def __init__(self):
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
self.cfg = Cfg()
# build network
self.network = UNet(self.cfg)
self.model_dict = torch.load('pretrained.pt',
map_location=lambda storage, loc: storage)
self.network.load_state_dict(self.model_dict)
self.network.to(self.device)
self.network.eval()
self.transforms = tf.Compose([
tf.ToTensor(),
tf.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
self.bin_mean_shift = Bin_Mean_Shift(device=self.device)
self.k_inv_dot_xy1 = get_coordinate_map(self.device)
self.instance_parameter_loss = InstanceParameterLoss(
self.k_inv_dot_xy1)
self.h, self.w = 192, 256
def eval(_run, _log):
cfg = edict(_run.config)
torch.manual_seed(cfg.seed)
np.random.seed(cfg.seed)
random.seed(cfg.seed)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if not (_run._id is None):
checkpoint_dir = os.path.join('experiments', str(_run._id),
'checkpoints')
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
# build network
network = UNet(cfg.model)
if not (cfg.resume_dir == 'None'):
model_dict = torch.load(cfg.resume_dir,
map_location=lambda storage, loc: storage)
network.load_state_dict(model_dict)
# load nets into gpu
if cfg.num_gpus > 1 and torch.cuda.is_available():
network = torch.nn.DataParallel(network)
network.to(device)
network.eval()
# data loader
data_loader = load_dataset('val', cfg.dataset)
pixel_recall_curve = np.zeros((13))
plane_recall_curve = np.zeros((13, 3))
bin_mean_shift = Bin_Mean_Shift(device=device)
k_inv_dot_xy1 = get_coordinate_map(device)
instance_parameter_loss = InstanceParameterLoss(k_inv_dot_xy1)
match_segmentatin = MatchSegmentation()
with torch.no_grad():
for iter, sample in enumerate(data_loader):
image = sample['image'].to(device)
instance = sample['instance'].to(device)
gt_seg = sample['gt_seg'].numpy()
semantic = sample['semantic'].to(device)
gt_depth = sample['depth'].to(device)
# gt_plane_parameters = sample['plane_parameters'].to(device)
valid_region = sample['valid_region'].to(device)
gt_plane_num = sample['num_planes'].int()
# gt_plane_instance_parameter = sample['plane_instance_parameter'].numpy()
# forward pass
logit, embedding, _, _, param = network(image)
prob = torch.sigmoid(logit[0])
# infer per pixel depth using per pixel plane parameter
_, _, per_pixel_depth = Q_loss(param, k_inv_dot_xy1, gt_depth)
# fast mean shift
segmentation, sampled_segmentation, sample_param = bin_mean_shift.test_forward(
prob, embedding[0], param, mask_threshold=0.1)
# since GT plane segmentation is somewhat noise, the boundary of plane in GT is not well aligned,
# we thus use avg_pool_2d to smooth the segmentation results
b = segmentation.t().view(1, -1, 192, 256)
pooling_b = torch.nn.functional.avg_pool2d(b, (7, 7),
stride=1,
padding=(3, 3))
b = pooling_b.view(-1, 192 * 256).t()
segmentation = b
# infer instance depth
instance_loss, instance_depth, instance_abs_disntace, instance_parameter = \
instance_parameter_loss(segmentation, sampled_segmentation, sample_param,
valid_region, gt_depth, False)
# greedy match of predict segmentation and ground truth segmentation using cross entropy
# to better visualization
matching = match_segmentatin(segmentation, prob.view(-1, 1),
instance[0], gt_plane_num)
# return cluster results
predict_segmentation = segmentation.cpu().numpy().argmax(axis=1)
# reindexing to matching gt segmentation for better visualization
matching = matching.cpu().numpy().reshape(-1)
used = set([])
max_index = max(matching) + 1
for i, a in zip(range(len(matching)), matching):
if a in used:
matching[i] = max_index
max_index += 1
else:
used.add(a)
predict_segmentation = matching[predict_segmentation]
# mask out non planar region
predict_segmentation[prob.cpu().numpy().reshape(-1) <= 0.1] = 20
predict_segmentation = predict_segmentation.reshape(192, 256)
