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MARTA_2.5.py
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MARTA_2.5.py
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# coding: utf-8
import time as tm
import os
import sys
import cv2
import imutils
import numpy as np
import matplotlib.pyplot as plt
import warnings
import PIL
from datetime import datetime, timezone
from tkinter import *
from tkinter.ttk import Progressbar
from tkinter import ttk
from tkinter.font import BOLD
from tkinter import simpledialog
from PIL import ImageTk, Image
class ToolTip(object):
def __init__(self, widget):
self.widget = widget
self.tipwindow = None
self.id = None
self.x = self.y = 0
def showtip(self, text):
"Display text in tooltip window"
self.text = text
if self.tipwindow or not self.text:
return
x, y, cx, cy = self.widget.bbox("insert")
x = x + self.widget.winfo_rootx() + 57
y = y + cy + self.widget.winfo_rooty() +27
self.tipwindow = tw = Toplevel(self.widget)
tw.wm_overrideredirect(1)
tw.wm_geometry("+%d+%d" % (x, y))
label = Label(tw, text=self.text, justify=LEFT,
background="#ffffe0", relief=SOLID, borderwidth=1,
font=("tahoma", "8", "normal"))
label.pack(ipadx=1)
def hidetip(self):
tw = self.tipwindow
self.tipwindow = None
if tw:
tw.destroy()
def CreateToolTip(widget, text):
toolTip = ToolTip(widget)
def enter(event):
toolTip.showtip(text)
def leave(event):
toolTip.hidetip()
widget.bind('<Enter>', enter)
widget.bind('<Leave>', leave)
class MyWindow:
def __init__(self, win):
self.master = win
self.lbl1=Label(win, text='Number of images \n(1 or 3)')
self.t1=Entry(win,textvariable=StringVar(window, value='3'), width=5, justify='center')
self.lbl1.grid(column=4,row=0, sticky=E)
self.t1.grid(column=5,row=0,sticky=W)
CreateToolTip(self.t1, text = 'One merged input file (1) or three input files (3)')
self.lbl2=Label(win, text='File label pattern \n(no ext)')
self.t2=Entry(window,textvariable=StringVar(window, value='a'), width=5, justify='center')
self.lbl2.grid(column=2,row=0, sticky=E)
self.t2.grid(column=3,row=0, sticky=W)
CreateToolTip(self.t2, text = 'Pattern filename with no extension.')
self.lb_33=Label(win, text='Input extension', justify= RIGHT)
self.t33=Entry(window,textvariable=StringVar(window, value='.tif'), width=5, justify='center')
self.lb_33.grid(column=6,row=0, sticky=E)
self.t33.grid(column=7,row=0, sticky=W)
self.lb_34=Label(win, text='Output extension')
self.t34=Entry(window,textvariable=StringVar(window, value='.tif'), width=5)
self.lb_34.grid(column=6,row=1, sticky=E)
self.t34.grid(column=7,row=1, sticky=W)
self.lbl4=Label(win, text='scaling (units/pixel)')
self.t4=Entry(window,textvariable=StringVar(window, value='0.21'), width=5, justify='center')
self.lbl4.grid(column=4,row=1, sticky=E)
self.t4.grid(column=5,row=1, sticky=W)
self.selected = StringVar()
rad1 = Radiobutton(window,text='Automatic', value='automatic', variable=self.selected)
rad2 = Radiobutton(window,text='Supervised', value='supervised', variable=self.selected)
self.selected.set("automatic")
CreateToolTip(rad1, text = 'Select for full automated processing')
CreateToolTip(rad2, text = 'Select for individual decision for all detections')
btn2 = Button(window, text="Generate Automatic mask", font= "bold", command=self.run, height=2, width=25)
btn3 = Button(window, text=" Generate Manual mask ", command=self.say_hi)
btn10 = Button(window, text=" Exit ", command=self.ex)
previewCM = Button(window, text="Preview \nCardiomyocytes", bg="#10FF10", command=self.sercaORG, width=15)
previewCx = Button(window, text="Preview \nConnexin 43", bg="#FFFFFF", command=self.cxORG, width=15)
previewWGA = Button(window, text="Preview \nWGA", bg="#F08080", command=self.wgaORG, width=15)
previewtres = Button(window, text="Preview cell masks", bg="#FFFFFF", command=self.sumar_canales, width=25)
rad1.grid(column=0, row=1)
rad2.grid(column=1, row=1)
btn2.grid(column=6, row=26, columnspan=2, rowspan=2)
btn3.grid(column=0,row=36, columnspan=2)
btn10.grid(column=4, row=27)
previewCM.grid(row=8, column=8)
previewCx.grid(row=9, column=8)
previewWGA.grid(row=10, column=8)
previewtres.grid(row=11, column=6, columnspan=2)
CreateToolTip(btn2, text = 'Click to run automated processing of input images with the parameter set.')
CreateToolTip(btn3, text = 'Click to create a manual delineated mask of the input images. \n Double click to generate vertices. \n Press ESC to go to next subimage in case of split. \n The output will be labeled as pattern_mM (i.e. a_mM).')
self.selcomb = StringVar()
rad21 = Radiobutton(window,text='Add', value='addch', variable=self.selcomb)
rad22 = Radiobutton(window,text='Independent', value='indepch', variable=self.selcomb)
self.selcomb.set("addch")
rad21.grid(column=0, row=2)
rad22.grid(column=1, row=2)
self.chk1_state = BooleanVar()
self.chk1_state.set(False)
chk1 = Checkbutton(window, text='Input Mask', var=self.chk1_state)
chk1.grid(column=0, row=3)
CreateToolTip(chk1, text = 'Check to process a provided input mask (i.e a_mM.tif)')
self.chk2_state = BooleanVar()
self.chk2_state.set(False)
chk2 = Checkbutton(window, text='Equalize', var=self.chk2_state)
chk2.grid(column=1, row=3)
CreateToolTip(chk2, text = 'Check to make equalization of input files before processing.')
