From 901b71c59ddce5700f51e762f8b45881a9df84dd Mon Sep 17 00:00:00 2001 From: arielleleon <54869022+arielleleon@users.noreply.github.com> Date: Fri, 6 Dec 2024 14:23:05 -0800 Subject: [PATCH] Revert/add generic inputs (#46) * Move command args into separate function * Add input args for TIFF and re-arrange frame rate. -Add data-type argument -Add tiff-input argument -Add look-one-level-down argument for multiple TIFF inputs -Move frame-rate pull from metadata files to separate function -Add data-path to args for suite2p input * Resolve data paths for generic inputs * Deleted datasets.json; edited registration.py, run * Move a parameter. * Docker build to use latest suite2p * Take in tiff inputs without a reference image * Edited run, metadata.yml * Load in reference image for h5py and tiff files. * Edited registration.py, run * Modify load_initial_frames and find_movie_start_end_empty_frames. * Parse inputs to find movie start / end empty frames. * Clean up inputs. * Save changes * Update run file. * Save dataset.json configuration * Save debug state. * Edited registration.py, run * Edited registration.py * Edited registration.py * Edited registration.py * Fix downsampled and normalized output of movie. * Add optical flow diagrams for tiff arrays. * Fix formatting issue. * Edited registration.py * Cache TIFF read results. * Edited registration.py * Move reference images to resources directory * Reference new image location. * Edited run * Revert "Edited run" This reverts commit 5472329ec2c00e5ffc2639038c79166c12fe98f0. Revert to processing tiff input --- README.md | 6 +- code/registration.py | 694 ++++++++++++++++++++++++--------- code/run | 2 +- environment/Dockerfile | 2 +- metadata/metadata.yml | 2 +- resources/PC_good_blocks.png | Bin 0 -> 66266 bytes resources/PC_large_blocks.png | Bin 0 -> 53500 bytes resources/good_blocks.png | Bin 0 -> 204590 bytes resources/too_small_blocks.png | Bin 0 -> 159357 bytes 9 files changed, 511 insertions(+), 195 deletions(-) create mode 100644 resources/PC_good_blocks.png create mode 100644 resources/PC_large_blocks.png create mode 100644 resources/good_blocks.png create mode 100644 resources/too_small_blocks.png diff --git a/README.md b/README.md index d750903..07cac97 100644 --- a/README.md +++ b/README.md @@ -33,15 +33,15 @@ For QC some videos and figures are created: - The correction offset in the registration summary image does not contain outliers. - The correction offsets of the individual patches in the nonrigid registration summary image do not contain outliers. If they do, the `block_size` was likely too small for the data's noise level. good block size -![good blocks](good_blocks.png) +![good blocks](resources/good_blocks.png) too small blocks -![too small blocks](too_small_blocks.png) +![too small blocks](resources/too_small_blocks.png) - Comparing the images of `PCxhigh` and `PCxlow` (x is number of the PC) does not reveal residual motion but different cells being active. PCxhigh and PCxlow are obtained from the top prinicipal components of the registered movie (each averaging similar frames together to reduce noise), so that they capture the variation in the data. This variation should be due to changes in neural activity not residual motion. - The residual optical flow `PCxrof` between `PCxhigh` and `PCxlow` has values close to zero. To obtain `PCxrof`, `PCxhigh` and `PCxlow` are aligned using OpenCV's implementation of Farneback's method, thus we further rely on an independent method not just Suite2p's own. Reducing a large block size (left) to a good block size (right) reduces the residual optical flow to almost zero. -![too large blocks](PC_large_blocks.png)      ![good blocks](PC_good_blocks.png) +![too large blocks](resources/PC_large_blocks.png)      ![good blocks](resources/PC_good_blocks.png) **Numbers** - The scores in `[input]_registered.h5`'s datasets `'reg_metrics/regDX'` and `'reg_metrics/farnebackDX'` are very close to zero. These are summary statistics for all computed 30 PCs, the PC images are created for PC x that shows the most extreme residual motion. diff --git a/code/registration.py b/code/registration.py index b8fd388..b51c2ba 100644 --- a/code/registration.py +++ b/code/registration.py @@ -8,13 +8,13 @@ import tempfile import warnings from datetime import datetime as dt -from functools import partial +from functools import lru_cache, partial from glob import glob from itertools import product from