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.load_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.save_im(rgb_out_mpath.format(scene_id, im_id), rgb)
inout.save_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
depth /= par['cam']['depth_scale']
# Render RGB image
rgb = renderer.render(model, im_size_rgb, K_rgb, view['R'], view['t'],
clip_near, clip_far, texture=model_texture,
ambient_weight=ambient_weight, shading=shading,
mode='rgb')
# The OpenCV function was used for rendering of the training images
# provided for the SIXD Challenge 2017.
rgb = cv2.resize(rgb, par['cam']['im_size'], interpolation=cv2.INTER_AREA)
#rgb = scipy.misc.imresize(rgb, par['cam']['im_size'][::-1], 'bicubic')
# Save the rendered images
inout.save_im(out_rgb_mpath.format(obj_id, im_id), rgb)
inout.save_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],
depth /= p['cam']['depth_scale']
# Render RGB image
rgb = renderer.render(model, im_size_rgb, K_rgb, view['R'], view['t'],
clip_near, clip_far, texture=model_texture,
ambient_weight=ambient_weight, shading=shading,
mode='rgb')
# The OpenCV function was used for rendering of the training images
# provided for the SIXD Challenge 2017.
rgb = cv2.resize(rgb, p['cam']['im_size'], interpolation=cv2.INTER_AREA)
# rgb = scipy.misc.imresize(rgb, par['cam']['im_size'][::-1], 'bicubic')
# Save the rendered images
inout.save_im(out_rgb_mpath.format(obj_id, im_id), rgb)
inout.save_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, p['cam']['im_size'])
obj_info[im_id] = {
'cam_K': p['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],
black_out_mask[mask] = white_mask[mask].astype(black_out_mask.dtype)
#################### save imgs##################
if im_id > 300:
now_test = True
# print "out"
if(not now_test):
inout.save_im(out_rgb_mpath.format(dataset, scene_id, im_id),
rgb.astype(np.uint8))
inout.save_depth(out_depth_mpath.format(dataset,obj_id, im_id), depth)
from numpngw import write_png
write_png(out_obj_mpath.format(dataset, scene_id, im_id), img_obj.astype(np.uint16))
inout.save_im(out_seg_mpath.format(dataset, scene_id, im_id),
black_out_mask.astype(np.uint8))
R_str = [[str(num) for num in item] for item in R.tolist() ]
R_str = [" ".join(item) for item in R_str]
R_str = [item+'\n' for item in R_str]
t_str = [str(item/1000) for item in t.squeeze().tolist()]
t_str = " ".join(t_str)
size_x = models_info[gt['obj_id']]["size_x"]
import os
import sys
import glob
import numpy as np
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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.load_depth(depth_path)
d *= scale
d = np.round(d).astype(np.uint16)
inout.save_depth(depth_path, d)
