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(),
'obj_bb': [int(x) for x in obj_bb],
import os
import sys
import glob
import numpy as np
sys.path.append(os.path.abspath('..'))
from pysixd import inout
from params.dataset_params import get_dataset_params
par = get_dataset_params('hinterstoisser')
# data_ids = range(1, par.obj_count + 1)
data_ids = range(1, par['scene_count'] + 1)
# depth_mpath = par.train_depth_mpath
depth_mpath = par['test_depth_mpath']
scale = 0.1
for data_id in data_ids:
print('Processing id: ' + str(data_id))
depth_paths = sorted(
glob.glob(
os.path.join(os.path.dirname(depth_mpath.format(data_id, 0)),
'*')))
for depth_path in depth_paths:
d = inout.read_depth(depth_path)
d *= scale
d = np.round(d).astype(np.uint16)
inout.write_depth(depth_path, d)
R_model_inv = np.linalg.inv(R_model)
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 = load_hinter_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), rgb)
inout.write_depth(depth_out_mpath.format(scene_id, im_id), depth)
# Load the GT pose
R_m2c = load_hinter_mat(rot_mpath.format(scene_id, im_id))
t_m2c = load_hinter_mat(tra_mpath.format(scene_id, im_id))
t_m2c *= 10 # Convert to [mm]
# Transfom the GT pose (to compensate transformation of the models)
R_m2c = R_m2c.dot(R_model_inv)
t_m2c = t_m2c + R_m2c.dot(R_model.dot(t_model))
# Get 2D bounding box of the object model at the ground truth pose
obj_bb = misc.calc_pose_2d_bbox(model, par['cam']['im_size'],
par['cam']['K'], R_m2c, t_m2c)
# Visualisation