multiprocessing import Pool from pathlib import Path from time import time -from typing import Callable, Optional, Tuple, Union +from typing import Callable, List, Optional, Tuple, Union import cv2 import h5py @@ -23,15 +23,16 @@ import pandas as pd import suite2p from aind_data_schema.core.processing import ( - Processing, DataProcess, PipelineProcess, + Processing, ProcessName, ) from aind_ophys_utils.array_utils import normalize_array -from aind_ophys_utils.video_utils import downsample_h5_video, encode_video +from aind_ophys_utils.video_utils import downsample_h5_video, encode_video, downsample_array from matplotlib import pyplot as plt # noqa: E402 from PIL import Image +from ScanImageTiffReader import ScanImageTiffReader from scipy.ndimage import median_filter from scipy.stats import sigmaclip from suite2p.registration.nonrigid import make_blocks @@ -60,8 +61,165 @@ def is_S3(file_path: str): ) +def h5py_byteorder_name(h5py_file: h5py.File, h5py_key: str) -> Tuple[str, str]: + """Get the byteorder and name of the dataset in the h5py file. + + Parameters + ---------- + h5py_file : h5py.File + h5py file object + h5py_key : str + key to the dataset + + Returns + ------- + str + byteorder of the dataset + str + name of the dataset + """ + with h5py.File(h5py_file, "r") as f: + byteorder = f[h5py_key].dtype.byteorder + name = f[h5py_key].dtype.name + return byteorder, name + + +def tiff_byteorder_name(tiff_file: Path) -> Tuple[str, str]: + """Get the byteorder and name of the dataset in the tiff file. + + Parameters + ---------- + tiff_file : Path + Location of the tiff file + + Returns + ------- + str + byteorder of the dataset + str + name of the dataset + """ + with ScanImageTiffReader(tiff_file) as reader: + byteorder = reader.data().dtype.byteorder + name = reader.data().dtype.name + return byteorder, name + + +def h5py_to_numpy( + h5py_file: str, + h5py_key: str, + trim_frames_start: int = 0, + trim_frames_end: int = 0, +) -> np.ndarray: + """Converts a h5py dataset to a numpy array + + Parameters + ---------- + h5py_file: str + h5py file path + h5py_key : str + key to the dataset + trim_frames_start : int + Number of frames to disregard from the start of the movie. Default 0. + trim_frames_end : int + Number of frames to disregard from the end of the movie. Default 0. + Returns + ------- + np.ndarray + numpy array + """ + with h5py.File(h5py_file, "r") as f: + n_frames = f[h5py_key].shape[0] + if trim_frames_start > 0 or trim_frames_end > 0: + return f[h5py_key][trim_frames_start : n_frames - trim_frames_end] + else: + return f[h5py_key][:] + +@lru_cache(maxsize=None) +def _tiff_to_numpy(tiff_file: Path) -> np.ndarray: + with ScanImageTiffReader(tiff_file) as reader: + return reader.data() + +def tiff_to_numpy( + tiff_list: List[Path], trim_frames_start: int = 0, trim_frames_end: int = 0 +) -> np.ndarray: + """ + Converts a list of TIFF files to a single numpy array, with optional frame trimming. + + Parameters + ---------- + tiff_list : List[str] + List of TIFF file paths to process + trim_frames_start : int, optional + Number of frames to remove from the start (default: 0) + trim_frames_end : int, optional + Number of frames to remove from the end (default: 0) + + Returns + ------- + np.ndarray + Combined array of all TIFF data with specified trimming + + Raises + ------ + ValueError + If trim values exceed total number of frames or are negative + """ + if trim_frames_start < 0 or trim_frames_end < 0: + raise ValueError("Trim values must be non-negative") + + def get_total_frames(tiff_files: List[Path]) -> int: + """Calculate total number of frames across all TIFF files.""" + total = 0 + for tiff in tiff_files: + with ScanImageTiffReader(tiff) as reader: + total += reader.shape()[0] + return total + + # Validate trim parameters + total_frames = get_total_frames(tiff_list) + if trim_frames_start + trim_frames_end >= total_frames: + raise ValueError( + f"Invalid trim values: start ({trim_frames_start}) + end ({trim_frames_end}) " + f"must be less than total frames ({total_frames})" + ) + + # Initialize variables for frame counting + processed_frames = 0 + arrays_to_stack = [] + + for tiff_path in tiff_list: + with ScanImageTiffReader(tiff_path) as reader: + current_frames = reader.shape()[0] + start_idx = max(0, trim_frames_start - processed_frames) + end_idx = current_frames + + if processed_frames + current_frames > total_frames - trim_frames_end: + end_idx = total_frames - trim_frames_end - processed_frames + + if start_idx < end_idx: # Only process if there are frames to include + data = np.array(_tiff_to_numpy(tiff_path)[start_idx:end_idx]) + arrays_to_stack.append(data) + + processed_frames += current_frames + + # Break if we've processed all needed frames + if processed_frames >= total_frames - trim_frames_end: + break + + # Stack all arrays along the appropriate axis + if not arrays_to_stack: + raise ValueError("No frames remained after trimming") + + return ( + np.concatenate(arrays_to_stack, axis=0) + if len(arrays_to_stack) > 1 + else arrays_to_stack[0] + ) + + def load_initial_frames( - file_path: str, + file_path: Union[str, list], h5py_key: str, n_frames: int, trim_frames_start: int = 0, @@ -88,17 +246,18 @@ def load_initial_frames( time axis. If n_frames > tot_frames, a number of frames equal to tot_frames is returned. """ - with h5py.File(file_path, "r") as hdf5_file: - # Load all frames as fancy indexing is slower than loading the full - # data. - max_frame = hdf5_file[h5py_key].shape[0] - trim_frames_end - frame_window = hdf5_file[h5py_key][trim_frames_start:max_frame] - # Total number of frames in the movie. - tot_frames = frame_window.shape[0] - requested_frames = np.linspace( - 0, tot_frames, 1 + min(n_frames, tot_frames), dtype=int - )[:-1] - frames = frame_window[requested_frames] + if isinstance(file_path, str): + array = h5py_to_numpy(file_path, h5py_key, trim_frames_start, trim_frames_end) + elif isinstance(file_path, list): + array = tiff_to_numpy(file_path, trim_frames_start, trim_frames_end) + else: + raise ValueError("File type not supported") + # Total number of frames in the movie. + tot_frames = array.shape[0] + requested_frames = np.linspace( + 0, tot_frames, 1 + min(n_frames, tot_frames), dtype=int + )[:-1] + frames = array[requested_frames] return frames @@ -342,7 +501,7 @@ def optimize_motion_parameters( ) start_time = time() for param_spatial, param_time in product(smooth_sigmas, smooth_sigma_times): - current_args = suite2p_args.copy() + current_args = suite2p_parser.copy() current_args["smooth_sigma"] = param_spatial current_args["smooth_sigma_time"] = param_time @@ -533,13 +692,13 @@ def add_modify_required_parameters(suite2p_args: dict): suite2p_args : dict Suite2p ops dictionary with potentially missing values. """ - if suite2p_args.get("1Preg") is None: + if suite2p_parser.get("1Preg") is None: suite2p_args["1Preg"] = False - if suite2p_args.get("bidiphase") is None: + if suite2p_parser.get("bidiphase") is None: suite2p_args["bidiphase"] = False - if suite2p_args.get("nonrigid") is None: + if suite2p_parser.get("nonrigid") is None: suite2p_args["nonrigid"] = False - if suite2p_args.get("norm_frames") is None: + if suite2p_parser.get("norm_frames") is None: suite2p_args["norm_frames"] = True # Don't use nonrigid for parameter search. suite2p_args["nonrigid"] = False @@ -579,44 +738,52 @@ def compute_acutance( return (grady**2 + gradx**2).mean() -def check_and_warn_on_datatype(h5py_name: str, h5py_key: str, logger: Callable): +def check_and_warn_on_datatype( + filepath: Path, logger: Callable, filetype: str = "h5", h5py_key: str = "" +): """Suite2p assumes int16 types throughout code. Check that the input data is type int16 else throw a warning. Parameters ---------- - h5py_name : str + filepath : Path Path to the HDF5 containing the data. - h5py_key : str - Name of the dataset to check. logger : Callable Logger to output logger warning to. - """ - with h5py.File(h5py_name, "r") as h5_file: - dataset = h5_file[h5py_key] + filetype : str + Type of file to check. Default is "h5". + h5py_key : str + Name of the dataset to check. Default is "". - if dataset.dtype.byteorder == ">": - logger( - "Data byteorder is big-endian which may cause issues in " - "suite2p. This may result in a crash or unexpected " - "results." - ) - if dataset.dtype.name != "int16": - logger( - f"Data type is {dataset.dtype.name} and not int16. Suite2p " - "assumes int16 data as input and throughout codebase. " - "Non-int16 data may result in unexpected results or " - "crashes." - ) + """ + if filetype == "h5": + byteorder, name = h5py_byteorder_name(filepath, h5py_key) + elif filetype == "tiff": + byteorder, name = tiff_byteorder_name(filepath) + else: + raise ValueError("File type not supported") + if byteorder == ">": + logger( + "Data byteorder is big-endian which may cause issues in " + "suite2p. This may result in a crash or