self.chk3_state = BooleanVar()
self.chk3_state.set(False)
chk3 = Checkbutton(window, text='Evaluate', var=self.chk3_state)
chk3.grid(column=2, row=3)
CreateToolTip(chk3, text = 'Check to generate evaluation using manual delineated mask (i.e a_mM.tif) \n and input files (i.e. a_c1.tif, a_c2.tif, a_c3.tif).')
lbl5=Label(win, text='Equalized \nbinary threshold')
self.t5=Entry(window,textvariable=StringVar(window, value='70'), width=5)
lbl5.grid(column=0,row=7, sticky=E)
self.t5.grid(column=1,row=7, sticky=W)
CreateToolTip(self.t5, text = '8-bit binary value for thresholding when equalization is used for channels c1 (cell) and c3 (intersticium) ')
lbl5_2=Label(win, text='Binary threshold for c4')
self.t5_2=Entry(window,textvariable=StringVar(window, value='254'), width=5)
lbl5_2.grid(column=2,row=7, sticky=E)
self.t5_2.grid(column=3,row=7, sticky=W)
CreateToolTip(self.t5_2, text = '8-bit binary value for thresholding in c4 channel')
lbl6=Label(win, text='Binary threshold \nfor c1 (CM)', bg="#10FF10")
self.t6=Entry(window,textvariable=StringVar(window, value='7'), width=5)
lbl6.grid(column=0,row=8, sticky=E)
self.t6.grid(column=1,row=8, sticky=W)
CreateToolTip(self.t6, text = '8-bit binary value for thresholding in c1 channel (Myocite fill, i.e. SERCA, F-actin,...)')
lbl7=Label(win, text='Binary threshold \nfor c2 (CX)', bg="#FFFFFF")
self.t7=Entry(window,textvariable=StringVar(window, value='12'), width=5)
lbl7.grid(column=0,row=9, sticky=E)
self.t7.grid(column=1,row=9, sticky=W)
CreateToolTip(self.t7, text = '8-bit binary value for thresholding in c2 channel (CX43 for longitudinal CM delimitation)')
lbl8=Label(win, text='Binary threshold \nfor c3 (INT)', bg="#F08080")
self.t8=Entry(window,textvariable=StringVar(window, value='9'), width=5)
lbl8.grid(column=0,row=10, sticky=E)
self.t8.grid(column=1,row=10, sticky=W)
CreateToolTip(self.t8, text = '8-bit binary value for thresholding in c3 channel (Intersticium fill, i.e. WGA)')
lbl9=Label(win, text='Noise removal matrix rank \nin c1 (CM)', bg="#10FF10")
self.t9=Entry(window,textvariable=StringVar(window, value='3'), width=5)
lbl9.grid(column=2,row=8, sticky=E)
self.t9.grid(column=3,row=8, sticky=W)
lbl_10=Label(win, text='Noise removal matrix rank \nin c2 (CX)', bg="#FFFFFF")
self.t10=Entry(window,textvariable=StringVar(window, value='4'), width=5)
lbl_10.grid(column=2,row=9, sticky=E)
self.t10.grid(column=3,row=9, sticky=W)
lb_11=Label(win, text='Noise removal matrix rank \nin c3 (INT)', bg="#F08080")
self.t11=Entry(window,textvariable=StringVar(window, value='4'), width=5)
lb_11.grid(column=2,row=10, sticky=E)
self.t11.grid(column=3,row=10, sticky=W)
lb_12=Label(win, text='Dilation matrix rank \nin c1 (CM)', bg="#10FF10")
self.t12=Entry(window,textvariable=StringVar(window, value='5'), width=5)
lb_12.grid(column=4,row=8, sticky=E)
self.t12.grid(column=5,row=8, sticky=W)
lb_13=Label(win, text='Dilation matrix rank \nin c2 (CX)', bg="#FFFFFF")
self.t13=Entry(window,textvariable=StringVar(window, value='7'), width=5)
lb_13.grid(column=4,row=9, sticky=E)
self.t13.grid(column=5,row=9, sticky=W)
lb_14=Label(win, text='Dilation matrix rank \nin c3 (INT)', bg="#F08080")
self.t14=Entry(window,textvariable=StringVar(window, value='2'), width=5)
lb_14.grid(column=4,row=10, sticky=E)
self.t14.grid(column=5,row=10, sticky=W)
lb_15=Label(win, text='Dilation iterations \nin c1 (CM)', bg="#10FF10")
self.t15=Entry(window,textvariable=StringVar(window, value='3'), width=5)
lb_15.grid(column=6,row=8, sticky=E)
self.t15.grid(column=7,row=8, sticky=W)
lb_16=Label(win, text='Dilation iterations \nin c2 (CX)', bg="#FFFFFF")
self.t16=Entry(window,textvariable=StringVar(window, value='6'), width=5)
lb_16.grid(column=6,row=9, sticky=E)
self.t16.grid(column=7,row=9, sticky=W)
lb_17=Label(win, text='Dilation iterations \nin c3 (INT)', bg="#F08080")
self.t17=Entry(window,textvariable=StringVar(window, value='3'), width=5)
lb_17.grid(column=6,row=10, sticky=E)
self.t17.grid(column=7,row=10, sticky=W)
lb_21=Label(win, text='First Filter \nminimum area (um**2)')
self.t21=Entry(window,textvariable=StringVar(window, value='100'), width=5)
lb_21.grid(column=0,row=11, sticky=E)
self.t21.grid(column=1,row=11, sticky=W)
lb_22=Label(win, text='First Filter \nminimum perimeter (um)')
