depth_im *= scene_camera[im_id]['depth_scale'] # to [mm]
dist_im = misc.depth_im_to_dist_im(depth_im, K)
'''
for gt_id, gt in enumerate(scene_gt[im_id]):
# Render the depth image.
depth_gt = ren.render_object(gt['obj_id'], gt['cam_R_m2c'],
gt['cam_t_m2c'], fx, fy, cx,
cy)['depth']
# Convert depth image to distance image.
dist_gt = misc.depth_im_to_dist_im(depth_gt, K)
# Mask of the full object silhouette.
mask = dist_gt > 0
'''
# Mask of the visible part of the object silhouette.
mask_visib = visibility.estimate_visib_mask_gt(
dist_im, dist_gt, p['delta'], visib_mode='bop19')
'''
# Save the calculated masks.
mask_path = dp_split['mask_tpath'].format(scene_id=scene_id,
im_id=im_id,
gt_id=gt_id)
inout.save_im(mask_path, 255 * mask.astype(np.uint8))
'''
mask_visib_path = dp_split['mask_visib_tpath'].format(
scene_id=scene_id, im_id=im_id, gt_id=gt_id)
inout.save_im(mask_visib_path, 255 * mask_visib.astype(np.uint8))
'''
# Convert depth so it is in the same units as other images in the dataset.
depth /= float(dp_camera['depth_scale'])
# The OpenCV function was used for rendering of the training images
# provided for the SIXD Challenge 2017.
rgb = cv2.resize(rgb,
dp_camera['im_size'],
interpolation=cv2.INTER_AREA)
# rgb = scipy.misc.imresize(rgb, par['cam']['im_size'][::-1], 'bicubic')
# Save the rendered images.
out_rgb_path = out_rgb_tpath.format(out_path=out_path,
obj_id=obj_id,
im_id=im_id)
inout.save_im(out_rgb_path, rgb)
out_depth_path = out_depth_tpath.format(out_path=out_path,
obj_id=obj_id,
im_id=im_id)
inout.save_depth(out_depth_path, depth)
# Get 2D bounding box of the object model at the ground truth pose.
# ys, xs = np.nonzero(depth > 0)
# obj_bb = misc.calc_2d_bbox(xs, ys, dp_camera['im_size'])
scene_camera[im_id] = {
'cam_K': dp_camera['K'],
'depth_scale': dp_camera['depth_scale'],
'view_level': int(views_level[view_id])
}
for obj_id in dp_model['obj_ids']:
# Load object model.
misc.log('Loading 3D model of object {}...'.format(obj_id))
model_path = dp_model['model_tpath'].format(obj_id=obj_id)
ren.add_object(obj_id, model_path)
poses = misc.get_symmetry_transformations(models_info[obj_id],
p['max_sym_disc_step'])
for pose_id, pose in enumerate(poses):
for view_id, view in enumerate(p['views']):
R = view['R'].dot(pose['R'])
t = view['R'].dot(pose['t']) + view['t']
vis_rgb = ren.render_object(obj_id, R, t, fx, fy, cx, cy)['rgb']
# Path to the output RGB visualization.
vis_rgb_path = p['vis_rgb_tpath'].format(vis_path=p['vis_path'],
dataset=p['dataset'],
obj_id=obj_id,
view_id=view_id,
pose_id=pose_id)
misc.ensure_dir(os.path.dirname(vis_rgb_path))
inout.save_im(vis_rgb_path, vis_rgb)
misc.log('Done.')
})
# Visualization of the visibility mask.
if p['vis_visibility_masks']:
depth_im_vis = visualization.depth_for_vis(depth, 0.2, 1.0)
depth_im_vis = np.dstack([depth_im_vis] * 3)
visib_gt_vis = visib_gt.astype(np.float)
zero_ch = np.zeros(visib_gt_vis.shape)
visib_gt_vis = np.dstack([zero_ch, visib_gt_vis, zero_ch])
vis = 0.5 * depth_im_vis + 0.5 * visib_gt_vis
vis[vis > 1] = 1
vis_path = p['vis_mask_visib_tpath'].format(
delta=p['delta'],
dataset=p['dataset'],
split=p['dataset_split'],
scene_id=scene_id,
im_id=im_id,
gt_id=gt_id)
misc.ensure_dir(os.path.dirname(vis_path))
inout.save_im(vis_path, vis)
# Save the info for the current scene.
scene_gt_info_path = dp_split['scene_gt_info_tpath'].format(
scene_id=scene_id)
misc.ensure_dir(os.path.dirname(scene_gt_info_path))
inout.save_json(scene_gt_info_path, scene_gt_info)
def vis_object_poses(poses,
K,
renderer,
rgb=None,
depth=None,
vis_rgb_path=None,
vis_depth_diff_path=None,
vis_rgb_resolve_visib=False):
"""Visualizes 3D object models in specified poses in a single image.
Two visualizations are created:
1. An RGB visualization (if vis_rgb_path is not None).
2. A Depth-difference visualization (if vis_depth_diff_path is not None).
:param poses: List of dictionaries, each with info about one pose:
- 'obj_id': Object ID.
- 'R': 3x3 ndarray with a rotation matrix.
- 't': 3x1 ndarray with a translation vector.
- 'text_info': Info to write at the object (see write_text_on_image).
:param K: 3x3 ndarray with an intrinsic camera matrix.
:param renderer: Instance of the Renderer class (see renderer.py).
