im_size_rgb,
K_rgb,
view['R'],
view['t'],
clip_near,
clip_far,
texture=model_texture,
ambient_weight=ambient_weight,
shading=shading,
mode='rgb')
rgb = cv2.resize(rgb,
par.cam['im_size'],
interpolation=cv2.INTER_AREA)
# Save the rendered images
inout.write_im(out_rgb_mpath.format(obj_id, im_id), rgb)
inout.write_depth(out_depth_mpath.format(obj_id, im_id), 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, par.cam['im_size'])
obj_info[im_id] = {
'cam_K': par.cam['K'].flatten().tolist(),
'view_level': int(views_level[view_id]),
#'sphere_radius': float(radius)
}
obj_gt[im_id] = [{
'cam_R_m2c': view['R'].flatten().tolist(),
'cam_t_m2c': view['t'].flatten().tolist(),
# 1) Translate the bounding box center to the origin
t_model = bbox_cens[obj_id - 1, :].reshape((3, 1))
im_id_out = 0
for im_id in im_ids:
# if im_id % 10 == 0:
print('scene,view: ' + str(scene_id) + ',' + str(im_id))
# Load the RGB and depth image
rgb = inout.read_im(rgb_in_mpath.format(scene_id, im_id))
depth = inout.read_depth(depth_in_mpath.format(scene_id, im_id))
depth *= 10.0 # Convert depth map to [100um]
# Save the RGB and depth image
inout.write_im(rgb_out_mpath.format(scene_id, im_id_out), rgb)
inout.write_depth(depth_out_mpath.format(scene_id, im_id_out), depth)
scene_info[im_id_out] = {
'cam_K': par.cam['K'].flatten().tolist()
}
# Process the GT poses
poses = load_tejani_poses(pose_mpath.format(scene_id, im_id))
scene_gt[im_id_out] = []
for pose in poses:
R_m2c = pose['R']
t_m2c = pose['t']
t_m2c *= 1000.0 # Convert to [mm]
mask = np.logical_and(m_depth != 0, visible_mask)
ren_depth[mask] = m_depth[mask].astype(ren_depth.dtype)
# Combine the RGB renderings
if vis_rgb:
if vis_rgb_resolve_visib:
ren_rgb[mask] = m_rgb[mask].astype(ren_rgb.dtype)
else:
ren_rgb += m_rgb.astype(ren_rgb.dtype)
# Save RGB visualization
if vis_rgb:
vis_im_rgb = 0.4 * rgb.astype(np.float) + 0.6 * ren_rgb
vis_im_rgb[vis_im_rgb > 255] = 255
inout.write_im(vis_rgb_mpath.format(scene_id, im_id),
vis_im_rgb.astype(np.uint8))
# Save image of depth differences
if vis_depth:
# Calculate the depth difference at pixels where both depth maps
# are valid
valid_mask = (depth > 0) * (ren_depth > 0)
depth_diff = valid_mask * (depth - ren_depth.astype(np.float))
plt.matshow(depth_diff)
plt.axis('off')
plt.title('measured - GT depth [mm]')
plt.colorbar()
plt.savefig(vis_depth_mpath.format(scene_id, im_id),
pad=0,
bbox_inches='tight')