Merge branch 'dmMaze:master' into master

        im_h, im_w = img.shape[:2]

        blks, mask, lines_map = self.net(img_in)

        if self.backend == 'opencv':

            if mask.shape[1] == 2:     # some version of opencv spit out reversed result

                tmp = mask

                mask = lines_map

                lines_map = tmp

        resize_ratio = (im_w / (detect_size[0] - dw), im_h / (detect_size[1] - dh))

        blks = postprocess_yolo(blks, self.conf_thresh, self.nms_thresh, resize_ratio)

    x2, y2 = min(im_w-1, x2+paddings), min(im_h-1, y2+paddings)

    return [x1, y1, x2, y2]

def enlarge_window(rect, im_w, im_h, ratio=2.5) -> List:

def enlarge_window(rect, im_w, im_h, ratio=2.5, aspect_ratio=1.0) -> List:

    assert ratio > 1.0

    x1, y1, x2, y2 = rect

    h = y2 - y1

    # https://numpy.org/doc/stable/reference/generated/numpy.roots.html

    coeff = [1, w+h, (1-ratio)*w*h]

    coeff = [aspect_ratio, w+h*aspect_ratio, (1-ratio)*w*h]

    roots = np.roots(coeff)

    roots.sort()

    delta = int(round(roots[-1] / 2 ))

    rect = np.array([x1-delta, y1-delta, x2+delta, y2+delta], dtype=np.int64)

    rect[[0, 2]] = np.clip(rect[[0, 2]], 0, im_w)

    rect[[1, 3]] = np.clip(rect[[1, 3]], 0, im_h)

    delta_w = int(delta * aspect_ratio)

    delta_w = min(x1, im_w - x2, delta_w)

    delta = min(y1, im_h - y2, delta)

    rect = np.array([x1-delta_w, y1-delta, x2+delta_w, y2+delta], dtype=np.int64)

    return rect.tolist()

def draw_connected_labels(num_labels, labels, stats, centroids, names="draw_connected_labels", skip_background=True):