Python cv2.TM_SQDIFF_NORMED Examples

def findAllMatches(self, needle, similarity):
        """ Find all matches for ``needle`` with confidence better than or equal to ``similarity``.

        Returns an array of tuples ``(position, confidence)`` if match(es) is/are found,
        or an empty array otherwise.
        """
        positions = []
        method = cv2.TM_CCOEFF_NORMED

        match = cv2.matchTemplate(self.haystack, self.needle, method)

        indices = (-match).argpartition(100, axis=None)[:100] # Review the 100 top matches
        unraveled_indices = numpy.array(numpy.unravel_index(indices, match.shape)).T
        for location in unraveled_indices:
            y, x = location
            confidence = match[y][x]
            if method == cv2.TM_SQDIFF_NORMED or method == cv2.TM_SQDIFF:
                if confidence <= 1-similarity:
                    positions.append(((x, y), confidence))
            else:
                if confidence >= similarity:
                    positions.append(((x, y), confidence))

        positions.sort(key=lambda x: (x[0][1], x[0][0]))
        return positions 

def find_watermark_from_gray(self, gray_img, watermark_template_gray_img):
        """
        从原图的灰度图中寻找水印位置
        :param gray_img: 原图的灰度图
        :param watermark_template_gray_img: 水印模板的灰度图
        :return: x1, y1, x2, y2
        """
        # Load the images in gray scale

        method = cv2.TM_CCOEFF
        # Apply template Matching
        res = cv2.matchTemplate(gray_img, watermark_template_gray_img, 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]:
            x, y = min_loc
        else:
            x, y = max_loc

        return x, y, x + self.watermark_template_w, y + self.watermark_template_h 

def locate_img(image, template):
    img = image.copy()
    res = cv2.matchTemplate(img, template, method)
    print res
    print res.shape
    cv2.imwrite('image/shape.png', res)
    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
    print cv2.minMaxLoc(res)
    if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:
        top_left = min_loc
    else:
        top_left = max_loc
    h, w = template.shape
    bottom_right = (top_left[0] + w, top_left[1]+h)
    cv2.rectangle(img, top_left, bottom_right, 255, 2)
    cv2.imwrite('image/tt.jpg', img) 

def locate_img(image, template):
    img = image.copy()
    res = cv2.matchTemplate(img, template, method)
    print res
    print res.shape
    cv2.imwrite('image/shape.png', res)
    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
    print cv2.minMaxLoc(res)
    if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:
        top_left = min_loc
    else:
        top_left = max_loc
    h, w = template.shape
    bottom_right = (top_left[0] + w, top_left[1]+h)
    cv2.rectangle(img, top_left, bottom_right, 255, 2)
    cv2.imwrite('image/tt.jpg', img) 

def count_occurrence(this):
        Screenshot.shot()
        this = this + '.png'
        img_rgb = cv2.imread(this)
        template = cv2.imread('playing.png')
        res = cv2.matchTemplate(img_rgb, template, cv2.TM_SQDIFF_NORMED)
        threshold = 0.8
        loc = np.where(res >= threshold)
        cv2.imwrite('result.png', img_rgb)
        return loc 

def find_substream(self, pattern, window_center, window_size):
        start_time = clip(window_center - window_size, -self.PADDING_SECONDS, self.duration_seconds)
        end_time = clip(window_center + window_size, 0, self.duration_seconds + self.PADDING_SECONDS)

        start_sample = self._get_sample_for_time(start_time)
        end_sample = self._get_sample_for_time(end_time) + len(pattern[0])

        search_source = self.data[:, start_sample:end_sample]
        result = cv2.matchTemplate(search_source, pattern, cv2.TM_SQDIFF_NORMED)
        min_idx = result.argmin(axis=1)[0]

        return result[0][min_idx], start_time + (min_idx / float(self.sample_rate)) 

def match_template(self,img,template,threshold=u"0.8"):
        """  Matches a template in an image using TM_CCOEFF_NORMED method