self.t22=Entry(window,textvariable=StringVar(window, value='40'), width=5)
lb_22.grid(column=0,row=12, sticky=E)
self.t22.grid(column=1,row=12, sticky=W)
lb_23=Label(win, text='Second Filter \nL min (um)')
self.t23=Entry(window,textvariable=StringVar(window, value='35'), width=5)
lb_23.grid(column=0,row=13, sticky=E)
self.t23.grid(column=1,row=13, sticky=W)
lb_24=Label(win, text='Second Filter \nL max (um)')
self.t24=Entry(window,textvariable=StringVar(window, value='200'), width=5)
lb_24.grid(column=2,row=13, sticky=E)
self.t24.grid(column=3,row=13, sticky=W)
lb_25=Label(win, text='Second Filter \nW min (um)')
self.t25=Entry(window,textvariable=StringVar(window, value='5'), width=5)
lb_25.grid(column=0,row=14, sticky=E)
self.t25.grid(column=1,row=14, sticky=W)
lb_26=Label(win, text='Second Filter \nW max (um)')
self.t26=Entry(window,textvariable=StringVar(window, value='50'), width=5)
lb_26.grid(column=2,row=14, sticky=E)
self.t26.grid(column=3,row=14, sticky=W)
lb_27=Label(win, text='Second Filter \nR min')
self.t27=Entry(window,textvariable=StringVar(window, value='1'), width=5)
lb_27.grid(column=0,row=15, sticky=E)
self.t27.grid(column=1,row=15, sticky=W)
lb_270=Label(win, text='Padding scale factor')
self.t270=Entry(window,textvariable=StringVar(window, value='1'), width=5)
lb_270.grid(column=2,row=11, sticky=E)
self.t270.grid(column=3,row=11, sticky=W)
CreateToolTip(self.t270, text = '0 for no padding, 1 or 2 are recommended to compensate noise removal effects in automated mask generation')
lb_28=Label(win, text='Gamma correction')
self.t28=Entry(window,textvariable=StringVar(window, value='0.5'), width=5)
lb_28.grid(column=0,row=16, sticky=E)
self.t28.grid(column=1,row=16, sticky=W)
CreateToolTip(self.t28, text = 'Set < 1 to obtain brighter merged output or > 1 to obtain darker merged image output')
lb_29=Label(win, text='Big scale \nbar lenght (um)')
self.t29=Entry(window,textvariable=StringVar(window, value='100'), width=5)
lb_29.grid(column=0,row=17, sticky=E)
self.t29.grid(column=1,row=17, sticky=W)
CreateToolTip(self.t29, text = 'Set the lenght of scale bar for full image')
lb_30=Label(win, text='Small scale \nbar lenght (um)')
self.t30=Entry(window,textvariable=StringVar(window, value='20'), width=5)
lb_30.grid(column=2,row=17, sticky=E)
self.t30.grid(column=3,row=17, sticky=W)
CreateToolTip(self.t30, text = 'Set the lenght of scale bar for individual retrieval images of detections')
lb_31=Label(win, text='Small window \nsize (um)')
self.t31=Entry(window,textvariable=StringVar(window, value='150'), width=5)
lb_31.grid(column=0,row=18, sticky=E)
self.t31.grid(column=1,row=18, sticky=W)
CreateToolTip(self.t31, text = 'Set the size of the window for individual retrievals')
lb_32=Label(win, text='Select \nCM ID plot')
self.t32=Entry(window,textvariable=StringVar(window, value='all'), width=5)
lb_32.grid(column=0,row=19, sticky=E)
self.t32.grid(column=1,row=19, sticky=W)
CreateToolTip(self.t32, text = 'Set a number to display only specific CM number on final output image. Set all to display all CMs')
self.bo1_state = BooleanVar()
self.bo1_state.set(False)
bo1 = Checkbutton(window, text='Plot \nID numbers', var=self.bo1_state)
bo1.grid(column=0, row=20)
CreateToolTip(bo1, text = 'Check to display ID numbers on final output')
self.bo2_state = BooleanVar()
self.bo2_state.set(True)
bo2 = Checkbutton(window, text='Histograms', var=self.bo2_state)
bo2.grid(column=1, row=20)
CreateToolTip(bo2, text = 'Check to generate histogram of detected CMs')
self.bo3_state = BooleanVar()
self.bo3_state.set(True)
bo3 = Checkbutton(window, text='Box Plots', var=self.bo3_state)
bo3.grid(column=2, row=20)
CreateToolTip(bo3, text = 'Check to generate Box Plots of detected CMs')
self.bo4_state = BooleanVar()
self.bo4_state.set(True)
bo4 = Checkbutton(window, text='Merge', var=self.bo4_state)
bo4.grid(column=3, row=20)
CreateToolTip(bo4, text = 'Check to generate the binary merged output in a subfolder. \n For instance: out_quantif_timestamp/a_timestamp_merged_binarized.tif')
self.bo5_state = BooleanVar()
self.bo5_state.set(True)
bo5 = Checkbutton(window, text='Binary Channels', var=self.bo5_state)
bo5.grid(column=2, row=22)
CreateToolTip(bo5, text = 'Check to generate binarized output channels in a subfolder. \n For instance: out_quantif_timestamp/a_timestamp_c1_binarized.tif')
