# -*- coding: utf-8 -*-
"""
Created on Thu Apr 30 07:53:52 2026

@author: chuffard
"""

# -*- coding: utf-8 -*-

"""

Created on Fri Mar 13 11:54:42 2026



@author: chuffard

"""



#!/Users/cdurkin/opt/anaconda3/bin/python3

#C:/Users/chuffard/AppData/Local/miniconda3







import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

import os , sys

import cv2

import glob



def create_bg_max_img(files, f,num_of_bg_imgs ):

    photo_max=[]

    #Create a max pixel image of the previous 20 images

    #But if within the first 20 images taken, create a max pixel image of the next 20 images that were taken afterward

    #This means we can't start calculating data until atleast 41 images have been collected.

    count=num_of_bg_imgs

    while count>0:

        if f>=num_of_bg_imgs:

            photo=cv2.imread(files[f-count])

        else:

            photo=cv2.imread(files[f+count])

        photo_value=photo[:,:,2]

        photo_value=cv2.normalize(photo_value,None,0, 255,cv2.NORM_MINMAX)

        if len(photo_max)==0:

            photo_max=photo_value.copy()

        else:

            max_values=photo_max.astype(int)-photo_value.astype(int)

            photo_max[max_values<0]=photo_value[max_values<0]

        count=count-1

    return(photo_max)

def subtract_bg(files,f,photo_max):
    photo=cv2.imread(files[f])
    photo_hsv=cv2.cvtColor(photo,cv2.COLOR_BGR2HSV)
    photo_value=photo_hsv[:,:,2]
    photo_norm=cv2.normalize(photo_value,None,0,255,cv2.NORM_MINMAX)
    photo_denoise1=photo_norm-photo_max.astype(int)
    if np.min(photo_denoise1)<0:
        photo_denoise=photo_denoise1+abs(np.min(photo_denoise1))
    else:
        photo_denoise=photo_denoise1.copy()
    photo_denoise=cv2.normalize(photo_denoise,None,0,255,cv2.NORM_MINMAX)

    photo_blur=cv2.medianBlur(cv2.convertScaleAbs(photo_max),251)
    photo_blur=abs(photo_blur.astype(int)-255)
    photo_nobg=photo_denoise-photo_blur
    if np.min(photo_nobg)<0:
        photo_nobg=photo_nobg+abs(np.min(photo_nobg))
    photo_nobg=cv2.normalize(photo_nobg,None,0, 255,cv2.NORM_MINMAX)
    return(photo_nobg)

def find_circle_mask(photo_max):

    circle_markers=cv2.Canny(cv2.convertScaleAbs(photo_max),25,25)

    kernel = np.ones((5, 5), np.uint8)

    circle_marker_dilation=cv2.dilate(circle_markers,kernel,1)  

    contours, hierarchy = cv2.findContours(circle_marker_dilation, cv2.RETR_EXTERNAL , cv2.CHAIN_APPROX_NONE)

    circle_markers2=np.zeros_like(circle_markers)

    for cnt in contours:

        cv2.drawContours(circle_markers2,[cnt],-1,255,-1)

    circle_markers2=cv2.erode(circle_markers2,kernel,1)  

    

    contours,_= cv2.findContours(cv2.convertScaleAbs(circle_markers2), cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

    areas = [cv2.contourArea(c) for c in contours]

    sorted_areas = np.sort(areas)

    cnt=contours[areas.index(sorted_areas[-1])] 

    (x,y),radius = cv2.minEnclosingCircle(cnt)

    center = (int(x),int(y))

    radius = int(radius)-50

    mask=np.zeros_like(photo_max)

    cv2.circle(mask,center,radius,(255),-1)

    return(mask)



def detect_marker_pixels(mask, photo_nobg):
    markers=cv2.adaptiveThreshold(cv2.convertScaleAbs(photo_nobg), 255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV,801, 3)
    markers[mask==0]=0
    kernel = np.ones((5, 5), np.uint8)
    marker_erosion=cv2.erode(markers,kernel,1) 
    marker_dilation=cv2.dilate(marker_erosion,kernel,1)  
    contours, hierarchy = cv2.findContours(marker_dilation, cv2.RETR_EXTERNAL , cv2.CHAIN_APPROX_NONE)
    markers2=np.zeros_like(markers)
    for cnt in contours:
        cv2.drawContours(markers2,[cnt],-1,255,-1)
                
