From 2b2dd69774c41e33edd74c204009e9c40bcb67b0 Mon Sep 17 00:00:00 2001 From: jdber1 Date: Wed, 20 Mar 2024 16:54:41 +1100 Subject: [PATCH] first commit --- .DS_Store | Bin 0 -> 10244 bytes LICENSE | 340 +++ README.md | 23 + conan.py | 220 ++ conan.pyc | Bin 0 -> 2456 bytes experimental data set/10.bmp | Bin 0 -> 444510 bytes experimental data set/111.031693.bmp | Bin 0 -> 559198 bytes experimental data set/113.66.bmp | Bin 0 -> 559198 bytes experimental data set/113.98.bmp | Bin 0 -> 559198 bytes experimental data set/114.47.bmp | Bin 0 -> 559198 bytes experimental data set/115.00.bmp | Bin 0 -> 559198 bytes experimental data set/115.553909.bmp | Bin 0 -> 559198 bytes experimental data set/115.714851.bmp | Bin 0 -> 559198 bytes experimental data set/118.174171.bmp | Bin 0 -> 559198 bytes .../2-s@M@Z-120.321945190429.bmp | Bin 0 -> 559198 bytes .../2-s@M@Z-121.057571411132.bmp | Bin 0 -> 559198 bytes .../2-s@M@Z-122.639770507812.bmp | Bin 0 -> 559198 bytes experimental data 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--- /dev/null +++ b/LICENSE @@ -0,0 +1,340 @@ + GNU GENERAL PUBLIC LICENSE + Version 2, June 1991 + + Copyright (C) 1989, 1991 Free Software Foundation, Inc., + 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The licenses for most software are designed to take away your +freedom to share and change it. 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See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License along + with this program; if not, write to the Free Software Foundation, Inc., + 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. + +Also add information on how to contact you by electronic and paper mail. + +If the program is interactive, make it output a short notice like this +when it starts in an interactive mode: + + Gnomovision version 69, Copyright (C) year name of author + Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. 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If this is what you want to do, use the GNU Lesser General +Public License instead of this License. + diff --git a/README.md b/README.md new file mode 100644 index 0000000..c806602 --- /dev/null +++ b/README.md @@ -0,0 +1,23 @@ +# Conan-ML + +Conan-ML software is a tool engineered for precise and automated +processing and analysis of contact angle data. The software utilizes image +processing algorithms to analyze captured images of droplets on surface. +Current implmentation is only optimised for high angle systems. + +# Dependencies + +Conan-ML is written to run on python3.6+. The packages required to run +conan ML must be installed in your Python environment. These are included +listed in requirements.txt and can be install using + +pip install -r requirements.txt + +# Usage + +To load Conan-ML GUI run the conan.py file using + +python conan.py + +However, functions from each file of the modules directory can be called +for a more customised approach. diff --git a/conan.py b/conan.py new file mode 100644 index 0000000..3cef411 --- /dev/null +++ b/conan.py @@ -0,0 +1,220 @@ +#!/usr/bin/env python +#coding=utf-8 +from __future__ import unicode_literals +from __future__ import print_function +# from modules.classes import ExperimentalDrop, DropData, Tolerances +# from modules.static_setup_class import ExperimentalSetup +# # from modules.ui import initialise_ui +# from modules.user_interface import call_user_input +# # from modules.load import load_data +# from modules.extract_data import extract_drop_profile +# from modules.initialise_parameters import initialise_parameters +# # from modules.fit_data import fit_raw_experiment +# # from modules.user_set_regions + + +from modules.classes import ExperimentalSetup, ExperimentalDrop, DropData, Tolerances +#from modules.PlotManager import PlotManager +from modules.ExtractData import ExtractedData + +from modules.user_interface import call_user_input +from modules.read_image import get_image +from modules.select_regions import set_drop_region,set_surface_line, correct_tilt +from modules.extract_profile import extract_drop_profile +from modules.extract_profile import image_crop +from modules.initialise_parameters import initialise_parameters +#from modules.analyse_needle import calculate_needle_diameter +#from modules.fit_data import fit_experimental_drop +from modules.fits import perform_fits +#from modules.generate_data import generate_full_data +# from modules. import add_data_to_lists + +import matplotlib.pyplot as plt + + +import os +import numpy as np +import tkinter as tk +from tkinter import font as tkFont + +import timeit +import time + +np.set_printoptions(suppress=True) +np.set_printoptions(precision=3) + +DELTA_TOL = 1.e-6 +GRADIENT_TOL = 1.e-6 +MAXIMUM_FITTING_STEPS = 10 +OBJECTIVE_TOL = 1.e-4 +ARCLENGTH_TOL = 1.e-6 +MAXIMUM_ARCLENGTH_STEPS = 10 +NEEDLE_TOL = 1.e-4 +NEEDLE_STEPS = 20 + + + +def main(): + clear_screen() + fitted_drop_data = DropData() + tolerances = Tolerances( + DELTA_TOL, + GRADIENT_TOL, + MAXIMUM_FITTING_STEPS, + OBJECTIVE_TOL, + ARCLENGTH_TOL, + MAXIMUM_ARCLENGTH_STEPS, + NEEDLE_TOL, + NEEDLE_STEPS) + user_inputs = ExperimentalSetup() + + call_user_input(user_inputs) + + if user_inputs.ML_boole == True: + from modules.ML_model.prepare_experimental import prepare4model_v03, experimental_pred + import tensorflow as tf + model_path = './modules/ML_model/' + model = tf.keras.models.load_model(model_path) + #model = tf.keras.Model.load_weights(model_path).expect_partial() + + n_frames = user_inputs.number_of_frames + extracted_data = ExtractedData(n_frames, fitted_drop_data.parameter_dimensions) + raw_experiment = ExperimentalDrop() + + #if user_inputs.interfacial_tension_boole: + # plots = PlotManager(user_inputs.wait_time, n_frames) + + #get_image(raw_experiment, user_inputs, -1) #is this needed? + + for i in range(n_frames): + print("\nProcessing frame %d of %d..." % (i+1, n_frames)) + input_file = user_inputs.import_files[i] + print("\nProceccing " + input_file) + time_start = timeit.default_timer() + raw_experiment = ExperimentalDrop() + get_image(raw_experiment, user_inputs, i) # save image in here... + set_drop_region(raw_experiment, user_inputs) + # extract_drop_profile(raw_experiment, user_inputs) + extract_drop_profile(raw_experiment, user_inputs) + + +# croppedImage = image_crop(raw_experiment.image, user_inputs.drop_region) +# xx = np.array([0,croppedImage.shape[1]]) + +# plt.imshow(croppedImage, origin='upper', cmap = 'gray') +# # plt.plot(contour_x,contour_y,"--",color="white",linewidth = 2.0) +# plt.plot(raw_experiment.drop_data[:,0],-raw_experiment.drop_data[:,1],color="white",linewidth=2.0) +# plt.plot(xx,raw_experiment.surface_data(xx),'r--',linewidth=2.0) + +# plt.show() + + # plt.hold() + if i == 0: + extracted_data.initial_image_time = raw_experiment.time + filename = user_inputs.filename[:-4] + '_' + user_inputs.time_string + ".csv" + export_filename = os.path.join(user_inputs.directory_string, filename) + #print("HERE!!") + set_surface_line(raw_experiment, user_inputs) #fits performed here if baseline_method is User-selected + + # these methods don't need tilt correction + if user_inputs.baseline_method == "Automated": + if user_inputs.tangent_boole == True or user_inputs.second_deg_polynomial_boole == True or user_inputs.circle_boole == True or user_inputs.ellipse_boole == True: + perform_fits(raw_experiment, tangent=user_inputs.tangent_boole, polynomial=user_inputs.second_deg_polynomial_boole, circle=user_inputs.circle_boole,ellipse=user_inputs.ellipse_boole) + + # YL fit and ML model need tilt correction + if user_inputs.ML_boole == True or user_inputs.YL_boole == True: + correct_tilt(raw_experiment, user_inputs) + extract_drop_profile(raw_experiment, user_inputs) + if user_inputs.baseline_method == "Automated": + set_surface_line(raw_experiment, user_inputs) + # experimental_setup.baseline_method == 'User-selected' should work as is + + #raw_experiment.contour = extract_edges_CV(raw_experiment.cropped_image, threshold_val=raw_experiment.ret, return_thresholed_value=False) + #experimental_drop.drop_contour, experimental_drop.contact_points = prepare_hydrophobic(experimental_drop.contour) + + if user_inputs.YL_boole == True: + print('Performing YL fit...') + perform_fits(raw_experiment, YL=user_inputs.YL_boole) + if user_inputs.ML_boole == True: + pred_ds = prepare4model_v03(raw_experiment.drop_contour) + ML_predictions, timings = experimental_pred(pred_ds, model) + raw_experiment.contact_angles['ML model'] = {} + raw_experiment.contact_angles['ML model']['angles'] = [ML_predictions[0,0],ML_predictions[1,0]] + raw_experiment.contact_angles['ML model']['timings'] = timings + + extracted_data.contact_angles = raw_experiment.contact_angles # DS 7/6/21 + + #print(extracted_data.contact_angles) #for the dictionary output + print('Extracted outputs:') + for key1 in extracted_data.contact_angles.keys(): + for key2 in extracted_data.contact_angles[key1].keys(): + print(key1+' '+key2+': ') + print(' ',extracted_data.contact_angles[key1][key2]) + print() + + # LMF least-squares fit (was commented out before) + #fit_experimental_drop(raw_experiment, fitted_drop_data, user_inputs, tolerances) + #generate_full_data(extracted_data, raw_experiment, fitted_drop_data, user_inputs, i) + #data_vector = extracted_data.time_IFT_vol_area(i) + #if user_inputs.interfacial_tension_boole: + # plots.append_data_plot(data_vector, i) + #if i != (n_frames - 1): + # time_loop = timeit.default_timer() - time_start + # pause_wait_time(time_loop, user_inputs.wait_time) + + + extracted_data.export_data(input_file,filename,i) +# cheeky_pause() + +def clear_screen(): + os.system('cls' if os.name == 'nt' else 'clear') + +def pause_wait_time(elapsed_time, requested_time): + if elapsed_time > requested_time: + print('WARNING: Fitting took longer than desired wait time') + else: + time.sleep(requested_time - elapsed_time) + +def cheeky_pause(): + import Tkinter + import tkMessageBox + import cv2 + # cv2.namedWindow("Pause") + # while 1: + # k = cv2.waitKey(1) & 0xFF + # if (k==27): + # break + #root = Tkinter.Tk() + # B = Tkinter.Button(top, text="Exit",command = cv2.destroyAllWindows()) + # B = Tkinter.Button(root, text="Exit",command = root.destroy()) + # + # B.pack() + # root.mainloop() + + root = Tkinter.Tk() + frame = Tkinter.Frame(root) + frame.pack() + + button = Tkinter.Button(frame) + button['text'] ="Good-bye." + button['command'] = root.destroy()#close_window(root) + button.pack() + + root.mainloop() + +def quit_(root): + root.quit() + +#def close_window(root): +# root.destroy() + + +if __name__ == '__main__': + main() + root = tk.Tk() + # quit button + buttonFont = tkFont.Font(family='Helvetica', size=48, weight='bold') #This isn't working for some reason (??) + quit_button = tk.Button(master=root, font=buttonFont,text='Quit',height=4,width=15, + command=lambda: quit_(root),bg='blue',fg='white',activeforeground='white',activebackground='red') + quit_button.pack() + root.mainloop() diff --git a/conan.pyc b/conan.pyc new file mode 100644 index 0000000000000000000000000000000000000000..15c073f22ce7ee7af4e55ebc79db0094a74e9de5 GIT binary patch literal 2456 zcmZ`)OK%)S5U!c^Yn}Bgb`r-#qH|Fn@pR=T4VK ze>MERM>Q&LIYc4;9Wq3*L%u@}%dW^S`EDiG$kZsFB7chFY4WEjo*{pxTALy>OYt1} zbCokj7tFk=pMMhVv zl0p*MC@@i>RTktytP%R?C|>SGR_Tc##mj+&S(I6p>!PrHE@s`RKyDG|*1z`UTTj1! z+}`Sc^~X=&{q@U_TM_5d|6=WjyV)F`acF>q_~Lv)0#Q)H*f&XAoYM1@#Twc{B&g8eDkQjL4%HJWEW^C z_D8Typ)4OYbv$35=>M#C98uEEKg 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