Source code for cloudreg.scripts.registration

# local imports
from .util import get_reorientations, aws_cli
from .visualization import (
from .download_data import download_data
from .ingest_image_stack import ingest_image_stack

import shlex
from cloudvolume import CloudVolume
from scipy.spatial.transform import Rotation
import numpy as np
import argparse
import subprocess
import os

[docs]def get_affine_matrix( translation, rotation, from_orientation, to_orientation, fixed_scale, s3_path, center=False, ): """Get Neuroglancer-compatible affine matrix transfrming precomputed volume given set of translations and rotations Args: translation (list of float): x,y,z translations respectively in microns rotation (list of float): x,y,z rotations respectively in degrees from_orientation (str): 3-letter orientation of source data to_orientation (str): 3-letter orientation of target data fixed_scale (float): Isotropic scale factor s3_path (str): S3 path to precomputed volume for source data center (bool, optional): If true, center image at it's origin. Defaults to False. Returns: np.ndarray: Returns 4x4 affine matrix representing the given translations and rotations of source data at S3 path """ # since neuroglancer uses corner 0 coordinates we need to center the volume at it's center vol = CloudVolume(s3_path) # volume size in um vol_size = np.multiply(vol.scales[0]["size"], vol.scales[0]["resolution"]) / 1e3 # make affine matrix in homogenous coordinates affine = np.zeros((4, 4)) affine[-1, -1] = 1 order, flips = get_reorientations(from_orientation, to_orientation) # reorder vol_size to match reorientation vol_size = vol_size[order] dim = affine.shape[0] # swap atlas axes to match target affine[range(len(order)), order] = 1 # flip across appropriate dimensions affine[:3, :3] = np.diag(flips) @ affine[:3, :3] if center: # for each flip add the size of image in that dimension affine[:3, -1] += np.array( [vol_size[i] if flips[i] == -1 else 0 for i in range(len(flips))] ) # make image centered at the middle of the image # volume is now centered affine[:3, -1] -= vol_size / 2 # get rotation matrix if np.array(rotation).any(): rotation_matrix = np.eye(4) rotation_matrix[:3, :3] = Rotation.from_euler( "xyz", rotation, degrees=True ).as_matrix() # compose rotation with affine affine = rotation_matrix @ affine # add translation components # note: for neuroglancer affine, we scale the translations by voxel size # because neuroglancer expects translation in voxels affine[:3, -1] += translation # scale by fixed_scale if isinstance(fixed_scale, float): affine = np.diag([fixed_scale, fixed_scale, fixed_scale, 1.0]) @ affine elif isinstance(fixed_scale, (list, np.ndarray)) and len(fixed_scale) == 3: affine = np.diag([fixed_scale[0], fixed_scale[1], fixed_scale[2], 1.0]) @ affine else: affine = np.diag([fixed_scale[0], fixed_scale[0], fixed_scale[0], 1.0]) @ affine return affine
[docs]def register( input_s3_path, atlas_s3_path, parcellation_s3_path, atlas_orientation, output_s3_path, log_s3_path, orientation, fixed_scale, translation, rotation, missing_data_correction, grid_correction, bias_correction, regularization, num_iterations, registration_resolution, output_local_path = "~/" ): """Run EM-LDDMM registration on precomputed volume at input_s3_path Args: input_s3_path (str): S3 path to precomputed data to be registered atlas_s3_path (str): S3 path to atlas to register to. parcellation_s3_path (str): S3 path to atlas to register to. atlas_orientation (str): 3-letter orientation of atlas output_s3_path (str): S3 path to store precomputed volume of atlas transformed to input data log_s3_path (str): S3 path to store intermediates at orientation (str): 3-letter orientation of input data fixed_scale (float): Isotropic scale factor on input data translation (list of float): Initial translations in x,y,z of input data rotation (list): Initial rotation in x,y,z for input data missing_data_correction (bool): Perform missing data correction to ignore zeros in image grid_correction (bool): Perform grid correction (for COLM data) bias_correction (bool): Perform illumination correction regularization (float): Regularization constat in cost function. Higher regularization constant means less regularization num_iterations (int): Number of iterations of EM-LDDMM to run registration_resolution (int): Minimum resolution at which the registration is run. """ download_atlas = True # get volume info s3_url = S3Url(input_s3_path) channel = s3_url.key.split("/")[-1] exp = s3_url.key.split("/")[-2] # only after stitching autofluorescence channel base_path = os.path.expanduser("~/") registration_prefix = f"{output_local_path}/{exp}_{channel}_registration/" atlas_prefix = f'{base_path}/CloudReg/cloudreg/registration/atlases/' target_name = f"{base_path}/autofluorescence_data.tif" atlas_name = f"{atlas_prefix}/atlas_data.nrrd" parcellation_name = f"{atlas_prefix}/parcellation_data.nrrd" parcellation_hr_name = f"{atlas_prefix}/parcellation_data.tif" # download downsampled autofluorescence channel print("downloading input data for registration...") # convert to nanometers registration_resolution *= 1000.0 # download raw data at lowest 15 microns voxel_size = download_data(input_s3_path, target_name, 15000) # download atlas and parcellations at registration resolution print(f"Download atlas: {download_atlas}") if download_atlas: _ = download_data(atlas_s3_path, atlas_name, registration_resolution, resample_isotropic=True) _ = download_data(parcellation_s3_path, parcellation_name, registration_resolution, resample_isotropic=True) # also