animalpose_utils.py

import math

import cv2
import numpy as np

from logging import getLogger

logger = getLogger(__name__)

__all__ = [
    'get_affine_transform',
    'keypoints_from_heatmaps',
    'show_result'
]


def get_affine_transform(
        center,
        scale,
        rot,
        output_size,
        shift=(0., 0.),
        inv=False):
    """Get the affine transform matrix, given the center/scale/rot/output_size.

    Args:
        center (np.ndarray[2, ]): Center of the bounding box (x, y).
        scale (np.ndarray[2, ]): Scale of the bounding box
            wrt [width, height].
        rot (float): Rotation angle (degree).
        output_size (np.ndarray[2, ] | list(2,)): Size of the
            destination heatmaps.
        shift (0-100%): Shift translation ratio wrt the width/height.
            Default (0., 0.).
        inv (bool): Option to inverse the affine transform direction.
            (inv=False: src->dst or inv=True: dst->src)

    Returns:
        np.ndarray: The transform matrix.
    """
    assert len(center) == 2
    assert len(scale) == 2
    assert len(output_size) == 2
    assert len(shift) == 2

    # pixel_std is 200.
    scale_tmp = scale * 200.0

    shift = np.array(shift)
    src_w = scale_tmp[0]
    dst_w = output_size[0]
    dst_h = output_size[1]

    rot_rad = np.pi * rot / 180
    src_dir = rotate_point([0., src_w * -0.5], rot_rad)
    dst_dir = np.array([0., dst_w * -0.5])

    src = np.zeros((3, 2), dtype=np.float32)
    src[0, :] = center + scale_tmp * shift
    src[1, :] = center + src_dir + scale_tmp * shift
    src[2, :] = _get_3rd_point(src[0, :], src[1, :])

    dst = np.zeros((3, 2), dtype=np.float32)
    dst[0, :] = [dst_w * 0.5, dst_h * 0.5]
    dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir
    dst[2, :] = _get_3rd_point(dst[0, :], dst[1, :])

    if inv:
        trans = cv2.getAffineTransform(np.float32(dst), np.float32(src))
    else:
        trans = cv2.getAffineTransform(np.float32(src), np.float32(dst))

    return trans


def _get_3rd_point(a, b):
    """To calculate the affine matrix, three pairs of points are required. This
    function is used to get the 3rd point, given 2D points a & b.

    The 3rd point is defined by rotating vector `a - b` by 90 degrees
    anticlockwise, using b as the rotation center.

    Args:
        a (np.ndarray): point(x,y)
        b (np.ndarray): point(x,y)

    Returns:
        np.ndarray: The 3rd point.
    """
    assert len(a) == 2
    assert len(b) == 2

    direction = a - b
    third_pt = b + np.array([-direction[1], direction[0]], dtype=np.float32)

    return third_pt


def rotate_point(pt, angle_rad):
    """Rotate a point by an angle.

    Args:
        pt (list[float]): 2 dimensional point to be rotated
        angle_rad (float): rotation angle by radian

    Returns:
        list[float]: Rotated point.
    """
    assert len(pt) == 2

    sn, cs = np.sin(angle_rad), np.cos(angle_rad)
    new_x = pt[0] * cs - pt[1] * sn
    new_y = pt[0] * sn + pt[1] * cs
    rotated_pt = [new_x, new_y]

    return rotated_pt


def _get_max_preds(heatmaps):
    """Get keypoint predictions from score maps.

    Note:
        batch_size: N
        num_keypoints: K
        heatmap height: H
        heatmap width: W

    Args:
        heatmaps (np.ndarray[N, K, H, W]): model predicted heatmaps.

    Returns:
        tuple: A tuple containing aggregated results.

        - preds (np.ndarray[N, K, 2]): Predicted keypoint location.
        - maxvals (np.ndarray[N, K, 1]): Scores (confidence) of the keypoints.
    """
    assert isinstance(heatmaps,
                      np.ndarray), ('heatmaps should be numpy.ndarray')
    assert heatmaps.ndim == 4, 'batch_images should be 4-ndim'

    N, K, _, W = heatmaps.shape
    heatmaps_reshaped = heatmaps.reshape((N, K, -1))
    idx = np.argmax(heatmaps_reshaped, 2).reshape((N, K, 1))
    maxvals = np.amax(heatmaps_reshaped, 2).reshape((N, K, 1))

    preds = np.tile(idx, (1, 1, 2)).astype(np.float32)
    preds[:, :, 0] = preds[:, :, 0] % W
    preds[:, :, 1] = preds[:, :, 1] // W

    preds = np.where(np.tile(maxvals, (1, 1, 2)) > 0.0, preds, -1)
    return preds, maxvals


def _taylor(heatmap, coord):
    """Distribution aware coordinate decoding method.

