185 lines
6.6 KiB
Python
185 lines
6.6 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Created on Thu Jun 28 15:30:23 2018
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@author: Armando
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"""
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#import os
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import ReadIM
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import numpy as np
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import matplotlib.pyplot as plt
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#import matplotlib.animation as animation
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#import glob
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from matplotlib.colors import LogNorm
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#from matplotlib.animation import FFMpegWriter
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#from skimage.filters.rank import mean_bilateral
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#from skimage.morphology import disk
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#import scipy.misc
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from skimage import exposure
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#Courbe fit values - as well as the maximum and minimum values for the inverse fit function
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global a, b, c, d
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a = 0.316617
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b = 1.26288
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c = -7.70744
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d = 0.6722
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y_min = 0.36
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y_max = 0.98
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#Numerical to physical equivalences
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nappe_laser = 0.250 #Thickness of laser en mm
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dimension_pixel = 0.026244 #Equivalence mm/px
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#Conversion to SI units
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dimension_pixel_m = dimension_pixel * 10**(-3) #Equivalence mm/px
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nappe_laser_m = nappe_laser * 10**(-3) #Thickness of laser en m
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surface_pixel = dimension_pixel_m**2 #Surface equivalence of a pixel (m^2)
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#The working directory, where all the images are stored
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working_directory = 'D:\\python_processing\\imagesV2\\eauXG\\'
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#help to accelerate script by not showing images
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#Plotting options
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global interpolation, vmax_value, vmin_value, colormap
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colormap = plt.get_cmap('viridis')
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vmin_value = 0.001 #Minimum value for the pixel value of a processed image
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vmax_value = 0.6 #Maximum value for the pixel value of a processed image
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interpolation = 'bicubic' #Output image interpolation (see matplotlib interpolations for more information)
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origin = []
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#Fit-inverse function
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def inverse_tanhfit(y):
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return (1/2*np.log((a+y-d)/(a-y+d))-c)/(b)
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cut_image_y0 = 0 #The borders that will be cropped from the image [y0,yf,x0,xf]
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cut_image_yf = 0 #xf and yf, are the values to be rested from the total images(hence if 0 the max values are used)
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cut_image_x0 = 0
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cut_image_xf = 0
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radius = 0
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r=[]
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#The experiment and image to process (only one of each will be used since it is a test)
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working_dir = 'C:\\Users\\Armando\\Desktop\\conf\\'
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image_to_process = 'B00210.im7'
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vbuff, vatts = ReadIM.extra.get_Buffer_andAttributeList(working_dir+ image_to_process)
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v_array, vbuff = ReadIM.extra.buffer_as_array(vbuff)
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original_array = v_array[1][cut_image_y0:len(v_array[0])-cut_image_yf,cut_image_x0:len(v_array[0][0])-cut_image_xf]
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original_array = np.array(original_array,dtype = float)
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modif_array = np.array(original_array,dtype = float)
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original_array = exposure.rescale_intensity(original_array)
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fig, ax = plt.subplots()
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#
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plt.rc('text', usetex=True)
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plt.rc('font', family='serif')
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plt.rcParams['figure.figsize'] = (10.0, 6.0)
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plt.rcParams['font.size'] = 24
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plt.rcParams['lines.linewidth'] = 2
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plt.rcParams['lines.markersize'] = 24
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plt.rcParams['xtick.direction'] = 'out'
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plt.rcParams['ytick.direction'] = 'out'
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plt.rcParams['legend.fontsize'] = 24
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plt.rcParams['axes.titlesize'] = 24
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plt.rcParams['axes.labelsize'] = 24
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#
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vmin = 0.05
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vmax = 0.8
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v =[0-17.8423, 33.59-17.8423, 44.88-6.72752, 0-6.72752]
