121 lines
4.0 KiB
Python
121 lines
4.0 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Created on Fri Apr 6 09:58:07 2018
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@author: Armando
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"""
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import cv2
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import glob
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import ReadIM
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from skimage import exposure
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import numpy as np
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from scipy.optimize import curve_fit
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from functions_utile import draw_rectangle
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import matplotlib.pyplot as plt
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#Variable initialization
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pH_images ={}
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courbe = []
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rectangle_final = []
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pH_max_value_array = []
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file_name = []
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images = {}
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#Obtain the ph images to make the courbe (these images have already been treated by DaVis - spacially )
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working_directory = "C:\\Users\\Armando\\Desktop\\python_processing\\ph\\zero-one"
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file_name = glob.glob(working_directory + "\\*.im7")
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for e in range(len(file_name)):
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if file_name[e].find('pH') != -1:
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vbuff, vatts = ReadIM.extra.get_Buffer_andAttributeList(file_name[e])
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v_array, vbuff = ReadIM.extra.buffer_as_array(vbuff)
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img_rescaled = exposure.rescale_intensity(v_array)
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name = file_name[e].rsplit('\\', 1)[1]
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images[name] = img_rescaled
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mean = str(np.nanmean(images[name]))
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print (name + ' ' + mean)
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del(v_array,name,file_name,img_rescaled, working_directory)
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#%%
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#Make a dictonarie (pH_images) with pH value as key and the array associated as value.
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#The max pH value is also obtained
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images_list_form = list(images.items())
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pH_max_value = 0
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for e in range(len(images_list_form)):
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if images_list_form[e][0].find('pH') != -1:
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pH = images_list_form[e][0].replace('.im7','').rsplit('pH', 1)[1]
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for y in range(len(images_list_form[e][1][0])-1):
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for x in range(len(images_list_form[ e][1][0][0])-1):
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if np.isinf(images_list_form[e][1][0][y][x]):
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images_list_form[e][1][0][y][x] = 0.0001
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elif np.isnan(images_list_form[e][1][0][y][x]):
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images_list_form[e][1][0][y][x] = 0.0001
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elif images_list_form[e][1][0][y][x] ==0:
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images_list_form[e][1][0][y][x] = 0.001
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pH_images[pH] = images_list_form[e][1]
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#cv2.namedWindow("Image" + str(e),cv2.WINDOW_NORMAL) #Create the window to show image
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#cv2.imshow("Image" + str(e), pH_images[pH])
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pH_value = float(pH)
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if pH_value > pH_max_value:
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pH_max_value = pH_value
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pH_max_value_array = images_list_form[e][1][0]
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rectangle_final = [[10,10],[y-10,x-10]]
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#del(images_list_form,e,images,x,y,pH_value,pH)
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#%%
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pH_images_list = list(pH_images.items())
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courbe = []
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for e in range(len(pH_images_list)):
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normalized_photo = np.divide(pH_max_value_array,pH_images_list[e][1][0])
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courbe.append([float(pH_images_list[e][0]),np.nanmean(normalized_photo, dtype = np.float64)])
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del(e)
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#%%
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courbe.sort()
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courbe = np.array(courbe, dtype=np.float64)
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x1 = courbe[:,0]
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y1 = courbe[:,1]
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def tanh_fit(x, a, b, c, d):
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return a*(np.tanh(b*x+c)) + d
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def inverse_tanhfit(y ,a ,b ,c ,d):
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return (1/2*np.log((y+a-d)/(-y+a+d))-c)/(b)
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popt, pcov = curve_fit(tanh_fit, x1, y1,bounds=([0,0,-15,0], [1., 2.,5,1.]))
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fig = plt.figure()
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plt.title('Normalized intensity in function of pH')
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plt.xlabel('pH', color='plum')
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plt.ylabel('Normalized intensity (I/I\u2080)', color='0.5') # grayscale color
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plt.grid(b=True, which='major', color='k', linestyle='-')
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plt.grid(b=True, which='minor', color='k', linestyle='--')
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plt.plot(x1,y1, 'r.')
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x_full = np.arange(-1,15,0.01)
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y_full = np.arange(0,1,0.01)
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plt.plot(x_full, tanh_fit(x_full, *popt), '--',color = 'black',label='fit: A=%.3f, B=%.3f, C=%.3f D=%.3f \n A*tanh(B*pH+C) + D' % tuple(popt))
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#plt.plot(y_full, inverse_tanhfit(y_full, *popt), '--',color = 'blue')
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plt.legend()
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plt.show()
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x_min = 0
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x_max = courbe[len(courbe)-1][0]
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y_min = tanh_fit(x_min, *popt)
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y_max = tanh_fit(x_max, *popt)
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del(x1,y1,x_full) |