def _run_single_scan(self, idx): in_ori_image = ScanWrapper(self._in_data_folder.get_file_path(idx)) in_ori_data = in_ori_image.get_data() in_effective_mask = (in_ori_data == in_ori_data).astype(int) effective_region_mask = in_effective_mask * self._in_ref_valid_mask.get_data( ) # We need to make sure the boundary elements are all 0 boundary_mask = np.zeros(in_ori_image.get_shape()) boundary_mask[1:-1, 1:-1, 1:-1] = 1 effective_region_mask = effective_region_mask * boundary_mask edt_img = ndi.distance_transform_edt(effective_region_mask) effective_region_mask = (edt_img > self._etch_radius).astype(int) out_mask_path = self._out_folder_obj.get_file_path(idx) in_ori_image.save_scan_same_space(out_mask_path, effective_region_mask)
def _run_single_scan(self, idx): in_img = ScanWrapper(self._in_data_folder.get_file_path(idx)) in_mask = None if self._in_mask_folder_obj is not None: in_mask = ScanWrapper(self._in_mask_folder_obj.get_file_path(idx)) if self._in_mask_file_obj is not None: in_mask = self._in_mask_file_obj out_path = self._out_folder_obj.get_file_path(idx) in_img_data = in_img.get_data() in_mask_data = in_mask.get_data() new_img_data = np.full(in_img.get_shape(), self._ambient_val) np.copyto(new_img_data, in_img_data, where=in_mask_data > 0) in_img.save_scan_same_space(out_path, new_img_data)
class AverageValidRegion(AbstractParallelRoutine): def __init__(self, in_folder_obj, num_process): super().__init__(in_folder_obj, num_process) self._ref_img = ScanWrapper(self._in_data_folder.get_first_path()) self._sum_map = None self._average_map = None self._sum_variance_map = None self._valid_count_map = None self._run_mode = None def run_get_average(self): logger.info('Calculating average') self._run_mode = 'get_average' result_list = self.run_parallel() im_shape = self._ref_img.get_shape() self._sum_map = np.zeros(im_shape) self._valid_count_map = np.zeros(im_shape) for result in result_list: self._sum_map += result['sum_image'] self._valid_count_map += result['region_count'] average_image = np.zeros(im_shape) average_image = np.divide(self._sum_map, self._valid_count_map, out=average_image, where=self._valid_count_map > 0.5) self._average_map = average_image def output_result_average(self, output_path, ambient_val): average_image_out_data = np.ma.masked_array( self._average_map, mask=self._valid_count_map == 0) self._ref_img.save_scan_same_space( output_path, average_image_out_data.filled(ambient_val)) def _run_chunk(self, chunk_list): result_list = [] if self._run_mode == 'get_average': result_list = self._run_chunk_get_average(chunk_list) elif self._run_mode == 'get_variance': result_list = self._run_chunk_get_variance(chunk_list) else: logger.info('Into the error') raise NotImplementedError return result_list def _run_chunk_get_average(self, chunk_list): result_list = [] im_shape = self._ref_img.get_shape() sum_image_union = np.zeros(im_shape) region_mask_count_image = np.zeros(im_shape) for idx in chunk_list: self._in_data_folder.print_idx(idx) img_obj = ScanWrapper(self._in_data_folder.get_file_path(idx)) img_data = img_obj.get_data() valid_mask = np.logical_not(np.isnan(img_data)).astype(int) np.add(img_data, sum_image_union, out=sum_image_union, where=valid_mask > 0) region_mask_count_image += valid_mask result = { 'sum_image': sum_image_union, 'region_count': region_mask_count_image } result_list.append(result) return result_list def _run_chunk_get_variance(self, chunk_list): result_list = [] im_shape = self._ref_img.get_shape() sum_image_union = np.zeros(im_shape) for idx in chunk_list: self._in_data_folder.print_idx(idx) img_obj = ScanWrapper(self._in_data_folder.get_file_path(idx)) img_data = img_obj.get_data() valid_mask = np.logical_not(np.isnan(img_data)).astype(int) residue_map = np.zeros(img_data.shape) np.subtract(img_data, self._average_map, out=residue_map, where=valid_mask > 0) residue_map = np.power(residue_map, 2) np.add(residue_map, sum_image_union, out=sum_image_union, where=valid_mask > 0) result = {'sum_image': sum_image_union} result_list.append(result) return result_list