unexpected " + "results." + ) + if name != "int16": + logger( + f"Data type is {name} and not int16. Suite2p " + "assumes int16 data as input and throughout codebase. " + "Non-int16 data may result in unexpected results or " + "crashes." + ) -def _mean_of_batch(i, h5py_name, h5py_key): - return h5py.File(h5py_name)[h5py_key][i : i + 1000].mean(axis=(1, 2)) +def _mean_of_batch(i, array): + return array[i : i + 1000].mean(axis=(1, 2)) def find_movie_start_end_empty_frames( - h5py_name: str, - h5py_key: str, + filepath: Union[str , list[str]], + h5py_key: str = "", n_sigma: float = 5, logger: Optional[Callable] = None, n_jobs: Optional[int] = None, @@ -630,10 +797,10 @@ def find_movie_start_end_empty_frames( Parameters ---------- - h5py_name : str - Name of the HDF5 file to load from. + filepath : str | list[str] + File path to HDF5 file or the list of TIFFS to process h5py_key : str - Name of the dataset to load from the HDF5 file. + Name of the dataset to load from the HDF5 file. Default is "" n_sigma : float Number of standard deviations beyond which a frame is considered an outlier and "empty". @@ -648,22 +815,28 @@ def find_movie_start_end_empty_frames( Tuple of the number of frames to cut from the start and end of the movie as (n_trim_start, n_trim_end). """ + + if isinstance(filepath, str): + array = h5py_to_numpy(filepath, h5py_key) + elif isinstance(filepath, list): + array = tiff_to_numpy(filepath) + else: + raise ValueError("File type not supported") # Find the midpoint of the movie. - with h5py.File(h5py_name, "r") as f: - n_frames = f[h5py_key].shape[0] - midpoint = n_frames // 2 - # We discover empty or extrema frames by comparing the mean of each frames - # to the mean of the full movie. - if n_jobs == 1 or n_frames < 2000: - means = f[h5py_key][:].mean(axis=(1, 2)) - else: - means = np.concatenate( - Pool(n_jobs).starmap( - _mean_of_batch, - product(range(0, n_frames, 1000), [h5py_name], [h5py_key]), - ) + n_frames = array.shape[0] + midpoint = n_frames // 2 + # We discover empty or extrema frames by comparing the mean of each frames + # to the mean of the full movie. + if n_jobs == 1 or n_frames < 2000: + means = array[:].mean(axis=(1, 2)) + else: + means = np.concatenate( + Pool(n_jobs).starmap( + _mean_of_batch, + product(range(0, n_frames, 1000), [array]), ) - mean_of_frames = means.mean() + ) + mean_of_frames = means.mean() # Compute a robust standard deviation that is not sensitive to the # outliers we are attempting to find. @@ -866,7 +1039,7 @@ def get_frame_rate_platform_json(input_dir: str) -> float: def write_output_metadata( metadata: dict, - raw_movie: Union[str, Path], + raw_movie: Union[str, list], motion_corrected_movie: Union[str, Path], output_dir: Union[str, Path], ) -> None: @@ -881,6 +1054,10 @@ def write_output_metadata( motion_corrected_movie: str path to motion corrected movies """ + if isinstance(raw_movie, Path): + raw_movie = str(raw_movie) + elif isinstance(raw_movie, list): + raw_movie = " ".join(raw_movie) processing = Processing( processing_pipeline=PipelineProcess( processor_full_name="Multplane Ophys Processing Pipeline", @@ -907,7 +1084,8 @@ def write_output_metadata( output_dir = Path(output_dir) print(f"~~~~~~~~~~~~~~Writing output: {output_dir}") processing.write_standard_file(output_directory=output_dir) - + + def check_trim_frames(data): """Make sure that if the user sets auto_remove_empty_frames and timing frames is already requested, raise an error. @@ -1021,7 +1199,7 @@ def make_nonrigid_png( def downsample_normalize( - movie_path: Path, + movie_path: Union[Path, np.array], frame_rate: float, bin_size: float, lower_quantile: float, @@ -1033,9 +1211,9 @@ def downsample_normalize( Parameters ---------- - movie_path: Path + movie_path: Union[Path, np.array] path to an h5 file, containing an (nframes x nrows x ncol) dataset - named 'data' + named 'data' or a numpy array. frame_rate: float frame rate of the movie specified by 'movie_path' bin_size: float @@ -1059,7 +1237,10 @@ def downsample_normalize( consistent visibility. """ - ds = downsample_h5_video(movie_path, input_fps=frame_rate, output_fps=1.0 / bin_size) + if isinstance(movie_path, Path): + ds = downsample_h5_video(movie_path, input_fps=frame_rate, output_fps=1.0 / bin_size) + else: + ds = downsample_array(movie_path, input_fps=frame_rate, output_fps=1.0 / bin_size) avg_projection = ds.mean(axis=0) lower_cutoff, upper_cutoff = np.quantile( avg_projection.flatten(), (lower_quantile, upper_quantile) @@ -1173,13 +1354,15 @@ def multiplane_motion_correction(data_dir: Path, output_dir: Path, debug: bool = frame_rate_hz: float frame rate in Hz """ + data_dir = next(data_dir.rglob("pophys")) + if not data_dir.is_dir(): + raise ValueError("Could not locate 'pophys' directory") try: unique_id = [i for i in data_dir.rglob("*") if "ophys_experiment" in str(i)][ 0 ].name.split("_")[-1] h5_file = [i for i in data_dir.rglob("*") if f"{unique_id}.h5" in str(i)][0] except IndexError: - unique_id = [i for i in data_dir.rglob("*") if i.is_dir()][0].name h5_file = [i for i in data_dir.rglob("*") if f"{unique_id}.h5" in str(i)][0] session_dir = h5_file.parent.parent @@ -1233,9 +1416,15 @@ def update_suite2p_args_reference_image( ) else: + if suite2p_args.get("h5py", None): + file_path = suite2p_args["h5py"] + h5py_key = suite2p_args["h5py_key"] + else: + file_path = suite2p_args["tiff_list"] + h5py_key = None initial_frames = load_initial_frames( - file_path=suite2p_args["h5py"], - h5py_key=suite2p_args["h5py_key"], + file_path=file_path, + h5py_key=h5py_key, n_frames=suite2p_args["nimg_init"], trim_frames_start=args["trim_frames_start"], trim_frames_end=args["trim_frames_end"], @@ -1395,12 +1584,33 @@ def singleplane_motion_correction( return h5_file, output_dir, reference_image_fp -if __name__ == "__main__": # pragma: nocover - # Set the log level and name the logger - logger = logging.getLogger("Suite2P motion correction") - logger.setLevel(logging.INFO) +def get_frame_rate(session: dict): + """Attempt to pull frame rate from session.json + Returns none if frame rate not in session.json - # Create an ArgumentParser object + Parameters + ---------- + session: dict + session metadata + + Returns + ------- + frame_rate: float + frame rate in Hz + """ + frame_rate_hz = None + for i in session.get("data_streams", ""): + frame_rate_hz = [j["frame_rate"] for j in i["ophys_fovs"]] + frame_rate_hz = frame_rate_hz[0] + if frame_rate_hz: + break + if isinstance(frame_rate_hz, str): + frame_rate_hz = float(frame_rate_hz) + return frame_rate_hz + + +def parse_arguments(): + """Parse command-line arguments""" parser = argparse.ArgumentParser(description="Suite2P motion correction") parser.add_argument( @@ -1415,9 +1625,27 @@ def singleplane_motion_correction( ) parser.add_argument( - "-d", "--debug", action="store_true", help="Run with only partial dset" + "-d", "--debug", action="store_true", help="Run with only first 500 frames" + ) + + parser.add_argument( + "--frame-rate", + type=float, + default=31.0, + help="Frame rate of the movie in Hz. If not provided, " + "the frame rate will here will be used.", ) + parser.add_argument( + "--data-type", type=str, default="h5", help="Specify h5 or TIFF input type" + ) + + parser.add_argument( + "--look-one-level-down", + type=bool, + default=False, + help="If True, search for TIFF files in subdirectories " "of the input directory", + ) parser.add_argument( "--tmp_dir", type=str, @@ -1549,45 +1777,56 @@ def singleplane_motion_correction( "steps=1.", ) - # Parse command-line arguments - args = parser.parse_args() + return parser.parse_args() + + +if __name__ == "__main__": # pragma: nocover + # Set the log level and name the logger + logger = logging.getLogger("Suite2P motion correction") + logger.setLevel(logging.INFO) + + # Create an ArgumentParser object + parser = parse_arguments() # General settings - output_dir = Path(args.output_dir) - data_dir = Path("../data") + data_dir = Path(parser.input) + output_dir = Path(parser.output_dir) session_fp = next(data_dir.rglob("session.json")) description_fp = next(data_dir.rglob("data_description.json")) with open(session_fp, "r") as j: session = json.load(j) with open(description_fp, "r") as j: data_description = json.load(j) - for i in session["data_streams"]: - frame_rate_hz = [j["frame_rate"] for j in i["ophys_fovs"]] - if frame_rate_hz: - break - frame_rate_hz = frame_rate_hz[0] - if isinstance(frame_rate_hz, str): - frame_rate_hz = float(frame_rate_hz) - reference_image_fp = "" + frame_rate_hz = get_frame_rate(session) unique_id = "_".join(str(data_description["name"]).split("_")[-3:]) - if "Bergamo" in session["rig_id"]: - h5_file, output_dir, reference_image_fp = singleplane_motion_correction( - data_dir, output_dir, session, unique_id, debug=args.debug - ) - else: - h5_file, output_dir, frame_rate_hz = multiplane_motion_correction( - data_dir, output_dir, debug=args.debug - ) + reference_image_fp = "" + if parser.data_type == "TIFF": + input_file = next(data_dir.rglob("pophys")) + else: + if "Bergamo" in session.get("rig_id", ""): + h5_file, output_dir, reference_image_fp = singleplane_motion_correction( + data_dir, output_dir, session, unique_id, debug=parser.debug + ) + else: + h5_file, output_dir, frame_rate_hz = multiplane_motion_correction( + data_dir, output_dir, debug=parser.debug + ) + input_file = str(h5_file) # We convert to dictionary - args = vars(args) - h5_file = str(h5_file) + args = vars(parser) + if not frame_rate_hz: + frame_rate_hz = parser.frame_rate + logging.warning("Using default frame rate of 31.0 Hz") reference_image = None - meta_jsons = list(data_dir.glob("*/*.json")) args["refImg"] = [] if reference_image_fp: args["refImg"] = [reference_image_fp] # We construct the paths to the outputs + if isinstance(input_file, list): + basename = os.path.basename(input_file[0]) + else: + basename = os.path.basename(input_file) args["movie_frame_rate_hz"] = frame_rate_hz for key, default in ( ("motion_corrected_output", "_registered.h5"), @@ -1598,9 +1837,7 @@ def singleplane_motion_correction( ("motion_correction_preview_output", "_motion_preview.webm"), ("output_json", "_motion_correction_output.json"), ): - args[key] = os.path.join( - output_dir, os.path.splitext(os.path.basename(h5_file))[0] + default - ) + args[key] = os.path.join(output_dir, os.path.splitext(basename)[0] + default) # These are hardcoded parameters of the wrapper. Those are tracked but # not exposed. @@ -1633,19 +1870,24 @@ def singleplane_motion_correction( # This is part of a complex scheme to pass an image that is a bit too # complicated. Will remove when tested. - # if not args.get("refImg", ""): + # if not parser.get("refImg", ""): # args["refImg"] = [] - # Set suite2p args. + # Set suite2p parser. suite2p_args = suite2p.default_ops() # Here we overwrite the parameters for suite2p that will not change in our # processing pipeline. These are parameters that are not exposed to # minimize code length. Those are not set to default. - suite2p_args["h5py"] = h5_file + if parser.data_type == "h5": + suite2p_args["h5py"] = input_file + else: + suite2p_args["data_path"] = str(input_file) + suite2p_args["look_one_level_down"] = True + suite2p_args["tiff_list"] = [str(i) for i in input_file.glob("*.tif*")] suite2p_args["roidetect"] = False suite2p_args["do_registration"] = 1 - suite2p_args["data_path"] = [] # TODO: remove this if not needed by suite2p + # suite2p_args["data_path"] = [] # TODO: remove this if not needed by suite2p suite2p_args["reg_tif"] = False # We save our own outputs here suite2p_args["nimg_init"] = ( 500 # Nb of images to compute reference. This value is a bit high. Suite2p has it at 300 normally @@ -1660,7 +1902,8 @@ def singleplane_motion_correction( 5.0 # Maximum shift allowed in pixels for a block in rigid registration. ) suite2p_args["batch_size"] = 500 # Number of frames to process at once - suite2p_args["h5py_key"] = "data" # h5 path in the file. + if suite2p_args.get("h5py", ""): + suite2p_args["h5py_key"] = "data" # h5 path in the file. suite2p_args["smooth_sigma"] = ( 1.15 # Standard deviation in pixels of the gaussian used to smooth the phase correlation. ) @@ -1678,25 +1921,38 @@ def singleplane_motion_correction( suite2p_args["force_refImg"] = args["force_refImg"] # if data is in a S3 bucket, copy it to /scratch for faster access - if is_S3(suite2p_args["h5py"]): - dst = "/scratch/" + Path(suite2p_args["h5py"]).name - logger.info(f"copying {suite2p_args['h5py']} from S3 bucket to {dst}") - shutil.copy(suite2p_args["h5py"], dst) - suite2p_args["h5py"] = dst - - check_and_warn_on_datatype( - h5py_name=suite2p_args["h5py"], - h5py_key=suite2p_args["h5py_key"], - logger=logger.warning, - ) + if suite2p_args.get("h5py", ""): + if is_S3(suite2p_args["h5py"]): + dst = "/scratch/" + Path(suite2p_args["h5py"]).name + logger.info(f"copying {suite2p_args['h5py']} from S3 bucket to {dst}") + shutil.copy(suite2p_args["h5py"], dst) + suite2p_args["h5py"] = dst + + if suite2p_args.get("tiff_list", ""): + check_and_warn_on_datatype( + filepath=suite2p_args["tiff_list"][0], logger=logger.warning, filetype="tiff" + ) + else: + check_and_warn_on_datatype( + filepath=suite2p_args["h5py"], + logger=logger.warning, + filetype="h5", + h5py_key=suite2p_args["h5py_key"], + ) if args["auto_remove_empty_frames"]: logger.info("Attempting to find empty frames