# visualization and evaluation
h, w = 192, 256
image = tensor_to_image(image.cpu()[0])
semantic = semantic.cpu().numpy().reshape(h, w)
mask = (prob > 0.1).float().cpu().numpy().reshape(h, w)
gt_seg = gt_seg.reshape(h, w)
depth = instance_depth.cpu().numpy()[0, 0].reshape(h, w)
per_pixel_depth = per_pixel_depth.cpu().numpy()[0, 0].reshape(h, w)
# use per pixel depth for non planar region
depth = depth * (predict_segmentation != 20) + per_pixel_depth * (
predict_segmentation == 20)
gt_depth = gt_depth.cpu().numpy()[0, 0].reshape(h, w)
# evaluation plane segmentation
pixelStatistics, planeStatistics = eval_plane_prediction(
predict_segmentation, gt_seg, depth, gt_depth)
pixel_recall_curve += np.array(pixelStatistics)
plane_recall_curve += np.array(planeStatistics)
print("pixel and plane recall of test image ", iter)
print(pixel_recall_curve / float(iter + 1))
print(plane_recall_curve[:, 0] / plane_recall_curve[:, 1])
print("********")
# visualization convert labels to color image
# change non-planar regions to zero, so non-planar regions use the black color
gt_seg += 1
gt_seg[gt_seg == 21] = 0
predict_segmentation += 1
predict_segmentation[predict_segmentation == 21] = 0
gt_seg_image = cv2.resize(
np.stack(
[colors[gt_seg, 0], colors[gt_seg, 1], colors[gt_seg, 2]],
axis=2), (w, h))
pred_seg = cv2.resize(
np.stack([
colors[predict_segmentation,
0], colors[predict_segmentation, 1],
colors[predict_segmentation, 2]
],
axis=2), (w, h))
# blend image
blend_pred = (pred_seg * 0.7 + image * 0.3).astype(np.uint8)
blend_gt = (gt_seg_image * 0.7 + image * 0.3).astype(np.uint8)
semantic = cv2.resize((semantic * 255).astype(np.uint8), (w, h))
semantic = cv2.cvtColor(semantic, cv2.COLOR_GRAY2BGR)
mask = cv2.resize((mask * 255).astype(np.uint8), (w, h))
mask = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)
depth_diff = np.abs(gt_depth - depth)
depth_diff[gt_depth == 0.] = 0
# visualize depth map as PlaneNet
depth_diff = np.clip(depth_diff / 5 * 255, 0, 255).astype(np.uint8)
depth_diff = cv2.cvtColor(cv2.resize(depth_diff, (w, h)),
cv2.COLOR_GRAY2BGR)
depth = 255 - np.clip(depth / 5 * 255, 0, 255).astype(np.uint8)
depth = cv2.cvtColor(cv2.resize(depth, (w, h)), cv2.COLOR_GRAY2BGR)
gt_depth = 255 - np.clip(gt_depth / 5 * 255, 0, 255).astype(
np.uint8)
gt_depth = cv2.cvtColor(cv2.resize(gt_depth, (w, h)),
cv2.COLOR_GRAY2BGR)
image_1 = np.concatenate((image, pred_seg, gt_seg_image), axis=1)
image_2 = np.concatenate((image, blend_pred, blend_gt), axis=1)
image_3 = np.concatenate((image, mask, semantic), axis=1)
image_4 = np.concatenate((depth_diff, depth, gt_depth), axis=1)
image = np.concatenate((image_1, image_2, image_3, image_4),
axis=0)
# cv2.imshow('image', image)
# cv2.waitKey(0)
# cv2.imwrite("%d_segmentation.png"%iter, image)
print("========================================")
print("pixel and plane recall of all test image")
print(pixel_recall_curve / len(data_loader))
print(plane_recall_curve[:, 0] / plane_recall_curve[:, 1])
print("****************************************")
def train(_run, _log):
cfg = edict(_run.config)
torch.manual_seed(cfg.seed)
np.random.seed(cfg.seed)
random.seed(cfg.seed)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if not (_run._id is None):
checkpoint_dir = os.path.join(_run.observers[0].basedir, str(_run._id),
'checkpoints')
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
# build network
network = UNet(cfg.model)
if not (cfg.resume_dir == 'None'):
model_dict = torch.load(cfg.resume_dir,
map_location=lambda storage, loc: storage)
network.load_state_dict(model_dict)
# load nets into gpu
if cfg.num_gpus > 1 and torch.cuda.is_available():
network = torch.nn.DataParallel(network)
network.to(device)
# set up optimizers
optimizer = get_optimizer(network.parameters(), cfg.solver)
