def main():
args = parser.parse_args()
if not (args.output_disp or args.output_depth):
print('You must at least output one value !')
return
disp_net = DispNetS().to(device)
weights = torch.load(args.pretrained)
disp_net.load_state_dict(weights['state_dict'])
disp_net.eval()
dataset_dir = Path(args.dataset_dir)
output_dir = Path(args.output_dir)
output_dir.makedirs_p()
if args.dataset_list is not None:
with open(args.dataset_list, 'r') as f:
test_files = [dataset_dir / file for file in f.read().splitlines()]
else:
test_files = sum([
list(dataset_dir.walkfiles('*.{}'.format(ext)))
for ext in args.img_exts
], [])
print('{} files to test'.format(len(test_files)))
for file in tqdm(test_files):
img = imread(file)
h, w, _ = img.shape
if (not args.no_resize) and (h != args.img_height
or w != args.img_width):
img = np.array(
Image.fromarray(img).resize((args.img_width, args.img_height)))
img = np.transpose(img, (2, 0, 1))
tensor_img = torch.from_numpy(img.astype(np.float32)).unsqueeze(0)
tensor_img = ((tensor_img / 255 - 0.5) / 0.5).to(device)
output = disp_net(tensor_img)[0]
file_path, file_ext = file.relpath(args.dataset_dir).splitext()
print(file_path)
print(file_path.splitall())
file_name = '-'.join(file_path.splitall()[1:])
print(file_name)
if args.output_disp:
disp = (255 * tensor2array(output, max_value=None,
colormap='bone')).astype(np.uint8)
imsave(output_dir / '{}_disp{}'.format(file_name, file_ext),
np.transpose(disp, (1, 2, 0)))
if args.output_depth:
depth = 1 / output
depth = (255 * tensor2array(
depth, max_value=None, colormap='magma')).astype(np.uint8)
imsave(output_dir / '{}_depth{}'.format(file_name, file_ext),
np.transpose(depth, (1, 2, 0)))
def main():
args = parser.parse_args()
if not (args.output_disp or args.output_depth):
print('You must at least output one value !')
return
disp_net = DispNetS().to(device)
weights = torch.load(args.pretrained)
disp_net.load_state_dict(weights['state_dict'])
disp_net.eval()
dataset_dir = Path(args.dataset_dir)
output_dir = Path(args.output_dir)
output_dir.makedirs_p()
if args.dataset_list is not None:
with open(args.dataset_list, 'r') as f:
test_files = [dataset_dir / file for file in f.read().splitlines()]
else:
test_files = sum(
[dataset_dir.files('*.{}'.format(ext)) for ext in args.img_exts],
[])
print('{} files to test'.format(len(test_files)))
for file in tqdm(test_files):
img = imread(file).astype(np.float32)
h, w, _ = img.shape
if (not args.no_resize) and (h != args.img_height
or w != args.img_width):
img = imresize(img, (args.img_height, args.img_width)).astype(
np.float32)
img = np.transpose(img, (2, 0, 1))
tensor_img = torch.from_numpy(img).unsqueeze(0)
tensor_img = ((tensor_img / 255 - 0.5) / 0.2).to(device)
output = disp_net(tensor_img)[0]
if args.output_disp:
disp = (255 * tensor2array(
output, max_value=None, colormap='bone',
channel_first=False)).astype(np.uint8)
imsave(output_dir / '{}_disp{}'.format(file.namebase, file.ext),
disp)
if args.output_depth:
depth = 1 / output
depth = (255 * tensor2array(
depth, max_value=10, colormap='rainbow',
channel_first=False)).astype(np.uint8)
imsave(output_dir / '{}_depth{}'.format(file.namebase, file.ext),
depth)
def main():
args = parser.parse_args()
if args.gt_type == 'KITTI':
from kitti_eval.depth_evaluation_utils import test_framework_KITTI as test_framework
elif args.gt_type == 'stillbox':
from stillbox_eval.depth_evaluation_utils import test_framework_stillbox as test_framework
disp_net = DispNetS().to(device)
weights = torch.load(args.pretrained_dispnet)
disp_net.load_state_dict(weights['state_dict'])
disp_net.eval()
if args.pretrained_posenet is None:
print('no PoseNet specified, scale_factor will be determined by median ratio, which is kiiinda cheating\
(but consistent with original paper)')
seq_length = 0