download high resolution parcellations for final transformation parcellation_voxel_size, parcellation_image_size = download_data(parcellation_s3_path, parcellation_hr_name, 10000, return_size=True) # initialize affine transformation for data # atlas_res = 100 # atlas_s3_path = ara_average_data_link(atlas_res) initial_affine = get_affine_matrix( translation, rotation, atlas_orientation, orientation, fixed_scale, atlas_s3_path, ) # run registration affine_string = [", ".join(map(str, i)) for i in initial_affine] affine_string = "; ".join(affine_string) print(affine_string) # make sure the version of matlab is correct (e.g. CIS computers may call old version of matlab) matlab_registration_command = f""" matlab.r2017a -nodisplay -nosplash -nodesktop -r \"niter={num_iterations};sigmaR={regularization};missing_data_correction={int(missing_data_correction)};grid_correction={int(grid_correction)};bias_correction={int(bias_correction)};base_path=\'{base_path}\';target_name=\'{target_name}\';registration_prefix=\'{registration_prefix}\';atlas_prefix=\'{atlas_prefix}\';dxJ0={voxel_size};fixed_scale={fixed_scale};initial_affine=[{affine_string}];parcellation_voxel_size={parcellation_voxel_size};parcellation_image_size={parcellation_image_size};run(\'~/CloudReg/cloudreg/registration/map_nonuniform_multiscale_v02_mouse_gauss_newton.m\'); exit;\" """ print(matlab_registration_command) ''' # save results to S3 if log_s3_path: # sync registration results to log_s3_path aws_cli(["s3", "sync", registration_prefix, log_s3_path]) ''' # upload high res deformed atlas and deformed target to S3 ingest_image_stack( output_s3_path, [v*1000 for v in voxel_size], f"{registration_prefix}/downloop_1_labels_to_target_highres.img", "img", "uint64", ) # print out viz link for visualization # visualize results at 5 microns viz_link = create_viz_link( [input_s3_path, output_s3_path], output_resolution=np.array([5] * 3) / 1e6 ) print("###################") print(f"VIZ LINK (atlas overlayed on data): {viz_link}") print("###################")
if __name__ == "__main__": parser = argparse.ArgumentParser( "Run COLM pipeline on remote EC2 instance with given input parameters" ) # data args parser.add_argument( "-input_s3_path", help="S3 path to precomputed volume used to register the data", type=str, ) parser.add_argument( "-log_s3_path", help="S3 path at which registration outputs are stored.", type=str, ) parser.add_argument( "--output_s3_path", help="S3 path to store atlas transformed to target as precomputed volume. Should be of the form s3://<bucket>/<path_to_precomputed>. Default is same as input s3_path with atlas_to_target as channel name", type=str, default=None, ) parser.add_argument( "--atlas_s3_path", help="S3 path to atlas we want to register to. Should be of the form s3://<bucket>/<path_to_precomputed>. Default is Allen Reference atlas path", type=str, default=ara_average_data_link(100), ) parser.add_argument( "--parcellation_s3_path", help="S3 path to corresponding atlas parcellations. If atlas path is provided, this should also be provided. Should be of the form s3://<bucket>/<path_to_precomputed>. Default is Allen Reference atlas parcellations path", type=str, default=ara_annotation_data_link(10), ) parser.add_argument( "--atlas_orientation", help="3-letter orientation of data. i.e. LPS", type=str, default='PIR' ) # affine initialization args parser.add_argument( "-orientation", help="3-letter orientation of data. i.e. LPS", type=str ) parser.add_argument( "--fixed_scale", help="Fixed scale of data, uniform in all dimensions. Default is 1.", nargs='+', type=float, default=[1.0, 1.0, 1.0] ) parser.add_argument( "--translation", help="Initial translation in x,y,z respectively in microns.", nargs="+", type=float, default=[0, 0, 0], ) parser.add_argument( "--rotation", help="Initial rotation in x,y,z respectively in degrees.", nargs="+", type=float, default=[0, 0, 0], ) # preprocessing args parser.add_argument( "--bias_correction", help="Perform bias correction prior to registration.", type=eval, choices=[True, False], default='True', ) parser.add_argument( "--missing_data_correction", help="Perform missing data correction by ignoring 0 values in image prior to registration.", type=eval, choices=[True, False], default='True', ) parser.add_argument( "--grid_correction", help="Perform correction for low-intensity grid artifact (COLM data)", type=eval, choices=[True, False], default='False', ) # registration params parser.add_argument( "--regularization", help="Weight of the regularization. Bigger regularization means less regularization. Default is 5e3", type=float, default=5e3, ) parser.add_argument( "--iterations", help="Number of iterations to do at low resolution. Default is 5000.", type=int, default=3000, ) parser.add_argument( "--registration_resolution", help="Minimum resolution that the registration is run at (in microns). Default is 100.", type=int, default=100, ) parser.add_argument( "--output_local_path", help="Output directory where transformation and data intermediates are stored. Default is ~/.", type=str, default="~/", ) args = parser.parse_args() register( args.input_s3_path, args.atlas_s3_path, args.parcellation_s3_path, args.atlas_orientation, args.output_s3_path, args.log_s3_path, args.orientation, args.fixed_scale, args.translation, args.rotation, args.missing_data_correction, args.grid_correction, args.bias_correction, args.regularization, args.iterations, args.registration_resolution, args.output_local_path )