self.bo6_state = BooleanVar()
self.bo6_state.set(True)
bo6 = Checkbutton(window, text='CM Mask (Ma)', var=self.bo6_state)
bo6.grid(column=0, row=21)
CreateToolTip(bo6, text = 'Check to generate cell mask in a subfolder. \n For instance: out_quantif_timestamp/a_timestamp_mask.tif')
self.bo7_state = BooleanVar()
self.bo7_state.set(True)
bo7 = Checkbutton(window, text='Retrievals \noverlapped', var=self.bo7_state)
bo7.grid(column=1, row=21)
CreateToolTip(bo7, text = 'Check to generate merged output with detections in a subfolder. \n For instance: out_quantif_timestamp/a_timestamp_combined_annotated.tif')
self.bo8_state = BooleanVar()
self.bo8_state.set(True)
bo8 = Checkbutton(window, text='Tissue Mask', var=self.bo8_state)
bo8.grid(column=2, row=21)
CreateToolTip(bo8, text = 'Check to generate tissue mask in a subfolder. \n For instance: out_quantif_timestamp/a_timestamp_tissue_mask.tif')
self.bo9_state = BooleanVar()
self.bo9_state.set(True)
bo9 = Checkbutton(window, text='Plot \nIndividual CMs', var=self.bo9_state)
bo9.grid(column=3, row=21)
CreateToolTip(bo9, text = 'Check to generate individual images for each detection in a subfolder inside main subfolder. \n For instance: out_quantif_timestamp/CMs/1.tif')
lbl520=Label(win, text=' ')
lbl520.grid(column=0,row=24)
lbl52=Label(win, text='Parameters for \nEVALUATION', bg="black", fg="white")
lbl52.grid(column=0,row=25)
# lbl521=Label(win, text=' ')
# lbl521.grid(column=0,row=26)
lbl53=Label(win, text='Minimum \nIntersection value')
self.t53=Entry(window,textvariable=StringVar(window, value='50'), width=5)
lbl53.grid(column=0,row=26, sticky=E)
self.t53.grid(column=1,row=26, sticky=W)
lbl54=Label(win, text='Intersection \nmode (max/ref)')
self.t54=Entry(window,textvariable=StringVar(window, value='ref'), width=5)
lbl54.grid(column=0,row=27, sticky=E)
self.t54.grid(column=1,row=27, sticky=W)
# lbl520=Label(win, text=' ')
# lbl520.grid(column=0,row=29)
lbl52=Label(win, text='Parameters for \nManual Mask Generation', bg="black", fg="white")
lbl52.grid(column=0,row=28)
# lbl521=Label(win, text=' ')
# lbl521.grid(column=0,row=29)
lbl530=Label(win, text='Number of vertices \nof polygon')
self.t530=Entry(window,textvariable=StringVar(window, value='8'), width=5)
lbl530.grid(column=0,row=29, sticky=E)
self.t530.grid(column=1,row=29, sticky=W)
CreateToolTip(self.t530, text = 'Number of vertices for each feature. High number will be more accurate but slower manual delineation')
lbl531=Label(win, text='X splits')
self.t531=Entry(window,textvariable=StringVar(window, value='1'), width=5)
lbl531.grid(column=0,row=30, sticky=E)
self.t531.grid(column=1,row=30, sticky=W)
CreateToolTip(self.t531, text = 'Number of splits in x direction to delineate CMs for big images. Press ESC to go to following split')
lbl532=Label(win, text='Y splits')
self.t532=Entry(window,textvariable=StringVar(window, value='1'), width=5)
lbl532.grid(column=0,row=31, sticky=E)
self.t532.grid(column=1,row=31, sticky=W)
CreateToolTip(self.t532, text = 'Number of splits in y direction CMs for big images. Press ESC to go to following split')
def ex(self):
sys.exit()
def sercaORG(self):
#serca = cv2.imread("a_c1.tif")
global fname_c1
idim=self.t2.get()
inpf=self.t33.get()
fname_c1=idim+'_c1'+inpf
serca = cv2.imread(fname_c1)
serca400 = imutils.resize(serca, width=285, height=280)
sercapreview = cv2.cvtColor(serca400, cv2.COLOR_BGR2RGB)
imserca= Image.fromarray(sercapreview)
imgserca=ImageTk.PhotoImage(image=imserca)
origserca = Label(window)
origserca.configure(image=imgserca)
origserca.image = imgserca
origserca.grid(column=9, row=0, rowspan=10)
imgc1 = cv2.imread(fname_c1, cv2.IMREAD_GRAYSCALE)
tresh_c1=int(self.t6.get())
dkremovec1=int(self.t9.get())
dkrgrowc1=int(self.t12.get())
iters_grow_c1=int(self.t15.get())
kernel_noise_remove_c1=np.ones((dkremovec1, dkremovec1),np.uint8)
kernel_grow_c1=np.ones((dkrgrowc1,dkrgrowc1),np.uint8)
thresh1serca = cv2.threshold(imgc1,tresh_c1,255,cv2.THRESH_BINARY)[1]
erserca=cv2.morphologyEx(thresh1serca, cv2.MORPH_OPEN, kernel_noise_remove_c1)
gimgserca=cv2.dilate(erserca,kernel_grow_c1,iterations = iters_grow_c1)
greyserca = np.copy(gimgserca)
greyserca400 = imutils.resize(greyserca, width=285, height=280)
gimserca= Image.fromarray(greyserca400)
gimgserca=ImageTk.PhotoImage(image=gimserca)