else:
weights = torch.load(args.pretrained_posenet)
seq_length = int(weights['state_dict']['conv1.0.weight'].size(1)/3)
pose_net = PoseExpNet(nb_ref_imgs=seq_length - 1, output_exp=False).to(device)
pose_net.load_state_dict(weights['state_dict'], strict=False)
dataset_dir = Path(args.dataset_dir)
if args.dataset_list is not None:
with open(args.dataset_list, 'r') as f:
test_files = list(f.read().splitlines())
else:
test_files = [file.relpathto(dataset_dir) for file in sum([dataset_dir.files('*.{}'.format(ext)) for ext in args.img_exts], [])]
framework = test_framework(dataset_dir, test_files, seq_length, args.min_depth, args.max_depth)
print('{} files to test'.format(len(test_files)))
errors = np.zeros((2, 7, len(test_files)), np.float32)
if args.output_dir is not None:
output_dir = Path(args.output_dir)
output_dir.makedirs_p()
for j, sample in enumerate(tqdm(framework)):
tgt_img = sample['tgt']
ref_imgs = sample['ref']
h,w,_ = tgt_img.shape
if (not args.no_resize) and (h != args.img_height or w != args.img_width):
tgt_img = imresize(tgt_img, (args.img_height, args.img_width)).astype(np.float32)
ref_imgs = [imresize(img, (args.img_height, args.img_width)).astype(np.float32) for img in ref_imgs]
tgt_img = np.transpose(tgt_img, (2, 0, 1))
ref_imgs = [np.transpose(img, (2,0,1)) for img in ref_imgs]
tgt_img = torch.from_numpy(tgt_img).unsqueeze(0)
tgt_img = ((tgt_img/255 - 0.5)/0.5).to(device)
for i, img in enumerate(ref_imgs):
img = torch.from_numpy(img).unsqueeze(0)
img = ((img/255 - 0.5)/0.5).to(device)
ref_imgs[i] = img
pred_disp = disp_net(tgt_img).cpu().numpy()[0,0]
if args.output_dir is not None:
if j == 0:
predictions = np.zeros((len(test_files), *pred_disp.shape))
predictions[j] = 1/pred_disp
gt_depth = sample['gt_depth']
pred_depth = 1/pred_disp
pred_depth_zoomed = zoom(pred_depth,
(gt_depth.shape[0]/pred_depth.shape[0],
gt_depth.shape[1]/pred_depth.shape[1])
).clip(args.min_depth, args.max_depth)
if sample['mask'] is not None:
pred_depth_zoomed = pred_depth_zoomed[sample['mask']]
gt_depth = gt_depth[sample['mask']]
if seq_length > 0:
# Reorganize ref_imgs : tgt is middle frame but not necessarily the one used in DispNetS
# (in case sample to test was in end or beginning of the image sequence)
middle_index = seq_length//2
tgt = ref_imgs[middle_index]
reorganized_refs = ref_imgs[:middle_index] + ref_imgs[middle_index + 1:]
_, poses = pose_net(tgt, reorganized_refs)
mean_displacement_magnitude = poses[0,:,:3].norm(2,1).mean().item()
scale_factor = sample['displacement'] / mean_displacement_magnitude
errors[0,:,j] = compute_errors(gt_depth, pred_depth_zoomed*scale_factor)
# scale_factor = np.median(gt_depth)/np.median(pred_depth_zoomed)
scale_factor=1
errors[1,:,j] = compute_errors(gt_depth, pred_depth_zoomed*scale_factor)
mean_errors = errors.mean(2)
error_names = ['abs_rel','sq_rel','rms','log_rms','a1','a2','a3']
if args.pretrained_posenet:
print("Results with scale factor determined by PoseNet : ")
print("{:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10}".format(*error_names))
print("{:10.4f}, {:10.4f}, {:10.4f}, {:10.4f}, {:10.4f}, {:10.4f}, {:10.4f}".format(*mean_errors[0]))
print("Results with scale factor determined by GT/prediction ratio (like the original paper) : ")
print("{:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10}".format(*error_names))
print("{:10.4f}, {:10.4f}, {:10.4f}, {:10.4f}, {:10.4f}, {:10.4f}, {:10.4f}".format(*mean_errors[1]))
if args.output_dir is not None:
np.save(output_dir/'predictions.npy', predictions)
def main():
global tgt_img, disp_net
args = parser.parse_args()
'''加载训练后的模型'''
weights = torch.load(args.pretrained_posenet)
seq_length = int(weights['state_dict']['conv1.0.weight'].size(1) / 3)
pose_net = PoseExpNet(nb_ref_imgs=seq_length - 1,
output_exp=False).to(device)
