import numpy as np

import cv2

from skimage.util import img_as_float

import matplotlib.pyplot as plt

import time

import scipy.io

from scipy.sparse import spdiags

import glob

from scipy.sparse import spdiags

import pandas as pd

import scipy.signal

import skvideo.io

from scipy.signal import butter

import scipy

import os

def find_frame(videoFilePath, duration, fs=60):

i = 0

vidObj = cv2.VideoCapture(videoFilePath)

totalFrames = int(vidObj.get(cv2.CAP_PROP_FRAME_COUNT))

success, img = vidObj.read()

selected_frame_idx = totalFrames - duration * fs

FS = fs

while success:

# t.append(vidObj.get(cv2.CAP_PROP_POS_MSEC))# current timestamp in milisecond

if i < selected_frame_idx:

success, img = vidObj.read() # read the next one

i = i + 1

continue

else:

vidLxL = img_as_float(img)

vidLxL = cv2.cvtColor(vidLxL.astype('float32'), cv2.COLOR_BGR2RGB)

return vidLxL

onedrive_path = r'D:\OneDrive - UW\rPPG Clinical Study\UW Medicine Data'

all_video_path = sorted(glob.glob(onedrive_path + "\*\Videos\*.mp4"))

# Parameters

FS = 60

DURATION = 120 # in seconds

# Save a middle frame. (60th second)

for video_path in all_video_path:

video_basename = os.path.basename(video_path).split('.')[0]

print(video_basename)

# Load Video and crop face and palm

selected_frame = find_frame(video_path, DURATION, fs=FS)

np.save(f'./cropping_frames/{video_basename}.npy', selected_frame)