Initial Commit

#Image Recognition Testing.py

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+import cv2
+import numpy as np
+import pyautogui as pag
+
+##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')