estimator.cuda()
refiner = PoseRefineNet(num_points=num_points, num_obj=num_objects)
refiner.cuda()
estimator.load_state_dict(torch.load(opt.model))
refiner.load_state_dict(torch.load(opt.refine_model))
estimator.eval()
refiner.eval()
testdataset = PoseDataset_linemod('eval', num_points, False, opt.dataset_root,
0.0, True)
testdataloader = torch.utils.data.DataLoader(testdataset,
batch_size=1,
shuffle=False,
num_workers=10)
sym_list = testdataset.get_sym_list() # symmetry_obj_idx
num_points_mesh = testdataset.get_num_points_mesh()
criterion = Loss(num_points_mesh, sym_list)
criterion_refine = Loss_refine(num_points_mesh, sym_list)
diameter = []
meta_file = open('{0}/models_info.yml'.format(dataset_config_dir), 'r')
meta = yaml.load(meta_file)
for obj in objlist:
diameter.append(meta[obj]['diameter'] / 1000.0 * 0.1)
print(diameter)
success_count = [0 for i in range(num_objects)]
num_count = [0 for i in range(num_objects)]
fw = open('{0}/eval_result_logs.txt'.format(output_result_dir), 'w')
estimator.cuda()
refiner = PoseRefineNet(num_points=num_points, num_obj=num_objects)
refiner.cuda()
estimator.load_state_dict(torch.load(opt.model))
refiner.load_state_dict(torch.load(opt.refine_model))
estimator.eval()
refiner.eval()
testdataset = PoseDataset_linemod('test', num_points, False, opt.dataset_root,
0.0, True)
testdataloader = torch.utils.data.DataLoader(testdataset,
batch_size=1,
shuffle=False,
num_workers=8)
sym_list = testdataset.get_sym_list()
num_points_mesh = testdataset.get_num_points_mesh()
criterion = Loss(num_points_mesh, sym_list)
criterion_refine = Loss_refine(num_points_mesh, sym_list)
diameter = []
meta_file = open('{0}/models_info.yml'.format(dataset_config_dir), 'r')
meta = yaml.load(meta_file)
for obj in objlist:
diameter.append(meta[obj]['diameter'] / 1000.0 * 0.1)
print(diameter)
success_count = [0 for i in range(num_objects)]
success_count_cpy = [0 for i in range(num_objects)]
num_count = [0 for i in range(num_objects)]
fw = open('{0}/eval_result_logs_ICP_DEL2.txt'.format(output_result_dir), 'w')
def main():
# g13: parameter setting -------------------
'''
posemodel is trained_checkpoints/linemod/pose_model_9_0.01310166542980859.pth
refine model is trained_checkpoints/linemod/pose_refine_model_493_0.006761023565178073.pth
'''
objlist = [1, 2, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15]
knn = KNearestNeighbor(1)
opt.dataset ='linemod'
opt.dataset_root = './datasets/linemod/Linemod_preprocessed'
estimator_path = 'trained_checkpoints/linemod/pose_model_9_0.01310166542980859.pth'
refiner_path = 'trained_checkpoints/linemod/pose_refine_model_493_0.006761023565178073.pth'
opt.model = estimator_path
opt.refine_model = refiner_path
dataset_config_dir = 'datasets/linemod/dataset_config'
output_result_dir = 'experiments/eval_result/linemod'
opt.refine_start = True
bs = 1 #fixed because of the default setting in torch.utils.data.DataLoader
opt.iteration = 2 #default is 4 in eval_linemod.py
t1_start = True
t1_idx = 0
t1_total_eval_num = 3
t2_start = False
t2_target_list = [22, 30, 172, 187, 267, 363, 410, 471, 472, 605, 644, 712, 1046, 1116, 1129, 1135, 1263]
#t2_target_list = [0, 1]
axis_range = 0.1 # the length of X, Y, and Z axis in 3D
vimg_dir = 'verify_img'
diameter = []
meta_file = open('{0}/models_info.yml'.format(dataset_config_dir), 'r')
