Testing the MLX90640 firmware support stack

Melexis, creator of the MLX90640 provide a firmware stack that is available directly on github. The stack was written to be run off a Teensy running mbed os. I thus ported the stack over to the ESP-IDF development environment including the test cases with raw data provided with an excel file by the repository maintainer.

The goal is to pass the raw camera parameters through the stack running on the embedded development platform and check that the results are equal to the reference results given by the manufacturer. My forays into doing this is detailed in the project's github issues. 

I checked and confirmed the results by comparing the SHA-256 hashes of the reference results against mine, some tests failed and on further inspection, which I suspect to be due to floating point representation and rounding error! I considered this as non impactful!

Testing the MLX90640 - Displaying frames

To test that the sensor worked, I printed the raw data of my own thermal representation sent by the thermal camera to the ESP32 processor, to my computer and formatted the data using python as a 2D numpy array(24x32) for plotting. The code is as seen below:

Bilinear interpolated test image of me using the plasma color palette in matplotlib.

A 3D temperature surface plot of a typical thermal image with 24x32 pixels

import matplotlib
#matplotlib.use('GTKAgg') 
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt

frame = np.array([30.825253,30.455252,30.720179,30.544859,30.427420,30.370266,29.946110,29.928658,29.878696,30.046879,30.317205,31.169159,33.990673,35.194588,35.997345,36.098236,36.031712,35.897179,36.039021,35.784195,35.328114,35.644867,35.863697,35.174343,31.032515,31.371862,30.977913,30.588465,30.133257,
30.008118,30.236732,
30.231815,30.976789,30.689404,30.787285,30.362507,30.149279,30.146961,30.035376,30.241760,30.489824,30.103189,30.620626,31.206104,35.185364,35.667862,36.217941,35.894447,35.881859,35.292969,35.147324,35.744308,35.361080,35.174133,35.637871,35.915424,32.157627,31.172319,30.133978,30.620142,30.077423,
29.957914,30.049553,30.963652,30.955786,30.545006,32.262329,30.053732,30.133377,30.106586,31.323080,29.745535,30.029339,30.132275,31.237171,33.113396,35.322514,35.405560,35.708084,35.426910,33.949474,34.209705,34.342945,34.208508,34.998226,35.118893,36.396896,35.762486,33.242210,32.095470,30.882133,
30.798094,30.593410,30.201653,30.372244,30.420557,30.871033,30.348743,30.257111,30.570904,30.083139,30.148439,29.986992,29.818211,29.564634,29.975609,31.276020,33.663185,35.581955,35.840919,35.192551,34.320091,32.080173,32.254898,32.203354,32.421917,33.327965,33.865158,34.779900,34.088661,33.353344,
32.041279,31.380322,30.768456,30.482023,31.081444,29.956898,30.223719,30.339663,30.205742,30.265926,30.145273,30.305040,30.179245,30.155495,29.484919,29.640444,30.064463,32.542854,34.320049,35.053146,35.165974,33.747772,33.833889,31.328650,31.578316,31.994202,32.109375,33.064148,32.973824,33.972363,
33.738358,32.720371,32.628704,31.945860,30.206486,30.670797,30.118706,29.981321,28.589109,30.237324,30.031094,30.333559,30.369200,29.809341,29.831825,29.624437,29.970108,29.929329,30.226299,32.220379,34.503220,34.974258,33.980598,34.773674,34.643135,32.091923,31.376667,32.039097,32.077644,32.826157,
34.001637,34.346165,34.727680,33.437244,32.627235,32.021400,31.101357,29.732843,29.929398,30.093491,29.902218,30.573723,29.981726,30.058355,29.946224,29.900709,29.617571,29.825027,29.898470,29.462370,30.350552,32.004219,34.320049,34.948685,34.968773,35.805622,35.928032,32.918358,32.612209,32.546959,
32.588249,34.324471,34.577042,35.043011,34.965393,33.684315,32.996124,31.555805,31.451670,29.801346,29.898396,29.734728,29.651402,30.844032,30.053579,29.465715,29.797607,30.025743,29.669643,29.299297,30.257536,29.700901,29.542891,31.691963,34.166092,35.519711,35.083809,34.974312,35.362972,34.507477,
33.471874,33.103764,34.492317,34.527534,34.690823,34.752567,35.613087,34.676266,34.103535,31.210436,30.876890,29.903629,30.533581,29.814299,30.476486,30.317810,30.021536,29.863836,29.769650,29.703615,29.780603,30.019096,29.404810,30.001093,30.188116,33.224419,33.800163,35.317280,35.078205,36.482384,
35.234932,34.978931,34.867256,35.064987,35.281227,35.230232,35.259289,35.083416,34.635693,35.542957,35.614059,31.853098,31.364258,29.786497,29.921522,30.255171,29.849384,30.452324,30.042599,29.736336,30.028849,29.816181,29.547615,29.537355,30.002766,29.929615,30.043802,32.593044,33.990944,35.553532,
35.364029,34.561729,36.252148,35.041500,34.712952,34.724964,35.289490,35.935913,35.569515,35.272633,35.817547,35.445282,35.569942,32.016716,30.955177,30.603487,30.104607,30.109179,29.824963,29.915388,29.816927,29.469576,30.034851,30.104675,29.862240,29.856619,29.539978,29.523264,29.876308,31.381310,
