I was able to get this to work using Python 2.7.13 and opencv-python==3.1.0.4
Here is the code for it.
import cv2
import numpy as np
import sys
if len(sys.argv) < 3:
print 'Usage: python match.py <template.png> <image.png>'
sys.exit()
template_path = sys.argv[1]
template = cv2.imread(template_path, cv2.IMREAD_UNCHANGED)
channels = cv2.split(template)
zero_channel = np.zeros_like(channels[0])
mask = np.array(channels[3])
image_path = sys.argv[2]
image = cv2.imread(image_path, cv2.IMREAD_UNCHANGED)
mask[channels[3] == 0] = 1
mask[channels[3] == 100] = 0
# transparent_mask = None
# According to http://www.devsplanet.com/question/35658323, we can only use
# cv2.TM_SQDIFF or cv2.TM_CCORR_NORMED
# All methods can be seen here:
# http://docs.opencv.org/2.4/doc/tutorials/imgproc/histograms/template_matching/template_matching.html#which-are-the-matching-methods-available-in-opencv
method = cv2.TM_SQDIFF # R(x,y) = sum _{x',y'} (T(x',y')-I(x+x',y+y'))^2 (essentially, sum of squared differences)
transparent_mask = cv2.merge([zero_channel, zero_channel, zero_channel, mask])
result = cv2.matchTemplate(image, template, method, mask=transparent_mask)
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
print 'Lowest squared difference WITH mask', min_val
# Now we'll try it without the mask (should give a much larger error)
transparent_mask = None
result = cv2.matchTemplate(image, template, method, mask=transparent_mask)
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
print 'Lowest squared difference WITHOUT mask', min_val
Here it is as a gist.
Essentially, you need to make sure you're using the right matching method.
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