dataprocessing.py

import numpy as np
import matplotlib.pyplot as plt


print("start processing . . .\n")

'''file will get two coordinate values seperated by a comma'''

file = open("/Users/hegderajesh/Desktop/demo/demo.txt","r")

a = []
b = []

'''the above mentioned comma is removed by using the split() function and then storing
the individual x and y coordinates in the list a and b seperately after
coverting it into integer'''

for line in file:
    p,q = line.split(",")
    p = int(p)
    q = int(q)
    a.append(-p)
    b.append(q)

'''plotting the unprocessed image for comparision with 
the processed image later, this will not be uploaded to the cloud server'''

gh=[0,0,0,-80]
hg=[0,80,0,0]
plt.plot(gh,hg)
plt.plot(a,b)

plt.show()

''' (-p) is stored in 'a' because our origin is from the right
unlike the common standard(from the left) supported
by the matplotlib'''


x = a
y = b


'''check if the x is staying sort of constant
for that take average of a calculated alpha(assumed) values and check if
the average is below a set tolerance level'''

length = len(x)

nx = []
ny = []
k = 0

'''alpha & tolerance is a key component in the vertical analysis
    reduce alpha value to get more straight lines
    increase it to get more smooth curves
    increase tolerance to get more straight lines
    reduce it to get smooth curves
'''


'''the average of 4 neighbours is taken for each value of a and b
and the coordinate value is stored in lists x and y seperately'''

alpha = 15

tolerance = 2


for i in range(0,length-(alpha+1),alpha):
    s = 0
    for j in range(i,i+alpha):
        s = s + x[j]
    avg = s/alpha
    s = 0
    for j in range(i,i+alpha):
        s = s + abs(avg - x[j])
    avg_std_x = s/20
    if(avg_std_x < tolerance): #if the deviation from mean indicates a vertical line
        nx.append(x[i])
        ny.append(y[i])
        nx.append(x[i+alpha])
        ny.append(y[i+alpha])
    else:
        for j in range(i-4,i+(alpha-4)): #if the deviation from mean indicates a non vertical or horizontal line
            nx.append((x[j-4]+x[j-3]+x[j-2]+x[j-1]+x[j]+x[j+1]+x[j+2]+x[j+3]+x[j+4])/9)
            ny.append((y[j-4]+y[j-3]+y[j-2]+y[j-1]+y[j]+y[j+1]+y[j+2]+y[j+3]+y[j+4])/9)
        


length = len(nx)


'''will create a plot that with x and y values'''
gh=[0,0,0,-80]
hg=[0,80,0,0]
plt.plot(gh,hg)
plt.plot(nx,ny)

'''will show the plot, similar to the imshow function in opencv'''

plt.show()

gh=[0,0,0,-80]
hg=[0,80,0,0]
plt.plot(gh,hg)
plt.plot(nx,ny)


'''if enabled, the command will save a pdf of the plotted figure'''
'''NOTE: only enable ONE of plt.show() or plt.savefig() at a time
i.e. either show or save figure'''

'''for our reference'''

plt.savefig("/Users/hegderajesh/Desktop/demo/demo.pdf")


print("Successfully processed data session\n")
print("\nPDF created\n")
print("\nUploading to cloud . . . \n")





print("end of run\n")