Subtracting a varying value from pixels in image opencv python

Question

I am trying to replicate the study in this paper to verify it.

Long story short:

I extracted the row of pixels from all the frames in the video using:

values_list = []

for filename in glob.glob('frames//*.png'):

    img = cv2.imread(filename,0)    
    values_list.append(img[100, :]) #Get all rows at y-axis 17 which is the row pixels

Then I created a plot using:

fig, ax = plt.subplots()
width = 10
xlim = 0, width*len(values_list)
ylim = 0, max([len(v) for v in values_list]) + 2


ax.set(xlim=xlim, ylim=ylim, autoscale_on=False)
for i in range(len(values_list)):
    plt.imshow(np.array(values_list[i]).reshape(-1, 1), extent=[i * width, (i + 1) * width, 0, len(values_list[i])],
               origin='lower', cmap='gray')

ax.set_aspect('auto')

fig.set_size_inches(20, 10.5)

plt.savefig('myimage.png', format='png', dpi=1000)

This gives the following plot

The above is equivalent to what they are showing at b) c) and d) from the authors' paper (but Horizontal instead of vertical and grayscale instead of colors). How did they convert it to the equivalent of e) f) and g) as shown below?

All the claim is

This modulation ( b) c) and d)) is poor visible in the figures. To make this modulation more evident, we subtracted a slowly varying component along vertical direction in a diagram separately from each pixel time-variable value thus enhancing an alternating component (AC) of light modulation, which is varying at the heartbeat or higher rate.

How to subtract a slowly varying component from the pixels stored in values_list which are the pixels of the image at every frame?

Extras:

The values_list to replicate the graph is as follows

Download the video link at the bottom of the paper under Electronic supplementary material and use the following code to convert from videos to frames, and then apply code above.

import cv2

vidcap = cv2.VideoCapture('video/2.mp4')
success, image = vidcap.read()
count = 0
while success:

    cv2.imwrite("frames/%d.png" % count, image)
    success, image = vidcap.read()
    count += 1

Answer 1

import glob
import cv2

import matplotlib.pyplot as plt
import numpy as np
import random

values_list = []
values_mean = []

count = 0

for filename in glob.glob('video//frames//*.png'):

    count +=1
    img = cv2.imread(filename,0)    

    values_list.append(img[100,:]) #Get all rows at x-axis 17 which is the row pixels
    values_mean.append(np.round(np.mean(img[100:]), decimals=0))

values_list = np.array(values_list)
values_mean = np.array(values_mean).reshape(-1,1)
new_column_value = values_mean - values_list
new_column_value_scaled = np.interp(new_column_value, (new_column_value.min(), new_column_value.max()),(0, 255))

plotted_values_list = new_column_value_scaled

fig, ax = plt.subplots()
width = 10
xlim = 0, width*len(values_list)
ylim = 0, max([len(v) for v in values_list]) + 2


ax.set(xlim=xlim, ylim=ylim, autoscale_on=False)

for i in range(len(plotted_values_list)):
    plt.imshow(np.array(plotted_values_list[i,:]).reshape(-1, 1), extent=[i * width, (i + 1) * width, 0, 
                                                                          len(plotted_values_list[i,:])],origin='lower', cmap='gray')

ax.set_aspect('auto')

fig.set_size_inches(20, 10.5)

plt.savefig('myimage_whole.png', format='png', dpi=500)
#plt.show()

Which gives out this image: