def test_imshow_keypoints():
# 2D keypoint
img = np.zeros((100, 100, 3), dtype=np.uint8)
kpts = np.array([[1, 1, 1], [10, 10, 1]], dtype=np.float32)
pose_result = [kpts]
skeleton = [[0, 1]]
pose_kpt_color = [(127, 127, 127)] * len(kpts)
pose_link_color = [(127, 127, 127)] * len(skeleton)
img_vis_2d = imshow_keypoints(
img,
pose_result,
skeleton=skeleton,
pose_kpt_color=pose_kpt_color,
pose_link_color=pose_link_color,
show_keypoint_weight=True)
# 3D keypoint
kpts_3d = np.array([[0, 0, 0, 1], [1, 1, 1, 1]], dtype=np.float32)
pose_result_3d = [{'keypoints_3d': kpts_3d, 'title': 'test'}]
_ = imshow_keypoints_3d(
pose_result_3d,
img=img_vis_2d,
skeleton=skeleton,
pose_kpt_color=pose_kpt_color,
pose_link_color=pose_link_color,
vis_height=400)
def test_imshow_keypoints_3d():
img = np.zeros((100, 100, 3), dtype=np.uint8)
kpts_3d = np.array([[0, 0, 0, 1], [1, 1, 1, 1]], dtype=np.float32)
pose_result_3d = [{'keypoints_3d': kpts_3d, 'title': 'test'}]
skeleton = [[0, 1]]
pose_kpt_color = [(127, 127, 127)] * len(kpts_3d)
pose_link_color = [(127, 127, 127)] * len(skeleton)
_ = imshow_keypoints_3d(pose_result_3d,
img=img,
skeleton=skeleton,
pose_kpt_color=pose_kpt_color,
pose_link_color=pose_link_color,
vis_height=400)
# multiview 3D keypoint
pose_result_3d = [kpts_3d]
_ = imshow_multiview_keypoints_3d(pose_result_3d,
skeleton=skeleton,
pose_kpt_color=pose_kpt_color,
pose_link_color=pose_link_color,
space_size=[8, 8, 8],
space_center=[0, 0, 0],
kpt_score_thr=0.0)
def show_result(self,
result,
img=None,
skeleton=None,
pose_kpt_color=None,
pose_link_color=None,
radius=8,
thickness=2,
vis_height=400,
num_instances=-1,
axis_azimuth=70,
win_name='',
show=False,
wait_time=0,
out_file=None):
"""Visualize 3D pose estimation results.
Args:
result (list[dict]): The pose estimation results containing:
- "keypoints_3d" ([K,4]): 3D keypoints
- "keypoints" ([K,3] or [T,K,3]): Optional for visualizing
2D inputs. If a sequence is given, only the last frame
will be used for visualization
- "bbox" ([4,] or [T,4]): Optional for visualizing 2D inputs
- "title" (str): title for the subplot
img (str or Tensor): Optional. The image to visualize 2D inputs on.
skeleton (list of [idx_i,idx_j]): Skeleton described by a list of
links, each is a pair of joint indices.
pose_kpt_color (np.array[Nx3]`): Color of N keypoints.
If None, do not draw keypoints.
pose_link_color (np.array[Mx3]): Color of M links.
If None, do not draw links.
radius (int): Radius of circles.
thickness (int): Thickness of lines.
vis_height (int): The image height of the visualization. The width
will be N*vis_height depending on the number of visualized
items.
num_instances (int): Number of instances to be shown in 3D. If
smaller than 0, all the instances in the result will be shown.
Otherwise, pad or truncate the result to a length of
num_instances.
axis_azimuth (float): axis azimuth angle for 3D visualizations.
win_name (str): The window name.
show (bool): Whether to directly show the visualization.
wait_time (int): Value of waitKey param.
Default: 0.
out_file (str or None): The filename to write the image.
Default: None.
Returns:
Tensor: Visualized img, only if not `show` or `out_file`.
"""
if num_instances < 0:
assert len(result) > 0
result = sorted(result, key=lambda x: x.get('track_id', 1e4))
# draw image and input 2d poses
if img is not None:
img = mmcv.imread(img)
bbox_result = []
pose_input_2d = []
for res in result:
if 'bbox' in res:
bbox = np.array(res['bbox'])
if bbox.ndim != 1:
assert bbox.ndim == 2
bbox = bbox[-1] # Get bbox from the last frame
bbox_result.append(bbox)
if 'keypoints' in res:
kpts = np.array(res['keypoints'])
if kpts.ndim != 2:
assert kpts.ndim == 3
kpts = kpts[-1] # Get 2D keypoints from the last frame
pose_input_2d.append(kpts)
if len(bbox_result) > 0:
bboxes = np.vstack(bbox_result)
imshow_bboxes(
img,
bboxes,
colors='green',
thickness=thickness,
show=False)
if len(pose_input_2d) > 0:
imshow_keypoints(
img,
pose_input_2d,
skeleton,
kpt_score_thr=0.3,
pose_kpt_color=pose_kpt_color,
pose_link_color=pose_link_color,
radius=radius,
thickness=thickness)
img = mmcv.imrescale(img, scale=vis_height / img.shape[0])
img_vis = imshow_keypoints_3d(
result,
img,
skeleton,
pose_kpt_color,
pose_link_color,
vis_height,
num_instances=num_instances,
axis_azimuth=axis_azimuth,
)
if show:
mmcv.visualization.imshow(img_vis, win_name, wait_time)
if out_file is not None:
mmcv.imwrite(img_vis, out_file)
return img_vis