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2021-010
Commit 92427af6 authored by Lakshan Wijewardana W.M.W-IT18132588's avatar Lakshan Wijewardana W.M.W-IT18132588
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Add ML files and update ino file

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Esp-32-wifi_module/test1/test1.ino
View file @ 92427af6
#include <SparkFunTSL2561.h>
#include <Adafruit_TSL2561_U.h>
#include <Adafruit_Sensor.h>
#include <Wire.h>
const int ledPin1 = 32;
const int ledPin2 = 33;
float ir;
float nir;
float full;
float vis;
float NDVI;
SFE_TSL2561 light;
// Global variables:
boolean gain; // Gain setting, 0 = X1, 1 = X16;
unsigned int ms; // Integration ("shutter") time in milliseconds
Adafruit_TSL2561_Unified tsl = Adafruit_TSL2561_Unified(TSL2561_ADDR_FLOAT, 12345);
void configureSensor(void)
{
tsl.setGain(TSL2561_GAIN_1X);
tsl.setIntegrationTime(TSL2561_INTEGRATIONTIME_101MS);
}
void setup()
{
// Initialize the Serial port:
pinMode (ledPin1, OUTPUT);
pinMode (ledPin2, OUTPUT);
Serial.begin(9600);
// Initialize the SFE_TSL2561 library
light.begin();
gain = 0;
// If time = 0, integration will be 13.7ms
// If time = 1, integration will be 101ms
// If time = 2, integration will be 402ms
// If time = 3, use manual start / stop to perform your own integration
unsigned char time = 2;
Serial.println("Set timing...");
light.setTiming(gain,time,ms);
Serial.println("Powerup...");
light.setPowerUp();
pinMode (ledPin1, OUTPUT);
pinMode (ledPin2, OUTPUT);
configureSensor();
Serial.begin(9600);
Wire.begin();
}
void loop()
{
ms = 500;
light.manualStart();
int ms = 500;
delay(ms);
light.manualStop();
digitalWrite (ledPin1, HIGH);
digitalWrite (ledPin2, HIGH);
unsigned int data0, data1;
if (light.analogRead(data0,data1))
{
// getData() returned true, communication was successful
Serial.print("data0: ");
Serial.print(data0);
Serial.print(" data1: ");
Serial.print(data1);
Serial.print(" red band: ");
Serial.print(data0 - data1);
double lux; // Resulting lux value
boolean good; // True if neither sensor is saturated
good = light.getLux(gain,ms,data0,data1,lux);
Serial.print(" lux: ");
Serial.print(lux);
if (good) Serial.println(" (good)"); else Serial.println(" (BAD)");
}
else
{
// getData() returned false because of an I2C error, inform the user.
byte error = light.getError();
printError(error);
}
}
void printError(byte error)
// If there's an I2C error, this function will
// print out an explanation.
{
Serial.print("I2C error: ");
Serial.print(error,DEC);
Serial.print(", ");
uint16_t y = 0;
uint16_t z = 0;
tsl.getLuminosity(&y, &z);
full = y;
ir=z;
vis = full - ir;
nir = ir;
NDVI = (nir - vis)/(nir + vis);
Serial.print(nir); Serial.println(" 1 (600-1100nm)");
Serial.print(vis); Serial.println(" 1 (Visible)");
Serial.print(NDVI); Serial.println(" 1 (NDVI)");
Serial.println("");
}
ML/Classify org-inorg/.gitignore 0 → 100644
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.ipynb_checkpoints/
organic dataset/
mobile captures/
\ No newline at end of file
ML/Classify org-inorg/Classify_cucumber_org.ipynb 0 → 100644
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ML/Classify org-inorg/organic_inorganic.csv 0 → 100644
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ML/Classify org-inorg/preprocess_extract_dataset_cucumber_org.ipynb 0 → 100644
View file @ 92427af6
{
"cells": [
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import cv2\n",
"import numpy as np\n",
"import pandas as pd\n",
"import mahotas as mht\n",
"from matplotlib import pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"dataset_path = \"organic dataset\"\n",
"img_files = os.listdir(dataset_path)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"def create_dataset():\n",
" names = ['area','perimeter','physiological_length','physiological_width','aspect_ratio','rectangularity','circularity', \\\n",
" 'mean_r','mean_g','mean_b','stddev_r','stddev_g','stddev_b', \\\n",
" 'contrast','correlation','inverse_difference_moments','entropy'\n",
" ]\n",
" df = pd.DataFrame([], columns=names)\n",
" for file in img_files:\n",
" imgpath = dataset_path + \"\\\\\" + file\n",
" main_img = cv2.imread(imgpath)\n",
" \n",
" #Preprocessing\n",
" img = cv2.cvtColor(main_img, cv2.COLOR_BGR2RGB)\n",
" gr_scale = cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)\n",
" blur = cv2.GaussianBlur(gr_scale, (5,5),0)\n",
" ret_otsu,im_bw_otsu = cv2.threshold(blur,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)\n",
" kernel = np.ones((50,50),np.uint8)\n",
" closing = cv2.morphologyEx(im_bw_otsu, cv2.MORPH_CLOSE, kernel)\n",
" \n",
" #Shape\n",
" contours, _ = cv2.findContours(closing,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)\n",
" cnt = contours[0]\n",
" M = cv2.moments(cnt)\n",
" area = cv2.contourArea(cnt)\n",
" perimeter = cv2.arcLength(cnt,True)\n",
" x,y,w,h = cv2.boundingRect(cnt)\n",
" aspect_ratio = float(w)/h\n",
" rectangularity = w*h/area\n",
" circularity = ((perimeter)**2)/area\n",
" \n",
" #Color\n",
" red_channel = img[:,:,0]\n",
" green_channel = img[:,:,1]\n",
" blue_channel = img[:,:,2]\n",
" blue_channel[blue_channel == 255] = 0\n",
" green_channel[green_channel == 255] = 0\n",
" red_channel[red_channel == 255] = 0\n",
" \n",
" red_mean = np.mean(red_channel)\n",
" green_mean = np.mean(green_channel)\n",
" blue_mean = np.mean(blue_channel)\n",
" \n",
" red_std = np.std(red_channel)\n",
" green_std = np.std(green_channel)\n",
" blue_std = np.std(blue_channel)\n",
" \n",
" #Texture\n",
" textures = mht.features.haralick(gr_scale)\n",
" ht_mean = textures.mean(axis=0)\n",
" contrast = ht_mean[1]\n",
" correlation = ht_mean[2]\n",
" inverse_diff_moments = ht_mean[4]\n",
" entropy = ht_mean[8]\n",
" \n",
" vector = [area,perimeter,w,h,aspect_ratio,rectangularity,circularity,\\\n",
" red_mean,green_mean,blue_mean,red_std,green_std,blue_std,\\\n",
" contrast,correlation,inverse_diff_moments,entropy\n",
" ]\n",
" \n",
" df_temp = pd.DataFrame([vector],columns=names)\n",
" df = df.append(df_temp)\n",
" print(file)\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"101.jpg\n",
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"205.jpg\n",
"206.jpg\n"
]
}
],
"source": [
"dataset = create_dataset()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(105, 17)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset.shape"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"pandas.core.frame.DataFrame"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(dataset)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"dataset.to_csv(\"organic_inorganic.csv\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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