# -*- 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_hsv=cv2.cvtColor(photo,cv2.COLOR_BGR2HSV)
        photo_value=photo_hsv[:,:,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 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 measure_atn(mask, photo_max,files, f):
    photo=cv2.imread(files[f])
    photo_red=photo[:,:,2] ##cv2 reads images as BGR, so red is the third channel
    photo_red_mask=photo_red[mask==255]
    photo_red_mask[photo_red_mask==0]=1
    photo_max_mask=photo_max[mask==255]
    photo_max_mask[photo_max_mask==0]=1
    attenuation=np.mean(-np.log(photo_red_mask/photo_max_mask))
    file_name=os.path.basename(files[f])
    data=pd.DataFrame([[file_name,attenuation]],columns=['file_name','atn'])
    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)+'_atn.csv')):
    data_link=open(os.path.join(out_path,os.path.basename(out_path)+'_atn.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)
        atn_data=measure_atn(mask,photo_max,sorted_filelist,f)
        all_atn_data=pd.concat([all_atn_data,atn_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)