scientific_comp_projects/CODE/[python]thesis_old_scripts/total_calibration.py

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2021-10-29 15:16:40 +02:00
# -*- coding: utf-8 -*-
"""
Created on Thu Mar 29 12:16:17 2018
@author: That guy that turns his pen all the fucking time.
The purpose of this code is to make the spatial calibration for DaVis images. It consist in taking a doble-image, separate them in two and superimpose them.
It also calculates the magnification in the picture [pixels]<->[mm]
"""
"We tried to do this as cleanly as possible so that some PhD student in the future won't get on a plane at 2:00am and come hunt us down with a crowbar."
import datetime
import cv2
import numpy as np
import ReadIM
import matplotlib.pyplot as plt
import glob
import os
from skimage import exposure
from scipy.optimize import curve_fit
from functions_utile import draw_rectangle, image_correlation, test_homography, magnification
"""
Variables you can change in fonction of the experiment you are doing (f.e. the image to do the calibration with)
"""
working_directory = "C:\\Users\\Armando\\Desktop\\python_processing\\total_processing\\"
nbr_of_experiments = 1
"""
Variables initiation (don't touch unless you know what you are doing)
Normally, this variables are the ones we will save to process the images later :)
"""
rectangle = {}
rectangle['left'] = []
rectangle['right'] = []
"""
This are the variables we need to process the spatial calibration and pH calibration,
but once it is done we can delete them to save memory and hence go faster and farther !!
"""
images = {}
file_name = []
pH_images ={}
courbe = []
rectangle_final = []
pH_max_value_image = []
experiments = []
#Unlock this code if you want to process just one image, be wise though cause farther in the code
#you have the sections to treat spatial and pH calibration
#If you are working with DaViS here is where you write the name of the file
#vbuff, vatts = ReadIM.extra.get_Buffer_andAttributeList(working_directory + 'calibration\\calibration_spatial\\' + 'mire.im7')
#v_array, vbuff = ReadIM.extra.buffer_as_array(vbuff)
#img_rescaled = exposure.rescale_intensity(v_array)
#img_rescaled = img_rescaled[0]
#images['image_mire'] = img_rescaled
#If you are working with jpg,png or other more normal type of images
#src_img = cv2.imread("E:\\Donnees\\ArmandoBulle\\python_treatement\\PLIF_test\\calibration\\calibration_spatial\\book1.jpg")
#src_img_grey = cv2.cvtColor(src_img, cv2.COLOR_BGR2GRAY)
#cv2.imshow('Grey Image',src_img_grey) #Test to see if the image you are using is the right one and is converted to grey
#%%
"""This section of the code looks for all the files named ***pH***.im7 and ***mire***.im7 and
stores them in image dictionary either with their pHname file eith with img_mire file.
"""
file_name = glob.glob(working_directory + "calibration\\**\\*.im7")
img_rescaled = []
for e in range(len(file_name)):
if file_name[e].find('pH') != -1:
vbuff, vatts = ReadIM.extra.get_Buffer_andAttributeList(file_name[e])
v_array, vbuff = ReadIM.extra.buffer_as_array(vbuff)
img_rescaled = exposure.rescale_intensity(v_array)
img_rescaled = img_rescaled[0]
name = file_name[e].rsplit('\\', 1)[1]
images[name] = img_rescaled
elif file_name[e].find('mire') != -1:
vbuff, vatts = ReadIM.extra.get_Buffer_andAttributeList(file_name[e])
v_array, vbuff = ReadIM.extra.buffer_as_array(vbuff)
img_rescaled = exposure.rescale_intensity(v_array)
img_rescaled = img_rescaled[0]
images['image_mire'] = img_rescaled
#Cleaning the already used variables/ Memory clean up
ReadIM.DestroyBuffer(vbuff)
ReadIM.DestroyAttributeListSafe(vatts)
del(img_rescaled, file_name )
#%%
"""
This section is the center of the spatial calibration, it takes a image, ask for the spatial points
and creates the homography. It also crops the images and deduces the magnification (mm<->pixels).
Finally it gives the superposed images.
