Initial Commit

fishing.py

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+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')