    Note:
        heatmap height: H
        heatmap width: W

    Args:
        heatmap (np.ndarray[H, W]): Heatmap of a particular joint type.
        coord (np.ndarray[2,]): Coordinates of the predicted keypoints.

    Returns:
        np.ndarray[2,]: Updated coordinates.
    """
    H, W = heatmap.shape[:2]
    px, py = int(coord[0]), int(coord[1])
    if 1 < px < W - 2 and 1 < py < H - 2:
        dx = 0.5 * (heatmap[py][px + 1] - heatmap[py][px - 1])
        dy = 0.5 * (heatmap[py + 1][px] - heatmap[py - 1][px])
        dxx = 0.25 * (
                heatmap[py][px + 2] - 2 * heatmap[py][px] + heatmap[py][px - 2])
        dxy = 0.25 * (
                heatmap[py + 1][px + 1] - heatmap[py - 1][px + 1] -
                heatmap[py + 1][px - 1] + heatmap[py - 1][px - 1])
        dyy = 0.25 * (
                heatmap[py + 2 * 1][px] - 2 * heatmap[py][px] +
                heatmap[py - 2 * 1][px])
        derivative = np.array([[dx], [dy]])
        hessian = np.array([[dxx, dxy], [dxy, dyy]])
        if dxx * dyy - dxy ** 2 != 0:
            hessianinv = np.linalg.inv(hessian)
            offset = -hessianinv @ derivative
            offset = np.squeeze(np.array(offset.T), axis=0)
            coord += offset

    return coord


def post_dark_udp(coords, batch_heatmaps, kernel=3):
    """DARK post-pocessing. Implemented by udp. Paper ref: Huang et al. The
    Devil is in the Details: Delving into Unbiased Data Processing for Human
    Pose Estimation (CVPR 2020). Zhang et al. Distribution-Aware Coordinate
    Representation for Human Pose Estimation (CVPR 2020).

    Note:
        batch size: B
        num keypoints: K
        num persons: N
        hight of heatmaps: H
        width of heatmaps: W
        B=1 for bottom_up paradigm where all persons share the same heatmap.
        B=N for top_down paradigm where each person has its own heatmaps.

    Args:
        coords (np.ndarray[N, K, 2]): Initial coordinates of human pose.
        batch_heatmaps (np.ndarray[B, K, H, W]): batch_heatmaps
        kernel (int): Gaussian kernel size (K) for modulation.

    Returns:
        res (np.ndarray[N, K, 2]): Refined coordinates.
    """
    B, K, H, W = batch_heatmaps.shape
    N = coords.shape[0]
    assert (B == 1 or B == N)
    for heatmaps in batch_heatmaps:
        for heatmap in heatmaps:
            cv2.GaussianBlur(heatmap, (kernel, kernel), 0, heatmap)
    np.clip(batch_heatmaps, 0.001, 50, batch_heatmaps)
    np.log(batch_heatmaps, batch_heatmaps)
    batch_heatmaps = np.transpose(batch_heatmaps,
                                  (2, 3, 0, 1)).reshape(H, W, -1)
    batch_heatmaps_pad = cv2.copyMakeBorder(
        batch_heatmaps, 1, 1, 1, 1, borderType=cv2.BORDER_REFLECT)
    batch_heatmaps_pad = np.transpose(
        batch_heatmaps_pad.reshape(H + 2, W + 2, B, K),
        (2, 3, 0, 1)).flatten()

    index = coords[..., 0] + 1 + (coords[..., 1] + 1) * (W + 2)
    index += (W + 2) * (H + 2) * np.arange(0, B * K).reshape(-1, K)
    index = index.astype(int).reshape(-1, 1)
    i_ = batch_heatmaps_pad[index]
    ix1 = batch_heatmaps_pad[index + 1]
    iy1 = batch_heatmaps_pad[index + W + 2]
    ix1y1 = batch_heatmaps_pad[index + W + 3]
    ix1_y1_ = batch_heatmaps_pad[index - W - 3]
    ix1_ = batch_heatmaps_pad[index - 1]
    iy1_ = batch_heatmaps_pad[index - 2 - W]