pose_net.load_state_dict(weights['state_dict'], strict=False)
# 网络模型的MD5 ID
net_ID = MD5_ID(args.pretrained_posenet)
# L和C的转换矩阵,对齐输入位姿到雷达坐标系
Transform_matrix_L2C = np.identity(4)
'''Kitti switch'''
if args.isKitti:
if not args.isDynamic:
from kitti_eval.pose_evaluation_utils import test_framework_KITTI as test_framework
else:
from kitti_eval.pose_evaluation_utils_forDynamicTest import test_framework_KITTI as test_framework
save_dir = os.path.join(args.output_dir, "kitti", args.sequences[0],
'net_' + net_ID)
if args.trainedOnMydataset:
downsample_img_height = args.img_height
downsample_img_width = args.img_width
else:
# on kitti train set
downsample_img_height = 128
downsample_img_width = 416
Transform_matrix_L2C[:3, :3] = np.array(
[[7.533745e-03, -9.999714e-01, -6.166020e-04],
[1.480249e-02, 7.280733e-04, -9.998902e-01],
[9.998621e-01, 7.523790e-03, 1.480755e-02]])
Transform_matrix_L2C[:3, -1:] = np.array(
[-4.069766e-03, -7.631618e-02, -2.717806e-01]).reshape(3, 1)
else:
from mydataset_eval.pose_evaluation_utils import test_framework_MYDATASET as test_framework
save_dir = os.path.join(args.output_dir, "mydataset",
args.sequences[0], 'net_' + net_ID)
if args.trainedOnMydataset:
downsample_img_height = args.img_height
downsample_img_width = args.img_width
else:
# on kitti train set
downsample_img_height = 128
downsample_img_width = 416
Transform_matrix_L2C[:3, :3] = np.array(
[[-1.51482698e-02, -9.99886648e-01, 5.36310553e-03],
[-4.65337018e-03, -5.36307196e-03, -9.99969412e-01],
[9.99870070e-01, -1.56647995e-02, -4.48880010e-03]])
Transform_matrix_L2C[:3, -1:] = np.array(
[4.29029924e-03, -6.08539196e-02, -9.20346161e-02]).reshape(3, 1)
Transform_matrix_L2C = GramSchmidtHelper(Transform_matrix_L2C)
Transform_matrix_C2L = np.linalg.inv(Transform_matrix_L2C)
# *************************可删除*********************************
# 为了进行动态场景下的Mask评估，这里需要引入disp net
if args.isDynamic:
from models import DispNetS
disp_net = DispNetS().to(device)
weights = torch.load(args.pretrained_dispnet)
disp_net.load_state_dict(weights['state_dict'])
disp_net.eval()
# normalize = custom_transforms.Normalize(mean=[0.5, 0.5, 0.5],
# std=[0.5, 0.5, 0.5])
# valid_transform = custom_transforms.Compose([custom_transforms.ArrayToTensor(), normalize])
# from datasets.sequence_folders import SequenceFolder
# val_set = SequenceFolder(
# '/home/sda/mydataset/preprocessing/formatted/data/',
# transform=valid_transform,
# seed=0,
# train=False,
# sequence_length=3,
# )
# val_loader = torch.utils.data.DataLoader(
# val_set, batch_size=1, shuffle=False,
# num_workers=4, pin_memory=True)
#
# intrinsics = None
# for i, (tgt_img, ref_imgs, intrinsics, intrinsics_inv) in enumerate(val_loader):
# intrinsics = intrinsics.to(device)
# break
# *************************************************************************
'''载入测试数据集'''
dataset_dir = Path(args.dataset_dir)
framework = test_framework(dataset_dir, args.sequences, seq_length)
print('{} snippets to test'.format(len(framework)))
errors = np.zeros((len(framework), 2), np.float32)
'''输出到文件夹中的数据'''
num_poses = len(framework) - (seq_length - 2)
predictions_array = np.zeros((len(framework), seq_length, 3, 4))
processing_time = np.zeros((num_poses - 1, 1))
# 输出文件夹
save_dir = Path(save_dir)
print('Output files wiil be saved in： ' + save_dir)
if not os.path.exists(save_dir): save_dir.makedirs_p()
# Pose Graph Manager (for back-end optimization) initialization
PGM = PoseGraphManager()
PGM.addPriorFactor()
# Result saver
num_frames = len(framework)
ResultSaver = PoseGraphResultSaver(init_pose=PGM.curr_se3,
save_gap=args.save_gap,
num_frames=num_frames,
seq_idx=args.sequences[0],
save_dir=save_dir)
# for save the results as a video
fig_idx = 1
fig = plt.figure(fig_idx)
writer = FFMpegWriter(fps=15)
