def vis_one_image_opencv(
        im, boxes, segms=None, keypoints=None, thresh=0.9, kp_thresh=2,
        show_box=False, dataset=None, show_class=False):
    """Constructs a numpy array with the detections visualized."""

    if isinstance(boxes, list):
        boxes, segms, keypoints, classes = convert_from_cls_format(
            boxes, segms, keypoints)

    if boxes is None or boxes.shape[0] == 0 or max(boxes[:, 4]) < thresh:
        return im

    if segms is not None and len(segms) > 0:
        masks = mask_util.decode(segms)
        color_list = colormap()
        mask_color_id = 0

    # Display in largest to smallest order to reduce occlusion
    areas = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
    sorted_inds = np.argsort(-areas)

    for i in sorted_inds:
        bbox = boxes[i, :4]
        score = boxes[i, -1]
        if score < thresh:
            continue

        # show box (off by default)
        if show_box:
            im = vis_bbox(
                im, (bbox[0], bbox[1], bbox[2] - bbox[0], bbox[3] - bbox[1]))

        # show class (off by default)
        if show_class:
            class_str = get_class_string(classes[i], score, dataset)
            im = vis_class(im, (bbox[0], bbox[1] - 2), class_str)

        # show mask
        if segms is not None and len(segms) > i:
            color_mask = color_list[mask_color_id % len(color_list), 0:3]
            mask_color_id += 1
            im = vis_mask(im, masks[..., i], color_mask)

        # show keypoints
        if keypoints is not None and len(keypoints) > i:
            im = vis_keypoints(im, keypoints[i], kp_thresh)

    return im 

def vis_one_image_opencv(
        im, boxes, segms=None, keypoints=None, thresh=0.9, kp_thresh=2,
        show_box=False, dataset=None, show_class=False):
    """Constructs a numpy array with the detections visualized."""

    if isinstance(boxes, list):
        boxes, segms, keypoints, classes = convert_from_cls_format(
            boxes, segms, keypoints)

    if boxes is None or boxes.shape[0] == 0 or max(boxes[:, 4]) < thresh:
        return im

    if segms is not None and len(segms) > 0:
        masks = mask_util.decode(segms)
        color_list = colormap()
        mask_color_id = 0

    # Display in largest to smallest order to reduce occlusion
    areas = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
    sorted_inds = np.argsort(-areas)

    for i in sorted_inds:
        bbox = boxes[i, :4]
        score = boxes[i, -1]
        if score < thresh:
            continue

        # show box (off by default)
        if show_box:
            im = vis_bbox(
                im, (bbox[0], bbox[1], bbox[2] - bbox[0], bbox[3] - bbox[1]))

        # show class (off by default)
        if show_class:
            class_str = get_class_string(classes[i], score, dataset)
            im = vis_class(im, (bbox[0], bbox[1] - 2), class_str)

        # show mask
        if segms is not None and len(segms) > i:
            color_mask = color_list[mask_color_id % len(color_list), 0:3]
            mask_color_id += 1
            im = vis_mask(im, masks[..., i], color_mask)

        # show keypoints
        if keypoints is not None and len(keypoints) > i:
            im = vis_keypoints(im, keypoints[i], kp_thresh)

    return im 

def vis_one_image_opencv(
        im, boxes, segms=None, keypoints=None, thresh=0.9, kp_thresh=2,
        show_box=False, dataset=None, show_class=False):
    """Constructs a numpy array with the detections visualized."""

    if isinstance(boxes, list):
        boxes, segms, keypoints, classes = convert_from_cls_format(
            boxes, segms, keypoints)

    if boxes is None or boxes.shape[0] == 0 or max(boxes[:, 4]) < thresh:
        return im

    if segms is not None and len(segms) > 0:
        masks = mask_util.decode(segms)
        color_list = colormap()
        mask_color_id = 0

    # Display in largest to smallest order to reduce occlusion
    areas = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
    sorted_inds = np.argsort(-areas)

    for i in sorted_inds:
        bbox = boxes[i, :4]
        score = boxes[i, -1]
        if score < thresh:
            continue

        # show box (off by default)
        if show_box:
            im = vis_bbox(
                im, (bbox[0], bbox[1], bbox[2] - bbox[0], bbox[3] - bbox[1]))

        # show class (off by default)
        if show_class:
            class_str = get_class_string(classes[i], score, dataset)
            im = vis_class(im, (bbox[0], bbox[1] - 2), class_str)

