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

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

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