del rgb, depth, mask
print('success: {}'.format(success))
for i in range(len(dep_anchors)):
if success[i] != -1:
aTemplateInfo = dict()
aTemplateInfo['cam_K'] = copy.deepcopy(dp['cam']['K'])
aTemplateInfo['cam_R_w2c'] = copy.deepcopy(view['R'])
aTemplateInfo['cam_t_w2c'] = copy.deepcopy(view['t'])
aTemplateInfo['cam_t_w2c'][2] = dep_anchors[i]
templateInfo = templateInfo_radius[dep_anchors[i]]
templateInfo[success[i]] = aTemplateInfo
for radius in dep_anchors:
inout.save_info(tempInfo_saved_to.format(obj_id, radius), templateInfo_radius[radius])
detector.writeClasses(template_saved_to)
# clear to save RAM
detector.clear_classes()
else:
for radius in dep_anchors:
# Sample views
# with camera tilt
# tilt_factor = (80 / 180)
tilt_factor = 1
views, views_level = view_sampler.sample_views(min_n_views, radius,
azimuth_range, elev_range,
tilt_range=(-math.pi * tilt_factor,
math.pi * tilt_factor),
# Get 2D bounding box of the object model at the ground truth pose
obj_bb = misc.calc_pose_2d_bbox(models[obj_id], par['cam']['im_size'],
par['cam']['K'], R_m2c, t_m2c)
# Visualisation
if False:
print(R_m2c)
print(t_m2c)
ren_rgb = renderer.render(models[obj_id], par['cam']['im_size'], par['cam']['K'],
R_m2c, t_m2c, mode='rgb')
vis_rgb = 0.4 * rgb.astype(np.float32) + 0.6 * ren_rgb.astype(np.float32)
vis_rgb = vis_rgb.astype(np.uint8)
vis_rgb = misc.draw_rect(vis_rgb, obj_bb)
plt.imshow(vis_rgb)
plt.show()
scene_gt[im_id_out].append(
{
'obj_id': obj_id,
'cam_R_m2c': R_m2c,
'cam_t_m2c': t_m2c,
'obj_bb': obj_bb
}
)
im_id_out += 1
inout.save_info(scene_info_mpath.format(scene_id), scene_info)
inout.save_gt(scene_gt_mpath.format(scene_id), scene_gt)
aTemplateInfo['cam_K'] = K
aTemplateInfo['cam_R_w2c'] = R
aTemplateInfo['cam_t_w2c'] = t
aTemplateInfo['width'] = int(xmax - xmin)
aTemplateInfo['height'] = int(ymax - ymin)
mask = (depth > 0).astype(np.uint8) * 255
visual = False
if visual:
cv2.namedWindow('rgb')
cv2.imshow('rgb', rgb)
cv2.namedWindow('depth')
cv2.imshow('depth', depth)
cv2.namedWindow('mask')
cv2.imshow('mask', mask)
cv2.waitKey(1000)
success = detector.addTemplate([rgb, depth],
'{}_templ'.format(obj_id), mask)
print('success {}'.format(success))
del rgb, depth, mask
if success != -1:
templateInfo[success] = aTemplateInfo
inout.save_info(tempInfo_saved_to.format(obj_id), templateInfo)
detector.writeClasses(template_saved_to)
elapsed_time = time.time() - start_time
print('train time: {}\n'.format(elapsed_time))
aTemplateInfo = dict()
aTemplateInfo['cam_K'] = K
aTemplateInfo['cam_R_w2c'] = R
aTemplateInfo['cam_t_w2c'] = t
aTemplateInfo['width'] = int(xmax - xmin)
aTemplateInfo['height'] = int(ymax - ymin)
mask = (depth > 0).astype(np.uint8) * 255
visual = False
if visual:
cv2.namedWindow('rgb')
cv2.imshow('rgb', rgb)
cv2.namedWindow('depth')
cv2.imshow('depth', depth)
cv2.namedWindow('mask')
cv2.imshow('mask', mask)
cv2.waitKey(1000)
success = detector.addTemplate([rgb, depth], '{}_templ'.format(obj_id), mask)
print('success {}'.format(success))
del rgb, depth, mask
if success != -1:
templateInfo[success] = aTemplateInfo
inout.save_info(tempInfo_saved_to.format(obj_id), templateInfo)
detector.writeClasses(template_saved_to)
elapsed_time = time.time() - start_time
print('train time: {}\n'.format(elapsed_time))