meta_d = yaml.load(meta_file)
for obj in objlist:
diameter.append(meta_d[obj]['diameter'] / 1000.0 * 0.1)
print(diameter)
if not os.path.exists(vimg_dir):
os.makedirs(vimg_dir)
#-------------------------------------------
if opt.dataset == 'ycb':
opt.num_objects = 21 #number of object classes in the dataset
opt.num_points = 1000 #number of points on the input pointcloud
opt.outf = 'trained_models/ycb' #folder to save trained models
opt.log_dir = 'experiments/logs/ycb' #folder to save logs
opt.repeat_epoch = 1 #number of repeat times for one epoch training
elif opt.dataset == 'linemod':
opt.num_objects = 13
opt.num_points = 500
opt.outf = 'trained_models/linemod'
opt.log_dir = 'experiments/logs/linemod'
opt.repeat_epoch = 20
else:
print('Unknown dataset')
return
estimator = PoseNet(num_points = opt.num_points, num_obj = opt.num_objects)
estimator.cuda()
refiner = PoseRefineNet(num_points = opt.num_points, num_obj = opt.num_objects)
refiner.cuda()
estimator.load_state_dict(torch.load(estimator_path))
refiner.load_state_dict(torch.load(refiner_path))
opt.refine_start = True
test_dataset = PoseDataset_linemod('test', opt.num_points, False, opt.dataset_root, 0.0, opt.refine_start)
testdataloader = torch.utils.data.DataLoader(test_dataset, batch_size=1, shuffle=False, num_workers=opt.workers)
opt.sym_list = test_dataset.get_sym_list()
opt.num_points_mesh = test_dataset.get_num_points_mesh()
print('>>>>>>>>----------Dataset loaded!---------<<<<<<<<\n\
length of the testing set: {0}\nnumber of sample points on mesh: {1}\n\
symmetry object list: {2}'\
.format( len(test_dataset), opt.num_points_mesh, opt.sym_list))
#load pytorch model
estimator.eval()
refiner.eval()
criterion = Loss(opt.num_points_mesh, opt.sym_list)
criterion_refine = Loss_refine(opt.num_points_mesh, opt.sym_list)
fw = open('{0}/t1_eval_result_logs.txt'.format(output_result_dir), 'w')
#Pose estimation
for j, data in enumerate(testdataloader, 0):
# g13: modify this part for evaluation target--------------------
if t1_start and j == t1_total_eval_num:
break
if t2_start and not (j in t2_target_list):
continue
#----------------------------------------------------------------
points, choose, img, target, model_points, idx = data
if len(points.size()) == 2:
print('No.{0} NOT Pass! Lost detection!'.format(j))
fw.write('No.{0} NOT Pass! Lost detection!\n'.format(j))
continue
points, choose, img, target, model_points, idx = Variable(points).cuda(), \
Variable(choose).cuda(), \
Variable(img).cuda(), \
Variable(target).cuda(), \
Variable(model_points).cuda(), \
Variable(idx).cuda()
pred_r, pred_t, pred_c, emb = estimator(img, points, choose, idx)
_, dis, new_points, new_target = criterion(pred_r, pred_t, pred_c, target, model_points, idx, points, opt.w, opt.refine_start)
#if opt.refine_start: #iterative poserefinement
# for ite in range(0, opt.iteration):
# pred_r, pred_t = refiner(new_points, emb, idx)
# dis, new_points, new_target = criterion_refine(pred_r, pred_t, new_target, model_points, idx, new_points)
pred_r = pred_r / torch.norm(pred_r, dim=2).view(1, opt.num_points, 1)
pred_c = pred_c.view(bs, opt.num_points)
how_max, which_max = torch.max(pred_c, 1)
pred_t = pred_t.view(bs * opt.num_points, 1, 3)
my_r = pred_r[0][which_max[0]].view(-1).cpu().data.numpy()
my_t = (points.view(bs * opt.num_points, 1, 3) + pred_t)[which_max[0]].view(-1).cpu().data.numpy()
