Problems in Python

Hello, how are you? I was using Python a few days ago and had no problem, yesterday I wanted to modify my code, I had a problem when compiling: UnicodeDecodeError: 'ascii' codec can not decode byte 0xf3 in position 30: ordinal not in range (128)

From what I've read they say it's a problem of accents or eñes, in my program there's nothing like that, now I copy it so they can see it, I need your help ... I'm desperate

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

"""
Created on Wed Dec 13 17:41:56 2017

@author: pc
"""



# when ddepth=-1, the output image will have the same depth as the source
# cv2.CV_64F

from matplotlib import pyplot as plt
import matplotlib.cm as cm
import matplotlib.patches as patches

import numpy as np
import cv2

from Class_2D_DoG import DoG_2D
from func_Custom import (Convert255ToBalanced, ConvertBalancedTo255)

import os
import sys
import scipy.io


#Ploting Settings
m=0.5
graph_size = (m*19.2, m*10.8)
graph_dpi = 300

if __name__ == "__main__":

#==============================================================================
#   Setting the DoG Filter
#==============================================================================

    #Set DoG Filter Parameters
    Rc = 1.5
    rR = 2.5
    rV = 0.95


    #Create Class: DoG Filter
    DoG = DoG_2D(Rc=Rc, rR=rR, rV=rV)

#==============================================================================
#   Setting First Derivative Filter    
#==============================================================================
    #First Derivate Filter: Horizontal and Veritical Directions
    dx1 = np.array([[0,0,0],[-1.0,0,1.0],[0,0,0]])
    dy1 = np.array([[0,0,0],[-1.0,0,1.0],[0,0,0]]).T

#==============================================================================
#   Setting the Folder
#==============================================================================
    #Set the folder
    main_path = os.path.dirname(sys.argv[0]) 
    pupil_name = 'p3'
    img_name = 'Flores'    

    #Get file names from AD-
    data_path_neg = os.path.join(main_path, pupil_name, img_name,'AD-')
    AD_neg  = os.listdir(data_path_neg)
    AD_neg  = AD_neg[0:13]
    AD_neg  = AD_neg[::-1]

    #Get file names from AD+
    data_path_pos = os.path.join(main_path, pupil_name, img_name, 'AD+')
    AD_pos  = os.listdir(data_path_pos)
    AD_pos  = AD_pos[0:13]

#==============================================================================
#   Processing Loop 
#==============================================================================
    n = 25
    AD = np.linspace(-3.0,+3.0, n)  
    Focus_DoG = np.zeros(n)
    Focus_D1 = np.zeros(n)

    for i in range(0,n):
        if i < 12:
            mat = scipy.io.loadmat(data_path_neg + '//' + AD_neg[i])  
            imgIn = mat['imag']
        else:
            mat = scipy.io.loadmat(data_path_pos + '//' + AD_pos[i-12])  
            imgIn = mat['imag']



        #Convert input image from [0...128...255] to [-1...0...+1] (Optional)
#        imgIn = Convert255ToBalanced(imgIn)
#        imgIn = ConvertBalancedTo255(imgIn)

        #Calculating: DoG Output
        imgOut = cv2.filter2D(imgIn, cv2.CV_64F, DoG.Filter)

        #Calculating: Focus Metric Based on DoG
        [r,c] = imgIn.shape
        Focus_DoG[i] = np.sum(np.sqrt(imgOut**2))/float(r*c)


        #Calculating: First Derivative
        Out_dx1 = cv2.filter2D(imgIn, cv2.CV_64F, dx1)
        Out_dy1 = cv2.filter2D(imgIn, cv2.CV_64F, dy1)

        #Calculating: Focus Metric Based on First Derivative
        Focus_D1[i] = np.sum(np.sqrt(Out_dx1**2 + Out_dy1**2))/float(r*c)



#==============================================================================
#   Correcting the Shift (Optional but recommended for "p3" data)
#==============================================================================

#    if pupil_name == 'p3':
#        AD = AD - 0.25
#        AD = AD[2:]
#        Focus_DoG = Focus_DoG[2:]
#        Focus_D1 = Focus_D1[2:]

#==============================================================================
#   Normalizing the Focus Metrics to the Maximum Value to make comparision.
#==============================================================================

    Focus_DoG_norm = Focus_DoG/Focus_DoG.max()
    Focus_D1_norm = Focus_D1/Focus_D1.max()

#==============================================================================
#   Plotting    
#==============================================================================

    fig, ax = plt.subplots(2,1)  
    fig.set_size_inches(graph_size)  

    ax[0].plot(AD, Focus_D1_norm,'-or', label='1D')
    ax[0].vlines(x=0, ymin=ax[0].axes.get_ylim()[0], ymax=ax[0].axes.get_ylim()[1], linestyle='--', color='k')  
    ax[0].hlines(y=1, xmin=ax[0].axes.get_xlim()[0], xmax=ax[0].axes.get_xlim()[1], linestyle='--', color='k')
    ax[0].legend()

    ax[1].plot(AD, Focus_DoG_norm,'-og', label='DoG')
    ax[1].vlines(x=0, ymin=ax[1].axes.get_ylim()[0], ymax=ax[1].axes.get_ylim()[1], linestyle='--', color='k')  
    ax[1].hlines(y=1, xmin=ax[1].axes.get_xlim()[0], xmax=ax[1].axes.get_xlim()[1], linestyle='--', color='k')    
    ax[1].legend()

    plt.show()