        Both `img` and `tempalte` are BGR ndarray object.
        The result is in the the center and boundary of the match.
        """
        _method = cv2.TM_CCOEFF_NORMED
        gray_img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
        gray_template = cv2.cvtColor(template,cv2.COLOR_BGR2GRAY)
        w,h = gray_template.shape[::-1]

        res = cv2.matchTemplate(gray_img,gray_template,_method)
        loc = np.where(res >= float(threshold))
        if len(loc[0]) != 0 and len(loc[1]) != 0:
            min_val,max_val,min_loc,max_loc = cv2.minMaxLoc(res)
            if _method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:
                top_left = min_loc
            else:
                top_left = max_loc
            bottom_right = (top_left[0] + w, top_left[1] + h)
            mx = int((top_left[0] + bottom_right[0])/2)
            my = int((top_left[1] + bottom_right[1])/2)
            result = ((mx,my),(top_left[0],top_left[1],bottom_right[0],bottom_right[1]))
            BuiltIn().log("Found image at %s" % str(result))
        else:
            result = (None,None)
            BuiltIn().log("WRN: Could not found the template")
        return result 

def find(search_file, image_file, threshold=0.7):
    '''
    Locate image position with cv2.templateFind

    Use pixel match to find pictures.

    Args:
        search_file(string): filename of search object
        image_file(string): filename of image to search on
        threshold: optional variable, to ensure the match rate should >= threshold

    Returns:
        A tuple like (x, y) or None if nothing found

    Raises:
        IOError: when file read error
    '''
    search = _cv2open(search_file)
    image  = _cv2open(image_file)

    w, h = search.shape[::-1]

    method = cv2.CV_TM_CCORR_NORMED
    res = cv2.matchTemplate(image, search, method)
    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
    if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:
        top_left = min_loc
    else:
        top_left = max_loc
    bottom_right = (top_left[0] + w, top_left[1] + h)
    middle_point = (top_left[0]+w/2, top_left[1]+h/2)
    print top_left, bottom_right
    return middle_point

    # if len(region_center):
    #     x = int(maxloc[0]+region_center[0]-source_width/2)
    #     y = int(maxloc[1]+region_center[1]-source_height/2)
    # else:
    #     [x,y] = maxloc
    # return max_val, [x,y] 

def coords(this, shot=True):
        this = this + '.png'
        if shot:
            Screenshot.shot()
        else:
            print('No screenshot')
        small_image = cv2.imread(this)
        h, w, c = small_image.shape
        large_image = cv2.imread('playing.png')
        result = cv2.matchTemplate(
            small_image, large_image, cv2.TM_SQDIFF_NORMED)
        min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
        # print('ImageCoordinate::coords => ' + this + ' ' + str(min_val))
        mn, _, mn_loc, mx_loc = cv2.minMaxLoc(result)
        mp_x, mp_y = mn_loc
        # #pyautogui.moveTo(mp_x, mp_y)
        # top_left = mn_loc
        # mx_right = mx_loc
        # bt_rt =(top_left[0]+h,top_left[1]+w)
        # cv2.rectangle(large_image,top_left,bt_rt,255,2)

        # bt_rt =(mx_right[0]+h,mx_right[1]+w)
        # cv2.rectangle(large_image,mx_right,bt_rt,255,2)
        # cv2.imwrite('result_'+this.replace('images/',''), large_image)
        # print('saved'+str(min_val))
        if min_val > 0.2:
            return [0, 0, min_val]
        mn, _, mn_loc, _ = cv2.minMaxLoc(result)
        mp_x, mp_y = mn_loc
        ordinal = random.randrange(1, 15)
        a = random.randrange(-ordinal, ordinal)
        b = random.randrange(-ordinal, ordinal)

        location = [mp_x + w / 2+a, mp_y + h / 2+b, min_val]
        return location 

def is_on_screen(this, accuracy=0.14):
        this = this + '.png'
        Screenshot.shot()
        small_image = cv2.imread(this)
        h, w, c = small_image.shape
        large_image = cv2.imread('playing.png')
        result = cv2.matchTemplate(
            small_image, large_image, cv2.TM_SQDIFF_NORMED)
        min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
        #print('ImageCoordinate::is_on_screen => ' + this + ' ' + str(min_val))
        mn, _, mn_loc, mx_loc = cv2.minMaxLoc(result)
        mp_x, mp_y = mn_loc
        m_x, m_y = mx_loc
        # print(mn_loc)
        # top_left = mn_loc
        # mx_right = mx_loc
        bt_rt = (mn_loc[0], mn_loc[1])
        bt_rtw = (mn_loc[0]+w, mn_loc[1]+h)
        # cv2.rectangle(large_image,top_left,bt_rt,255,2)

