btlorch

import argparse
from PIL import Image
import numpy as np
from glob import glob
import os
from tqdm import tqdm
from diffusers import DiffusionPipeline
import torch
import torchvision.transforms as T

btlorch

import numpy as np
import struct


def read_hog(filename, batch_size=5000):
    """
    Read HoG features file created by OpenFace.
    For each frame, OpenFace extracts 12 * 12 * 31 HoG features, i.e., num_features = 4464. These features are stored in row-major order.
    :param filename: path to .hog file created by OpenFace
    :param batch_size: how many rows to read at a time

btlorch

from nfft.nfft import Plan
import nibabel as nib
import numpy as np
import time

# Load image
img = nib.load("./merge_cartesian_2D_0cycles.nii.gz").get_data()
width = img.shape[1]

# Zeropadding