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I am using an algorithm to measure fractal dimention (https://francescoturci.net/2016/03/31/box-counting-in-numpy/) or box counting method over 3 images:

1 First image 2 Second image 3 Third image

My adapted code is:

`

#importing pandas (library needed)
import pandas as pd
import openpyxl
import io
import numpy as np
import pylab as pl
 
def rgb2gray(rgb):
    r, g, b = rgb[:,:,0], rgb[:,:,1], rgb[:,:,2]
    gray = 0.2989 * r + 0.5870 * g + 0.1140 * b
    return gray
randshapes = 3
counter = 1
for fractal_cicle in range(randshapes):
  
  path = "/content/lienzo_particula_cutted"+str(counter)+".png"
  image=rgb2gray(pl.imread(path))
  
  # finding all the non-zero pixels
  pixels=[]
  for i in range(image.shape[0]):
    for j in range(image.shape[1]):
      if image[i,j]>0:
        pixels.append((i,j))
  
  Lx=image.shape[1]
  Ly=image.shape[0]
  print (Lx, Ly)
  pixels=pl.array(pixels)
  print (pixels.shape)
  
  #computing the fractal dimension
  #considering only scales in a logarithmic list
  scales=np.logspace(0.01, 1, num=10, endpoint=False, base=2)
  Ns=[]
  
  # looping over several scales
  for scale in scales:
    print ("======= Scale :",scale)
    # computing the histogram
    H, edges=np.histogramdd(pixels, bins=(np.arange(0,Lx,scale),np.arange(0,Ly,scale)))
    Ns.append(np.sum(H>0))
  
  # linear fit, polynomial of degree 1
  coeffs=np.polyfit(np.log(scales), np.log(Ns), 1)
  
  pl.plot(np.log(scales),np.log(Ns), 'o', mfc='none')
  pl.plot(np.log(scales), np.polyval(coeffs,np.log(scales)))
  pl.xlabel('log $\epsilon$')
  pl.ylabel('log N')
  pl.savefig('Coeff graph'+str(counter)+'.pdf')

  print ("The Hausdorff dimension is", -coeffs[0]) 
  np.savetxt('/content/lienzo_particula_fracdim'+str(counter)+'.txt', list(zip(scales,Ns)))
  
  
  #creating a dictionary
  raw_data = {'#': [], 'Fractal_dim': []}
  
  #creating the data frame
  df = pd.DataFrame(raw_data, columns = ['#', 'Fractal_dim'])
  
  print(counter)
  print(fractal_cicle)
  df.loc[fractal_cicle,'#'] = counter
  df.loc[fractal_cicle,'Fractal_dim'] = -coeffs[0]
  #print(df)
  counter += 1
print(df)
df.to_csv('raw_data.csv', index=False)
#io.excel.xls.writer('raw_data.xls', df)
df.to_excel('raw_data.xls', index=False)

` among the results, I want to register each fractal dimention in a row from my data frame, however, it is registered just the last calculation.

The results I have are:

   #  Fractal_dim
0  0     0.000000
1  0     0.000000
2  3     2.000385

what I wan is as follows:

   #  Fractal_dim
0  1     2.000442
1  2     2.000390
2  3     2.000385

Thank you very much in advance.

Daniel Molina
  • 11
  • 3

1 Answers1

0

As matter of fact I couldn´n answer the exact requierment I wanted to solve, however, I when around by, saving the data in txt format, then converting the txt file to csv, while I can use the Pandas(r) environment.

The code was like

#Calculating fractal dimensión over cutted images step instead saving  a csv , it saved a txt


#importing pandas (library needed)
import pandas as pd
import openpyxl
import io
import numpy as np
import pylab as pl
 
def rgb2gray(rgb):
    r, g, b = rgb[:,:,0], rgb[:,:,1], rgb[:,:,2]
    gray = 0.2989 * r + 0.5870 * g + 0.1140 * b
    return gray
#randshapes = 3 #that variable was initialy randomly set up
counter = 1

#Defining a variable to hol da list
Measures = ["Fractal dimension"]

for fractal_cicle in range(randshapes):
  
  path = "/content/lienzo_particula_cutted"+str(counter)+".png"
  image=rgb2gray(pl.imread(path))
  
  # finding all the non-zero pixels
  pixels=[]
  for i in range(image.shape[0]):
    for j in range(image.shape[1]):
      if image[i,j]>0:
        pixels.append((i,j))
  
  Lx=image.shape[1]
  Ly=image.shape[0]
  #print (Lx, Ly)
  pixels=pl.array(pixels)
  #print (pixels.shape)
  
  #computing the fractal dimension
  #considering only scales in a logarithmic list
  scales=np.logspace(0.01, 1, num=10, endpoint=False, base=2)
  Ns=[]
  
  # looping over several scales
  for scale in scales:
    #print ("======= Scale :",scale)
    # computing the histogram
    H, edges=np.histogramdd(pixels, bins=(np.arange(0,Lx,scale),np.arange(0,Ly,scale)))
    Ns.append(np.sum(H>0))
  
  # linear fit, polynomial of degree 1
  coeffs=np.polyfit(np.log(scales), np.log(Ns), 1)
  
  pl.plot(np.log(scales),np.log(Ns), 'o', mfc='none')
  pl.plot(np.log(scales), np.polyval(coeffs,np.log(scales)))
  pl.xlabel('log $\epsilon$')
  pl.ylabel('log N')
  pl.savefig('Coeff graph'+str(counter)+'.pdf')

  print ("The Hausdorff dimension is", -coeffs[0]) #the fractal dimension is the OPPOSITE of the fitting coefficient
  np.savetxt('/content/lienzo_particula_fracdim'+str(counter)+'.txt', list(zip(scales,Ns)))
  
  #Saving the calculated fractal dimension in the variable measures
  Measures.append(str(-coeffs[0]))
  print(counter)
  print(fractal_cicle)

np.savetxt('/content/raw_data_fracdim_measures.txt', Measures, fmt='%s')

#import pandas as pd
#reading the txt file
read_file = pd.read_csv (r'/content/raw_data_fracdim_measures.txt')
#converting to csv
#df_txt2csv = read_file.to_csv (r'/content/raw_data_fracdim_measures.csv', index=None)
read_file.to_csv (r'/content/raw_data_fracdim_measures.csv', index=None)


#saving csv ans xls file
#df_txt2csv.to_csv('raw_data_bis.csv', index=False)  ## line -->read_file.to_csv (r'/content/raw_data_fracdim_measures.csv', index=None)<-- already convert and saved the csv file
#io.excel.xls.writer('raw_data.xls', df)
#df_txt2csv.to_excel('raw_data.xls', index=False)
read_file.to_excel('raw_data_fracdim_measures.xls', index=False)

Despite I had a warning because of the xlwt package no longer maintaining, at the moment, the xls file still being saved.

Daniel Molina
  • 11
  • 3