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test_image.py
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import cv2
import numpy as np
import pyautogui
import pygetwindow as gw
"""
leftB=1524
topB=369
widthB=1918-1524
heightB=671-369
leftt=1679
topt=164
widtht=1759-1679
heightt=264-164"""
window = gw.getWindowsWithTitle('Paused')[0]
left, top, width, height = window.left, window.top, window.width, window.height
data1 = cv2.imread('images/template_mario.png')
data2 = cv2.imread('images/template_yoshi.png')
data3 = cv2.imread('images/template_luigi.png')
data4 = cv2.imread('images/template_wario.png')
# Load the bigger image and the template image
#bigger_image = cv2.imread("bigger_image.png")
bigger_image = np.array(pyautogui.screenshot(region=(leftB, topB, widthB, heightB)))
#template_image = cv2.imread("template_image.png")
template_image = np.array(pyautogui.screenshot(region=(leftt, topt, widtht, heightt)))
# Convert the template and data images to grayscale
template_gray = cv2.cvtColor(template_image, cv2.COLOR_BGR2GRAY)
data1_gray = cv2.cvtColor(data1, cv2.COLOR_BGR2GRAY)
data2_gray = cv2.cvtColor(data2, cv2.COLOR_BGR2GRAY)
data3_gray = cv2.cvtColor(data3, cv2.COLOR_BGR2GRAY)
data4_gray = cv2.cvtColor(data4, cv2.COLOR_BGR2GRAY)
# Perform template matching using normalized cross-correlation (NCC)
result1 = cv2.matchTemplate(data1_gray, template_gray, cv2.TM_CCORR_NORMED)
result2 = cv2.matchTemplate(data2_gray, template_gray, cv2.TM_CCORR_NORMED)
result3 = cv2.matchTemplate(data3_gray, template_gray, cv2.TM_CCORR_NORMED)
result4 = cv2.matchTemplate(data4_gray, template_gray, cv2.TM_CCORR_NORMED)
# Find the position with the highest matching score in each data image
min_val1, max_val1, min_loc1, max_loc1 = cv2.minMaxLoc(result1)
min_val2, max_val2, min_loc2, max_loc2 = cv2.minMaxLoc(result2)
min_val3, max_val3, min_loc3, max_loc3 = cv2.minMaxLoc(result3)
min_val4, max_val4, min_loc4, max_loc4 = cv2.minMaxLoc(result4)
# Determine which data image has the highest matching score
if max_val1 > max_val2 and max_val1 > max_val3 and max_val1 > max_val4:
print('Mario is the best match')
elif max_val2 > max_val1 and max_val2 > max_val3 and max_val2 > max_val4:
print('yoshi is the best match')
elif max_val3 > max_val1 and max_val3 > max_val2 and max_val3 > max_val4:
print('Luigi is the best match')
else:
print('Wario is the best match')
"""
cv2.imshow("Result", bigger_image)
cv2.imshow("template", template_image)
# Convert both images to grayscale
bigger_gray = cv2.cvtColor(bigger_image, cv2.COLOR_BGR2GRAY)
template_gray = cv2.cvtColor(template_image, cv2.COLOR_BGR2GRAY)
# Find the template in the bigger image using template matching
result = cv2.matchTemplate(bigger_gray, template_gray, cv2.TM_CCOEFF_NORMED)
# Define a threshold value for the similarity score
threshold = 0.8
# Get the location of the template in the bigger image
locations = np.where(result >= threshold)
locations = list(zip(*locations[::-1]))
# Draw a bounding box around each location where the template was found
for loc in locations:
top_left = loc
bottom_right = (top_left[0] + template_image.shape[1], top_left[1] + template_image.shape[0])
cv2.rectangle(bigger_image, top_left, bottom_right, (0, 0, 255), 1)
# Display the result
cv2.imshow("Result", bigger_image)
cv2.imshow("template", template_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
"""