        # show mask
        if segms is not None and len(segms) > i:
            color_mask = color_list[mask_color_id % len(color_list), 0:3]
            mask_color_id += 1
            im = vis_mask(im, masks[..., i], color_mask)

        # show keypoints
        if keypoints is not None and len(keypoints) > i:
            im = vis_keypoints(im, keypoints[i], kp_thresh)

    return im 

def vis_one_image_opencv(
        im, boxes, segms=None, keypoints=None, thresh=0.9, kp_thresh=2,
        show_box=False, dataset=None, show_class=False):
    """Constructs a numpy array with the detections visualized."""

    if isinstance(boxes, list):
        boxes, segms, keypoints, classes = convert_from_cls_format(
            boxes, segms, keypoints)

    if boxes is None or boxes.shape[0] == 0 or max(boxes[:, 4]) < thresh:
        return im

    if segms is not None and len(segms) > 0:
        masks = mask_util.decode(segms)
        color_list = colormap()
        mask_color_id = 0

    # Display in largest to smallest order to reduce occlusion
    areas = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
    sorted_inds = np.argsort(-areas)

    for i in sorted_inds:
        bbox = boxes[i, :4]
        score = boxes[i, -1]
        if score < thresh:
            continue

        # show box (off by default)
        if show_box:
            im = vis_bbox(
                im, (bbox[0], bbox[1], bbox[2] - bbox[0], bbox[3] - bbox[1]))

        # show class (off by default)
        if show_class:
            class_str = get_class_string(classes[i], score, dataset)
            im = vis_class(im, (bbox[0], bbox[1] - 2), class_str)

        # show mask
        if segms is not None and len(segms) > i:
            color_mask = color_list[mask_color_id % len(color_list), 0:3]
            mask_color_id += 1
            im = vis_mask(im, masks[..., i], color_mask)

        # show keypoints
        if keypoints is not None and len(keypoints) > i:
            im = vis_keypoints(im, keypoints[i], kp_thresh)

    return im 

def vis_one_image_opencv(
        im, boxes, segms=None, keypoints=None, thresh=0.9, kp_thresh=2,
        show_box=False, dataset=None, show_class=False):
    """Constructs a numpy array with the detections visualized."""

    if isinstance(boxes, list):
        boxes, segms, keypoints, classes = convert_from_cls_format(
            boxes, segms, keypoints)

    if boxes is None or boxes.shape[0] == 0 or max(boxes[:, 4]) < thresh:
        return im

    if segms is not None and len(segms) > 0:
        masks = mask_util.decode(segms)
        color_list = colormap()
        mask_color_id = 0

    # Display in largest to smallest order to reduce occlusion
    areas = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
    sorted_inds = np.argsort(-areas)

    for i in sorted_inds:
        bbox = boxes[i, :4]
        score = boxes[i, -1]
        if score < thresh:
            continue

        # show box (off by default)
        if show_box:
            im = vis_bbox(
                im, (bbox[0], bbox[1], bbox[2] - bbox[0], bbox[3] - bbox[1]))

        # show class (off by default)
        if show_class:
            class_str = get_class_string(classes[i], score, dataset)
            im = vis_class(im, (bbox[0], bbox[1] - 2), class_str)

        # show mask
        if segms is not None and len(segms) > i:
            color_mask = color_list[mask_color_id % len(color_list), 0:3]
            mask_color_id += 1
            im = vis_mask(im, masks[..., i], color_mask)

        # show keypoints
        if keypoints is not None and len(keypoints) > i:
            im = vis_keypoints(im, keypoints[i], kp_thresh)

    return im 

def vis_one_image_opencv(
        im, boxes, segms=None, keypoints=None, thresh=0.9, kp_thresh=2,
        show_box=False, dataset=None, show_class=False):
    """Constructs a numpy array with the detections visualized."""

    if isinstance(boxes, list):
        boxes, segms, keypoints, classes = convert_from_cls_format(
            boxes, segms, keypoints)

    if boxes is None or boxes.shape[0] == 0 or max(boxes[:, 4]) < thresh:
        return im

    if segms is not None and len(segms) > 0:
        masks = mask_util.decode(segms)
        color_list = colormap()
        mask_color_id = 0