32.963490,34.966473,35.476376,35.671581,35.654423,35.130768,34.991806,35.327049,35.032814,35.497482,35.715199,35.276897,35.672832,35.499878,35.575603,31.823814,31.048864,30.119349,30.561926,30.875475,29.927982,30.058186,29.882174,29.607641,30.251472,30.052780,29.553158,29.557384,29.457729,29.795660,
29.478170,29.928938,32.299774,34.415218,35.018600,35.351013,35.636173,35.374035,35.161896,35.246452,35.848503,36.202412,35.723236,34.755775,35.102516,35.231533,35.530571,32.096180,31.031857,30.212738,29.815725,29.897425,30.116806,30.212532,30.792463,29.779476,29.893263,29.760498,29.900221,30.248545,
29.799126,29.528423,29.963467,30.036573,29.926020,31.808561,33.425022,36.108452,35.534931,35.283348,35.616695,35.959675,35.981495,35.971260,35.681622,35.081394,35.192375,34.709564,35.591793,31.162937,30.651421,30.126072,29.982489,30.289963,29.935642,29.484997,29.946280,29.849260,30.358604,30.562708,
29.728052,29.588160,29.995047,29.802187,29.447811,29.581106,29.747078,30.400469,32.341225,35.020535,36.541882,35.483341,35.788380,35.678303,36.010071,36.626423,35.384724,35.108875,35.001595,34.889576,34.876366,31.162891,30.516249,30.219873,29.814800,30.749279,29.990110,29.898378,30.490925,29.957872,
30.042242,29.994278,29.553961,30.236063,29.563148,29.877455,29.551201,29.713213,29.791931,30.150269,31.319578,35.301083,35.054108,35.941132,35.674313,36.400726,36.220665,35.983143,36.248146,36.214397,36.363438,35.631176,35.090679,30.752977,30.461384,29.742481,30.137491,30.102514,28.290979,29.591667,
29.672857,29.823454,29.661844,29.545959,29.941406,29.219667,29.816172,29.649904,29.423286,29.608559,29.611916,29.740639,31.032679,34.412640,35.208286,35.884430,36.081642,36.125725,36.216583,36.310204,35.914783,36.279846,36.510159,35.585140,33.878525,30.536989,30.453133,29.845497,29.812941,30.151926,
29.966724,30.328518,30.212074,30.092100,29.768299,29.948242,29.652149,29.457760,29.744308,29.456697,29.649361,29.736805,28.499578,30.140648,30.503004,34.715698,34.584217,35.656910,35.659832,35.670086,35.771759,35.526474,36.120697,36.169987,36.210056,35.018471,34.267387,32.360340,31.394144,30.283535,
30.111923,29.988897,30.383509,30.244726,30.009436,28.952450,30.052336,29.697035,29.860157,29.413933,29.759481,29.932318,29.530575,29.787077,29.600149,29.877132,30.049713,33.285236,34.903492,35.601048,35.680439,35.558037,35.336216,35.131886,34.968597,34.490410,35.369781,34.227779,34.229904,32.629860,
32.496052,30.382954,30.340776,30.338923,30.150995,30.307499,30.410322,30.228249,30.116241,29.638229,30.019041,29.840868,29.939632,29.614403,29.879114,29.857929,29.913263,31.942949,33.113129,35.090935,35.276627,35.651634,35.244732,35.570637,35.514740,34.449360,35.311104,34.834583,34.785351,35.048225,
35.298717,34.094040,33.261524,32.994980,32.861629,33.268528,33.819786,30.240597,29.990622,29.870834,30.424391,29.751329,30.152893,29.897278,30.435659,29.564898,29.537609,29.743263,31.286535,33.060127,34.018021,35.307545,35.928196,35.490273,35.445309,35.626991,35.519154,35.052029,35.177189,35.399738,
35.822407,35.320961,35.689850,33.769344,34.018810,33.894356,35.570854,34.102001,34.233429,30.222151,30.132812,30.430403,30.015600,29.737064,29.904163,29.941334,30.009123,29.907103,29.808104,31.739523,32.106396,32.762123,33.216309,34.993397,35.553715,35.248356,35.708889,35.510681,35.593075,35.511066,
36.413120,35.933678,36.416039,36.000542,35.376503,33.190861,33.493649,34.241016,34.172115,34.584034,34.319748,30.860155,30.170086,30.058989,30.125769,30.088377,29.929113,29.814045,30.206425,29.118309,30.547298,32.336330,33.400879,32.468079,32.649548,33.709805,33.894958,34.250042,34.229904,33.882778,
34.423611,34.403744,34.887779,36.002701,35.902077,35.960785,35.264378,33.250889,33.691711,33.759762,33.965618,33.751312,34.094242,30.881582,30.921249,30.969425,29.857355,30.671698,31.749584,33.012306,33.056740,33.741997,33.754776,34.046505,32.778145,33.817608,34.238117,34.701591,34.220093,34.362137,
34.215816,34.393341,34.302555,34.043175,34.107567,33.923306,34.018215,35.085987,34.207722,33.374298,33.747631,33.205704,34.102360,34.654442,35.118324,31.081171,31.064100,30.640053,30.944107,32.583111,34.110058,34.158962,33.938618,34.074863,33.820190,34.209820,34.226067,33.996681,33.592789,34.336464,
34.351791,34.029461,34.649178,34.331161,34.184895,33.877117,34.889751,33.661694,33.707085,33.702087,33.866566,33.549461,33.617970,33.610279,33.578182,34.359665,34.729172])

ir=frame.reshape((24, 32))

graph = plt.imshow(ir, cmap=matplotlib.cm.plasma,interpolation='bilinear')

plt.colorbar()
plt.draw()
plt.clim(np.amin(frame),np.amax(frame))

2D image code snippet