maskserca = Label(window)
maskserca.configure(image=gimgserca)
maskserca.image = gimgserca
maskserca.grid(column=10, row=0, rowspan=10)
def cxORG(self):
#serca = cv2.imread("a_c1.tif")
idim=self.t2.get()
inpf=self.t33.get()
fname_c2=idim+'_c2'+inpf
connexinorig = cv2.imread(fname_c2)
connexinorig400 = imutils.resize(connexinorig, width=285, height=280)
cxpreview = cv2.cvtColor(connexinorig400, cv2.COLOR_BGR2RGB)
imcx= Image.fromarray(cxpreview)
imgcx=ImageTk.PhotoImage(image=imcx)
origcx = Label(window)
origcx.configure(image=imgcx)
origcx.image = imgcx
origcx.grid(column=9, row=10, rowspan=10)
# Process channel 4 (Binarized Connexin for quantification)
imgc2=cv2.imread(fname_c2,0)
eqthrcx=int(self.t5_2.get())
tresh_c2=int(self.t7.get())
if(self.chk2_state.get()==True):
equalize='y'
else:
equalize='n'
if(equalize == 'y'):
equ=cv2.equalizeHist(imgc2)
ret,imgcxb2 = cv2.threshold(equ,eqthrcx,255,cv2.THRESH_BINARY)
ret,threshcx = cv2.threshold(equ,eqthrcx,255,cv2.THRESH_BINARY)
else:
ret,threshcx = cv2.threshold(imgc2,tresh_c2,255,cv2.THRESH_BINARY)
ret,imgcxb2 = cv2.threshold(cv2.equalizeHist(imgc2),eqthrcx,255,cv2.THRESH_BINARY)
gimgcx=np.copy(imgcxb2)
greycx = np.copy(gimgcx)
greycx400 = imutils.resize(greycx, width=285, height=280)
gimcx= Image.fromarray(greycx400)
gimgcx=ImageTk.PhotoImage(image=gimcx)
maskcx = Label(window)
maskcx.configure(image=gimgcx)
maskcx.image = gimgcx
maskcx.grid(column=10, row=10, rowspan=10)
def wgaORG(self):
#serca = cv2.imread("a_c1.tif")
idim=self.t2.get()
inpf=self.t33.get()
fname_c3=idim+'_c3'+inpf
wgaorig = cv2.imread(fname_c3)
wgaorig400 = imutils.resize(wgaorig, width=285, height=280)
wgapreview = cv2.cvtColor(wgaorig400, cv2.COLOR_BGR2RGB)
imwga= Image.fromarray(wgapreview)
imgwga=ImageTk.PhotoImage(image=imwga)
origwga = Label(window)
origwga.configure(image=imgwga)
origwga.image = imgwga
origwga.grid(column=9, row=20, rowspan=10)
# Para mostrar la previsualización del canal de wga
imgc3 = cv2.imread(fname_c3, cv2.IMREAD_GRAYSCALE)
tresh_c3=int(self.t8.get())
dkremovec3=int(self.t11.get())
dkrgrowc3=int(self.t14.get())
iters_grow_c3=int(self.t17.get())
kernel_noise_remove_c3=np.ones((dkremovec3, dkremovec3),np.uint8)
kernel_grow_c3=np.ones((dkrgrowc3,dkrgrowc3),np.uint8)
thresh1wga = cv2.threshold(imgc3,tresh_c3,255,cv2.THRESH_BINARY)[1]
erwga=cv2.morphologyEx(thresh1wga, cv2.MORPH_OPEN, kernel_noise_remove_c3)
gimgwga=cv2.dilate(erwga,kernel_grow_c3,iterations = iters_grow_c3)
greywga = np.copy(gimgwga)
greywga400 = imutils.resize(greywga, width=285, height=280)
gimwga= Image.fromarray(greywga400)
gimgwga=ImageTk.PhotoImage(image=gimwga)
maskwga = Label(window)
maskwga.configure(image=gimgwga)
maskwga.image = gimgwga
maskwga.grid(column=10, row=20, rowspan=10)
def sumar_canales(self):
idim=self.t2.get()
inpf=self.t33.get()
fname_c1=idim+'_c1'+inpf
imgc1 = cv2.imread(fname_c1, cv2.IMREAD_GRAYSCALE)
tresh_c1=int(self.t6.get())
dkremovec1=int(self.t9.get())
dkrgrowc1=int(self.t12.get())
iters_grow_c1=int(self.t15.get())
kernel_noise_remove_c1=np.ones((dkremovec1, dkremovec1),np.uint8)
kernel_grow_c1=np.ones((dkrgrowc1,dkrgrowc1),np.uint8)
thresh1serca = cv2.threshold(imgc1,tresh_c1,255,cv2.THRESH_BINARY)[1]
erserca=cv2.morphologyEx(thresh1serca, cv2.MORPH_OPEN, kernel_noise_remove_c1)
gimgserca=cv2.dilate(erserca,kernel_grow_c1,iterations = iters_grow_c1)
greyserca = np.copy(gimgserca)
# greyserca400 = imutils.resize(greyserca, width=285, height=280)
greenserca= cv2.cvtColor(greyserca, cv2.COLOR_GRAY2RGB)
idim=self.t2.get()
inpf=self.t33.get()
fname_c2=idim+'_c2'+inpf
imgc2 = cv2.imread(fname_c2, cv2.IMREAD_GRAYSCALE)
tresh_c2=int(self.t7.get())
dkremovec2=int(self.t10.get())
dkrgrowc2=int(self.t13.get())
iters_grow_c2=int(self.t16.get())
kernel_noise_remove_c2=np.ones((dkremovec2, dkremovec2),np.uint8)
kernel_grow_c2=np.ones((dkrgrowc2,dkrgrowc2),np.uint8)
thresh1cx = cv2.threshold(imgc2,tresh_c2,255,cv2.THRESH_BINARY)[1]
ercx=cv2.morphologyEx(thresh1cx, cv2.MORPH_OPEN, kernel_noise_remove_c2)
gimgcx=cv2.dilate(ercx,kernel_grow_c2,iterations = iters_grow_c2)
greycx = np.copy(gimgcx)
# greycx400 = imutils.resize(greycx, width=285, height=280)
idim=self.t2.get()
inpf=self.t33.get()
fname_c3=idim+'_c3'+inpf
imgc3 = cv2.imread(fname_c3, cv2.IMREAD_GRAYSCALE)
tresh_c3=int(self.t8.get())