video_path = save_dir + '/' + args.sequences[0] + ".mp4"
num_frames_to_skip_to_show = 5
num_frames_to_save = np.floor(num_frames / num_frames_to_skip_to_show)
with writer.saving(
fig, video_path,
num_frames_to_save): # this video saving part is optional
for j, sample in enumerate(tqdm(framework)):
'''
VO部分
'''
imgs = sample['imgs']
w, h = imgs[0].size
if (not args.no_resize) and (h != downsample_img_height
or w != downsample_img_width):
imgs = [
imresize(img, (downsample_img_height,
downsample_img_width)).astype(np.float32)
for img in imgs
]
imgs = [np.transpose(img, (2, 0, 1)) for img in imgs]
ref_imgs = []
for i, img in enumerate(imgs):
img = torch.from_numpy(img).unsqueeze(0)
img = ((img / 255 - 0.5) / 0.5).to(device)
if i == len(imgs) // 2:
tgt_img = img
else:
ref_imgs.append(img)
startTimeVO = time.time()
_, poses = pose_net(tgt_img, ref_imgs)
processing_time[j] = (time.time() - startTimeVO) / (seq_length - 1)
# **************************可删除********************************
if args.isDynamic:
'''测试Photo mask的效果'''
if args.isKitti:
intrinsics = [[
2.416744631239935472e+02, 0.000000000000000000e+00,
2.041680103059581199e+02
],
[
0.000000000000000000e+00,
2.462848682666666491e+02,
5.900083200000000261e+01
],
[
0.000000000000000000e+00,
0.000000000000000000e+00,
1.000000000000000000e+00
]]
else:
intrinsics = [[279.1911, 0.0000, 210.8265],
[0.0000, 279.3980, 172.3114],
[0.0000, 0.0000, 1.0000]]
PhotoMask_Output(_, disp_net, intrinsics, j, poses, ref_imgs,
save_dir)
# ***************************************************************
final_poses = pose2tf_mat(args.rotation_mode, imgs, poses)
predictions_array[j] = final_poses
# rel_VO_pose取final poses的第2项，整体则是取T10,T21,T32。。。
rel_VO_pose = np.identity(4)
rel_VO_pose[:3, :] = final_poses[1]
# 引入尺度因子对单目VO输出的位姿进行修正,并进行坐标系对齐到雷达坐标系
scale_factor = 7
rel_VO_pose[:3, -1:] = rel_VO_pose[:3, -1:] * scale_factor
rel_VO_pose = Transform_matrix_C2L @ rel_VO_pose @ np.linalg.inv(
Transform_matrix_C2L)
rel_VO_pose = GramSchmidtHelper(rel_VO_pose)
ResultSaver.saveRelativePose(rel_VO_pose)
PGM.curr_node_idx = j + 1
PGM.curr_se3 = np.matmul(PGM.curr_se3, rel_VO_pose)
PGM.addOdometryFactor(rel_VO_pose)
PGM.prev_node_idx = PGM.curr_node_idx
ResultSaver.saveUnoptimizedPoseGraphResult(PGM.curr_se3,
PGM.curr_node_idx)
# if (j % num_frames_to_skip_to_show == 0):
# ResultSaver.vizCurrentTrajectory(fig_idx=fig_idx)
# writer.grab_frame()
if args.isKitti:
ATE, RE = compute_pose_error(sample['poses'], final_poses)
errors[j] = ATE, RE
'''save output files'''
if save_dir is not None:
# np.save(save_dir / 'predictions.npy', predictions_array)
ResultSaver.saveFinalPoseGraphResult(filename='abs_VO_poses.txt')
ResultSaver.saveRelativePosesResult(filename='rel_VO_poses.txt')
np.savetxt(save_dir / 'processing_time.txt', processing_time)
if args.isKitti:
np.savetxt(save_dir / 'errors.txt', errors)
mean_errors = errors.mean(0)
std_errors = errors.std(0)
error_names = ['ATE', 'RE']
print('')
print("Results")
print("\t {:>10}, {:>10}".format(*error_names))
print("mean \t {:10.4f}, {:10.4f}".format(*mean_errors))
print("std \t {:10.4f}, {:10.4f}".format(*std_errors))
def main():
args = parser.parse_args()
if not(args.output_disp or args.output_depth):
# print("args.output_disp:\n", args.output_disp)
# print("args.output_depth:\n", args.output_depth)
print('You must at least output one value !')
return
disp_net = DispNetS().to(device)
weights = torch.load(args.pretrained)
disp_net.load_state_dict(weights['state_dict'])
disp_net.eval()
dataset_dir = Path(args.dataset_dir)
output_dir = Path(args.output_dir)
output_dir.makedirs_p()
print("dataset_list:\n", args.dataset_list)
if args.dataset_list is not None:
with open(args.dataset_list, 'r') as f:
test_files = [dataset_dir/file for file in f.read().splitlines()]
else:
print("Else!")