:param rgb: ndarray with the RGB input image.
:param depth: ndarray with the depth input image.
:param vis_rgb_path: Path to the output RGB visualization.
:param vis_depth_diff_path: Path to the output depth-difference visualization.
:param vis_rgb_resolve_visib: Whether to resolve visibility of the objects
(i.e. only the closest object is visualized at each pixel).
"""
fx, fy, cx, cy = K[0, 0], K[1, 1], K[0, 2], K[1, 2]
# Indicators of visualization types.
vis_rgb = vis_rgb_path is not None
vis_depth_diff = vis_depth_diff_path is not None
if vis_rgb and rgb is None:
raise ValueError(
'RGB visualization triggered but RGB image not provided.')
if (vis_depth_diff or
(vis_rgb and vis_rgb_resolve_visib)) and depth is None:
raise ValueError(
'Depth visualization triggered but D image not provided.')
# Prepare images for rendering.
im_size = None
ren_rgb = None
ren_rgb_info = None
ren_depth = None
if vis_rgb:
im_size = (rgb.shape[1], rgb.shape[0])
ren_rgb = np.zeros(rgb.shape, np.uint8)
ren_rgb_info = np.zeros(rgb.shape, np.uint8)
if vis_depth_diff:
if im_size and im_size != (depth.shape[1], depth.shape[0]):
raise ValueError('The RGB and D images must have the same size.')
else:
im_size = (depth.shape[1], depth.shape[0])
if vis_depth_diff or (vis_rgb and vis_rgb_resolve_visib):
ren_depth = np.zeros((im_size[1], im_size[0]), np.float32)
# Render the pose estimates one by one.
for pose in poses:
# Rendering.
ren_out = renderer.render_object(pose['obj_id'], pose['R'], pose['t'],
fx, fy, cx, cy)
m_rgb = None
if vis_rgb:
m_rgb = ren_out['rgb']
m_mask = None
if vis_depth_diff or (vis_rgb and vis_rgb_resolve_visib):
m_depth = ren_out['depth']
# Get mask of the surface parts that are closer than the
# surfaces rendered before.
visible_mask = np.logical_or(ren_depth == 0, m_depth < ren_depth)
m_mask = np.logical_and(m_depth != 0, visible_mask)
ren_depth[m_mask] = m_depth[m_mask].astype(ren_depth.dtype)
# Combine the RGB renderings.
if vis_rgb:
if vis_rgb_resolve_visib:
ren_rgb[m_mask] = m_rgb[m_mask].astype(ren_rgb.dtype)
else:
ren_rgb_f = ren_rgb.astype(np.float32) + m_rgb.astype(
np.float32)
ren_rgb_f[ren_rgb_f > 255] = 255
ren_rgb = ren_rgb_f.astype(np.uint8)
# Draw 2D bounding box and write text info.
obj_mask = np.sum(m_rgb > 0, axis=2)
ys, xs = obj_mask.nonzero()
if len(ys):
# bbox_color = model_color
# text_color = model_color
bbox_color = (0.3, 0.3, 0.3)
text_color = (1.0, 1.0, 1.0)
text_size = 11
bbox = misc.calc_2d_bbox(xs, ys, im_size)
im_size = (obj_mask.shape[1], obj_mask.shape[0])
ren_rgb_info = draw_rect(ren_rgb_info, bbox, bbox_color)
if 'text_info' in pose:
text_loc = (bbox[0] + 2, bbox[1])
ren_rgb_info = write_text_on_image(ren_rgb_info,
pose['text_info'],
text_loc,
color=text_color,
size=text_size)
# Blend and save the RGB visualization.
if vis_rgb:
misc.ensure_dir(os.path.dirname(vis_rgb_path))
vis_im_rgb = 0.5 * rgb.astype(np.float32) + \
0.5 * ren_rgb.astype(np.float32) + \
1.0 * ren_rgb_info.astype(np.float32)
vis_im_rgb[vis_im_rgb > 255] = 255
inout.save_im(vis_rgb_path,
vis_im_rgb.astype(np.uint8),
jpg_quality=95)
# Save the image of depth differences.
if vis_depth_diff:
misc.ensure_dir(os.path.dirname(vis_depth_diff_path))
# Calculate the depth difference at pixels where both depth maps are valid.
valid_mask = (depth > 0) * (ren_depth > 0)
depth_diff = valid_mask * (ren_depth.astype(np.float32) - depth)
delta = 15
below_delta = valid_mask * (depth_diff < delta)
below_delta_vis = (255 * below_delta).astype(np.uint8)
depth_diff_vis = 255 * depth_for_vis(depth_diff - depth_diff.min())
depth_diff_vis = np.dstack(
[below_delta_vis, depth_diff_vis, depth_diff_vis]).astype(np.uint8)
depth_diff_vis[np.logical_not(valid_mask)] = 0
depth_diff_valid = depth_diff[valid_mask]
depth_info = [
{
'name': 'min diff',
'fmt': ':.3f',
'val': np.min(depth_diff_valid)
},
{
'name': 'max diff',
'fmt': ':.3f',
'val': np.max(depth_diff_valid)
},
{
'name': 'mean diff',
'fmt': ':.3f',
'val': np.mean(depth_diff_valid)
},
]
depth_diff_vis = write_text_on_image(depth_diff_vis, depth_info)
inout.save_im(vis_depth_diff_path, depth_diff_vis)