img_obj    = sitk.ReadImage(filename)
    data_array = sitk.GetArrayFromImage(img_obj)
    origin     = img_obj.GetOrigin()
    spacing    = img_obj.GetSpacing()
    direction  = img_obj.GetDirection()
    shape = data_array.shape
    if(len(shape) == 4):
        assert(shape[3] == 1) 
    elif(len(shape) == 3):
        data_array = np.expand_dims(data_array, axis = 0)
    else:
        raise ValueError("unsupported image dim: {0:}".format(len(shape)))
    output = {}
    output['data_array'] = data_array
    output['origin']     = origin
    output['spacing']    = (spacing[2], spacing[1], spacing[0])
    output['direction']  = direction
    return output 

# 需要导入模块: import SimpleITK [as 别名]
# 或者: from SimpleITK import ReadImage [as 别名]
def read_images(self, path, affix):
        t1 = sitk.GetArrayFromImage(sitk.ReadImage(glob.glob(os.path.join(path, '*_t1' + affix))[0]))
        t1ce = sitk.GetArrayFromImage(sitk.ReadImage(glob.glob(os.path.join(path, '*_t1ce' + affix))[0]))
        t2 = sitk.GetArrayFromImage(sitk.ReadImage(glob.glob(os.path.join(path, '*_t2' + affix))[0]))
        flair = sitk.GetArrayFromImage(sitk.ReadImage(glob.glob(os.path.join(path, '*_flair' + affix))[0]))
        # scale to 0 to 1
        t1 = (t1 - np.amin(t1)) / (np.amax(t1) - np.amin(t1))
        t1ce = (t1ce - np.amin(t1ce)) / (np.amax(t1ce) - np.amin(t1ce))
        t2 = (t2 - np.amin(t2)) / (np.amax(t2) - np.amin(t2))
        flair = (flair - np.amin(flair)) / (np.amax(flair) - np.amin(flair))
        # scale to 0 mean, 1 std
        #t1 = (t1 - np.mean(t1)) / np.std(t1)
        #t1ce = (t1ce - np.mean(t1ce)) / np.std(t1ce)
        #t2 = (t2 - np.mean(t2)) / np.std(t2)
        #flair = (flair - np.mean(flair)) / np.std(flair)
        images = np.stack((t1, t1ce, t2, flair), axis=-1).astype(np.float32)
        return images 

# 需要导入模块: import SimpleITK [as 别名]
# 或者: from SimpleITK import ReadImage [as 别名]
def getImages(testFilename, resultFilename):
    """Return the test and result images, thresholded and non-WMH masked."""
    testImage   = sitk.ReadImage(testFilename)
    resultImage = sitk.ReadImage(resultFilename)
    assert testImage.GetSize() == resultImage.GetSize()
    
    # Get meta data from the test-image, needed for some sitk methods that check this
    resultImage.CopyInformation(testImage)
    
    # Remove non-WMH from the test and result images, since we don't evaluate on that
    maskedTestImage = sitk.BinaryThreshold(testImage, 0.5,  1.5, 1, 0) # WMH == 1    
    nonWMHImage     = sitk.BinaryThreshold(testImage, 1.5,  2.5, 0, 1) # non-WMH == 2
    maskedResultImage = sitk.Mask(resultImage, nonWMHImage)
    
    # Convert to binary mask
    if 'integer' in maskedResultImage.GetPixelIDTypeAsString():
        bResultImage = sitk.BinaryThreshold(maskedResultImage, 1, 1000, 1, 0)
    else:
        bResultImage = sitk.BinaryThreshold(maskedResultImage, 0.5, 1000, 1, 0)
        
    return maskedTestImage, bResultImage 

# 需要导入模块: import SimpleITK [as 别名]
# 或者: from SimpleITK import ReadImage [as 别名]
def pp_patient(self, path):

        pid = path.split('/')[-1]
        img = sitk.ReadImage(os.path.join(path, '{}_ct_scan.nrrd'.format(pid)))
        img_arr = sitk.GetArrayFromImage(img)
        print('processing {} with GT(s) {}, spacing {} and img shape {}.'.format(
            pid, " and ".join(self.cf.gts_to_produce), img.GetSpacing(), img_arr.shape))
        img_arr = resample_array(img_arr, img.GetSpacing(), self.cf.target_spacing)
        img_arr = np.clip(img_arr, -1200, 600)
        #img_arr = (1200 + img_arr) / (600 + 1200) * 255  # a+x / (b-a) * (c-d) (c, d = new)
        img_arr = img_arr.astype(np.float32)
        img_arr = (img_arr - np.mean(img_arr)) / np.std(img_arr).astype('float16')

        df = pd.read_csv(os.path.join(self.cf.root_dir, 'characteristics.csv'), sep=';')
        df = df[df.PatientID == pid]

        np.save(os.path.join(self.cf.pp_dir, '{}_img.npy'.format(pid)), img_arr)
        if 'single_annotator' in self.cf.gts_to_produce or 'sa' in self.cf.gts_to_produce:
            self.produce_sa_gt(path, pid, df, img.GetSpacing(), img_arr.shape)
        if 'merged' in self.cf.gts_to_produce:
            self.produce_merged_gt(path, pid, df, img.GetSpacing(), img_arr.shape) 