test_files = sum([list(dataset_dir.walkfiles('*.{}'.format(ext))) for ext in args.img_exts], [])
print(dataset_dir)
print("dataset_list:\n", args.dataset_list)
print("test_files:\n", test_files)
print('{} files to test'.format(len(test_files)))
for file in tqdm(test_files):
# print("file:\n", file)
img = imread(file)
h,w,_ = img.shape
if (not args.no_resize) and (h != args.img_height or w != args.img_width):
img = np.array(Image.fromarray(img).imresize((args.img_height, args.img_width)))
img = np.transpose(img, (2, 0, 1))
tensor_img = torch.from_numpy(img.astype(np.float32)).unsqueeze(0)
tensor_img = ((tensor_img/255 - 0.5)/0.5).to(device)
output = disp_net(tensor_img)[0]
file_path, file_ext = file.relpath(args.dataset_dir).splitext()
print(file_path)
print(file_path.splitall())
file_name = '-'.join(file_path.splitall()[1:])
print(file_name)
if args.output_disp:
disp = (255*tensor2array(output, max_value=None, colormap='bone')).astype(np.uint8)
# imsave(output_dir/'{}_disp{}'.format(file_name, file_ext), np.transpose(disp, (1,2,0)))
if args.output_depth:
depth = 1/output
# depth = (255*tensor2array(depth, max_value=10, colormap='rainbow')).astype(np.uint8)
# depth = (2550*tensor2array(depth, max_value=10, colormap='bone')).astype(np.uint8)
# print(depth.shape)
# imsave(output_dir/'{}_depth{}'.format(file_name, file_ext), np.transpose(depth, (1,2,0)))
depth = depth.to(device)
errors = np.zeros((2, 9, len(test_files)), np.float32)
mean_errors = errors.mean(2)
gt = tifffile.imread('/home/zyd/respository/sfmlearner_results/endo_testset/left_depth_map_d4k1_000000.tiff')
gt = gt[:, :, 2]
abs_diff, abs_rel, sq_rel, a1, a2, a3 = 0,0,0,0,0,0
if 1:
crop_mask = gt[0] != gt[0]
y1,y2 = int(0.40810811 * 1024), int(0.99189189 * 1024)
x1,x2 = int(0.03594771 * 1280), int(0.96405229 * 1280)
crop_mask[y1:y2,x1:x2] = 1
for current_gt, current_pred in zip(gt, pred):
valid = (current_gt > 0) & (current_gt < 80)
if 1:
valid = valid & crop_mask
valid_gt = current_gt[valid]
valid_pred = current_pred[valid].clamp(1e-3, 80)
valid_pred = valid_pred * torch.median(valid_gt)/torch.median(valid_pred)
thresh = torch.max((valid_gt / valid_pred), (valid_pred / valid_gt))
a1 += (thresh < 1.25).float().mean()
a2 += (thresh < 1.25 ** 2).float().mean()
a3 += (thresh < 1.25 ** 3).float().mean()
abs_diff += torch.mean(torch.abs(valid_gt - valid_pred))
abs_rel += torch.mean(torch.abs(valid_gt - valid_pred) / valid_gt)
sq_rel += torch.mean(((valid_gt - valid_pred)**2) / valid_gt)
error_names = ['abs_diff', 'abs_rel','sq_rel','rms','log_rms', 'abs_log', 'a1','a2','a3']
print("Results with scale factor determined by GT/prediction ratio (like the original paper) : ")
print("{:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10}".format(*error_names))
print("{:10.4f}, {:10.4f}, {:10.4f}, {:10.4f}, {:10.4f}, {:10.4f}, {:10.4f}, {:10.4f}, {:10.4f}".format(*mean_errors[1]))
def main():
args = parser.parse_args()
if args.gt_type == 'KITTI':
from kitti_eval.depth_evaluation_utils import test_framework_KITTI as test_framework
disp_net = DispNetS().cuda()
weights = torch.load(args.pretrained_dispnet)
disp_net.load_state_dict(weights['state_dict'])
disp_net.eval()
if args.pretrained_posenet is None:
print(
'no PoseNet specified, scale_factor will be determined by median ratio, which is kiiinda cheating\
(but consistent with original paper)')
seq_length = 0
else:
weights = torch.load(args.pretrained_posenet)
seq_length = int(weights['state_dict']['conv1.0.weight'].size(1) / 3)
pose_net = PoseExpNet(nb_ref_imgs=seq_length - 1,
output_exp=False).cuda()
pose_net.load_state_dict(weights['state_dict'], strict=False)
dataset_dir = Path(args.dataset_dir)
if args.dataset_list is not None:
with open(args.dataset_list, 'r') as f:
test_files = list(f.read().splitlines())
else:
test_files = [
file.relpathto(dataset_dir) for file in sum([
dataset_dir.files('*.{}'.format(ext)) for ext in args.img_exts
], [])
]
framework = test_framework(dataset_dir, test_files, seq_length,
args.min_depth, args.max_depth)
output_dir = Path(args.output_dir)
output_dir.makedirs_p()
print('{} files to test'.format(len(test_files)))
errors = np.zeros((2, 7, len(test_files)), np.float32)
for j, sample in enumerate(tqdm(framework)):
tgt_img = sample['tgt']
ref_imgs = sample['ref']
h, w, _ = tgt_img.shape
if (not args.no_resize) and (h != args.img_height
or w != args.img_width):
tgt_img = imresize(
tgt_img, (args.img_height, args.img_width)).astype(np.float32)
ref_imgs = [
imresize(img,
(args.img_height, args.img_width)).astype(np.float32)