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im = ax.imshow(original_array, cmap=colormap, origin='upper', extent = v, norm = LogNorm(vmin , vmax))
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plt.ylabel('Z (mm)')
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plt.axis('scaled')
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#ax.set_yticks([0, 44.88,10])
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plt.xlabel('X (mm)')
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cbar = fig.colorbar(im, ticks=[vmin, vmax])
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cbar.ax.set_yticklabels(['Low', 'High']) # horizontal colorbar
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plt.tight_layout()
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plt.show()
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#%%
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vbuff, vatts = ReadIM.extra.get_Buffer_andAttributeList(working_dir+ image_to_process)
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v_array, vbuff = ReadIM.extra.buffer_as_array(vbuff)
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original_array = v_array[0][cut_image_y0:len(v_array[0])-cut_image_yf,cut_image_x0:len(v_array[0][0])-cut_image_xf]
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original_array = np.array(original_array,dtype = float)
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modif_array = np.array(original_array,dtype = float)
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#original_array = exposure.rescale_intensity(original_array)
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for y in range(len(modif_array)-1):
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for x in range(len(modif_array[0])-1):
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if modif_array[y][x] >= y_max:
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modif_array[y][x] = 9
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elif modif_array[y][x] <= y_min:
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modif_array[y][x] = 9
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else:
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modif_array[y][x] = inverse_tanhfit(modif_array[y][x])
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#The CO2 concentration is then obtained from the pH value
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#modif_array = 207608319.9386*np.power(modif_array,-9.7399) #Concentration CO2 en g/m^3 (equal to mg/l)(from measures made by Tom LACASSAGNE)
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modif_array = 115467070469*np.exp(-4.63*modif_array) #Concentration CO2 en g/m^3 (equal to mg/l)(theoretical values)
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modif_array[modif_array<8e-13] = 0
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modif_array[modif_array>1] = 0
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#The values under 0.1 and over 40 (g/m^3) are considered noise and hence turned to 0
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cut_image1 = modif_array[0:len(modif_array),0:len(modif_array[0])//2]
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cut_image2 = np.fliplr(modif_array[0:len(modif_array),len(modif_array[0])//2:len(modif_array[0])-1])
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#Then we take the mean value for both images.
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modif_array2 = np.mean(np.array([cut_image1,cut_image2]), axis=0,dtype=np.float64)
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#This section, creates the r-vector (which have the same size as cutted images)
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if r == []:
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for k in range(len(modif_array2[0])):
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radius += dimension_pixel_m
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r.append(radius)
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#Multiplication of the image [C] by the {r} radius vector
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modif_array2 = (modif_array2) @ r
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#Final calculation to obtain the mass of CO2 in the image
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mass_co2 = (surface_pixel * np.pi * 2 * modif_array2.sum())/nappe_laser_m #The mass is obtained in (g)
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#%%This is the second section that will be used to plot the data obtained
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modif_array2 = modif_array.copy()
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#modif_array = exposure.rescale_intensity(modif_array)
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fig, ax = plt.subplots()
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#
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plt.rc('text', usetex=True)
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plt.rc('font', family='serif')
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plt.rcParams['figure.figsize'] = (10.0, 6.0)
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plt.rcParams['font.size'] = 24
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plt.rcParams['lines.linewidth'] = 2
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plt.rcParams['lines.markersize'] = 24
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plt.rcParams['xtick.direction'] = 'out'
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plt.rcParams['ytick.direction'] = 'out'
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plt.rcParams['legend.fontsize'] = 24
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plt.rcParams['axes.titlesize'] = 24
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plt.rcParams['axes.labelsize'] = 24
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#
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v =[0-5.95617, 11.3637-5.95617, 27.976, 0]
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im = ax.imshow(modif_array, cmap=colormap, origin='upper',
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norm = LogNorm(vmin = 0.00001, vmax = 1), extent = v)
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plt.ylabel('Z (mm)')
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plt.axis('scaled')
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#ax.set_yticks([0, 44.88,10])
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plt.xlabel('X (mm)')
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cbar = fig.colorbar(im, ticks=[0.00001, 1] )
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cbar.ax.set_yticklabels(['Low', 'High']) # horizontal colorbar
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plt.tight_layout()
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plt.show()
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