    dx = 0.5 * (ix1 - ix1_)
    dy = 0.5 * (iy1 - iy1_)
    derivative = np.concatenate([dx, dy], axis=1)
    derivative = derivative.reshape(N, K, 2, 1)
    dxx = ix1 - 2 * i_ + ix1_
    dyy = iy1 - 2 * i_ + iy1_
    dxy = 0.5 * (ix1y1 - ix1 - iy1 + i_ + i_ - ix1_ - iy1_ + ix1_y1_)
    hessian = np.concatenate([dxx, dxy, dxy, dyy], axis=1)
    hessian = hessian.reshape(N, K, 2, 2)
    hessian = np.linalg.inv(hessian + np.finfo(np.float32).eps * np.eye(2))
    coords -= np.einsum('ijmn,ijnk->ijmk', hessian, derivative).squeeze()
    return coords


def _gaussian_blur(heatmaps, kernel=11):
    """Modulate heatmap distribution with Gaussian.
     sigma = 0.3*((kernel_size-1)*0.5-1)+0.8
     sigma~=3 if k=17
     sigma=2 if k=11;
     sigma~=1.5 if k=7;
     sigma~=1 if k=3;

    Note:
        batch_size: N
        num_keypoints: K
        heatmap height: H
        heatmap width: W

    Args:
        heatmaps (np.ndarray[N, K, H, W]): model predicted heatmaps.
        kernel (int): Gaussian kernel size (K) for modulation, which should
            match the heatmap gaussian sigma when training.
            K=17 for sigma=3 and k=11 for sigma=2.

    Returns:
        np.ndarray[N, K, H, W]: Modulated heatmap distribution.
    """
    assert kernel % 2 == 1

    border = (kernel - 1) // 2
    batch_size = heatmaps.shape[0]
    num_joints = heatmaps.shape[1]
    height = heatmaps.shape[2]
    width = heatmaps.shape[3]
    for i in range(batch_size):
        for j in range(num_joints):
            origin_max = np.max(heatmaps[i, j])
            dr = np.zeros((height + 2 * border, width + 2 * border),
                          dtype=np.float32)
            dr[border:-border, border:-border] = heatmaps[i, j].copy()
            dr = cv2.GaussianBlur(dr, (kernel, kernel), 0)
            heatmaps[i, j] = dr[border:-border, border:-border].copy()
            heatmaps[i, j] *= origin_max / np.max(heatmaps[i, j])
    return heatmaps


def keypoints_from_heatmaps(
        heatmaps,
        center,
        scale,
        unbiased=False,
        post_process='default',
        kernel=11,
        valid_radius_factor=0.0546875,
        use_udp=False,
        target_type='GaussianHeatMap'):
    """Get final keypoint predictions from heatmaps and transform them back to
    the image.

    Note:
        batch size: N
        num keypoints: K
        heatmap height: H
        heatmap width: W

    Args:
        heatmaps (np.ndarray[N, K, H, W]): model predicted heatmaps.
        center (np.ndarray[N, 2]): Center of the bounding box (x, y).
        scale (np.ndarray[N, 2]): Scale of the bounding box
            wrt height/width.
        post_process (str/None): Choice of methods to post-process
            heatmaps. Currently supported: None, 'default', 'unbiased',
            'megvii'.
        unbiased (bool): Option to use unbiased decoding. Mutually
            exclusive with megvii.
            Note: this arg is deprecated and unbiased=True can be replaced
            by post_process='unbiased'
            Paper ref: Zhang et al. Distribution-Aware Coordinate
            Representation for Human Pose Estimation (CVPR 2020).
        kernel (int): Gaussian kernel size (K) for modulation, which should
            match the heatmap gaussian sigma when training.
            K=17 for sigma=3 and k=11 for sigma=2.
        valid_radius_factor (float): The radius factor of the positive area
            in classification heatmap for UDP.
        use_udp (bool): Use unbiased data processing.
        target_type (str): 'GaussianHeatMap' or 'CombinedTarget'.
            GaussianHeatMap: Classification target with gaussian distribution.
            CombinedTarget: The combination of classification target
            (response map) and regression target (offset map).
            Paper ref: Huang et al. The Devil is in the Details: Delving into
            Unbiased Data Processing for Human Pose Estimation (CVPR 2020).