for img in ref_imgs
]
tgt_img = np.transpose(tgt_img, (2, 0, 1))
ref_imgs = [np.transpose(img, (2, 0, 1)) for img in ref_imgs]
tgt_img = torch.from_numpy(tgt_img).unsqueeze(0)
tgt_img = ((tgt_img / 255 - 0.5) / 0.2).cuda()
tgt_img_var = Variable(tgt_img, volatile=True)
ref_imgs_var = []
for i, img in enumerate(ref_imgs):
img = torch.from_numpy(img).unsqueeze(0)
img = ((img / 255 - 0.5) / 0.2).cuda()
ref_imgs_var.append(Variable(img, volatile=True))
pred_disp = disp_net(tgt_img_var).data.cpu().numpy()[0, 0]
gt_depth = sample['gt_depth']
pred_depth = 1 / pred_disp
pred_depth_zoomed = zoom(
pred_depth,
(gt_depth.shape[0] / pred_depth.shape[0], gt_depth.shape[1] /
pred_depth.shape[1])).clip(args.min_depth, args.max_depth)
if sample['mask'] is not None:
pred_depth_zoomed = pred_depth_zoomed[sample['mask']]
gt_depth = gt_depth[sample['mask']]
if seq_length > 0:
_, poses = pose_net(tgt_img_var, ref_imgs_var)
displacements = poses[0, :, :3].norm(
2, 1).cpu().data.numpy() # shape [1 - seq_length]
scale_factors = (sample['displacements'] /
displacements)[sample['displacements'] > 0]
scale_factors = [
s1 / s2
for s1, s2 in zip(sample['displacements'], displacements)
if s1 > 0
]
scale_factor = np.mean(
scale_factors) if len(scale_factors) > 0 else 0
if len(scale_factors) == 0:
print('not good ! ', sample['path'], sample['displacements'])
errors[0, :, j] = compute_errors(gt_depth,
pred_depth_zoomed * scale_factor)
scale_factor = np.median(gt_depth) / np.median(pred_depth_zoomed)
errors[1, :, j] = compute_errors(gt_depth,
pred_depth_zoomed * scale_factor)
mean_errors = errors.mean(2)
error_names = ['abs_rel', 'sq_rel', 'rms', 'log_rms', 'a1', 'a2', 'a3']
if args.pretrained_posenet:
print("Results with scale factor determined by PoseNet : ")
print("{:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10}".format(
*error_names))
print(
"{:10.4f}, {:10.4f}, {:10.4f}, {:10.4f}, {:10.4f}, {:10.4f}, {:10.4f}"
.format(*mean_errors[0]))
print(
"Results with scale factor determined by GT/prediction ratio (like the original paper) : "
)
print("{:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10}".format(
*error_names))
print(
"{:10.4f}, {:10.4f}, {:10.4f}, {:10.4f}, {:10.4f}, {:10.4f}, {:10.4f}".
format(*mean_errors[1]))
def main():
args = parser.parse_args()
'''
#还挑着生成哪个, 这里都输出了
if not(args.output_disp or args.output_depth):
print('You must at least output one value !')
return
'''
disp_net = DispNetS().to(device)
weights = torch.load(args.pretrained)
disp_net.load_state_dict(weights['state_dict'])
disp_net.eval()
dataset_dir = Path(args.dataset_dir) #str2Path
output_disp_dir = Path(args.output_dir + '/disp')
output_depth_dir = Path(args.output_dir + '/depth')
output_disp_dir.makedirs_p() #如果没有就创建,甚至可以创建子文件夹
output_depth_dir.makedirs_p()
if args.dataset_list is not None:
with open(args.dataset_list, 'r') as f:
test_files = [dataset_dir / file for file in f.read().splitlines()]
else:
test_files = sum(
[dataset_dir.files('*.{}'.format(ext)) for ext in args.img_exts],
[])
print('{} files to test'.format(len(test_files)))
for file in tqdm(test_files): #测试图片
img = imread(file).astype(np.float32)
h, w, _ = img.shape #h :375 w:1242 _: 3
if (not args.no_resize) and (h != args.img_height
or w != args.img_width):
img = imresize(img, (args.img_height, args.img_width)).astype(
np.float32)
img = np.transpose(img, (2, 0, 1))
tensor_img = torch.from_numpy(img).unsqueeze(0)
tensor_img = ((tensor_img / 255 - 0.5) / 0.2).to(device)
#网络输入
output = disp_net(tensor_img) #1,1,h,w
output = output[0]
file_path, file_ext = file.relpath(args.dataset_dir).splitext()
file_name = '-'.join(file_path.splitall())
#save to disk
disp = (255 *
tensor2array(output, max_value=None, colormap='bone')).astype(
np.uint8) #4x375x1242
imsave(output_disp_dir / '{}_disp{}'.format(file_name, file_ext),
np.transpose(disp, (1, 2, 0))) #多通道图像转至（1,2,0）,375x1242x4
depth = 1 / output
depth = (255 *
tensor2array(depth, max_value=10, colormap='rainbow')).astype(
np.uint8)
imsave(output_depth_dir / '{}_depth{}'.format(file_name, file_ext),
np.transpose(depth, (1, 2, 0))[:, :, 1])
def init_disp_net(pretrained): weights = torch.load(pretrained, map_location=device_name) disp_net = DispNetS().to(device) disp_net.load_state_dict(weights['state_dict']) disp_net.eval() return disp_net
def main():
args = parser.parse_args()
if not (args.output_disp or args.output_depth):
# print("args.output_disp:\n", args.output_disp)
# print("args.output_depth:\n", args.output_depth)
print('You must at least output one value !')