        # bt_rt =(mx_right[0]+h,mx_right[1]+w)
        # cv2.rectangle(large_image,mx_right,bt_rt,255,2)
        # cv2.imwrite('result_'+this.replace('images/',''), large_image)
        # print('saved')
        #pyautogui.moveTo(mp_x, mp_y)
        print(min_val)
        if min_val > accuracy:
            return False
        else:
            mn, _, mn_loc, _ = cv2.minMaxLoc(result)
            mp_x, mp_y = mn_loc
            ordinal = random.randrange(1, 15)
            a = random.randrange(-ordinal, ordinal)
            b = random.randrange(-ordinal, ordinal)
            location = [mp_x + w / 2+a, mp_y + h / 2+b, bt_rt, bt_rtw, min_val]
            return location 

def match_template1(template, img, plot=False, method=cv2.TM_SQDIFF_NORMED):
    img = cv2.imread(img, 0).copy()
    template = cv2.imread(template, 0)
    w, h = template.shape[::-1]
    if lib == OPENCV:
        res = cv2.matchTemplate(img, template, method)
        min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
        if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:
            top_left = min_loc
        else:
            top_left = max_loc
    else:
        result = match_template(img, template)
        ij = np.unravel_index(np.argmax(result), result.shape)
        top_left = ij[::-1]

    bottom_right = (top_left[0] + w, top_left[1] + h)

    if plot:
        cv2.rectangle(img, top_left, bottom_right, 255, 5)
        plt.subplot(121)
        plt.imshow(img)
        plt.title('Detected Point'), plt.xticks([]), plt.yticks([])
        plt.subplot(122)
        plt.imshow(template)

        plt.show()

    return top_left, bottom_right 

def oneBattle():
    turn,stage,stageTurn,servant=0,0,0,[0,1,2]
    while True:
        if Check(.1).isTurnBegin():
            turn+=1
            stage,stageTurn,skill,newPortrait=(lambda chk:(lambda x:[x,stageTurn+1if stage==x else 1])(chk.getStage())+[chk.isSkillReady(),chk.getPortrait()])(Check(.2))
            if turn==1:stageTotal=check.getStageTotal()
            else:servant=(lambda m,p:[m+p.index(i)+1if i in p else servant[i]for i in range(3)])(max(servant),[i for i in range(3)if servant[i]<6and cv2.matchTemplate(newPortrait[i],portrait[i],cv2.TM_SQDIFF_NORMED)[0][0]>=.03])
            if stageTurn==1:doit('\x69\x68\x67\x66\x65\x64'[dangerPos[stage-1]]+'P',(250,500))
            portrait=newPortrait
            logger.info(f'{turn} {stage} {stageTurn} {servant}')
            for i,j in((i,j)for i in range(3)if servant[i]<6for j in range(3)if skill[i][j]and skillInfo[servant[i]][j][0]and stage<<4|stageTurn>=min(skillInfo[servant[i]][j][0],stageTotal)<<4|skillInfo[servant[i]][j][1]):
                doit(('ASD','FGH','JKL')[i][j],(300,))
                if skillInfo[servant[i]][j][2]:doit(chr(skillInfo[servant[i]][j][2]+49),(300,))
                sleep(1.7)
                while not Check(.1).isTurnBegin():pass
                sleep(.16)
            for i in(i for i in range(3)if stage==min(masterSkill[i][0],stageTotal)and stageTurn==masterSkill[i][1]):
                doit('Q'+'WER'[i],(300,300))
                if masterSkill[i][2]:doit(chr(masterSkill[i][2]+49),(300,))
                sleep(1.7)
                while not Check(.1).isTurnBegin():pass
                sleep(.16)
            doit(' ',(2250,))
            doit((lambda chk:(lambda c,h:([chr(i+54)for i in sorted((i for i in range(3)if h[i]),key=lambda x:-houguInfo[servant[x]][1])]if any(h)else[chr(j+49)for i in range(3)if c.count(i)>=3for j in range(5)if c[j]==i])+[chr(i+49)for i in sorted(range(5),key=lambda x:(c[x]&2)>>1|(c[x]&1)<<1)])(chk.getABQ(),(lambda h:[servant[i]<6and h[i]and houguInfo[servant[i]][0]and stage>=min(houguInfo[servant[i]][0],stageTotal)for i in range(3)])(chk.isHouguReady())))(Check())[:3],(350,350,10000))
        elif check.isBattleFinished():
            logger.info('Battle Finished')
            return True
        elif check.tapFailed():
            logger.warning('Battle Failed')
            return False 