dkremovec3=int(self.t11.get())
dkrgrowc3=int(self.t14.get())
iters_grow_c3=int(self.t17.get())
kernel_noise_remove_c3=np.ones((dkremovec3, dkremovec3),np.uint8)
kernel_grow_c3=np.ones((dkrgrowc3,dkrgrowc3),np.uint8)
thresh1wga = cv2.threshold(imgc3,tresh_c3,255,cv2.THRESH_BINARY)[1]
erwga=cv2.morphologyEx(thresh1wga, cv2.MORPH_OPEN, kernel_noise_remove_c3)
gimgwga=cv2.dilate(erwga,kernel_grow_c3,iterations = iters_grow_c3)
greywga = np.copy(gimgwga)
# greywga400 = imutils.resize(greywga, width=285, height=280)
# sumaSercaWGA = cv2.add(greyserca, greywga)
restarSercaWGA = cv2.subtract(greyserca, greywga)
# sumarlostres = cv2.add(sumaSercaWGA, greycx)
restarcx= cv2.subtract(restarSercaWGA, greycx)
# sumarlostres400 = imutils.resize(sumarlostres, width=285, height=280)
restarlostres400 = imutils.resize(restarcx, width=415, height=400)
lostres = np.copy(restarlostres400)
imlostres = Image.fromarray(lostres)
imgtres = ImageTk.PhotoImage(image=imlostres)
masktres = Label(window)
masktres.configure(image=imgtres)
masktres.image = imgtres
masktres.grid(column=5, row=12, rowspan=14, columnspan=4)
def say_hi(self):
global cnt,drect,boxvv,numboxes,nvect
cnt=0
drect=[]
box=[]
# mouse callback function
def draw_trpz(event,x,y,flags,param):
global cnt,drect,boxvv,numboxes,nvect
if event == cv2.EVENT_LBUTTONDBLCLK:
print(x,y)
drect.append([x,y])
cnt=cnt+1
vect=[]
cv2.circle(subpic,(x,y),5,(255,255,255),-1)
cv2.putText(subpic,str(cnt),(x+5,y), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 1, cv2.LINE_AA)
if(cnt==nvert):
cnt=0
numboxes=numboxes+1
for i in range(0,nvert,1):
vect.append(drect[i])
box.append([np.array([vect])])
cv2.drawContours(subpic,[np.array([vect])],-1,(255,255,255),3)
cv2.drawContours(sublack,[np.array([vect])],-1,(255,255,255),-1)
cv2.drawContours(sublack,[np.array([vect])],-1,(255,0,0),1)
print(drect)
drect=[]
print('---------------------------------------------------------------------------------------------------------------')
print('--------------------------- MANUAL MASK GENERATION --------------------------------------------------------')
print('---------------------------------------------------------------------------------------------------------------')
# Start counting time seconds to evaluate total time spent by running the program
t0= tm.localtime()
# Assign args to program variables
subdx=int(self.t531.get())
subdy=int(self.t532.get())
nvert=int(self.t530.get())
gamma=float(self.t28.get())
Nim=int(self.t1.get())
idim=self.t2.get()
scale=float(self.t4.get())
inpf=self.t33.get() #".tif"
outf=self.t34.get() #".png"
numboxes=0
# Read and merge channels
if(Nim==3):
fname_c3=idim+'_c3'+inpf
fname_c2=idim+'_c2'+inpf
fname_c1=idim+'_c1'+inpf
# Overlap image:
img1 = cv2.imread(fname_c1)
img3 = cv2.imread(fname_c3)
img2 = cv2.imread(fname_c2)
comb=cv2.add(img1,img3)
comb2=cv2.add(comb,img2)
# Apply a gamma correction here
lookUpTable = np.empty((1,256), np.uint8)
for i in range(256):
lookUpTable[0,i] = np.clip(pow(i / 255.0, gamma) * 255.0, 0, 255)
img = cv2.LUT(comb2, lookUpTable)
else:
img = cv2.imread(idim+inpf)
thresh, blackAndWhiteImage = cv2.threshold(img, 0, 0, cv2.THRESH_BINARY)
cv2.imwrite("blackblack"+outf,blackAndWhiteImage)
imblack = cv2.imread("blackblack"+outf)
xs=np.shape(img)[0]
ys=np.shape(img)[1]
# definition of input image subdivision for each dymension (height, width)
dh=int(xs/subdx)
dw=int(ys/subdy)
x0=0
y0=0
for k in range(0,subdx,1):
for j in range(0,subdy,1):
subpic=np.copy(img[k*dh:(k+1)*dh,j*dw:(j+1)*dw])
sublack=np.copy(imblack[k*dh:(k+1)*dh,j*dw:(j+1)*dw])
cv2.namedWindow('image')
cv2.setMouseCallback('image',draw_trpz)
while(1):
rows, cols, _channels = map(int, subpic.shape)
cv2.imshow('image',subpic)
#k =
if cv2.waitKey(20) & 0xFF == 27:
break
cv2.destroyAllWindows()
img[k*dh:(k+1)*dh,j*dw:(j+1)*dw]=subpic
imblack[k*dh:(k+1)*dh,j*dw:(j+1)*dw]=sublack
cv2.imwrite(idim+'_mM.tif',imblack)
cv2.imwrite(idim+'_mM_overlap.tif',img)
print("************************************************************************************************************")
print("************************************************************************************************************")
t1 = tm.localtime()
print("Job time (s) : ", np.round(t1 - t0,2))
