python opencv cv2 matchTemplate with transparency

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.


My environment is using opencv 3.1.0 and python 2.7.11.

Here is the code that is looking for images in another image where the template is using transparency (alpha channel). I hope this can help you.

def getMultiFullInfo(all_matches,w,h):
    #This function will rearrange the data and calculate the tuple
    #   for the square and the center and the tolerance for each point
    result = []
    for match in all_matches:
        tlx = match[0]
        tly = match[1]
        top_left = (tlx,tly)
        brx = match[0] + w
        bry = match[1] + h 
        bottom_right = (brx,bry)     
        centerx = match[0] + w/2
        centery = match[1] + h/2
        center = [centerx,centery]
        result.append({'top_left':top_left,'bottom_right':bottom_right,'center':center,'tolerance':match[2]})
    return result

def getMulti(res, tolerance,w,h):
    #We get an opencv image in the form of a numpy array and we need to
    #   find all the occurances in there knowing that 2 squares cannot intersect
    #This will give us exactly the matches that are unique

    #First we need to get all the points where value is >= tolerance
    #This wil get sometimes some squares that vary only from some pixels and that are overlapping
    all_matches_full = np.where (res >= tolerance)
    logging.debug('*************Start of getMulti function')
    logging.debug('All >= tolerance')
    logging.debug(all_matches_full)
    #Now we need to arrange it in x,y coordinates
    all_matches_coords = []
    for pt in zip(*all_matches_full[::-1]):
        all_matches_coords.append([pt[0],pt[1],res[pt[1]][pt[0]]])
    logging.debug('In coords form')
    logging.debug(all_matches_coords)
    #Let's sort the new array
    all_matches_coords = sorted(all_matches_coords)
    logging.debug('Sorted')
    logging.debug(all_matches_coords)

    #This function will be called only when there is at least one match so if matchtemplate returns something
    #This means we have found at least one record so we can prepare the analysis and loop through each records 
    all_matches = [[all_matches_coords[0][0],all_matches_coords[0][1],all_matches_coords[0][2]]]
    i=1
    for pt in all_matches_coords:
        found_in_existing = False
        logging.debug('%s)',i)
        for match in all_matches:
            logging.debug(match)
            #This is the test to make sure that the square we analyse doesn't overlap with one of the squares already found
            if pt[0] >= (match[0]-w) and pt[0] <= (match[0]+w) and pt[1] >= (match[1]-h) and pt[1] <= (match[1]+h):
                found_in_existing = True
                if pt[2] > match[2]:
                    match[0] = pt[0]
                    match[1] = pt[1]
                    match[2] = res[pt[1]][pt[0]]
        if not found_in_existing:
            all_matches.append([pt[0],pt[1],res[pt[1]][pt[0]]])
        i += 1
    logging.debug('Final')
    logging.debug(all_matches)
    logging.debug('Final with all info')
    #Before returning the result, we will arrange it with data easily accessible
    all_matches = getMultiFullInfo(all_matches,w,h)
    logging.debug(all_matches)   
    logging.debug('*************End of getMulti function')
    return all_matches

def checkPicture(screenshot,templateFile, tolerance, multiple = False):
    #This is an intermediary function so that the actual function doesn't include too much specific arguments
    #We open the config file

    configFile = 'test.cfg'

    config = SafeConfigParser()

    config.read(configFile)
    basepics_dir = config.get('general', 'basepics_dir')

    debug_dir = config.get('general', 'debug_dir')

    font = cv2.FONT_HERSHEY_PLAIN

    #The value -1 means we keep the file as is meaning with color and alpha channel if any
    #   btw, 0 means grayscale and 1 is color
    template = cv2.imread(basepics_dir+templateFile,-1)

    #Now we search in the picture
    result = findPicture(screenshot,template, tolerance, multiple)
    #If it didn't get any result, we log the best value

    if not result['res']:
        logging.debug('Best value found for %s is: %f',templateFile,result['best_val'])  

    elif logging.getLogger().getEffectiveLevel() == 10:
        screenshot_with_rectangle = screenshot.copy()
        for pt in result['points']:
            cv2.rectangle(screenshot_with_rectangle, pt['top_left'], pt['bottom_right'], 255, 2)
            fileName_top_left = (pt['top_left'][0],pt['top_left'][1]-10)
            cv2.putText(screenshot_with_rectangle,str(pt['tolerance'])[:4],fileName_top_left, font, 1,(255,255,255),2)
            #Now we save to the file if needed
            filename = time.strftime("%Y%m%d-%H%M%S") + '_' + templateFile[:-4] + '.jpg'
            cv2.imwrite(debug_dir + filename, screenshot_with_rectangle)

    result['name']=templateFile

    return result

def extractAlpha(img, hardedge = True):
    if img.shape[2]>3:
        logging.debug('Mask detected')
        channels = cv2.split(img)

        mask = np.array(channels[3])
        if hardedge:
            for idx in xrange(len(mask[0])):
                if mask[0][idx] <=128:
                    mask[0][idx] = 0
                else:
                    mask[0][idx] = 255


        mask = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)
        img = cv2.cvtColor(img, cv2.COLOR_BGRA2BGR)


        return {'res':True,'image':img,'mask':mask}
    else:
        return {'res':False,'image':img}


def findPicture(screenshot,template, tolerance, multiple = False):

    #This function will work with color images 3 channels minimum
    #The template can have an alpha channel and we will extract it to have the mask


    logging.debug('Looking for %s' , template)

    logging.debug('Tolerance to check is %f' , tolerance)


    logging.debug('*************Start of checkPicture')


    h = template.shape[0]
    w = template.shape[1]

    #We will now extract the alpha channel
    tmpl = extractAlpha(template)

    logging.debug('Image width: %d - Image heigth: %d',w,h)


    # the method used for comparison, can be ['cv2.TM_CCOEFF', 'cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR','cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED']

    meth = 'cv2.TM_CCORR_NORMED'

    method = eval(meth)



    # Apply template Matching
    if tmpl['res']:

        res = cv2.matchTemplate(screenshot,tmpl['image'],method, mask = tmpl['mask'])
    else:
        res = cv2.matchTemplate(screenshot,tmpl['image'],method)


    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)



    # If the method is TM_SQDIFF or TM_SQDIFF_NORMED, take minimum

    if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:

        top_left = min_loc

        best_val = 1 - min_val

    else:

        top_left = max_loc

        best_val = max_val
    #We need to ensure we found at least one match otherwise we return false
    if best_val >= tolerance:

        if multiple:
            #We need to find all the time the image is found
            all_matches = getMulti(res, float(tolerance),int(w),int(h))
        else:
            bottom_right = (top_left[0] + w, top_left[1] + h)

            center = (top_left[0] + (w/2), top_left[1] + (h/2))
            all_matches = [{'top_left':top_left,'bottom_right':bottom_right,'center':center,'tolerance':best_val}]

        #point will be in the form: [{'tolerance': 0.9889718890190125, 'center': (470, 193), 'bottom_right': (597, 215), 'top_left': (343, 172)}]
        logging.debug('The points found will be:')
        logging.debug(all_matches)
        logging.debug('*************End of checkPicture')

        return {'res': True,'points':all_matches}

    else:
        logging.debug('Could not find a value above tolerance')
        logging.debug('*************End of checkPicture')

        return {'res': False,'best_val':best_val}