EIT Real-time plotting using matplotlib

[UmiTooko]

Hi, it's me again.
Thanks for your support, we have managed to improve our reconstructed image quality.

We are currently developing a feature to read data from the tank and plot the image along with the time. To achieve that, we modified the code as follows:

# coding: utf-8
""" demo on dynamic eit using JAC method """
# Copyright (c) Benyuan Liu. All Rights Reserved.
# Distributed under the (new) BSD License. See LICENSE.txt for more info.
from __future__ import absolute_import, division, print_function

import matplotlib.pyplot as plt
import numpy as np
import pyeit.eit.jac as jac
import pyeit.mesh as mesh
from pyeit.eit.fem import EITForward
from pyeit.eit.interp2d import sim2pts
from pyeit.mesh.shape import thorax
import pyeit.eit.protocol as protocol
from pyeit.mesh.wrapper import PyEITAnomaly_Circle

import serial
from datetime import datetime
import time
from matplotlib.animation import FuncAnimation

"""-2. Initial vars """
arduino = serial.Serial('COM8', 115200 ,timeout=4)
v0 = np.loadtxt('example_data/ref_data.txt')
fig, ax = plt.subplots(constrained_layout=True)
n_el = 16

"""-1. Functions """
'''To read data from Arduino via COM port
Each frame is 16 lines, each line has 13 values, 
in total there are 208 values representing the voltage measured from electrodes. 
Frames are separated by an enter character.
'''

def readfromArduino():
    while(True):
        try:
            data = arduino.readline().decode('ascii')
            print("data: ", data)
            break
        except UnicodeDecodeError:
            print("UnicodeDecodeError found! Retrying...")
            continue
    return data

def get_difference_img_array(n_el = n_el, NewFrameSearchFlag = 1, idx = 0):
    difference_image_array = ''
    # Read difference image f1:
    while idx < n_el:
        data = readfromArduino()
        #skip until the empty line is found to catch the whole frame
        while(NewFrameSearchFlag == 1):
            if  len(data) < 4:
                print("Searching for new frame.")
                data = readfromArduino()
                continue
            else:
                print("New frame found.")
                data = readfromArduino()
                NewFrameSearchFlag = 0
                break


        data=data.strip('\r\n')
        difference_image_array += data
        difference_image_array += ' '
        idx = idx + 1
        #print("String: {0}".format(data))
 
    return difference_image_array

#Convert data to np type
def convert_data_in(s):
    data=s
    items=[]
    for item in data.split(' '):
        item = item.strip()
        if not item:
            continue
        try:
            items.append(float(item))
        except ValueError:
            print("Value Error found! Handling...")
            items.append(float(0))
    return np.array(items)


""" 0. build mesh """
n_el = 16  # nb of electrodes
use_customize_shape = False
if use_customize_shape:
    # Mesh shape is specified with fd parameter in the instantiation, e.g : fd=thorax
    mesh_obj = mesh.create(n_el, h0=0.065, fd=thorax)
else:
    mesh_obj = mesh.create(n_el, h0=0.065)

# extract node, element, alpha
pts = mesh_obj.node
tri = mesh_obj.element
x, y = pts[:, 0], pts[:, 1]

""" 1. problem setup """
# mesh_obj["alpha"] = np.random.rand(tri.shape[0]) * 200 + 100 # NOT USED
anomaly = PyEITAnomaly_Circle(center=[0.5, 0.5], r=0.1, perm=1000.0)
mesh_new = mesh.set_perm(mesh_obj, anomaly=anomaly)

""" 2. FEM simulation """
# setup EIT scan conditions
protocol_obj = protocol.create(n_el, dist_exc=1, step_meas=1, parser_meas="fmmu")

# calculate simulated data
fwd = EITForward(mesh_obj, protocol_obj)

#v0 = fwd.solve_eit()
#v1 = fwd.solve_eit(perm=mesh_new.perm)
""" 3. JAC solver """
# Note: if the jac and the real-problem are generated using the same mesh,
# then, data normalization in solve are not needed.
# However, when you generate jac from a known mesh, but in real-problem
# (mostly) the shape and the electrode positions are not exactly the same
# as in mesh generating the jac, then data must be normalized.
eit = jac.JAC(mesh_obj, protocol_obj)
eit.setup(p=0.5, lamb=0.001, method="kotre", perm=1000, jac_normalized=True)

def animating(i):  
    while arduino.inWaiting()==0:
        print("waiting")
        time.sleep(0.5)
        pass

    s1 = get_difference_img_array()

    v1 = convert_data_in(s1)

    try:
        ds = eit.solve(v1, v0, normalize=True)
    except Exception as e:
        ani.event_source.stop()  # Stop the current animation if error occurred
        ani.event_source.start()  # Start a new animation

    ds_n = sim2pts(pts, tri, np.real(ds))
  
    # Clear the graph after each animating frame
    ax.clear()
    
    # plot EIT reconstruction
    im = ax.tripcolor(x, y, tri, ds_n, shading="flat", cmap=plt.cm.magma)
    for i, e in enumerate(mesh_obj.el_pos):
        ax.annotate(str(i + 1), xy=(x[e], y[e]), color="r")
    ax.set_aspect("equal")

    # plt.savefig('../doc/images/demo_jac.png', dpi=96)

ani = FuncAnimation(fig, animating, interval = 50, cache_frame_data= False)
plt.show()

It is noteworthy that our electrodes were messy, and there are obvious contact problems with some, which severely affect the quality of the reconstructed image. Still, it should not be a big problem since we can replace them in no time. Besides, it works. We managed to show the movement of the objects inside our tank. You can watch the video at this Google Drive link: https://drive.google.com/drive/folders/1Y_9w8cCYSJimbiGiwunk6UIfk7JaeHzk?usp=sharing

However, it's clear to be seen that our fps is a little bit modest, and some frames are bad-reconstructed (we are currently fixing our hardware so the signal will me much more stable which should fix this problem).

I would like to ask you (and all other people who are reading this issue) to take a look at my code and give me some feedback.

Is there a better way to do this real-time data plotting? Please enlighten me.

Thank you so much!

[genji0306]

Hi Kiet,
I, Dung Trinh, have worked on EIT for a while. I am glad to know you are also working on this device.
Are you open to connect and discuss about this technology? - my facebook is https://www.facebook.com/Genji0306
Regards,
Steve Trinh

[liubenyuan]

@TooNakko Hi, we use pyqtgraph for real time ploting and EIT imaging visualization. It is very fluent for a prototyping app, but for serious application, you may consider C++.