print("************************************************************************************************************")
print("************************************************************************************************************")
def run(self):
# Time initialization
t0= tm.localtime()
# Ignore warnings
warnings.filterwarnings('ignore')
warnings.simplefilter('ignore')
# Create a time dependent output folder filename format
dirstr=str(round(tm.time()))
distr=dirstr
os.mkdir("out_quantif_"+dirstr)
# Processing mode (supervised, automatic)
procmode=self.selected.get()
# Input images must be labelled as: idim_c1.tif (CM MARKER), idim_c2.tif (CX43 MARKER), idim_c3.tif (INTERSTICIUM MARKER)
# Number of input channels (3 or 1)
Nim=int(self.t1.get())
# Filename input start pattern for biomarked channels
idim=self.t2.get()
# scale
scale=float(self.t4.get())
# Input mask (y/n). If y, an input mask labelled as idim_Mm.tif must be provided as input
if(self.chk1_state.get()==True):
Inpmask='y'
else:
Inpmask='n'
if(self.chk2_state.get()==True):
equalize='y'
else:
equalize='n'
if(self.chk3_state.get()==True):
evaluate='y'
else:
evaluate='n'
# Mode of mask combination (addch/indepch):
modecomb=self.selcomb.get() #'addch'
# scale parameter (in micrometers/pixel)
eqthr=int(self.t5.get())
# Threshold set (if equalize = no):
tresh_c1=int(self.t6.get())
tresh_c2=int(self.t7.get())
tresh_c3=int(self.t8.get())
# Threshold equalization for c4 channel
eqthrcx=int(self.t5_2.get())
# Kernel window size for noise removal in c1 (parameter nr_c1)
dkremovec1=int(self.t9.get())
# kernel window size for dilate of c1 (parameter ng_c1)
dkrgrowc1=int(self.t12.get())
# Iterations for dilate in c2 (parameter ngit_c1)
iters_grow_c1=int(self.t15.get())
# Kernel window size for noise removal in c2 (parameter nr_c2)
dkremovec2=int(self.t10.get())
# kernel window size for dilate of c2 (parameter ng_c2)
dkrgrowc2=int(self.t13.get())
# Iterations for dilate in c2 (parameter ngit_c2)
iters_grow_c2=int(self.t16.get())
# Kernel window size for noise removal in c3 (parameter nr_c3)
dkremovec3=int(self.t11.get())
# kernel window size for dilate of c3 ((parameter ng_c3)
dkrgrowc3=int(self.t14.get())
# Iterations for dilate in c3 (parameter ngit_c3)
iters_grow_c3=int(self.t17.get())
# First filtered parameters
# Minimum area of retrieved contour
ff_areamin=int(self.t21.get()) #100 # squared micrometers
# Minimum perimeter of retrieved contour
ff_permin=int(self.t22.get()) #40 # micrometers
# Second filtered parameters
# Minimum lenght of CM
sf_lboxmin=int(self.t23.get()) #20 # micrometers
# Maximum lenght of CM
sf_lboxmax=int(self.t24.get()) #200 # micrometers
# Minimum width of CM
sf_wmin=int(self.t25.get()) #5 # micrometers
# Maximum width of CM
sf_wmax=int(self.t26.get()) #50 # micrometers
# Minimum aspect ratio (Lenght/Width) of CM
sf_rmin=float(self.t27.get()) #1
# Padding applied to cell enclosing rectangles as function of dilate parameters
scpad=int(self.t270.get())
pad= scpad*dkrgrowc2*iters_grow_c2
# Select specific Cardiomyocite ID (all/ID number)
select=self.t32.get()
# Plot ID numbers (y/n)
if(self.bo1_state.get()==True):
plotidnumb='y'
else:
plotidnumb='n'
# Font type in image annotations index
font = cv2.FONT_HERSHEY_SIMPLEX
# Gamma correction factor (improve brightness of merged image: >1 => darker than original)
gamma=float(self.t28.get())
# 0-3 (number of half-sized reductions for final overlaped image output, it's recommended 2 or 3 for large size input files)
redfactor=0
# Font size
fsize=2
# Contour width
contwidth= 4
# Window size for individual plots (um)
wxsize=float(self.t31.get())/scale
# Plot scale bars
scalebars='y'
# lenght of scale bar (um)
scalebarlen=float(self.t29.get())
# mini scale bar (y/n)
miniscbar='y'
# mini scale bar lenght (um)
minisbarlen=float(self.t30.get())
# Graphical output (generate (y) or not (n)):
if(self.bo2_state.get()==True):
plothist='y'
else:
plothist='n'
if(self.bo3_state.get()==True):
plotboxplot='y'
else:
plotboxplot='n'
plotdens='y'
if(self.bo4_state.get()==True):
plotcombin='y'
else:
plotcombin='n'
if(self.bo5_state.get()==True):
plotchbin='y'
else:
plotchbin='n'
if(self.bo6_state.get()==True):
plotmask='y'
else:
plotmask='n'
if(self.bo7_state.get()==True):
plotanot='y'
else:
plotanot='n'
if(self.bo8_state.get()==True):
plotissuemask='y'
else:
plotissuemask='n'