    # Display in largest to smallest order to reduce occlusion
    areas = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
    sorted_inds = np.argsort(-areas)

    for i in sorted_inds:
        bbox = boxes[i, :4]
        score = boxes[i, -1]
        if score < thresh:
            continue

        # show box (off by default)
        if show_box:
            im = vis_bbox(
                im, (bbox[0], bbox[1], bbox[2] - bbox[0], bbox[3] - bbox[1]))

        # show class (off by default)
        if show_class:
            class_str = get_class_string(classes[i], score, dataset)
            im = vis_class(im, (bbox[0], bbox[1] - 2), class_str)

        # show mask
        if segms is not None and len(segms) > i:
            color_mask = color_list[mask_color_id % len(color_list), 0:3]
            mask_color_id += 1
            im = vis_mask(im, masks[..., i], color_mask)

        # show keypoints
        if keypoints is not None and len(keypoints) > i:
            im = vis_keypoints(im, keypoints[i], kp_thresh)

    return im 

def vis_one_image_opencv(
        im, boxes, segms=None, keypoints=None, thresh=0.9, kp_thresh=2,
        show_box=False, dataset=None, show_class=False):
    """Constructs a numpy array with the detections visualized."""

    if isinstance(boxes, list):
        boxes, segms, keypoints, classes = convert_from_cls_format(
            boxes, segms, keypoints)

    if boxes is None or boxes.shape[0] == 0 or max(boxes[:, 4]) < thresh:
        return im

    if segms is not None and len(segms) > 0:
        masks = mask_util.decode(segms)
        color_list = colormap()
        mask_color_id = 0

    # Display in largest to smallest order to reduce occlusion
    areas = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
    sorted_inds = np.argsort(-areas)

    for i in sorted_inds:
        bbox = boxes[i, :4]
        score = boxes[i, -1]
        if score < thresh:
            continue

        # show box (off by default)
        if show_box:
            im = vis_bbox(
                im, (bbox[0], bbox[1], bbox[2] - bbox[0], bbox[3] - bbox[1]))

        # show class (off by default)
        if show_class:
            class_str = get_class_string(classes[i], score, dataset)
            im = vis_class(im, (bbox[0], bbox[1] - 2), class_str)

        # show mask
        if segms is not None and len(segms) > i:
            color_mask = color_list[mask_color_id % len(color_list), 0:3]
            mask_color_id += 1
            im = vis_mask(im, masks[..., i], color_mask)

        # show keypoints
        if keypoints is not None and len(keypoints) > i:
            im = vis_keypoints(im, keypoints[i], kp_thresh)

    return im 

def vis_one_image_opencv(
        im, boxes, segms=None, keypoints=None, thresh=0.9, kp_thresh=2,
        show_box=False, dataset=None, show_class=False,
        alpha=0.4, show_border=True, border_thick=1, bbox_thick=1, font_scale=0.35):
    """Constructs a numpy array with the detections visualized."""

    if isinstance(boxes, list):
        boxes, segms, keypoints, classes = convert_from_cls_format(
            boxes, segms, keypoints)

    if boxes is None or boxes.shape[0] == 0 or max(boxes[:, 4]) < thresh:
        return im

    if segms is not None and len(segms) > 0:
        masks = mask_util.decode(segms)
        color_list = colormap()
        mask_color_id = 0

    # Display in largest to smallest order to reduce occlusion
    areas = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
    sorted_inds = np.argsort(-areas)

    for i in sorted_inds:
        bbox = boxes[i, :4]
        score = boxes[i, -1]
        if score < thresh:
            continue

        # show box (off by default)
        if show_box:
            im = vis_bbox(
                im, (bbox[0], bbox[1], bbox[2] - bbox[0], bbox[3] - bbox[1]), thick=bbox_thick)

        # show class (off by default)
        if show_class:
            class_str = get_class_string(classes[i], score, dataset)
            im = vis_class(im, (bbox[0], bbox[1] - 2), class_str, font_scale=font_scale)

        # show mask
        if segms is not None and len(segms) > i:
            color_mask = color_list[mask_color_id % len(color_list), 0:3]
            mask_color_id += 1
            im = vis_mask(im, masks[..., i], color_mask, alpha=alpha,
             show_border=show_border, border_thick=border_thick)

        # show keypoints
        if keypoints is not None and len(keypoints) > i:
            im = vis_keypoints(im, keypoints[i], kp_thresh)

    return im