utils.py

import tensorflow as tf
import numpy as np
from einops import rearrange
from decord import VideoReader

num_frames = 32
input_size = 224
patch_size = (16, 16)
IMAGENET_MEAN = np.array([123.675, 116.28, 103.53])
IMAGENET_STD = np.array([58.395, 57.12, 57.375])

def format_frames(frame, output_size):
    frame = tf.image.convert_image_dtype(frame, tf.uint8)
    frame = tf.image.resize(frame, size=output_size)
    frame = frame - IMAGENET_MEAN
    frame = frame / IMAGENET_STD
    return frame

def read_video(file_path):
    container = VideoReader(file_path)
    return container

def frame_sampling(container, num_frames):
    interval = len(container) // num_frames
    bids = np.arange(num_frames) * interval
    offset = np.random.randint(interval, size=bids.shape)
    frame_index = bids + offset
    frames = container.get_batch(frame_index).asnumpy()
    frames = np.stack(frames)
    frames = format_frames(frames, [input_size] * 2)
    return frames

def denormalize(z):
    mean = np.array([123.675, 116.28, 103.53])
    variance = np.array([np.square(58.395), np.square(57.12), np.square(57.375)]) 
    std = np.sqrt(variance) # no need var and std, todo: update here!
    x = (z * std) + mean
    x = x.clip(0, 255)
    return x