# data loader
data_loader = load_dataset('train', cfg.dataset)
# save losses per epoch
history = {
'losses': [],
'losses_pull': [],
'losses_push': [],
'losses_binary': [],
'losses_depth': [],
'ioues': [],
'rmses': []
}
network.train(not cfg.model.fix_bn)
bin_mean_shift = Bin_Mean_Shift(device=device)
k_inv_dot_xy1 = get_coordinate_map(device)
instance_parameter_loss = InstanceParameterLoss(k_inv_dot_xy1)
# main loop
for epoch in range(cfg.num_epochs):
batch_time = AverageMeter()
losses = AverageMeter()
losses_pull = AverageMeter()
losses_push = AverageMeter()
losses_binary = AverageMeter()
losses_depth = AverageMeter()
losses_normal = AverageMeter()
losses_instance = AverageMeter()
ioues = AverageMeter()
rmses = AverageMeter()
instance_rmses = AverageMeter()
mean_angles = AverageMeter()
tic = time.time()
for iter, sample in enumerate(data_loader):
image = sample['image'].to(device)
instance = sample['instance'].to(device)
semantic = sample['semantic'].to(device)
gt_depth = sample['depth'].to(device)
gt_seg = sample['gt_seg'].to(device)
gt_plane_parameters = sample['plane_parameters'].to(device)
valid_region = sample['valid_region'].to(device)
gt_plane_instance_parameter = sample[
'plane_instance_parameter'].to(device)
# forward pass
logit, embedding, _, _, param = network(image)
segmentations, sample_segmentations, sample_params, centers, sample_probs, sample_gt_segs = \
bin_mean_shift(logit, embedding, param, gt_seg)
# calculate loss
loss, loss_pull, loss_push, loss_binary, loss_depth, loss_normal, loss_parameters, loss_pw, loss_instance \
= 0., 0., 0., 0., 0., 0., 0., 0., 0.
batch_size = image.size(0)
for i in range(batch_size):
_loss, _loss_pull, _loss_push = hinge_embedding_loss(
embedding[i:i + 1], sample['num_planes'][i:i + 1],
instance[i:i + 1], device)
_loss_binary = class_balanced_cross_entropy_loss(
logit[i], semantic[i])
_loss_normal, mean_angle = surface_normal_loss(
param[i:i + 1], gt_plane_parameters[i:i + 1],
valid_region[i:i + 1])
_loss_L1 = parameter_loss(param[i:i + 1],
gt_plane_parameters[i:i + 1],
valid_region[i:i + 1])
_loss_depth, rmse, infered_depth = Q_loss(
param[i:i + 1], k_inv_dot_xy1, gt_depth[i:i + 1])
if segmentations[i] is None:
continue
_instance_loss, instance_depth, instance_abs_disntace, _ = \
instance_parameter_loss(segmentations[i], sample_segmentations[i], sample_params[i],
valid_region[i:i+1], gt_depth[i:i+1])
_loss += _loss_binary + _loss_depth + _loss_normal + _instance_loss + _loss_L1
# planar segmentation iou
prob = torch.sigmoid(logit[i])
mask = (prob > 0.5).float().cpu().numpy()
iou = eval_iou(mask, semantic[i].cpu().numpy())
ioues.update(iou * 100)
instance_rmses.update(instance_abs_disntace.item())
rmses.update(rmse.item())
mean_angles.update(mean_angle.item())
loss += _loss
loss_pull += _loss_pull
loss_push += _loss_push
loss_binary += _loss_binary
loss_depth += _loss_depth
loss_normal += _loss_normal
loss_instance += _instance_loss
loss /= batch_size
loss_pull /= batch_size
loss_push /= batch_size
loss_binary /= batch_size
loss_depth /= batch_size
loss_normal /= batch_size
loss_instance /= batch_size
# Backward
optimizer.zero_grad()
loss.backward()
optimizer.step()
# update loss
losses.update(loss.item())
losses_pull.update(loss_pull.item())
losses_push.update(loss_push.item())
losses_binary.update(loss_binary.item())
losses_depth.update(loss_depth.item())
losses_normal.update(loss_normal.item())
losses_instance.update(loss_instance.item())
# update time
batch_time.update(time.time() - tic)
tic = time.time()
if iter % cfg.print_interval == 0:
_log.info(
f"[{epoch:2d}][{iter:5d}/{len(data_loader):5d}] "
f"Time: {batch_time.val:.2f} ({batch_time.avg:.2f}) "
f"Loss: {losses.val:.4f} ({losses.avg:.4f}) "
f"Pull: {losses_pull.val:.4f} ({losses_pull.avg:.4f}) "