"""
#Make two images out of the double-image
#Obtain the image zone left
images['image_left_mire'] = images['image_mire'].copy()
images['image_left_mire'], rectangle['left'] = draw_rectangle(images['image_left_mire'])
#Obtain the image zone right
images['image_right_mire'] = images['image_mire'].copy()
images['image_right_mire'], rectangle['right'] = draw_rectangle(images['image_right_mire'])
#Get the magnification of the image
magnification = magnification(images['image_left_mire'])
#Obtain the homography between both images
h , status = image_correlation(images['image_left_mire'],images['image_right_mire'])
#Apply the homography transformation
images['img_final'] = test_homography(h, status, images['image_left_mire'], images['image_right_mire'])
cv2.namedWindow("Final Source Image",cv2.WINDOW_NORMAL)
cv2.imshow("Final Source Image", images['img_final'])
cv2.waitKey(0)
cv2.destroyAllWindows()
Super_position_images = images['img_final'].copy()
#Cleaning the already used variables/ Memory clean up
del(images['image_left_mire'],images['image_right_mire'],images['image_mire'], images['img_final'])
#%%
"""pH image processing
This is where the pH_images get normalized by the max value, which is get from the higher pH value
The first boucle uses the spatial calibration to superpose the pH images. It also gets searches for
the highest pH and hence the highest intensity.
The second part divides the images by the highest pH image, to get values from 0<x<=1
"""
titles = list(images.items())
pH_max_value = 0
for e in range(len(titles)):
if titles[e][0].find('pH') != -1:
pH = titles[e][0].replace('.im7','').rsplit('pH', 1)[1]
pH_images[pH] = titles[e][1]
pH_images_left = pH_images[pH][rectangle['left'][0][1]:rectangle['left'][1][1],rectangle['left'][0][0]:rectangle['left'][1][0]]
pH_images_right = pH_images[pH][rectangle['right'][0][1]:rectangle['right'][1][1],rectangle['right'][0][0]:rectangle['right'][1][0]]
pH_images_right = cv2.warpPerspective(pH_images_right, h, (pH_images_left.shape[1],pH_images_left.shape[0]))
pH_ratio = np.divide(pH_images_right,pH_images_left)
for y in range(len(pH_ratio)-1):
for x in range(len(pH_ratio[0])-1):
if np.isinf(pH_ratio[y][x]):
pH_ratio[y][x] = 0
elif np.isnan(pH_ratio[y][x]):
pH_ratio[y][x] = 0
pH_images[pH] = pH_ratio
pH_value = float(pH)
if pH_value > pH_max_value:
pH_max_value = pH_value
pH_max_value_image = pH_ratio
del(pH_images_left, pH_images_right, pH, images, pH_ratio, titles)
rectangle_final = draw_rectangle(pH_max_value_image)[1]
cv2.waitKey(0)
cv2.destroyAllWindows()
pH_max_value_image = np.reciprocal(pH_max_value_image)
for e in pH_images.keys():
pH_images[e] = np.divide(np.reciprocal(pH_images.get(e)),pH_max_value_image)
pH_images[e] = pH_images[e][rectangle_final[0][1]:rectangle_final[1][1],rectangle_final[0][0]:rectangle_final[1][0]]
if np.nanmean(pH_images[e], dtype = np.float64) <= 1:
courbe.append([float(e),np.nanmean(pH_images[e], dtype = np.float64)])
elif np.nanmean(pH_images[e], dtype = np.float64) >1 and np.nanmean(pH_images[e], dtype = np.float64) <10 :
courbe.append([float(e),1])
del(pH_images, pH_max_value, pH_max_value_image,)
#%%
courbe = np.array(courbe, dtype=np.float64)
#courbe1 = np.array(courbe1, dtype=np.float64)
courbe = np.sort(courbe,0)
x1 = courbe[:,0]
y1 = courbe[:,1]
def tanh_fit(x, a, b, c, d):
return a*(np.tanh(b*x+c)) + d
def inverse_tanhfit(y ,a ,b ,c ,d):
return (1/2*np.log((y+a-d)/(-y+a+d))-c)/(b)
popt, pcov = curve_fit(tanh_fit, x1, y1,bounds=([0,0,-15,0], [1., 2.,5,1.]))
fig = plt.figure()
plt.title('Normalized intensity in function of pH')
plt.xlabel('pH', color='plum')
plt.ylabel('Normalized intensity (I/I\u2080)', color='0.5') # grayscale color
plt.grid(b=True, which='major', color='k', linestyle='-')
plt.grid(b=True, which='minor', color='k', linestyle='--')
plt.plot(x1,y1, 'r.')