def tapOnCmp(self,img,rect=(0,0,1920,1080),delta=.05):return(lambda loc:loc[0]<delta and(base.touch((rect[0]+loc[2][0]+(img.shape[1]>>1),rect[1]+loc[2][1]+(img.shape[0]>>1))),fuse.reset())[1])(cv2.minMaxLoc(cv2.matchTemplate(self.im[rect[1]:rect[3],rect[0]:rect[2]],img,cv2.TM_SQDIFF_NORMED))) 

def select(self,img,rect=(0,0,1920,1080)):return(lambda x:x.index(min(x)))([cv2.minMaxLoc(cv2.matchTemplate(self.im[rect[1]:rect[3],rect[0]:rect[2]],i,cv2.TM_SQDIFF_NORMED))[0]for i in img]) 

def compare(self,img,rect=(0,0,1920,1080),delta=.05):return cv2.minMaxLoc(cv2.matchTemplate(self.im[rect[1]:rect[3],rect[0]:rect[2]],img,cv2.TM_SQDIFF_NORMED))[0]<delta and fuse.reset() 

def getRefCoordinate(self, image, template):
#        method = cv2.TM_SQDIFF                     #2
        method = cv2.TM_SQDIFF_NORMED              #1
#        method = cv2.TM_CCORR_NORMED                #3
        method = cv2.TM_CCOEFF_NORMED                #4
        res = cv2.matchTemplate(image, template, 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
        else:
            top_left = max_loc
#        bottom_right = (top_left[0] + w, top_left[1] + h)
        return top_left 

def findBestMatch(self, needle, similarity):
        """ Find the best match for ``needle`` that has a similarity better than or equal to ``similarity``.

        Returns a tuple of ``(position, confidence)`` if a match is found, or ``None`` otherwise.

        *Developer's Note - Despite the name, this method actually returns the **first** result
        with enough similarity, not the **best** result.*
        """
        method = cv2.TM_CCOEFF_NORMED
        position = None

        match = cv2.matchTemplate(self.haystack, needle, method)
        min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(match)
        if method == cv2.TM_SQDIFF_NORMED or method == cv2.TM_SQDIFF:
            confidence = min_val
            if min_val <= 1-similarity:
                # Confidence checks out
                position = min_loc
        else:
            confidence = max_val
            if max_val >= similarity:
                # Confidence checks out
                position = max_loc

        if not position:
            return None

        return (position, confidence) 

def MatchingMethod(param):

   global match_method
   match_method = param

   ## [copy_source]
   img_display = img.copy()
   ## [copy_source]
   ## [match_template]
   method_accepts_mask = (cv2.TM_SQDIFF == match_method or match_method == cv2.TM_CCORR_NORMED)
   if (use_mask and method_accepts_mask):
       result = cv2.matchTemplate(img, templ, match_method, None, mask)
   else:
       result = cv2.matchTemplate(img, templ, match_method)
   ## [match_template]

   ## [normalize]
   cv2.normalize( result, result, 0, 1, cv2.NORM_MINMAX, -1 )
   ## [normalize]
   ## [best_match]
   minVal, maxVal, minLoc, maxLoc = cv2.minMaxLoc(result, None)
   ## [best_match]

   ## [match_loc]
   if (match_method == cv2.TM_SQDIFF or match_method == cv2.TM_SQDIFF_NORMED):
       matchLoc = minLoc
   else:
       matchLoc = maxLoc
   ## [match_loc]

   ## [imshow]
   cv2.rectangle(img_display, matchLoc, (matchLoc[0] + templ.shape[0], matchLoc[1] + templ.shape[1]), (0,0,0), 2, 8, 0 )
   cv2.rectangle(result, matchLoc, (matchLoc[0] + templ.shape[0], matchLoc[1] + templ.shape[1]), (0,0,0), 2, 8, 0 )
   cv2.imshow(image_window, img_display)
   cv2.imshow(result_window, result)
   ## [imshow]
   pass