if(self.bo9_state.get()==True):
plotsepCMs='y'
else:
plotsepCMs='n'
if (plotsepCMs=='y'):
os.mkdir("out_quantif_"+dirstr+"/CMs/")
# Input format
inpf=self.t33.get() #".tif"
outf=self.t34.get() #".png"
#*************************************** END OF PARAMETER SET ********************************************
# define kernels in a proper way
kernel_noise_remove_c2 = np.ones((dkremovec2,dkremovec2),np.uint8)
kernel_grow_c2 =np.ones((dkrgrowc2,dkrgrowc2),np.uint8)
kernel_noise_remove_c1=np.ones((dkremovec1,dkremovec1),np.uint8)
kernel_grow_c1=np.ones((dkrgrowc1,dkrgrowc1),np.uint8)
kernel_noise_remove_c3 = np.ones((dkremovec3,dkremovec3),np.uint8)
kernel_grow_c3 =np.ones((dkrgrowc3,dkrgrowc3),np.uint8)
# Selective color mask (greenchannel)
up_mask_g=255
dwn_mask_g=250
f3 = open("out_quantif_"+dirstr+"/"+idim+"_"+distr+"_paramout.txt", 'w')
f2 = open("out_quantif_"+dirstr+"/"+idim+"_"+distr+"_data.csv", 'w')
if(procmode=='supervised'):
f2.write('ID,value,L(um),W(um),ang(degree),CxLat2,AreaBox(um2),Area(um2),perimeter(um),xc(pix),yc(pix),CXTOT\n')
else:
f2.write('ID,L(um),W(um),ang(degree),CxLat2,AreaBox(um2),Area(um2),perimeter(um),xc(pix),yc(pix),CXTOT\n')
# Set actual time variables
now = datetime.now()
year = now.strftime("%Y")
month = now.strftime("%m")
day = now.strftime("%d")
time = now.strftime("%H:%M:%S")
date_time = now.strftime("%Y%m%d_%H%M%S")
# Start binarization
if(Nim == 3):
fname_c3=idim+'_c3'+inpf
fname_c2=idim+'_c2'+inpf
fname_c1=idim+'_c1'+inpf
if(Nim==1):
fname=idim+inpf
neq = cv2.imread(fname,0)
if(equalize == 'y'):
img=cv2.equalizeHist(neq)
tresh_c1=eqthr
tresh_c2=254
else:
img=neq
ret,backg = cv2.threshold(img,tresh_c1,255,cv2.THRESH_BINARY)
ret,tiss = cv2.threshold(img,tresh_c1,255,cv2.THRESH_BINARY_INV)
ret,foreg = cv2.threshold(img,tresh_c2,255,cv2.THRESH_BINARY)
thresh, blackAndWhiteImage = cv2.threshold(img, 0, 0, cv2.THRESH_BINARY)
cv2.imwrite("blackback"+inpf,blackAndWhiteImage)
imgback = cv2.imread("blackback"+inpf)
imgfor = cv2.imread("blackback"+inpf)
imgtis = cv2.imread("blackback"+inpf)
imgback[:,:,2]=tiss
imgback[:,:,1]=0
imgback[:,:,0]=0
imgfor[:,:,2]=foreg
imgfor[:,:,1]=foreg
imgfor[:,:,0]=foreg
imgtis[:,:,2]=0
imgtis[:,:,1]=backg
imgtis[:,:,0]=0
cv2.imwrite(idim+"_c3"+inpf, imgback)
cv2.imwrite(idim+"_c1"+inpf, imgtis)
cv2.imwrite(idim+"_c2"+inpf, imgfor)
fname_c3=idim+'_c3'+inpf
fname_c2=idim+'_c2'+inpf
fname_c1=idim+'_c1'+inpf
# Process channel 1 (Binarization+noise removal+growth)
thresh, blackAndWhiteImage = cv2.threshold(cv2.imread(fname_c1,0), 0, 0, cv2.THRESH_BINARY)
cv2.imwrite("blackback"+inpf,blackAndWhiteImage)
imgc1 = cv2.imread(fname_c1,0)
if(equalize == 'y'):
equ=cv2.equalizeHist(imgc1)
ret,thresh1serca = cv2.threshold(equ,eqthr,255,cv2.THRESH_BINARY)
else:
ret,thresh1serca = cv2.threshold(imgc1,tresh_c1,255,cv2.THRESH_BINARY)
erserca=cv2.morphologyEx(thresh1serca, cv2.MORPH_OPEN, kernel_noise_remove_c1)
imgserca=cv2.dilate(erserca,kernel_grow_c1,iterations = iters_grow_c1)
grayserca = np.copy(imgserca) #cv2.cvtColor(imgserca, cv2.COLOR_BGR2GRAY)
# Process channel 2 (Binarization+noise removal+growth)
imgc2=cv2.imread(fname_c2,0)
if(equalize == 'y'):
equ=cv2.equalizeHist(imgc2)
ret,imgcxb2 = cv2.threshold(equ,eqthrcx,255,cv2.THRESH_BINARY)
ret,threshcx = cv2.threshold(equ,eqthrcx,255,cv2.THRESH_BINARY)
else:
ret,threshcx = cv2.threshold(imgc2,tresh_c2,255,cv2.THRESH_BINARY)
ret,imgcxb2 = cv2.threshold(cv2.equalizeHist(imgc2),eqthrcx,255,cv2.THRESH_BINARY)
ercx=cv2.morphologyEx(threshcx, cv2.MORPH_OPEN, kernel_noise_remove_c2)
imgcx=cv2.dilate(ercx,kernel_grow_c2,iterations = iters_grow_c2)
imgcxb=np.copy(imgc2)
graycx = np.copy(imgc2)
# Process channel 3 (Binarization+noise removal+growth)
imgc3 = cv2.imread(fname_c3,0)
if(equalize == 'y'):
equ=cv2.equalizeHist(imgc3)
ret,thresh1 = cv2.threshold(equ,eqthr,255,cv2.THRESH_BINARY)
else:
ret,thresh1 = cv2.threshold(imgc3,tresh_c3,255,cv2.THRESH_BINARY)
er=cv2.morphologyEx(thresh1, cv2.MORPH_OPEN, kernel_noise_remove_c3)
imgwga=cv2.dilate(er,kernel_grow_c3,iterations = iters_grow_c3)
graywga = np.copy(imgwga)#cv2.cvtColor(imgwga, cv2.COLOR_BGR2GRAY)
# Write a black background image for mask combination
thresh, blackAndWhiteImage = cv2.threshold(cv2.imread(fname_c2), 0, 0, cv2.THRESH_BINARY)
cv2.imwrite("blackback"+outf,blackAndWhiteImage)