    Returns:
        tuple: A tuple containing keypoint predictions and scores.

        - preds (np.ndarray[N, K, 2]): Predicted keypoint location in images.
        - maxvals (np.ndarray[N, K, 1]): Scores (confidence) of the keypoints.
    """
    # detect conflicts
    if unbiased:
        assert post_process not in [False, None, 'megvii']
    if post_process in ['megvii', 'unbiased']:
        assert kernel > 0
    if use_udp:
        assert not post_process == 'megvii'

    # normalize configs
    if post_process is False:
        logger.warning(
            'post_process=False is deprecated, '
            'please use post_process=None instead', DeprecationWarning)
        post_process = None
    elif post_process is True:
        if unbiased is True:
            logger.warning(
                'post_process=True, unbiased=True is deprecated,'
                " please use post_process='unbiased' instead",
                DeprecationWarning)
            post_process = 'unbiased'
        else:
            logger.warning(
                'post_process=True, unbiased=False is deprecated, '
                "please use post_process='default' instead",
                DeprecationWarning)
            post_process = 'default'
    elif post_process == 'default':
        if unbiased is True:
            logger.warning(
                'unbiased=True is deprecated, please use '
                "post_process='unbiased' instead", DeprecationWarning)
            post_process = 'unbiased'

    # start processing
    if post_process == 'megvii':
        heatmaps = _gaussian_blur(heatmaps, kernel=kernel)

    N, K, H, W = heatmaps.shape
    if use_udp:
        assert target_type in ['GaussianHeatMap', 'CombinedTarget']
        if target_type == 'GaussianHeatMap':
            preds, maxvals = _get_max_preds(heatmaps)
            preds = post_dark_udp(preds, heatmaps, kernel=kernel)
        elif target_type == 'CombinedTarget':
            for person_heatmaps in heatmaps:
                for i, heatmap in enumerate(person_heatmaps):
                    kt = 2 * kernel + 1 if i % 3 == 0 else kernel
                    cv2.GaussianBlur(heatmap, (kt, kt), 0, heatmap)
            # valid radius is in direct proportion to the height of heatmap.
            valid_radius = valid_radius_factor * H
            offset_x = heatmaps[:, 1::3, :].flatten() * valid_radius
            offset_y = heatmaps[:, 2::3, :].flatten() * valid_radius
            heatmaps = heatmaps[:, ::3, :]
            preds, maxvals = _get_max_preds(heatmaps)
            index = preds[..., 0] + preds[..., 1] * W
            index += W * H * np.arange(0, N * K / 3)
            index = index.astype(int).reshape(N, K // 3, 1)
            preds += np.concatenate((offset_x[index], offset_y[index]), axis=2)
    else:
        preds, maxvals = _get_max_preds(heatmaps)
        if post_process == 'unbiased':  # alleviate biased coordinate
            # apply Gaussian distribution modulation.
            heatmaps = np.log(
                np.maximum(_gaussian_blur(heatmaps, kernel), 1e-10))
            for n in range(N):
                for k in range(K):
                    preds[n][k] = _taylor(heatmaps[n][k], preds[n][k])
        elif post_process is not None:
            # add +/-0.25 shift to the predicted locations for higher acc.
            for n in range(N):
                for k in range(K):
                    heatmap = heatmaps[n][k]
                    px = int(preds[n][k][0])
                    py = int(preds[n][k][1])
                    if 1 < px < W - 1 and 1 < py < H - 1:
                        diff = np.array([
                            heatmap[py][px + 1] - heatmap[py][px - 1],
                            heatmap[py + 1][px] - heatmap[py - 1][px]
                        ])
                        preds[n][k] += np.sign(diff) * .25
                        if post_process == 'megvii':
                            preds[n][k] += 0.5

    # Transform back to the image
    for i in range(N):
        preds[i] = transform_preds(
            preds[i], center[i], scale[i], [W, H], use_udp=use_udp)

    if post_process == 'megvii':
        maxvals = maxvals / 255.0 + 0.5

    return preds, maxvals


def transform_preds(
        coords, center, scale, output_size, use_udp=False):
    """Get final keypoint predictions from heatmaps and apply scaling and
    translation to map them back to the image.

