# init 
import glob
import numpy as np
import os, sys, re
from urllib.parse import urlparse
import matplotlib.pyplot as plt
import matplotlib as mpl
from os import listdir
from os.path import isfile, join
from skimage.transform import rescale, resize, downscale_local_mean,  warp, AffineTransform
from PIL import Image, ImageDraw, ImageFilter
from skimage import measure, img_as_ubyte, color 
from skimage.measure import ransac
import pandas as pd
import math 
from sklearn.metrics.pairwise import euclidean_distances
import cv2
from cellpose import utils, io, models, plot
from numpy import genfromtxt
import json

# these are a result from the TrackEM plugin in fiji and can be found in the project .xml file that is generated 
matrix1 = [[1.0,0.0],[0.0,1.0],[363.0,174.0]]
matrix2 = [[-0.2624898117336124,-0.9670515249339174],[0.9970949986936836,-0.2545807300700232],[421.616674241217,1757.8246406687272]]
matrix3 = [[0.5652114984004705,0.8249460358568282],[-0.8249460358568282,0.5652114984004705],[1253.4216002238893,776.4859181156044]]

M1 = np.asmatrix(np.array(matrix1)).T
M2 = np.asmatrix(np.array(matrix2)).T
M3 = np.asmatrix(np.array(matrix3)).T


dst1 = cv2.warpAffine(img1,M1,(2000,2500))
dst2 = cv2.warpAffine(img2,M2,(2000,2500))
dst3 = cv2.warpAffine(img3,M3,(2000,2500))
plt.figure(dpi=200)
# plt.imshow(dst1)
# plt.figure(dpi=200)
# plt.imshow(dst2)
# plt.figure(dpi=200)
# plt.imshow(dst3)


result = cv2.addWeighted(dst1, 0.33, dst2, 0.33, 0)
result2 = cv2.addWeighted(result, 0.66, dst3, 0.33, 0)
plt.figure(dpi=400)
plt.imshow(result2)
res1 = Image.fromarray(dst1)
res1.save('tissue_hires_image_0.png')
res2 = Image.fromarray(dst2)
res2.save('tissue_hires_image_1.png')
res3 = Image.fromarray(dst3)
res3.save('tissue_hires_image_2.png')
res = Image.fromarray(result2)
res.save('tissue_hires_image_all.png')

import json
spots1 = pd.read_csv('spatial_0/tissue_positions_list_0.csv',header=None)
f1 = open('spatial_0/scalefactors_json_0.json'); data1 = json.load(f1); scale1 = data1['tissue_hires_scalef'] 
spots2 = pd.read_csv('spatial_1/tissue_positions_list_1.csv',header=None)
f2 = open('spatial_1/scalefactors_json_1.json'); data2 = json.load(f2); scale2 = data2['tissue_hires_scalef'] 
spots3 = pd.read_csv('spatial_2/tissue_positions_list_2.csv',header=None)
f3 = open('spatial_2/scalefactors_json_2.json'); data3 = json.load(f3); scale3 = data3['tissue_hires_scalef'] 
spots1

#correct overlap 
for p in range(sp1TTarget[0].size):
    dist = np.min(np.sum(np.abs(sp1TTarget[:-1,:]-sp3TTarget[:-1,p]),axis=0))
    # print(dist)
    if dist<10:
        spots3.iloc[p,1] = 0
        
for p in range(sp1TTarget[0].size):
    dist = np.min(np.sum(np.abs(sp1TTarget[:-1,:]-sp2TTarget[:-1,p]),axis=0))
    # print(dist)
    if dist<10:
        spots2.iloc[p,1] = 0
        
color = [(spots1.iloc[:,1].values).tolist(),(spots2.iloc[:,1].values*2).tolist(),(spots3.iloc[:,1].values*3).tolist()]
plt.figure(dpi=600)
plt.scatter([sp1TTarget[0,:].tolist(),sp2TTarget[0,:].tolist(),sp3TTarget[0,:].tolist()],[sp1TTarget[1,:].tolist(),sp2TTarget[1,:].tolist(),sp3TTarget[1,:].tolist()],s=0.1,c=color,alpha=0.7,facecolor=None)
plt.axis('equal')
plt.gca().invert_yaxis()

#correct barcodes
spots11 = spots1.copy()
# spots11.iloc[:,0] =  'a-' + spots11.iloc[:,0]
spots22 = spots2.copy()
# spots22.iloc[:,0] =  'b-' + spots22.iloc[:,0]
spots33 = spots3.copy()
# spots33.iloc[:,0] =  'c-' + spots33.iloc[:,0]
spots33

# ocrrect coordinates
spots11.iloc[:,-2:] =  (sp1TTarget[:-1,:].T*(1/scale1)).tolist()
spots22.iloc[:,-2:] =  (sp2TTarget[:-1,:].T*(1/scale2)).tolist()
spots33.iloc[:,-2:] =  (sp3TTarget[:-1,:].T*(1/scale3)).tolist()
spots33

#make sure everything is ok 
color = [(spots11.iloc[:,1].values).tolist(),(spots22.iloc[:,1].values*2).tolist(),(spots33.iloc[:,1].values*3).tolist()]
plt.figure(dpi=600)
plt.scatter([spots11.iloc[:,4].values.tolist(),spots22.iloc[:,4].values.tolist(),spots33.iloc[:,4].values.tolist()],[spots11.iloc[:,5].values.tolist(),spots22.iloc[:,5].values.tolist(),spots33.iloc[:,5].values.tolist()],s=0.1,c=color,alpha=0.7,facecolor=None)
plt.axis('equal')
plt.gca().invert_yaxis()