x_full = np.arange(-1,15,0.01)
y_full = np.arange(0,1,0.01)
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))
#plt.plot(y_full, inverse_tanhfit(y_full, *popt), '--',color = 'blue')
plt.legend()
plt.show()
x_min = 0
x_max = courbe[len(courbe)-1][0]
y_min = tanh_fit(x_min, *popt)
y_max = tanh_fit(x_max, *popt)
del(x1,y1,x_full)
#%%
#%%
with open(working_directory + 'workfile.txt', mode='a+') as file:
file.write('Printing the date recorded at %s.\n' % (datetime.datetime.now()))
file.write('The homography matrix(3x3) is \n')
[[file.write('%f \n' % x) for x in row]for row in h]
file.write("This is the magnification %f \n" % magnification)
file.write("This are the coordinates for image 1 crop upper corner : x = %d and y = %d \n" %(rectangle['left'][0][1],rectangle['left'][0][0]))
file.write("lower corner : x = %d and y = %d \n" %(rectangle['left'][1][1],rectangle['left'][1][0]))
file.write("This are the coordinates for image 2 crop upper corner : x = %d and y = %d \n" %(rectangle['right'][0][1],rectangle['right'][0][0]))
file.write("lower corner : x = %d and y = %d \n" %(rectangle['right'][1][1],rectangle['right'][1][0]))
file.write('The fitting curve with form : A*tanh(B*pH+C) + D \n')
[[file.write('%f \n' % x) for x in popt]]
file.write('------------------------------------*-------------------*------------------------------------\n')
cv2.imwrite(working_directory + 'Super_position_images.tiff', Super_position_images)
plt.savefig( working_directory +'Normalized intensity in function of pH.pdf')
plt.savefig( working_directory + 'Normalized intensity in function of pH.png' )
#%%
"""
Traitement des données
"""
for root, dirs, files in os.walk(working_directory + "donnees"):
for name in dirs:
experiments.append(name)
experiments_files = {}
for e in range(len(experiments)):
for root, dirs, files in os.walk(working_directory + "donnees\\"+ experiments[e]+ '\\'):
experiments_files[experiments[e]] = files
for i in range(len(experiments_files[experiments[e]])-1):
vbuff, vatts = ReadIM.extra.get_Buffer_andAttributeList(working_directory + "donnees\\"+experiments[e]+'\\'+ experiments_files[experiments[e]][i])
v_array, vbuff = ReadIM.extra.buffer_as_array(vbuff)
img_rescaled = exposure.rescale_intensity(v_array)
image_experiment = img_rescaled[0]
experiment_left = image_experiment.copy()
experiment_left = experiment_left[rectangle['left'][0][1]:rectangle['left'][1][1],rectangle['left'][0][0]:rectangle['left'][1][0]]
experiment_right = image_experiment.copy()
experiment_right = experiment_right[rectangle['right'][0][1]:rectangle['right'][1][1],rectangle['right'][0][0]:rectangle['right'][1][0]]
experiment_right = cv2.warpPerspective(experiment_right, h, (experiment_left.shape[1],experiment_left.shape[0]))
experiment_ratio = np.divide(experiment_right,experiment_left)
#experiment_ratio2 = np.divide(experiment_left,experiment_right)
for y in range(len(experiment_ratio)-1):
for x in range(len(experiment_ratio[0])-1):
if np.isinf(experiment_ratio[y][x]):
experiment_ratio[y][x] = 0
elif np.isnan(experiment_ratio[y][x]):
experiment_ratio[y][x] = 0
elif experiment_ratio[y][x] < 1:
experiment_ratio[y][x] = 1
#for y in range(len(experiment_ratio)-1):
# for x in range(len(experiment_ratio[0])-1):
# if np.isinf(experiment_ratio[y][x]):
# experiment_ratio2[y][x] = 0
# elif np.isnan(experiment_ratio[y][x]):
# experiment_ratio2[y][x] = 0
# elif experiment_ratio[y][x] < 1:
# experiment_ratio2[y][x] = 1
path = working_directory + "donnees\\"+ 'results\\' + experiments[e]
foto_name = path + '\\' + experiments_files[experiments[e]][i].rsplit('.')[0]
#foto_name2 = path + '\\' + experiments_files[experiments[e]][i] + '2.tiff'
if not os.path.exists(path):
os.makedirs(path)
#experiment_ratio = experiment_ratio/255.
cv2.imshow('Grey Image1',experiment_ratio)
experiment_ratio2 =np.reciprocal(experiment_ratio)
cv2.imshow('Grey Image3',experiment_ratio)
experiment_ratio=experiment_ratio.astype(np.float32)
experiment_ratio=cv2.cvtColor(experiment_ratio , cv2.COLOR_GRAY2BGR)
#cv2.imwrite(foto_name+ '.tiff', experiment_ratio)
#cv2.imwrite(foto_name+ '.png', experiment_ratio)
#cv2.imwrite(foto_name+ '.jpg', experiment_ratio)
cv2.imshow('Grey Image2',experiment_ratio)
#experiment_ratio2=experiment_ratio2.astype(np.float32)
#experiment_ratio2=cv2.cvtColor(experiment_ratio2 , cv2.COLOR_GRAY2BGR)
#cv2.imwrite(foto_name2, experiment_ratio)