my_pred = np.append(my_r, my_t)
for ite in range(0, opt.iteration):
T = Variable(torch.from_numpy(my_t.astype(np.float32))).cuda().view(1, 3).repeat(opt.num_points, 1).contiguous().view(1, opt.num_points, 3)
my_mat = quaternion_matrix(my_r)
R = Variable(torch.from_numpy(my_mat[:3, :3].astype(np.float32))).cuda().view(1, 3, 3)
my_mat[0:3, 3] = my_t
new_points = torch.bmm((points - T), R).contiguous()
pred_r, pred_t = refiner(new_points, emb, idx)
pred_r = pred_r.view(1, 1, -1)
pred_r = pred_r / (torch.norm(pred_r, dim=2).view(1, 1, 1))
my_r_2 = pred_r.view(-1).cpu().data.numpy()
my_t_2 = pred_t.view(-1).cpu().data.numpy()
my_mat_2 = quaternion_matrix(my_r_2)
my_mat_2[0:3, 3] = my_t_2
my_mat_final = np.dot(my_mat, my_mat_2)
my_r_final = copy.deepcopy(my_mat_final)
my_r_final[0:3, 3] = 0
my_r_final = quaternion_from_matrix(my_r_final, True)
my_t_final = np.array([my_mat_final[0][3], my_mat_final[1][3], my_mat_final[2][3]])
my_pred = np.append(my_r_final, my_t_final)
my_r = my_r_final
my_t = my_t_final
# Here 'my_pred' is the final pose estimation result after refinement ('my_r': quaternion, 'my_t': translation)
#g13: checking the dis value
success_count = [0 for i in range(opt.num_objects)]
num_count = [0 for i in range(opt.num_objects)]
model_points = model_points[0].cpu().detach().numpy()
my_r = quaternion_matrix(my_r)[:3, :3]
pred = np.dot(model_points, my_r.T) + my_t
target = target[0].cpu().detach().numpy()
if idx[0].item() in opt.sym_list:
pred = torch.from_numpy(pred.astype(np.float32)).cuda().transpose(1, 0).contiguous()
target = torch.from_numpy(target.astype(np.float32)).cuda().transpose(1, 0).contiguous()
inds = knn(target.unsqueeze(0), pred.unsqueeze(0))
target = torch.index_select(target, 1, inds.view(-1) - 1)
dis = torch.mean(torch.norm((pred.transpose(1, 0) - target.transpose(1, 0)), dim=1), dim=0).item()
else:
dis = np.mean(np.linalg.norm(pred - target, axis=1))
if dis < diameter[idx[0].item()]:
success_count[idx[0].item()] += 1
print('No.{0} Pass! Distance: {1}'.format(j, dis))
fw.write('No.{0} Pass! Distance: {1}\n'.format(j, dis))
else:
print('No.{0} NOT Pass! Distance: {1}'.format(j, dis))
fw.write('No.{0} NOT Pass! Distance: {1}\n'.format(j, dis))
num_count[idx[0].item()] += 1
# g13: start drawing pose on image------------------------------------
# pick up image
print('{0}:\nmy_r is {1}\nmy_t is {2}\ndis:{3}'.format(j, my_r, my_t, dis.item()))
print("index {0}: {1}".format(j, test_dataset.list_rgb[j]))
img = Image.open(test_dataset.list_rgb[j])
# pick up center position by bbox
meta_file = open('{0}/data/{1}/gt.yml'.format(opt.dataset_root, '%02d' % test_dataset.list_obj[j]), 'r')
meta = {}
meta = yaml.load(meta_file)
which_item = test_dataset.list_rank[j]
which_obj = test_dataset.list_obj[j]
which_dict = 0
dict_leng = len(meta[which_item])
#print('get meta[{0}][{1}][obj_bb]'.format(which_item, which_obj))
k_idx = 0
while 1:
if meta[which_item][k_idx]['obj_id'] == which_obj:
which_dict = k_idx
break
k_idx = k_idx+1
bbx = meta[which_item][which_dict]['obj_bb']
draw = ImageDraw.Draw(img)
# draw box (ensure this is the right object)
draw.line((bbx[0],bbx[1], bbx[0], bbx[1]+bbx[3]), fill=(255,0,0), width=5)
draw.line((bbx[0],bbx[1], bbx[0]+bbx[2], bbx[1]), fill=(255,0,0), width=5)
draw.line((bbx[0],bbx[1]+bbx[3], bbx[0]+bbx[2], bbx[1]+bbx[3]), fill=(255,0,0), width=5)
