import pyautogui as pag
from random import randint, uniform
import cv2
import numpy as np
import math
import time
import sys 

#For Fishing
##https://zaxrosenberg.com/how-to-write-a-runescape-autoclicker-with-python-part-ii/

def inventoryCount():

    ##Inventory Count

    # Take a screenshot and store it in a numpy array
    screenshot = np.array(pag.screenshot(region = (0, 1079, 1920, 1080)))

    screenshot = cv2.cvtColor(screenshot, cv2.COLOR_BGR2GRAY)

    # Load the image to be searched for in grayscale
    image_to_search = cv2.imread('Images/shrimp.png', 0)

    # Get the dimensions of the image to be searched for
    h, w = image_to_search.shape[::-1]

    # Use cv2.matchTemplate() to find the template image within the screenshot
    result = cv2.matchTemplate(screenshot, image_to_search, cv2.TM_CCOEFF_NORMED)

    # Set a threshold value to count only those template images that have a high enough correlation
    threshold = 0.9
    counter = 0

    loc = np.where(result >= threshold)
    for pt in zip(*loc[::-1]):
        cv2.rectangle(screenshot, pt, (pt[0] + w, pt[1] + h), (0, 0, 255), 2)
        counter += 1
        
    print(counter)

    if counter == 27:
        print('Inventory full, time to drop')
    else:
        print('Inventory not full, keep fishing')

#Function to check if we are currently fishing
def checkIfFishing():
    # Take a screenshot and store it in a numpy array
    screenshot = np.array(pag.screenshot(region = (0, 1079, 1920, 1080)))

    screenshot = cv2.cvtColor(screenshot, cv2.COLOR_BGR2GRAY)

    # Load the image to be searched for in grayscale
    image_to_search = cv2.imread('Images/fishing.png', 0)

    # Get the dimensions of the image to be searched for
    h, w = image_to_search.shape[::-1]

    # Use cv2.matchTemplate() to find the template image within the screenshot
    #This checks if the green Fishing icon is being displayed in the top left
    result = cv2.matchTemplate(screenshot, image_to_search, cv2.TM_CCOEFF_NORMED)

    # Set a threshold value to count only those template images that have a high enough correlation
    threshold = 0.8
    #Assume we are not fishing until proven otherwise
    Fishing = False


    loc = np.where(result >= threshold)
    for pt in zip(*loc[::-1]):
        cv2.rectangle(screenshot, pt, (pt[0] + w, pt[1] + h), (0, 0, 255), 2)
        Fishing = True
        
    print(Fishing)

    if Fishing:
        print('Currently Fishing')
    else:
        print('Not Fishing')

#Function for finding the fishing spots
def fishingSpots():
    print('Searching for fishing spot')