mkvenkit

/*

This code sends VL53L1X 12 x 12 or 8 x 8 ROI distance data over serial.

Python code for plottomg:

import serial
import struct 

import numpy as np

mkvenkit

#version 450 core

uniform bool enableRimLight;
uniform sampler2D sampler;

in VS_OUT {
	in vec3 L;
	in vec3 V;
	in vec2 tc;
} fs_in;

mkvenkit

#version 450 core

layout(location = 0) in vec3 aVert;
layout(location = 1) in vec3 aNorm;
layout(location = 2) in vec2 aTexCoord;
layout(location = 3) in vec3 aTangent;

uniform mat4 vMat;
uniform mat4 pMat;
uniform mat4 mMat;

mkvenkit

import numpy as np 
from PIL import Image
from math import sqrt

def main():

    NX, NY = 256, 256 

    nmap = np.zeros([NX, NY, 3], np.float32)

mkvenkit

// stripes 
float val = clamp(round(sin( 20 * fs_in.tc.x * 3.14156)), 0, 1);
vec3 col1 = vec3(255, 237, 81) / 255.0;
vec3 col2 = vec3(133, 202, 93) / 255.0;
vec3 col = mix(col1, col2, val);

mkvenkit

float rim = (1.0 - dot(N, V));
rim = smoothstep(0.0, 1.0, rim);
float rim_exp = 3.5;
rim = pow(rim, rim_exp);
vec3 rim_col = vec3(0.1, 0.1, 0.1);
crim = rim * rim_col;

mkvenkit

#version 450 core

uniform bool enableRimLight;

in VS_OUT {
	in vec3 N;
	in vec3 L;
	in vec3 V;
} fs_in;

mkvenkit

#version 450 core

layout(location = 0) in vec3 aVert;
layout(location = 1) in vec3 aNorm;

uniform mat4 vMat;
uniform mat4 pMat;
uniform mat4 mMat;

out VS_OUT {

mkvenkit

#version 450 core

uniform bool enableRimLight;
uniform sampler2D sampler;

in VS_OUT {
	in vec3 N;
	in vec3 L;
	in vec3 V;
	in vec2 tc;

mkvenkit

model.compile(
    optimizer=tf.keras.optimizers.Adam(),
    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    metrics=['accuracy'],
)

EPOCHS = 10
history = model.fit(
    train_ds, 
    validation_data=val_ds,