#!/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_circular_mask(h, w, center=None, radius=None):
    """Creates a circular mask for an image."""

    if center is None: # Use the center of the image
        center = (w*0.44526, h*0.47561)
    if radius is None: # Use the smallest distance between the center and image walls
        radius = w*0.35539

    Y, X = np.ogrid[:h, :w]
    dist_from_center = np.sqrt((X - center[0])**2 + (Y-center[1])**2)

    mask = dist_from_center <= radius
    return mask


def create_masked_bg_max_img(files, f,num_of_bg_imgs ):
    photo_max=None
    #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 np.array(photo_max).any() == None:
            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
    mask = create_circular_mask(2050, 2448, center=None, radius=867)
    maskedphotomax = photo_max.copy()
    maskedphotomax[~mask] = 0  # Set pixels outside the circle to black
    return(maskedphotomax)

def create_masked_image(files, f):
    masked_img=cv2.imread(files[f])
    mask = create_circular_mask(2050, 2448, center=None, radius=867)
    masked_img[~mask] = 0  # Set pixels outside the circle to black
    return(masked_img)


def measure_atn(maskedphotomax,files, f, masked_img):
    photo=masked_img
    photo_red=photo[:,:,2] ##cv2 reads images as BGR, so red is the third channel
    attenuation=np.mean(-np.log(photo_red/maskedphotomax))
    file_name=os.path.basename(files[f])
    data=pd.DataFrame([[file_name,attenuation]],columns=['file_name','atn'])
    return(data)

#im_path=sys.argv[1]
out_path='C:/SES_out'

#import the files names
im_path='C:/SES_in'
#im_path='C:/SES_mask'


#files= glob.glob(os.path.join(im_path,'*.jpg'))
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)


files = filesdf[filesdf['filenames'].str.contains('Ext')]
files['date'] = files['filenames'].str.split('_').str[-1]
files['collect_time'] = files['filenames'].str.split('_').str[-3]
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_data = pd.DataFrame(columns=['Number','Area','ESD','perimeter','file_name', 'topleft_x','topleft_y','width','height'])
        all_atn_data=pd.DataFrame(columns=['file_name','atn'])
        maskedphotomax=create_masked_bg_max_img(sorted_filelist,f,num_of_bg_imgs)
        image=np.array(maskedphotomax) 
        h = image.shape[0] 
        w = image.shape[1] 
        masked_img = create_masked_image(files, f)
        cv2.imwrite(os.path.join(out_path,'Photomax_'+str(os.path.basename(sorted_filelist[f]))),maskedphotomax)
        cv2.imwrite(os.path.join(out_path,'MaskedImg'+str(os.path.basename(sorted_filelist[f]))),masked_img)
        atn_data=measure_atn(maskedphotomax,files, f, masked_img)
     #   photo_nobg=subtract_bg(sorted_filelist,f,photo_max)
      #  detected_img = detect_marker_pixels(mask,photo_nobg)
      #  example_img=outline_detected(detected_img,sorted_filelist,f)
      #  data=measure_particle_props(detected_img,sorted_filelist,f)
      #  all_data=pd.concat([all_data,data])
        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)+'_All_particle_measurements.csv')):
        #     all_data.to_csv(os.path.join(out_path,os.path.basename(out_path)+'_All_particle_measurements.csv'), mode='a', header=False)
        # else:
        #     all_data.to_csv(os.path.join(out_path,os.path.basename(out_path)+'_All_particle_measurements.csv'))
        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)