f"Push: {losses_push.val:.4f} ({losses_push.avg:.4f}) "
f"INS: {losses_instance.val:.4f} ({losses_instance.avg:.4f}) "
f"Binary: {losses_binary.val:.4f} ({losses_binary.avg:.4f}) "
f"IoU: {ioues.val:.2f} ({ioues.avg:.2f}) "
f"LN: {losses_normal.val:.4f} ({losses_normal.avg:.4f}) "
f"AN: {mean_angles.val:.4f} ({mean_angles.avg:.4f}) "
f"Depth: {losses_depth.val:.4f} ({losses_depth.avg:.4f}) "
f"INSDEPTH: {instance_rmses.val:.4f} ({instance_rmses.avg:.4f}) "
f"RMSE: {rmses.val:.4f} ({rmses.avg:.4f}) ")
_log.info(f"* epoch: {epoch:2d}\t"
f"Loss: {losses.avg:.6f}\t"
f"Pull: {losses_pull.avg:.6f}\t"
f"Push: {losses_push.avg:.6f}\t"
f"Binary: {losses_binary.avg:.6f}\t"
f"Depth: {losses_depth.avg:.6f}\t"
f"IoU: {ioues.avg:.2f}\t"
f"RMSE: {rmses.avg:.4f}\t")
# save history
history['losses'].append(losses.avg)
history['losses_pull'].append(losses_pull.avg)
history['losses_push'].append(losses_push.avg)
history['losses_binary'].append(losses_binary.avg)
history['losses_depth'].append(losses_depth.avg)
history['ioues'].append(ioues.avg)
history['rmses'].append(rmses.avg)
# save checkpoint
if not (_run._id is None):
torch.save(
network.state_dict(),
os.path.join(checkpoint_dir, f"network_epoch_{epoch}.pt"))
pickle.dump(
history, open(os.path.join(checkpoint_dir, 'history.pkl'),
'wb'))
def predict(_run, _log):
cfg = edict(_run.config)
torch.manual_seed(cfg.seed)
np.random.seed(cfg.seed)
random.seed(cfg.seed)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# build network
network = UNet(cfg.model)
if not (cfg.resume_dir == 'None'):
model_dict = torch.load(cfg.resume_dir,
map_location=lambda storage, loc: storage)
network.load_state_dict(model_dict)
# load nets into gpu
if cfg.num_gpus > 1 and torch.cuda.is_available():
network = torch.nn.DataParallel(network)
network.to(device)
network.eval()
transforms = tf.Compose([
tf.ToTensor(),
tf.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
bin_mean_shift = Bin_Mean_Shift(device=device)
k_inv_dot_xy1 = get_coordinate_map(device)
instance_parameter_loss = InstanceParameterLoss(k_inv_dot_xy1)
h, w = 192, 256
with torch.no_grad():
image = cv2.imread(cfg.image_path)
# the network is trained with 192*256 and the intrinsic parameter is set as ScanNet
image = cv2.resize(image, (w, h))
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = Image.fromarray(image)
image = transforms(image)
image = image.to(device).unsqueeze(0)
# forward pass
logit, embedding, _, _, param = network(image)
prob = torch.sigmoid(logit[0])
# infer per pixel depth using per pixel plane parameter, currently Q_loss need a dummy gt_depth as input
_, _, per_pixel_depth = Q_loss(param, k_inv_dot_xy1,
torch.ones_like(logit))
# fast mean shift
segmentation, sampled_segmentation, sample_param = bin_mean_shift.test_forward(
prob, embedding[0], param, mask_threshold=0.1)
# since GT plane segmentation is somewhat noise, the boundary of plane in GT is not well aligned,
# we thus use avg_pool_2d to smooth the segmentation results
b = segmentation.t().view(1, -1, h, w)
pooling_b = torch.nn.functional.avg_pool2d(b, (7, 7),
stride=1,
padding=(3, 3))
b = pooling_b.view(-1, h * w).t()
segmentation = b
# infer instance depth
instance_loss, instance_depth, instance_abs_disntace, instance_parameter = instance_parameter_loss(
segmentation, sampled_segmentation, sample_param,
torch.ones_like(logit), torch.ones_like(logit), False)
# infer instance normal
_, _, manhattan_norm, instance_norm = surface_normal_loss(
param, torch.ones_like(image), None)
# return cluster results
predict_segmentation = segmentation.cpu().numpy().argmax(axis=1)
# mask out non planar region
predict_segmentation[prob.cpu().numpy().reshape(-1) <= 0.1] = 20
predict_segmentation = predict_segmentation.reshape(h, w)
# visualization and evaluation
image = tensor_to_image(image.cpu()[0])
