Python cv2 模块，TM_CCOEFF 实例源码

def test_templating(self):

        bbox = self.wh.create_boundingbox()
        scaled_bbox = self.wh.bbox_scale(bbox,0.5)
        sub_image = self.px.grab_window(scaled_bbox)
        sub_image = self.px.img_to_numpy(sub_image)

        w, h = sub_image.shape[0:2]

        main_image = cv2.imread('pytomatic/tests/assets/calc_clean.PNG')
        res = cv2.matchTemplate(main_image, sub_image, cv2.TM_CCOEFF)
        min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)

        top_left = max_loc
        bottom_right = (top_left[0] + w, top_left[1] + h)
        cv2.rectangle(main_image, top_left, bottom_right, 255, 2)
        assert top_left == (89,89)
        assert bottom_right == (290,290)

        #plt.imshow(main_image)
        #plt.show()

def __init__(self, root_patch=None):
        super(TDLTracker, self).__init__(root_patch)
        self.descriptor = ColorHistogramExtractor()
        self.match_method = cv2.TM_CCOEFF
        self.current_frame = None
        self.patch_h, self.patch_w = 0, 0
        self.tracker = None

        self.dispatcher = {
            TDLTracker.STATE_UNINITIATED: self.findTarget,
            TDLTracker.STATE_INITIATED: self.updateTarget,
            TDLTracker.STATE_INTERRUPTED: self.restartTarget,
            TDLTracker.STATE_FINISHED: self.updateTarget,
        }

def click_image(image, notify=True):

    if notify:
        _notify("starting to click " + image)

    if isinstance(image, str) or isinstance(image, unicode):
        template = cv2.imread(image, 0)
    elif isinstance(image, PngImageFile):
        pass # need to convert to cv2 image type

    sleep(2)

    #GET SCREENSHOT
    call(["gnome-screenshot", "--file=/tmp/beryl.png"])
    sleep(1)

    #FIND LOCATION OF NAME
    source = cv2.imread('/tmp/beryl.png', 0)

    points = []
    w, h = template.shape[::-1]
    methods = [cv2.TM_CCOEFF,cv2.TM_CCOEFF_NORMED,cv2.TM_CCORR,cv2.TM_CCORR_NORMED,cv2.TM_SQDIFF,cv2.TM_SQDIFF_NORMED]
    for method in methods:
        # Apply Template Matching
        result = cv2.matchTemplate(source.copy(), template, method)
        min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)

        #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)
        # (x,y)
        point = (  top_left[0] + (float(w)/2), top_left[1] + (float(h)/2)  )
        points.append(point)

    best_point = sorted([(point, avg_distance(point, points)) for point in points], key=lambda tup: tup[1])[0][0]

    click_location(best_point)

    if notify:
        _notify("finished clicking image")

def match_template_opencv(template, image, options):
    """
    Match template using OpenCV template matching implementation.
        Limited by number of channels as maximum of 3.
        Suitable for direct RGB or Gray-scale matching

    :param options: Other options:
        - distance: Distance measure to use. (euclidean | correlation | ccoeff).
            Default: 'correlation'
        - normalize: Heatmap values will be in the range of 0 to 1. Default: True
        - retain_size: Whether to retain the same size as input image. Default: True
    :return: Heatmap
    """
    # if image has more than 3 channels, use own implementation
    if len(image.shape) > 3:
        return match_template(template, image, options)

    op = _DEF_TM_OPT.copy()
    if options is not None:
        op.update(options)

    method = cv.TM_CCORR_NORMED
    if op['normalize'] and op['distance'] == 'euclidean':
        method = cv.TM_SQDIFF_NORMED
    elif op['distance'] == 'euclidean':
        method = cv.TM_SQDIFF
    elif op['normalize'] and op['distance'] == 'ccoeff':
        method = cv.TM_CCOEFF_NORMED
    elif op['distance'] == 'ccoeff':
        method = cv.TM_CCOEFF
    elif not op['normalize'] and op['distance'] == 'correlation':
        method = cv.TM_CCORR

    heatmap = cv.matchTemplate(image, template, method)

    # make minimum peak heatmap
    if method not in [cv.TM_SQDIFF, cv.TM_SQDIFF_NORMED]:
        heatmap = heatmap.max() - heatmap

    if op['normalize']:
        heatmap /= heatmap.max()

    # size
    if op['retain_size']:
        hmap = np.ones(image.shape[:2]) * heatmap.max()
        h, w = heatmap.shape
        hmap[:h, :w] = heatmap
        heatmap = hmap

    return heatmap

def find_subimage_in_array(self, sub_image, main_image, threshold=0.40, value=False, debug=False):
        """
        http://docs.opencv.org/3.1.0/d4/dc6/tutorial_py_template_matching.html

        Args:
            sub_image: A numby matrix containing the template we are trying to match
            main_image: A numpy array containing the main image we are trying to find the template in
            value: If true: Similarity is sent back.
            threshold: A treshhold regarding hos sensitive the matching should be.
        Returns:
            A list containing touples:
                If value is true:
                    The touples got he following elements(left,top,right,down,similarity)
                    Where similarity is a measure toward one
                Else:
                    The touples got he following elements(left,top,right,down)

        """
        # TODO: Check the test_init_wnd test for how to implement this :)
        logging.debug("Doing a template match with {} as threshold".format(threshold))
        methods = [cv2.TM_CCOEFF, cv2.TM_CCOEFF_NORMED, cv2.TM_CCORR, cv2.TM_CCORR_NORMED, cv2.TM_SQDIFF,
                   cv2.TM_SQDIFF_NORMED]
        method = methods[0]

        h, w = sub_image.shape[0:2]

        res = cv2.matchTemplate(main_image, sub_image, method)

        loc = np.where(res >= threshold)
        locations = []
        for pt in zip(*loc[::-1]):
            if value:
                locations.append((pt[0], pt[1], pt[0] + w, pt[1] + h, res[pt[1], pt[0]]))
            else:
                locations.append((pt[0], pt[1], pt[0] + w, pt[1] + h))

        logging.debug("Found {} locations".format(len(locations)))
        if debug:
            plt.subplot(121), plt.imshow(res, cmap='gray')
            plt.title('Matching Result'), plt.xticks([]), plt.yticks([])
            plt.subplot(122), plt.imshow(main_image, cmap='gray')
            plt.title('Detected Point'), plt.xticks([]), plt.yticks([])
            for pt in zip(*loc[::-1]):
                cv2.rectangle(main_image, pt, (pt[0] + w, pt[1] + h), (255, 0, 255), 2)
            plt.imshow(main_image)
            plt.show()

        if value:
            locations.sort(reverse=True, key=operator.itemgetter(4))
        return list(map(operator.itemgetter(0, 1, 2, 3), locations))