    detected_img=markers2.astype('ubyte')
    return(detected_img)

def measure_particle_props(detected_img,files,f):
    particle_mask=detected_img.copy()
    contours, hierarchy = cv2.findContours(particle_mask, cv2.RETR_EXTERNAL , cv2.CHAIN_APPROX_NONE)
    count=0
    particle_area = []
    particle_ESD = []
    particle_perimeter = []
    file_name = []
    topleft_x = []
    topleft_y = []
    width=[]
    height=[]
    particle_numberID=[]

    scale= 0.07194
    scale_area = scale**2 
    for cnt in contours:
        count=count+1
        px_area=cv2.contourArea(cnt)
        um_area = px_area / scale_area
        perimeter=cv2.arcLength(cnt, True)/scale
        ESD=2*(np.sqrt(um_area/np.pi))
        x,y,w,h=cv2.boundingRect(cnt)
        particle_area.append(um_area)
        particle_ESD.append(ESD)
        particle_perimeter.append(perimeter)
        file_name.append(os.path.basename(files[f]))
        topleft_x.append(x)
        topleft_y.append(y)
        width.append(w)
        height.append(h)
        particle_numberID.append(count)
    data = pd.DataFrame(np.stack((particle_numberID, particle_area, particle_ESD, particle_perimeter, file_name, topleft_x,topleft_y,width,height),-1),columns=['Number','Area','ESD','perimeter','file_name', 'topleft_x','topleft_y','width','height'])
    return(data) 



out_path='C:/SES_out'



#import the files names

im_path='C:/SES_in'





fileslist = glob.glob("C:/SES_in/*.jpg")



#for some reason it's making the second slash a backslash instead of forward. Replace it

fileslist = [path.replace('\\', '/') for path in fileslist]



filesdf = pd.DataFrame(fileslist, columns=['filenames'])

if os.path.isdir(out_path)==False:

    os.mkdir(out_path)



###Crissy I added/modified this code to illiminate warnings###

filenames = filesdf[filesdf['filenames'].str.contains('Ext')]

dates= filenames['filenames'].str.split('_').str[-1]

collect_time=filenames['filenames'].str.split('_').str[-3]



files=pd.DataFrame()

files['filenames']=filenames

files['date']=dates

files['collect_time']=collect_time

files=files.sort_values(by=['date'])

sorted_filelist = files['filenames'].tolist()



#Identify the files that needs to be processed

#Open existing data file and find the name of the last image file that was analyzed and analyze every image after that one

        

if os.path.isfile(os.path.join(out_path,os.path.basename(out_path)+'_particles.csv')):

    data_link=open(os.path.join(out_path,os.path.basename(out_path)+'_particles.csv'),'r')

    last_line=data_link.readlines()[-1]

    data_link.close()

    for x in np.flip(np.arange(0,len(sorted_filelist))):

        if sorted_filelist[x].split('/')[-1] in last_line:

            files_to_be_analyzed=np.arange(x+1,len(sorted_filelist))

            break

#If no data file exists, then just start with the first image

else:

    files_to_be_analyzed=np.arange(0,len(sorted_filelist))



#Create an empty dataframe for new data to be recorded

num_of_bg_imgs=20

if len(sorted_filelist)>(num_of_bg_imgs*2):

    for f in files_to_be_analyzed:

        all_atn_data=pd.DataFrame(columns=['file_name','atn'])

        photo_max=create_bg_max_img(sorted_filelist,f,num_of_bg_imgs)

        mask=find_circle_mask(photo_max)
        
        photo_nobg=subtract_bg(files,f,photo_max)
        detected_img = detect_marker_pixels(mask,photo_nobg)
     #   example_img=outline_detected(detected_img,files,f)
        data=measure_particle_props(detected_img,files,f)

        all_data=pd.concat([data,data])

        #Save the data to the existing file, or create a new file

        if os.path.isfile(os.path.join(out_path,os.path.basename(out_path)+'_atn.csv')):

            all_atn_data.to_csv(os.path.join(out_path,os.path.basename(out_path)+'_atn.csv'), mode='a', header=False)

        else:

            all_atn_data.to_csv(os.path.join(out_path,os.path.basename(out_path)+'_atn.csv'))

 #       cv2.imwrite(os.path.join(out_path,'Outline_'+str(os.path.basename(sorted_filelist[f]))),example_img)