/projects/016/Reaction_Monitoring/RealTime/TitrationCode_15.py

#!/usr/bin/env python3

# -*- coding: utf-8 -*-

"""

Created on Mon Jun 26 14:29:07 2017



@author: Chris and Louise

"""







# import the necessary packages

import cv2

import pandas as pd

import numpy as np

import glob

import sys

import matplotlib.pyplot as plt

import statistics as stats





#uses a command line argument to define what the directory being used is called

path_to_file = sys.argv[1]









################################

#uses commandline argument to know what directory to go into

def filepull():

    #defines location of directory

    #function that reads images from folder designated on command line

    #path_to_file is the folder defining argument

    #def readFolder(path_to_file):

    

    path_to_file = sys.argv[1]

    



    #use the glod tool to go into folder and look at the files/images in the folder

    #places all of the images into a list calle 'filenames'

    filenames = [img for img in glob.glob(path_to_file)]

        

    #this lines sorts all of the images in alphabetical order,

    #therefore also in chronological order

    #added this when the code was reading the images in a random order

    filenames.sort()

    

    return filenames



def fileread(filenames):

    

    image = filenames

    

    #empty list to store the images in

    read_image = cv2.imread(image)

    



    #returns wanted file

    return read_image 





    

#############################################################################    

       



#function to perform RGB image analysis on image 'n'

def RGBanalysis(n):    

    

    #modifiable pixel coordinates for center of sampling area

    x_coord = 400

    y_coord = 500

 

    #this will take the RGB data from the specified region

    #talk to Louise to run atleast 3-4 titrations in a marked position

    #on the stirrer to find an average position range to for general use

    b = n[x_coord - 100 : x_coord + 100 ,y_coord - 100 : y_coord + 100, 0]

    g = n[x_coord - 100 : x_coord + 100 ,y_coord - 100 : y_coord + 100, 1]

    r = n[x_coord - 100 : x_coord + 100 ,y_coord - 100 : y_coord + 100, 2]

                

    #averages the RGB values of all the pixels in the above region

    bAvg = np.mean(b)

    gAvg = np.mean(g)

    rAvg = np.mean(r)

                

    #defines averages a their color values

    blue = bAvg

    green = gAvg

    red = rAvg

        

        

     #returns the three values that make up an RGB code   

    return blue,green,red

################################################################

################################################################   



def initial_analysis(green_list):

    #list of values to look at, this will be the list of 20 first green values

    #on the plot to find the avg and stdev

    

    average_knot = stats.mean(green_list[0:20])

    

    stdev_knot = stats.stdev(green_list[0:20])

    

    return average_knot, stdev_knot     



#################################################################

#################################################################

    

def comparison_analysis(green_list):

        

    new_Avg = stats.mean(green_list[-20])

    new_stdev = stats.stdev(green_list[-20])

    

    return new_Avg, new_stdev

        

###################################################################

###################################################################

    



#function of code    

def maincode():

     

    #creates a plot with y-axis from 0 to 300

    #and x-axis from 0 to 100

    #axis can be changed to accomodate metrics

    fig = plt.figure()

    plt.axis([0,1000,0,300])

    plt.ion()

    plt.show() 

    

    #empty lists to hold data after analysis

    #used to create a datafram with all of the data

    #datafram will then be exported into csv

    images = []

    blue_list = []

    green_list = []

    red_list = []



    #list of averages and stdevs calculated as code progresses

    #these are values used to compare to fist calculated avg and stdev

    #to see if values have reached expected threshold value    

    New_avg_list = []

    New_stdev_list = []

    

    

    #starts loop to read and analyze images

    #set range to be number of images intended to capture plus 100

    #make sure range value is smaller than amount of image files in directory

    for (i,cap) in enumerate(range(900)):

        

        #images are capture one a second

        #index starts at 0 so add 1 

        second = i + 1

        

        #defines file finding function

        filenames = filepull()

        print(filenames[i])

        

        #file reading fucntion

        image = fileread(filenames[i])

                

        

        #stores RGB values from analysis function

        blue,green,red = RGBanalysis(image)

        

        #uses empty lists to store data as they're obtained

        images.append(second)

        blue_list.append(blue)

        green_list.append(green)

        red_list.append(red)



        #defines the green value

        Green_point = green

        

        #begin analysis

        if i == 20:

            average_knot, stdev_knot = initial_analysis(green_list)

            print(average_knot, stdev_knot)

            

        elif i > 20:

            new_Avg, new_stdev = comparison_analysis(green_list)

            

            New_avg_list.append(new_Avg)

            New_stdev_list.append(new_stdev)

            

            if new_Avg > (average_knot - stdev_knot):

                print("Threshold not reached")

                

            elif new_Avg <= (average_knot - stdev_knot):

                print("Threshold reached")

                

                #####add alert stuff

        

        #plots the time(s) vs. the green color channel value

        #green circles for the points

        plt.plot([second],[Green_point],'g.')

        #draws the plot

        plt.draw()

        

        

        

        #pause added, needs to be in time with camera

        plt.pause(.5)

    

        

    fig.savefig('titration_curve.png')    

    #after analysis is done, data is placed into dataframe

    #esports data

    dataframe = pd.DataFrame(images, columns = ['Image #'])

    dataframe['blue'] = blue_list

    dataframe['green'] = green_list

    dataframe['red'] = red_list

    print(dataframe) 

    

    #export datafram onto as csv

    dataframe.to_csv('_data.csv')







 



        

#calls code

maincode()