    Note:
        num_keypoints: K

    Args:
        coords (np.ndarray[K, ndims]):

            * If ndims=2, corrds are predicted keypoint location.
            * If ndims=4, corrds are composed of (x, y, scores, tags)
            * If ndims=5, corrds are composed of (x, y, scores, tags,
              flipped_tags)

        center (np.ndarray[2, ]): Center of the bounding box (x, y).
        scale (np.ndarray[2, ]): Scale of the bounding box
            wrt [width, height].
        output_size (np.ndarray[2, ] | list(2,)): Size of the
            destination heatmaps.
        use_udp (bool): Use unbiased data processing

    Returns:
        np.ndarray: Predicted coordinates in the images.
    """
    assert coords.shape[1] in (2, 4, 5)
    assert len(center) == 2
    assert len(scale) == 2
    assert len(output_size) == 2

    # Recover the scale which is normalized by a factor of 200.
    scale = scale * 200.0

    if use_udp:
        scale_x = scale[0] / (output_size[0] - 1.0)
        scale_y = scale[1] / (output_size[1] - 1.0)
    else:
        scale_x = scale[0] / output_size[0]
        scale_y = scale[1] / output_size[1]

    target_coords = np.ones_like(coords)
    target_coords[:, 0] = coords[:, 0] * scale_x + center[0] - scale[0] * 0.5
    target_coords[:, 1] = coords[:, 1] * scale_y + center[1] - scale[1] * 0.5

    return target_coords


def color_val(color):
    d = {
        'red': (0, 0, 255),
        'green': (0, 255, 0),
        'blue': (255, 0, 0),
        'cyan': (255, 255, 0),
        'yellow': (0, 255, 255),
        'magenta': (255, 0, 255),
        'white': (255, 255, 255),
        'black': (0, 0, 0)
    }
    return d[color]


def imshow_bboxes(
        img,
        bboxes,
        colors='green',
        top_k=-1,
        thickness=1):
    """Draw bboxes on an image.
    Args:
        img (str or ndarray): The image to be displayed.
        bboxes (list or ndarray): A list of ndarray of shape (k, 4).
        colors (list[str or tuple or Color]): A list of colors.
        top_k (int): Plot the first k bboxes only if set positive.
        thickness (int): Thickness of lines.
    Returns:
        ndarray: The image with bboxes drawn on it.
    """

    if isinstance(bboxes, np.ndarray):
        bboxes = [bboxes]
    if not isinstance(colors, list):
        colors = [colors for _ in range(len(bboxes))]
    colors = [color_val(c) for c in colors]
    assert len(bboxes) == len(colors)

    for i, _bboxes in enumerate(bboxes):
        _bboxes = _bboxes.astype(np.int32)
        if top_k <= 0:
            _top_k = _bboxes.shape[0]
        else:
            _top_k = min(top_k, _bboxes.shape[0])
        for j in range(_top_k):
            left_top = (_bboxes[j, 0], _bboxes[j, 1])
            right_bottom = (_bboxes[j, 2], _bboxes[j, 3])
            cv2.rectangle(
                img, left_top, right_bottom, colors[i], thickness=thickness)

    return img


def imshow_keypoints(
        img,
        pose_result,
        skeleton=None,
        kpt_score_thr=0.3,
        pose_kpt_color=None,
        pose_limb_color=None,
        radius=4,
        thickness=1,
        show_keypoint_weight=False):
    """Draw keypoints and limbs on an image.

    Args:
            img (str or Tensor): The image to draw poses on. If an image array
                is given, id will be modified in-place.
            pose_result (list[kpts]): The poses to draw. Each element kpts is
                a set of K keypoints as an Kx3 numpy.ndarray, where each
                keypoint is represented as x, y, score.
            kpt_score_thr (float, optional): Minimum score of keypoints
                to be shown. Default: 0.3.
            pose_kpt_color (np.array[Nx3]`): Color of N keypoints. If None,
                the keypoint will not be drawn.
            pose_limb_color (np.array[Mx3]): Color of M limbs. If None, the
                limbs will not be drawn.
            thickness (int): Thickness of lines.
    """