class AverageValidRegion(AbstractParallelRoutine): def __init__(self, in_folder_obj, in_omat_folder_obj, out_corrected_folder_obj, num_process): super().__init__(in_folder_obj, num_process) self._in_omat_folder_obj = in_omat_folder_obj self._out_corrected_folder_obj = out_corrected_folder_obj self._ref_img = ScanWrapper(self._in_data_folder.get_first_path()) self._sum_map = None self._average_map = None self._sum_variance_map = None self._variance_map = None self._valid_count_map = None self._run_mode = None def run_get_average(self): logger.info('Calculating average') self._run_mode = 'get_average' result_list = self.run_parallel() im_shape = self._ref_img.get_shape() self._sum_map = np.zeros(im_shape) self._valid_count_map = np.zeros(im_shape) for result in result_list: self._sum_map += result['sum_image'] self._valid_count_map += result['region_count'] average_image = np.zeros(im_shape) average_image = np.divide(self._sum_map, self._valid_count_map, out=average_image, where=self._valid_count_map > 0.5) self._average_map = average_image def run_get_variance(self): logger.info('Calculating variance') self._run_mode = 'get_variance' result_list = self.run_parallel() im_shape = self._ref_img.get_shape() self._sum_variance_map = np.zeros(im_shape) for result in result_list: self._sum_variance_map += result['sum_image'] self._variance_map = np.zeros(im_shape) self._variance_map = np.divide(self._sum_variance_map, self._valid_count_map, out=self._variance_map, where=self._valid_count_map > 0.5) epsilon = 1.0e-5 self._variance_map = np.log(np.add(self._variance_map, epsilon)) def output_result_folder(self, output_folder, ambient_val): average_img_path = os.path.join(output_folder, 'average.nii.gz') variance_img_path = os.path.join(output_folder, 'variance.nii.gz') count_map_path = os.path.join(output_folder, 'count_map.nii.gz') average_image_out_data = np.ma.masked_array( self._average_map, mask=self._valid_count_map == 0) variance_image_out_data = np.ma.masked_array( self._variance_map, mask=self._valid_count_map == 0) self._ref_img.save_scan_same_space( average_img_path, average_image_out_data.filled(ambient_val)) self._ref_img.save_scan_same_space( variance_img_path, variance_image_out_data.filled(ambient_val)) self._ref_img.save_scan_same_space(count_map_path, self._valid_count_map) def _run_chunk(self, chunk_list): result_list = [] if self._run_mode == 'get_average': result_list = self._run_chunk_get_average(chunk_list) elif self._run_mode == 'get_variance': result_list = self._run_chunk_get_variance(chunk_list) else: logger.info('Into the error') raise NotImplementedError return result_list def _run_chunk_get_average(self, chunk_list): result_list = [] im_shape = self._ref_img.get_shape() sum_image_union = np.zeros(im_shape) region_mask_count_image = np.zeros(im_shape) for idx in chunk_list: self._in_data_folder.print_idx(idx) # img_obj = ScanWrapper(self._in_data_folder.get_file_path(idx)) # img_data = img_obj.get_data() img_data = self._get_img_data(idx) valid_mask = np.logical_not(np.isnan(img_data)).astype(int) np.add(img_data, sum_image_union, out=sum_image_union, where=valid_mask > 0) region_mask_count_image += valid_mask result = { 'sum_image': sum_image_union, 'region_count': region_mask_count_image } result_list.append(result) return result_list def _run_chunk_get_variance(self, chunk_list): result_list = [] im_shape = self._ref_img.get_shape() sum_image_union = np.zeros(im_shape) for idx in chunk_list: self._in_data_folder.print_idx(idx) # img_obj = ScanWrapper(self._in_data_folder.get_file_path(idx)) # img_data = img_obj.get_data() img_data = self._get_img_data(idx) valid_mask = np.logical_not(np.isnan(img_data)).astype(int) residue_map = np.zeros(img_data.shape) np.subtract(img_data, self._average_map, out=residue_map, where=valid_mask > 0) residue_map = np.power(residue_map, 2) np.add(residue_map, sum_image_union, out=sum_image_union, where=valid_mask > 0) result = {'sum_image': sum_image_union} result_list.append(result) return result_list def _get_img_data(self, idx): img_obj = ScanWrapper(self._in_data_folder.get_file_path(idx)) img_data = img_obj.get_data() img_data = np.abs(img_data) np.copyto(img_data, np.nan, where=img_data < 0.01) img_data = np.log10(img_data) in_omat_path = self._in_omat_folder_obj.get_file_path(idx) omat = np.loadtxt(in_omat_path) img_data = img_data + np.log10(np.linalg.det(omat)) save_corrected_path = self._out_corrected_folder_obj.get_file_path(idx) img_obj.save_scan_same_space(save_corrected_path, img_data) return img_data
def _run_single_scan(self, idx): in_mask = ScanWrapper(self._in_data_folder.get_file_path(idx)) mask_diff = np.zeros(in_mask.get_shape(), dtype=int) mask_diff[in_mask.get_data() != self._ref_mask_obj.get_data()] = 1 out_path = self._out_folder_obj.get_file_path(idx) self._ref_mask_obj.save_scan_same_space(out_path, mask_diff)