at the start and end of the movie.") - lowside, highside = find_movie_start_end_empty_frames( - h5py_name=suite2p_args["h5py"], - h5py_key=suite2p_args["h5py_key"], - logger=logger.warning, - ) + if suite2p_args.get("tiff_list", ""): + lowside, highside = find_movie_start_end_empty_frames( + filepath=suite2p_args["tiff_list"], + logger=logger.warning, + ) + else: + lowside, highside = find_movie_start_end_empty_frames( + filepath=suite2p_args["h5py"], + h5py_key=suite2p_args["h5py_key"], + logger=logger.warning, + ) args["trim_frames_start"] = lowside args["trim_frames_end"] = highside logger.info(f"Found ({lowside}, {highside}) at the start/end of the movie.") @@ -1711,8 +1967,8 @@ def singleplane_motion_correction( suite2p_args, args, reference_image_fp=reference_image_fp ) - # register with Suite2P - logger.info(f"attempting to motion correct {suite2p_args['h5py']}") + # register with Suite2P + logger.info(f"attempting to motion correct {suite2p_args['h5py']}") # make a tempdir for Suite2P's output tmp_dir = tempfile.TemporaryDirectory(dir=args["tmp_dir"]) tdir = tmp_dir.name @@ -1734,10 +1990,17 @@ def singleplane_motion_correction( if suite2p_args["force_refImg"]: logger.info(f"\tUsing custom reference image: {suite2p_args['refImg']}") - suite2p_args["h5py"] = [suite2p_args["h5py"]] + if suite2p_args.get("h5py", ""): + suite2p_args["h5py"] = suite2p_args["h5py"] suite2p.run_s2p(suite2p_args) - suite2p_args["h5py"] = suite2p_args["h5py"][0] + data_path = "" + if suite2p_args.get("h5py", ""): + data_path = suite2p_args["h5py"][0] + else: + data_path = suite2p_args["data_path"][0] + if not data_path: + raise ValueError("No data path found in suite2p_args") bin_path = list(Path(tdir).rglob("data.bin"))[0] ops_path = list(Path(tdir).rglob("ops.npy"))[0] # Suite2P ops file contains at least the following keys: @@ -1830,8 +2093,12 @@ def singleplane_motion_correction( # make projections mx_proj = projection_process(data, projection="max") av_proj = projection_process(data, projection="avg") + if not suite2p_args.get("h5py", []): + filepath = suite2p_args["tiff_list"] + else: + filepath = suite2p_args["h5py"] write_output_metadata( - args_copy, Path(suite2p_args["h5py"]), args["motion_corrected_output"], output_dir + args_copy, suite2p_args["h5py"], args["motion_corrected_output"], output_dir ) # TODO: normalize here, if desired # save projections @@ -1949,13 +2216,25 @@ def singleplane_motion_correction( lower_quantile=args["movie_lower_quantile"], upper_quantile=args["movie_upper_quantile"], ) - processed_vids = [ - ds_partial(i) - for i in [ - Path(h5_file), - Path(args["motion_corrected_output"]), + if suite2p_args.get("h5py", ""): + h5_file = suite2p_args["h5py"] + processed_vids = [ + ds_partial(i) + for i in [ + Path(h5_file), + Path(args["motion_corrected_output"]), + ] ] - ] + else: + tiff_array = tiff_to_numpy(suite2p_args["tiff_list"]) + processed_vids = [ + ds_partial(i) + for i in [ + tiff_array, + Path(args["motion_corrected_output"]), + ] + ] + logger.info("finished downsampling motion corrected and non-motion corrected movies") # tile into 1 movie, raw on left, motion corrected on right @@ -1969,63 +2248,100 @@ def singleplane_motion_correction( except: logger.info("Could not write motion correction preview") # compute crispness of mean image using raw and registered movie - with ( - h5py.File(h5_file) as f_raw, - h5py.File(args["motion_corrected_output"], "r+") as f, - ): - mov_raw = f_raw["data"] - mov = f["data"] - crispness = [ - np.sqrt(np.sum(np.array(np.gradient(np.mean(m, 0))) ** 2)) - for m in (mov_raw, mov) - ] - logger.info("computed crispness of mean image before and after registration") - - # compute residual optical flow using Farneback method - if f["reg_metrics/regPC"][:].any(): - regPC = f["reg_metrics/regPC"] - flows = np.zeros(regPC.shape[1:] + (2,), np.float32) - for i in range(len(flows)): - pclow, pchigh = regPC[:, i] - flows[i] = cv2.calcOpticalFlowFarneback( - pclow, - pchigh, - None, - pyr_scale=0.5, - levels=3, - winsize=100, - iterations=15, - poly_n=5, - poly_sigma=1.2 / 5, - flags=0, + if suite2p_args.get("h5py", ""): + with ( + h5py.File(h5_file) as f_raw, + h5py.File(args["motion_corrected_output"], "r+") as f, + ): + mov_raw = f_raw["data"] + mov = f["data"] + crispness = [ + np.sqrt(np.sum(np.array(np.gradient(np.mean(m, 0))) ** 2)) + for m in (mov_raw, mov) + ] + logger.info("computed crispness of mean image before and after