Match template to unit bounding box at different orientation

template_matching_unit.py

# load base image and template
img = cv2.imread(sample_img_path, 0)
template = cv2.imread(template_path, 0)

# canny edge detection to get edges of base image
edged = cv2.Canny(img, 50, 200)

# define the orientations
methods_arr = [('original',0), ('original',90), ('original',180), ('original',270),
              ('flipped',0), ('flipped',90), ('flipped',180), ('flipped',270)]
              
for method in methods_arr:
    template_copy = template.copy()
    if method[0] == 'flipped':
        # flips image
        template_copy = cv2.flip(template, 1)
    
    # rotates image
    template_copy = rotate_image(template_copy, method[1])
    # perform template matching
    result = cv2.matchTemplate(edged, template_copy, cv2.TM_SQDIFF)
    # get maximum and minimum distance => we focus on minimum distance
    (_, maxVal, _, maxLoc) = cv2.minMaxLoc(result)