    img_h, img_w, _ = img.shape

    for kpts in pose_result:
        # draw each point on image
        if pose_kpt_color is not None:
            assert len(pose_kpt_color) == len(kpts)
            for kid, kpt in enumerate(kpts):
                x_coord, y_coord, kpt_score = int(kpt[0]), int(kpt[1]), kpt[2]
                if kpt_score > kpt_score_thr:
                    if show_keypoint_weight:
                        img_copy = img.copy()
                        r, g, b = pose_kpt_color[kid]
                        cv2.circle(img_copy, (int(x_coord), int(y_coord)),
                                   radius, (int(r), int(g), int(b)), -1)
                        transparency = max(0, min(1, kpt_score))
                        cv2.addWeighted(
                            img_copy,
                            transparency,
                            img,
                            1 - transparency,
                            0,
                            dst=img)
                    else:
                        r, g, b = pose_kpt_color[kid]
                        cv2.circle(img, (int(x_coord), int(y_coord)), radius,
                                   (int(r), int(g), int(b)), -1)

        # draw limbs
        if skeleton is not None and pose_limb_color is not None:
            assert len(pose_limb_color) == len(skeleton)
            for sk_id, sk in enumerate(skeleton):
                pos1 = (int(kpts[sk[0] - 1, 0]), int(kpts[sk[0] - 1, 1]))
                pos2 = (int(kpts[sk[1] - 1, 0]), int(kpts[sk[1] - 1, 1]))
                if (pos1[0] > 0 and pos1[0] < img_w and pos1[1] > 0
                        and pos1[1] < img_h and pos2[0] > 0 and pos2[0] < img_w
                        and pos2[1] > 0 and pos2[1] < img_h
                        and kpts[sk[0] - 1, 2] > kpt_score_thr
                        and kpts[sk[1] - 1, 2] > kpt_score_thr):
                    r, g, b = pose_limb_color[sk_id]
                    if show_keypoint_weight:
                        img_copy = img.copy()
                        X = (pos1[0], pos2[0])
                        Y = (pos1[1], pos2[1])
                        mX = np.mean(X)
                        mY = np.mean(Y)
                        length = ((Y[0] - Y[1]) ** 2 + (X[0] - X[1]) ** 2) ** 0.5
                        angle = math.degrees(
                            math.atan2(Y[0] - Y[1], X[0] - X[1]))
                        stickwidth = 2
                        polygon = cv2.ellipse2Poly(
                            (int(mX), int(mY)),
                            (int(length / 2), int(stickwidth)), int(angle), 0,
                            360, 1)
                        cv2.fillConvexPoly(img_copy, polygon,
                                           (int(r), int(g), int(b)))
                        transparency = max(
                            0,
                            min(
                                1, 0.5 *
                                   (kpts[sk[0] - 1, 2] + kpts[sk[1] - 1, 2])))
                        cv2.addWeighted(
                            img_copy,
                            transparency,
                            img,
                            1 - transparency,
                            0,
                            dst=img)
                    else:
                        cv2.line(
                            img,
                            pos1,
                            pos2, (int(r), int(g), int(b)),
                            thickness=thickness)

    return img


def show_result(
        img,
        result,
        skeleton=None,
        kpt_score_thr=0.3,
        bbox_color='green',
        pose_kpt_color=None,
        pose_limb_color=None,
        radius=4,
        thickness=1):
    """Draw `result` over `img`.

    Args:
        img (str or Tensor): The image to be displayed.
        result (list[dict]): The results to draw over `img`
            (bbox_result, pose_result).
        skeleton (list[list]): The connection of keypoints.
        kpt_score_thr (float, optional): Minimum score of keypoints
            to be shown. Default: 0.3.
        bbox_color (str or tuple or :obj:`Color`): Color of bbox lines.
        pose_kpt_color (np.array[Nx3]`): Color of N keypoints.
            If None, do not draw keypoints.
        pose_limb_color (np.array[Mx3]): Color of M limbs.
            If None, do not draw limbs.
        radius (int): Radius of circles.
        thickness (int): Thickness of lines.
    Returns:
        Tensor: Visualized img
    """

    img_h, img_w, _ = img.shape

    bbox_result = []
    pose_result = []
    for res in result:
        bbox_result.append(res['bbox'])
        pose_result.append(res['keypoints'])

    if len(bbox_result) > 0:
        bboxes = np.vstack(bbox_result)

        # draw bounding boxes
        imshow_bboxes(
            img,
            bboxes,
            colors=bbox_color,
            top_k=-1,
            thickness=thickness)

        imshow_keypoints(img, pose_result, skeleton, kpt_score_thr,
                         pose_kpt_color, pose_limb_color, radius,
                         thickness)

    return img