# -*- coding: utf-8 -*-
"""
Created on Mon Mar 30 14:40:40 2020

@author: koray
"""
import matplotlib.pyplot as plt
from tkinter import *
from PIL import Image
from PIL import ImageTk
from tkinter import filedialog
import cv2
import numpy as np
from skimage.util import random_noise
import random
import pandas as pd
from skimage.restoration import estimate_sigma
from skimage.measure import compare_ssim, compare_mse, compare_psnr
from scipy.stats import pearsonr

filename = "pies.jpg"
org_obraz = cv2.imread(filename,cv2.IMREAD_GRAYSCALE)
org_obraz_im_arr = np.asarray(org_obraz)
counter = 3
data_frame = pd.DataFrame(columns=["mwynik","sim","mse","psnr","szum"])

def generate_img():
    szum_img = random_noise(org_obraz_im_arr, mode="gaussian", var=random.uniform(0.02, 0.08)**2)
    szum_img = (255*szum_img).astype(np.uint8)
    return szum_img

def clicked(panel, mwynik):
    global counter
    global data_frame
    print(counter)
    if counter == 0:
        counter -= 1
        data_frame["mwynik"] = data_frame["mwynik"].shift(-1)
        data_frame = data_frame.iloc[:-1]
        data_frame.to_csv("dane.csv", mode="a", header=False)
        toplevel = Toplevel()
        label1 = Label(toplevel, text="Dziekuje", height=0, width=100)
        label1.pack()
        butt = Button(toplevel, text="zamknij", command=root.destroy)
        butt.pack()
    else:
        counter -= 1
        img2 = generate_img()

#        #szum lvl
        szum= estimate_sigma(img2, multichannel=True, average_sigmas=True)
#        #sim
        (sim, diff) = compare_ssim(org_obraz_im_arr, img2, full=True)
#        #mse
        mse = compare_mse(org_obraz_im_arr, img2)
        psnr = compare_psnr(org_obraz_im_arr, img2)

        data_frame = data_frame.append(pd.DataFrame([[mwynik, sim, mse, psnr, szum]],
                                                    columns=["mwynik","sim","mse","psnr","szum"]), ignore_index=True)
        img2 = Image.fromarray(img2)
        img2 = ImageTk.PhotoImage(img2)
        panel.configure(image=img2)
        panel.image = img2

root = Tk()
image = Image.fromarray(org_obraz_im_arr)
image = ImageTk.PhotoImage(image)
panel = Label(root, image = image)

label1 = Label(root, text="Ocen obrazek: 1-5 zle, bardzo dobrze", height=0, width=100)
v = DoubleVar()
scale = Scale(root, variable = v, from_ = 1, to = 5, orient = HORIZONTAL)
next_button = Button(root, text="Dalej", command=lambda: clicked(panel, scale.get()))
next_button.pack(side = "bottom", fill = "both", expand = "yes")
scale.pack(side = "bottom", fill = "both", expand = "yes")
label1.pack(side = "bottom", fill = "both", expand = "yes")
panel.pack(side = "bottom", fill = "both", expand = "yes")
root.mainloop()

data = pd.read_csv("dane.csv", header=None)
data.columns = ["index", "mwynik","sim","mse","psnr","szum"]


m, b = np.polyfit(data["mwynik"], data["sim"], 1)
plt.plot(data["mwynik"], data["sim"], "o", data["mwynik"], m*data["mwynik"] + b)

m, b = np.polyfit(data["mwynik"], data["mse"], 1)
plt.plot(data["mwynik"], data["mse"], "o", data["mwynik"], m*data["mwynik"] + b)

m, b = np.polyfit(data["mwynik"], data["psnr"], 1)
plt.plot(data["mwynik"], data["psnr"], "o", data["mwynik"], m*data["mwynik"] + b)


m, b = np.polyfit(data["mwynik"], data["szum"], 1)
plt.plot(data["mwynik"], data["szum"], "o", data["mwynik"], m*data["mwynik"] + b)

data.plot(x="mwynik")

plt.matshow(data.corr())
plt.xticks(range(len(data.columns)), data.columns)
plt.yticks(range(len(data.columns)), data.columns)
plt.colorbar()
plt.show()