# 需要导入模块: import SimpleITK [as 别名]
# 或者: from SimpleITK import ReadImage [as 别名]
def __init__ (self, uid):
        CaseBase.__init__(self)
        self.uid = uid
        self.path = os.path.join(LUNA_DIR_LOOKUP[uid], uid + '.mhd')
        if not os.path.exists(self.path):
            raise Exception('data not found for uid %s at %s' % (uid, self.path))
        pass
        #self.thumb_path = os.path.join(DATA_DIR, 'thumb', uid)
        # load path
        itkimage = itk.ReadImage(self.path)
        self.HU = (1.0, 0.0)
        self.images = itk.GetArrayFromImage(itkimage).astype(np.float32)
        #print type(self.images), self.images.dtype
        self.origin = list(reversed(itkimage.GetOrigin()))
        self.spacing = list(reversed(itkimage.GetSpacing()))
        self.view = AXIAL
        _, a, b = self.spacing
        self.anno = LUNA_ANNO.get(uid, None)
        assert a == b
        # sanity check
        pass 

# 需要导入模块: import SimpleITK [as 别名]
# 或者: from SimpleITK import ReadImage [as 别名]
def __init__ (self, path, uid=None):
        VolumeBase.__init__(self)
        self.uid = uid
        self.path = path
        if not os.path.exists(self.path):
            raise Exception('data not found for uid %s at %s' % (uid, self.path))
        pass
        #self.thumb_path = os.path.join(DATA_DIR, 'thumb', uid)
        # load path
        itkimage = itk.ReadImage(self.path)
        self.HU = (1.0, 0.0)
        self.images = itk.GetArrayFromImage(itkimage).astype(np.float32)
        #print type(self.images), self.images.dtype
        self.origin = list(reversed(itkimage.GetOrigin()))
        self.spacing = list(reversed(itkimage.GetSpacing()))
        self.view = AXIAL
        _, a, b = self.spacing
        #self.anno = LUNA_ANNO.get(uid, None)
        assert a == b
        # sanity check
        pass 

# 需要导入模块: import SimpleITK [as 别名]
# 或者: from SimpleITK import ReadImage [as 别名]
def copy_BraTS_segmentation_and_convert_labels(in_file, out_file):
    # use this for segmentation only!!!
    # nnUNet wants the labels to be continuous. BraTS is 0, 1, 2, 4 -> we make that into 0, 1, 2, 3
    img = sitk.ReadImage(in_file)
    img_npy = sitk.GetArrayFromImage(img)

    uniques = np.unique(img_npy)
    for u in uniques:
        if u not in [0, 1, 2, 4]:
            raise RuntimeError('unexpected label')

    seg_new = np.zeros_like(img_npy)
    seg_new[img_npy == 4] = 3
    seg_new[img_npy == 2] = 1
    seg_new[img_npy == 1] = 2
    img_corr = sitk.GetImageFromArray(seg_new)
    img_corr.CopyInformation(img)
    sitk.WriteImage(img_corr, out_file) 

# 需要导入模块: import SimpleITK [as 别名]
# 或者: from SimpleITK import ReadImage [as 别名]
def load_itk_image(filename):
    with open(filename) as f:
        contents = f.readlines()
        line = [k for k in contents if k.startswith('TransformMatrix')][0]
        transformM = np.array(line.split(' = ')[1].split(' ')).astype('float')
        transformM = np.round(transformM)
        if np.any(transformM != np.array([1, 0, 0, 0, 1, 0, 0, 0, 1])):
            isflip = True
        else:
            isflip = False

    itkimage = sitk.ReadImage(filename)
    numpyImage = sitk.GetArrayFromImage(itkimage)

    numpyOrigin = np.array(list(reversed(itkimage.GetOrigin())))
    numpySpacing = np.array(list(reversed(itkimage.GetSpacing())))

    return numpyImage, numpyOrigin, numpySpacing, isflip 

# 需要导入模块: import SimpleITK [as 别名]
# 或者: from SimpleITK import ReadImage [as 别名]
def load_itk_image(filename):
    with open(filename) as f:
        contents = f.readlines()
        line = [k for k in contents if k.startswith('TransformMatrix')][0]
        transformM = np.array(line.split(' = ')[1].split(' ')).astype('float')
        transformM = np.round(transformM)
        if np.any( transformM!=np.array([1,0,0, 0, 1, 0, 0, 0, 1])):
            isflip = True
        else:
            isflip = False

    itkimage = sitk.ReadImage(filename)
    numpyImage = sitk.GetArrayFromImage(itkimage)
    numpyOrigin = np.array(list(reversed(itkimage.GetOrigin())))
    numpySpacing = np.array(list(reversed(itkimage.GetSpacing())))

    return numpyImage, numpyOrigin, numpySpacing, isflip 

# 需要导入模块: import SimpleITK [as 别名]
# 或者: from SimpleITK import ReadImage [as 别名]
def load_itk_image(filename):
    with open(filename) as f:
        contents = f.readlines()
        line = [k for k in contents if k.startswith('TransformMatrix')][0]
        transformM = np.array(line.split(' = ')[1].split(' ')).astype('float')
        transformM = np.round(transformM)
        if np.any( transformM!=np.array([1,0,0, 0, 1, 0, 0, 0, 1])):
            isflip = True
        else:
            isflip = False

    itkimage = sitk.ReadImage(filename)
    numpyImage = sitk.GetArrayFromImage(itkimage)
     
    numpyOrigin = np.array(list(reversed(itkimage.GetOrigin())))
    numpySpacing = np.array(list(reversed(itkimage.GetSpacing())))
     
    return numpyImage, numpyOrigin, numpySpacing, isflip