return
disp_net = DispNetS().to(device)
weights = torch.load(args.pretrained)
disp_net.load_state_dict(weights['state_dict'])
disp_net.eval()
dataset_dir = Path(args.dataset_dir)
output_dir = Path(args.output_dir)
output_dir.makedirs_p()
print("dataset_list:\n", args.dataset_list)
if args.dataset_list is not None:
with open(args.dataset_list, 'r') as f:
test_files = [dataset_dir / file for file in f.read().splitlines()]
else:
print("Else!")
test_files = sum([
list(dataset_dir.walkfiles('*.{}'.format(ext)))
for ext in args.img_exts
], [])
print(dataset_dir)
print("dataset_list:\n", args.dataset_list)
print("test_files:\n", test_files)
print('{} files to test'.format(len(test_files)))
for file in tqdm(test_files):
# print("file:\n", file)
img = imread(file)
h, w, _ = img.shape
if (not args.no_resize) and (h != args.img_height
or w != args.img_width):
img = np.array(
Image.fromarray(img).imresize(
(args.img_height, args.img_width)))
img = np.transpose(img, (2, 0, 1))
tensor_img = torch.from_numpy(img.astype(np.float32)).unsqueeze(0)
tensor_img = ((tensor_img / 255 - 0.5) / 0.5).to(device)
output = disp_net(tensor_img)[0]
file_path, file_ext = file.relpath(args.dataset_dir).splitext()
print(file_path)
print(file_path.splitall())
file_name = '-'.join(file_path.splitall()[1:])
print(file_name)
if args.output_disp:
disp = (255 * tensor2array(output, max_value=None,
colormap='bone')).astype(np.uint8)
# imsave(output_dir/'{}_disp{}'.format(file_name, file_ext), np.transpose(disp, (1,2,0)))
if args.output_depth:
depth = 1 / output
depth = (255 * tensor2array(
depth, max_value=10, colormap='rainbow')).astype(np.uint8)
depth = (2550 * tensor2array(depth, max_value=10,
colormap='bone')).astype(np.uint8)
print(depth.shape)
imsave(output_dir / '{}_depth{}'.format(file_name, file_ext),
np.transpose(depth, (1, 2, 0)))
# added by ZYD
gt = tifffile.imread(
'/home/zyd/respository/sfmlearner_results/endo_testset/left_depth_map_d3k1_000000.tiff'
)
gt = gt[:, :, 2]
# np.savetxt('d4k1_gt.txt',gt,fmt='%0.8f')
print("groundtruth:\n", gt)
print("gt's mean:\n", np.mean(gt))
tensor = depth.detach().cpu()
arr = tensor.squeeze().numpy()
mask = (gt > 1e-3)
# mask = np.logical_and(gt > 1e-3, gt < 80)
gt_mask = gt[mask]
arr_mask = arr[mask]
scale_factor = np.median(gt_mask) / np.median(arr_mask)
print("scale_factor:\n", scale_factor)
arr = scale_factor * arr
print("array's mean:\n", np.mean(arr))
np.savetxt('d4k1_pred_depth_1epoch.txt', arr, fmt='%0.8f')
rmse = np.sqrt(mean_squared_error(arr, gt))
print("RMSE without masks:\n", rmse)
RMSE, logR, AbsRel, SqRel, count = 0, 0, 0, 0, 0
b1, b2, b3 = 0, 0, 0
for i in range(1024):
for j in range(1280):
# if (1e-3 < gt[i, j] < 80):
if (gt[i, j] > 1e-3):
RMSE = RMSE + (gt[i, j] - arr[i, j])**2
logR = logR + (np.log(gt[i, j]) - np.log(arr[i, j]))**2
AbsRel = AbsRel + abs(gt[i, j] - arr[i, j]) / gt[i, j]
SqRel = SqRel + ((gt[i, j] - arr[i, j])**2) / gt[i, j]
count = count + 1
if (0.75 * gt[i, j] < arr[i, j]
and arr[i, j] < 1.25 * gt[i, j]):
b1 = b1 + 1
if (0.4375 * gt[i, j] < arr[i, j]
and arr[i, j] < 1.5625 * gt[i, j]):
b2 = b2 + 1
if (0.046875 * gt[i, j] < arr[i, j]
and arr[i, j] < 1.953125 * gt[i, j]):
b3 = b3 + 1
RMSE = (RMSE / count)**0.5
logR = (logR / count)**0.5
AbsRel = AbsRel / count
SqRel = SqRel / count
print("count = ", count)
print("RMSE = ", RMSE)
print("logR = ", logR)
print("AbsRel = ", AbsRel)
print("SqRel = ", SqRel)
print("1.25 percentage: ", b1 / count)
print("1.25^2 percentage: ", b2 / count)
print("1.25^3 percentage: ", b3 / count)
def main():
args = parser.parse_args()
o_dir = Path(args.output_dir)
o_dir.makedirs_p()
if not(args.output_disp or args.output_depth):
print('You must at least output one value !')