registration") + + # compute residual optical flow using Farneback method + if f["reg_metrics/regPC"][:].any(): + regPC = f["reg_metrics/regPC"] + flows = np.zeros(regPC.shape[1:] + (2,), np.float32) + for i in range(len(flows)): + pclow, pchigh = regPC[:, i] + flows[i] = cv2.calcOpticalFlowFarneback( + pclow, + pchigh, + None, + pyr_scale=0.5, + levels=3, + winsize=100, + iterations=15, + poly_n=5, + poly_sigma=1.2 / 5, + flags=0, + ) + flows_norm = np.sqrt(np.sum(flows**2, -1)) + farnebackDX = np.transpose([flows_norm.mean((1, 2)), flows_norm.max((1, 2))]) + f.create_dataset("reg_metrics/crispness", data=crispness) + f.create_dataset("reg_metrics/farnebackROF", data=flows) + f.create_dataset("reg_metrics/farnebackDX", data=farnebackDX) + logger.info( + "computed residual optical flow of top PCs using Farneback method" ) - flows_norm = np.sqrt(np.sum(flows**2, -1)) - farnebackDX = np.transpose([flows_norm.mean((1, 2)), flows_norm.max((1, 2))]) - f.create_dataset("reg_metrics/crispness", data=crispness) - f.create_dataset("reg_metrics/farnebackROF", data=flows) - f.create_dataset("reg_metrics/farnebackDX", data=farnebackDX) - logger.info( - "computed residual optical flow of top PCs using Farneback method" - ) - logger.info( - "appended additional registration metrics to" - f"{args['motion_corrected_output']}" - ) - - # create image of PC_low, PC_high, and the residual optical flow between them - if f["reg_metrics/regDX"][:].any(): - for iPC in set( - ( - np.argmax(f["reg_metrics/regDX"][:, -1]), - np.argmax(farnebackDX[:, -1]), + logger.info( + "appended additional registration metrics to" + f"{args['motion_corrected_output']}" + ) + else: + mov_raw = tiff_array + with h5py.File(args["motion_corrected_output"], "r+") as f: + crispness = [ + np.sqrt(np.sum(np.array(np.gradient(np.mean(m, 0))) ** 2)) + for m in (mov_raw, f["data"]) + ] + logger.info("computed crispness of mean image before and after registration") + if f["reg_metrics/regPC"][:].any(): + regPC = f["reg_metrics/regPC"] + flows = np.zeros(regPC.shape[1:] + (2,), np.float32) + for i in range(len(flows)): + pclow, pchigh = regPC[:, i] + flows[i] = cv2.calcOpticalFlowFarneback( + pclow, + pchigh, + None, + pyr_scale=0.5, + levels=3, + winsize=100, + iterations=15, + poly_n=5, + poly_sigma=1.2 / 5, + flags=0, + ) + flows_norm = np.sqrt(np.sum(flows**2, -1)) + farnebackDX = np.transpose([flows_norm.mean((1, 2)), flows_norm.max((1, 2))]) + f.create_dataset("reg_metrics/crispness", data=crispness) + f.create_dataset("reg_metrics/farnebackROF", data=flows) + f.create_dataset("reg_metrics/farnebackDX", data=farnebackDX) + logger.info( + "computed residual optical flow of top PCs using Farneback method" ) - ): - p = Path(args["registration_summary_output"]) - flow_png( - Path(args["motion_corrected_output"]), - str(p.parent / p.stem), - iPC, + logger.info( + "appended additional registration metrics to" + f"{args['motion_corrected_output']}" ) + # create image of PC_low, PC_high, and the residual optical flow between them + if f["reg_metrics/regDX"][:].any(): + for iPC in set( + ( + np.argmax(f["reg_metrics/regDX"][:, -1]), + np.argmax(farnebackDX[:, -1]), + ) + ): + p = Path(args["registration_summary_output"]) + flow_png( + Path(args["motion_corrected_output"]), + str(p.parent / p.stem), + iPC, + ) logger.info(f"created images of PC_low, PC_high, and PC_rof for PC {iPC}") # Clean up temporary directory diff --git a/code/run b/code/run index 816b75e..4ed8ebc 100755 --- a/code/run +++ b/code/run @@ -2,4 +2,4 @@ set -ex ls ../data -python registration.py "$@" +python registration.py "$@" --input /data/single-plane-2024-10-21_11-15-00 --data-type TIFF diff --git a/environment/Dockerfile b/environment/Dockerfile index ab37b47..8cd8eec 100644 --- a/environment/Dockerfile +++ b/environment/Dockerfile @@ -17,6 +17,6 @@ WORKDIR / RUN pip3 install -U --no-cache-dir \ aind-ophys-utils \ sbxreader \ - git+https://github.com/arielleleon/suite2p@bug-pull-from-defined-h5py \ + suite2p \ aind-data-schema==0.22.0 \ pydantic==2.8.1 \ No newline at end of file diff --git a/metadata/metadata.yml b/metadata/metadata.yml index 8414a10..bbb2d48 100644 --- a/metadata/metadata.yml +++ b/metadata/metadata.yml @@ -1,5 +1,5 @@ metadata_version: 1 -name: aind-ophys-motion-correction +name: GENERIC INPUTS aind-ophys-motion-correction description: This capsule performs motion correction using Suite2p. 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