draw.line((bbx[0]+bbx[2],bbx[1], bbx[0]+bbx[2], bbx[1]+bbx[3]), fill=(255,0,0), width=5)
#get center
c_x = bbx[0]+int(bbx[2]/2)
c_y = bbx[1]+int(bbx[3]/2)
draw.point((c_x,c_y), fill=(255,255,0))
print('center:({0},{1})'.format(c_x, c_y))
#get the 3D position of center
cam_intrinsic = np.zeros((3,3))
cam_intrinsic.itemset(0, test_dataset.cam_fx)
cam_intrinsic.itemset(4, test_dataset.cam_fy)
cam_intrinsic.itemset(2, test_dataset.cam_cx)
cam_intrinsic.itemset(5, test_dataset.cam_cy)
cam_intrinsic.itemset(8, 1)
cam_extrinsic = my_mat_final[0:3, :]
cam2d_3d = np.matmul(cam_intrinsic, cam_extrinsic)
cen_3d = np.matmul(np.linalg.pinv(cam2d_3d), [[c_x],[c_y],[1]])
# replace img.show() with plt.imshow(img)
#transpose three 3D axis point into 2D
x_3d = cen_3d + [[axis_range],[0],[0],[0]]
y_3d = cen_3d + [[0],[axis_range],[0],[0]]
z_3d = cen_3d + [[0],[0],[axis_range],[0]]
x_2d = np.matmul(cam2d_3d, x_3d)
y_2d = np.matmul(cam2d_3d, y_3d)
z_2d = np.matmul(cam2d_3d, z_3d)
#draw the axis on 2D
draw.line((c_x, c_y, x_2d[0], x_2d[1]), fill=(255,255,0), width=5)
draw.line((c_x, c_y, y_2d[0], y_2d[1]), fill=(0,255,0), width=5)
draw.line((c_x, c_y, z_2d[0], z_2d[1]), fill=(0,0,255), width=5)
#g13: draw the estimate pred obj
for pti in pred:
pti.transpose()
pti_2d = np.matmul(cam_intrinsic, pti)
#print('({0},{1})\n'.format(int(pti_2d[0]),int(pti_2d[1])))
draw.point([int(pti_2d[0]),int(pti_2d[1])], fill=(255,255,0))
#g13: show image
#img.show()
#save file under file
img_file_name = '{0}/batch{1}_pred_obj{2}_pic{3}.png'.format(vimg_dir, j, test_dataset.list_obj[j], which_item)
img.save( img_file_name, "PNG" )
img.close()
# plot ground true ----------------------------
img = Image.open(test_dataset.list_rgb[j])
draw = ImageDraw.Draw(img)
draw.line((bbx[0],bbx[1], bbx[0], bbx[1]+bbx[3]), fill=(255,0,0), width=5)
draw.line((bbx[0],bbx[1], bbx[0]+bbx[2], bbx[1]), fill=(255,0,0), width=5)
draw.line((bbx[0],bbx[1]+bbx[3], bbx[0]+bbx[2], bbx[1]+bbx[3]), fill=(255,0,0), width=5)
draw.line((bbx[0]+bbx[2],bbx[1], bbx[0]+bbx[2], bbx[1]+bbx[3]), fill=(255,0,0), width=5)
target_r = np.resize(np.array(meta[which_item][k_idx]['cam_R_m2c']), (3, 3))
target_t = np.array(meta[which_item][k_idx]['cam_t_m2c'])
target_t = target_t[np.newaxis, :]
cam_extrinsic_GT = np.concatenate((target_r, target_t.T), axis=1)
#get center 3D
cam2d_3d_GT = np.matmul(cam_intrinsic, cam_extrinsic_GT)
cen_3d_GT = np.matmul(np.linalg.pinv(cam2d_3d_GT), [[c_x],[c_y],[1]])
#transpose three 3D axis point into 2D
x_3d = cen_3d_GT + [[axis_range],[0],[0],[0]]
y_3d = cen_3d_GT + [[0],[axis_range],[0],[0]]
z_3d = cen_3d_GT + [[0],[0],[axis_range],[0]]
x_2d = np.matmul(cam2d_3d_GT, x_3d)
y_2d = np.matmul(cam2d_3d_GT, y_3d)
z_2d = np.matmul(cam2d_3d_GT, z_3d)
#draw the axis on 2D
draw.line((c_x, c_y, x_2d[0], x_2d[1]), fill=(255,255,0), width=5)
draw.line((c_x, c_y, y_2d[0], y_2d[1]), fill=(0,255,0), width=5)
draw.line((c_x, c_y, z_2d[0], z_2d[1]), fill=(0,0,255), width=5)
print('pred:\n{0}\nGT:\n{1}\n'.format(cam_extrinsic,cam_extrinsic_GT))
print('pred 3D:{0}\nGT 3D:{1}\n'.format(cen_3d, cen_3d_GT))
img_file_name = '{0}/batch{1}_pred_obj{2}_pic{3}_gt.png'.format(vimg_dir, j, test_dataset.list_obj[j], which_item)
img.save( img_file_name, "PNG" )
img.close()
meta_file.close()
print('\nplot_result_img.py completed the task\n')