return
disp_net = DispNetS().to(device)
weights = torch.load(args.pretrained)
disp_net.load_state_dict(weights['state_dict'])
disp_net.eval()
vid_list = [i for i in list(Path(args.dataset_dir).walkdirs()) if i[-4:] == 'data']
vid_list.sort()
N = len(vid_list)
print('{} videos to demo'.format(N))
for ii, vid_path in enumerate(vid_list):
vid_name = vid_path.split('/')[-2]
dataset_dir = Path(vid_path)
output_dir = Path(args.output_dir + vid_name)
output_dir.makedirs_p()
test_files = sum([list(dataset_dir.walkfiles('*.{}'.format(ext))) for ext in args.img_exts], [])
vid_save_name = str(args.output_dir)+'{}.mp4'.format(vid_name)
print('{}/{} - {} - {} files to test::video saved to \'{}\''.format(ii, N, vid_name, len(test_files), vid_save_name))
for file in tqdm(test_files):
img = imread(file)
h,w,_ = img.shape
if (not args.no_resize) and (h != args.img_height or w != args.img_width):
img = cv2.resize(img, (args.img_width, args.img_height))
img = np.transpose(img, (2, 0, 1))
tensor_img = torch.from_numpy(img.astype(np.float32)).unsqueeze(0)
tensor_img = ((tensor_img/255 - 0.5)/0.5).to(device)
output = disp_net(tensor_img)[0]
file_path, file_ext = file.relpath(args.dataset_dir).splitext()
# print(file_path)
# print(file_path.splitall())
file_name = '-'.join(file_path.splitall()[1:])
# print(file_name)
if args.output_disp:
disp = (255*tensor2array(output, max_value=None, colormap='bone')).astype(np.uint8)
imsave(output_dir/'{}_disp{}'.format(file_name, file_ext), np.transpose(disp, (1,2,0)))
if args.output_depth:
depth = 1/output
depth = (255*tensor2array(depth, max_value=10, colormap='rainbow')).astype(np.uint8)
imsave(output_dir/'{}_depth{}'.format(file_name, file_ext), np.transpose(depth, (1,2,0)))
#make vid
img_dir = output_dir
test_files.sort()
video = cv2.VideoWriter(vid_save_name, 0x7634706d, 10, (args.img_width, args.img_height*2))
for file in test_files:
file_core_name, file_ext = str(file).split('.')[-2:]
file_core_name = '-'.join(file_core_name.split('/')[-3:])
input_img = cv2.imread(file)
h,w,_ = input_img.shape
if (not args.no_resize) and (h != args.img_height or w != args.img_width):
input_img = cv2.resize(input_img, (args.img_width, args.img_height))
disp_img = cv2.imread(output_dir + '/{}_disp.{}'.format(file_core_name, file_ext))
video.write(np.concatenate((input_img, disp_img)))
cv2.destroyAllWindows()
video.release()
self.count = 0
def now(self):
self.count += 1
print("[%d]time elasped = %f" % (self.count, time.time() - self.start))
tl = time_lapse()
tl.now()
device = torch.device("cuda") if torch.cuda.is_available() else torch.device(
"cpu")
disp_net = DispNetS().to(device)
weights = torch.load("pretrained/dispnet_model_best.pth.tar",
map_location='cpu')
disp_net.load_state_dict(weights['state_dict'])
disp_net.eval()
tgt_img0 = io.imread("samples/street1.jpeg")
h, w, c = tgt_img0.shape
#print(h, w, c)
print(h, w)
ww = 600
hh = int(h * ww / w + 0.5)
tgt_img0 = cv2.resize(tgt_img0, (ww, hh))
print(ww, hh)
#tgt_img0 = transform.resize(tgt_img0, (hh, ww))
tgt_img0 = torch.from_numpy(tgt_img0)
tgt_img = np.transpose(tgt_img0, (2, 0, 1))
print(tgt_img.shape)
tgt_img = tgt_img.unsqueeze(0)
