Basic template matching involves having an identical object image (template) and searching for that same pattern within a larger base image. OpenCV provides several methods for template matching:

methods = [cv2.TM_CCOEFF, cv2.TM_CCOEFF_NORMED, cv2.TM_CCORR, cv2.TM_CCORR_NORMED, cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]

OpenCV recommends trying all the methods and choosing whichever gives the best match for your use case.

img = cv2.imread('path', 0)
template = cv2.imread('path', 0)
height, width = template.shape

for method in methods:
    img2 = img.copy()
    result = cv2.matchTemplate(img2, template, method)
    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
    if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:
        location = min_loc
    else:
        location = max_loc
    bottom_right = (location[0] + width, location[1] + height)
    cv2.rectangle(img2, location, bottom_right, 255, 5)
    cv2.imshow("Match", img2)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

This slides the template across the base image and scores how well it matches at each position, returning an array of size (W_base - W_temp + 1, H_base - H_temp + 1). The best-matching location is used to draw a rectangle on the base image: SQDIFF-based methods use min_loc, while all other methods use max_loc.