import cv2
import numpy as np
from matplotlib import pyplot as plt
import mss
import mss
import mss.tools
from PIL import Image
import time
import sys
import pyautogui
import threading
import math
import uinput
from pymouse import PyMouse

def sim_score(min_val, max_val, method):
    if (method == cv2.TM_SQDIFF_NORMED):
        if(1 - min_val) <= 1:
            return(1-min_val)
        else:
            return(0)

    if (method == cv2.TM_SQDIFF):
        if(min_val <= 1):
            return(min_val)
        else:
            return(0)

    else:
        if max_val <= 1:
            return(max_val)
        else:
            return(0)

def check_tabela():

    while(1):
        try:
            tabelalocation = pyautogui.locateOnScreen('images/retry.png', confidence=0.90)
            tabelapoint = pyautogui.center(tabelalocation)
            tabelax, tabelay = tabelapoint
            pyautogui.click(tabelax, tabelay)
            print("tabelaya tıklandı")
        except:
            print("tabela bulunamadı")

        print("test")
        time.sleep(5)

m = PyMouse()

template = cv2.imread('images/cene.png', 0)
tmp1 = cv2.imread('images/cene1.png', 0)
tmp2 = cv2.imread('images/cene2.png', 0)
tmp3 = cv2.imread('images/cene3.png', 0)
tmp4 = cv2.imread('images/cene4.png', 0)
tmp5 = cv2.imread('images/1.png', 0)
w, h = template.shape[::-1]
w2, h2 = tmp5.shape[::-1]
method = eval('cv2.TM_SQDIFF_NORMED')
with mss.mss() as sct:
    monitor = {"top": 184, "left":772 , "width": 500, "height": 500}
    x = 0
    starttime = time.time()
    y= threading.Thread(target=check_tabela)
    y.start()
    while (1):
        last_time = time.time()
        img = cv2.cvtColor(np.array(sct.grab(monitor)), cv2.COLOR_BGR2GRAY)

        res0 = cv2.matchTemplate(img, template, method)
        min_val0, max_val0, min_loc0, max_loc0 = cv2.minMaxLoc(res0)
        res1 = cv2.matchTemplate(img, tmp1, method)
        min_val1, max_val1, min_loc1, max_loc1 = cv2.minMaxLoc(res1)
        res2 = cv2.matchTemplate(img, tmp2, method)
        min_val2, max_val2, min_loc2, max_loc2 = cv2.minMaxLoc(res2)
        res3 = cv2.matchTemplate(img, tmp3, method)
        min_val3, max_val3, min_loc3, max_loc3 = cv2.minMaxLoc(res3)
        res4 = cv2.matchTemplate(img, tmp4, method)
        min_val4, max_val4, min_loc4, max_loc4 = cv2.minMaxLoc(res4)
        res5 = cv2.matchTemplate(img, tmp5, cv2.TM_CCORR_NORMED)
        min_val5, max_val5, min_loc5, max_loc5 = cv2.minMaxLoc(res5)
        sınır = 0.90
        debug = 1
        sim0 = sim_score(min_val0, max_val0, method)
        sim0 = int(str(sim0)[2:9] + "0")
        sim1 = sim_score(min_val1, max_val1, method)
        sim1 = int(str(sim1)[2:9] + "1")
        sim2 = sim_score(min_val2, max_val2, method)
        sim2 = int(str(sim2)[2:9] + "2")
        sim3 = sim_score(min_val3, max_val3, method)
        sim3 = int(str(sim3)[2:9] + "3")
        sim4 = sim_score(min_val4, max_val4, method)
        sim4 = int(str(sim4)[2:9] + "4")
        sims = [sim0, sim1, sim2, sim3, sim4]
        similarity = max(sims)
        str_similarity = str(similarity)
        t_min_loc = eval("min_loc" + str_similarity[len(str_similarity)-1])
        t_max_loc = eval("max_loc" + str_similarity[len(str_similarity)-1])
        top_left = t_min_loc
        button_top_left = max_loc5
        relx = 0
        if float("0." + str(similarity)) > sınır:
            relx = (top_left[0] - button_top_left[0]) * -1
            print(float("0." + str(similarity)))
        if max_val5 > sınır:
            m.move(button_top_left[0] + int(w2/2) + 772 + relx, button_top_left[1] + int(h2/2) + 184)
            #pyautogui.moveTo(button_top_left[0] + w2/2 + 772, button_top_left[1] + h2/2 + 184)
            #win32api.SetCursorPos((button_top_left[0] + int(w2/2) + 772, button_top_left[1] + int(h2/2) + 184))
        x = x + 1

        #pyautogui.moveTo((button_top_left[0] + int(w2/2) + 772 + relx, button_top_left[1] + int(h2/2) + 184))
        #cv2.circle(img, (top_left[0] + int(w/2), top_left[1] + int(h/2)), 6, (0, 255, 255), -1)
        #cv2.imshow("OpenCV/Numpy normal", img)
        print("fps: {}".format(1 / (time.time() - last_time)))

        #time.sleep(0.001 - ((time.time() - starttime) % 0.001))