mask = (prob > 0.1).float().cpu().numpy().reshape(h, w)
depth = instance_depth.cpu().numpy()[0, 0].reshape(h, w)
per_pixel_depth = per_pixel_depth.cpu().numpy()[0, 0].reshape(h, w)
manhattan_normal_2d = manhattan_norm.cpu().numpy().reshape(
h, w, 3) * np.expand_dims((predict_segmentation != 20), -1)
instance_normal_2d = instance_norm.cpu().numpy().reshape(
h, w, 3) * np.expand_dims((predict_segmentation != 20), -1)
pcd = o3d.geometry.PointCloud()
norm_colors = cm.Set3(predict_segmentation.reshape(w * h))
pcd.points = o3d.utility.Vector3dVector(
np.reshape(manhattan_normal_2d, (w * h, 3)))
pcd.colors = o3d.utility.Vector3dVector(norm_colors[:, 0:3])
o3d.io.write_point_cloud('./manhattan_sphere.ply', pcd)
pcd.points = o3d.utility.Vector3dVector(
np.reshape(instance_normal_2d, (w * h, 3)))
o3d.io.write_point_cloud('./instance_sphere.ply', pcd)
normal_plot = cv2.resize(
((manhattan_normal_2d + 1) * 128).astype(np.uint8), (w, h))
# use per pixel depth for non planar region
depth = depth * (predict_segmentation !=
20) + per_pixel_depth * (predict_segmentation == 20)
# change non planar to zero, so non planar region use the black color
predict_segmentation += 1
predict_segmentation[predict_segmentation == 21] = 0
pred_seg = cv2.resize(
np.stack([
colors[predict_segmentation, 0],
colors[predict_segmentation, 1], colors[predict_segmentation,
2]
],
axis=2), (w, h))
# blend image
blend_pred = (pred_seg * 0.7 + image * 0.3).astype(np.uint8)
mask = cv2.resize((mask * 255).astype(np.uint8), (w, h))
mask = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)
# visualize depth map as PlaneNet
depth_real = cv2.cvtColor(cv2.resize(depth, (w, h)),
cv2.COLOR_GRAY2BGR)
depth = 255 - np.clip(depth / 5 * 255, 0, 255).astype(np.uint8)
depth = cv2.cvtColor(cv2.resize(depth, (w, h)), cv2.COLOR_GRAY2BGR)
Camera_fx = 481.2
Camera_fy = 480.0
Camera_cx = -319.5
Camera_cy = 239.50
##
# Camera_fx = 535.4
# Camera_fy = 539.2
# Camera_cx = 320.1
# Camera_cy = 247.6
# correct parameters
Camera_fx = 518.8
Camera_fy = 518.8
Camera_cx = 320
Camera_cy = 240
# cabin parrameters
Camera_fx = 440.66
Camera_fy = 531.72
Camera_cx = 361.77
Camera_cy = 215.79
points = []
points_instance = []
ratio_x = 640 / 256.0
ratio_y = 480 / 192.0
scalingFactor = 1.0
for v in range(h):
for u in range(w):
color = image[v, u]
color_instance = pred_seg[v, u]
Z = (depth_real[v, u] / scalingFactor)[0]
if Z == 0: continue
X = (u - Camera_cx / ratio_x) * Z / Camera_fx * ratio_x
Y = (v - Camera_cy / ratio_y) * Z / Camera_fy * ratio_y
# points.append("%f %f %f %d %d %d 0\n" % (X, Y, Z, color[2], color[1], color[0]))
points_instance.append("%f %f %f %d %d %d 0\n" %
(X, Y, Z, color_instance[2],
color_instance[1], color_instance[0]))
file1 = open('./pointCloud_instance.ply', "w")
file1.write('''ply
format ascii 1.0
element vertex %d
property float x
property float y
property float z
property uchar red
property uchar green
property uchar blue
property uchar alpha
end_header
%s
''' % (len(points_instance), "".join(points_instance)))
file1.close()
image = np.concatenate(
(image, pred_seg, blend_pred, mask, depth, normal_plot), axis=1)
cv2.imshow('image', image)
cv2.waitKey(0)
def predict(_run, _log):
cfg = edict(_run.config)
torch.manual_seed(cfg.seed)
np.random.seed(cfg.seed)
random.seed(cfg.seed)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# build network
network = UNet(cfg.model)
if not (cfg.resume_dir == 'None'):
model_dict = torch.load(cfg.resume_dir,
map_location=lambda storage, loc: storage)
network.load_state_dict(model_dict)
# load nets into gpu
if cfg.num_gpus > 1 and torch.cuda.is_available():
network = torch.nn.DataParallel(network)
network.to(device)
network.eval()
transforms = tf.Compose([
tf.ToTensor(),
tf.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
bin_mean_shift = Bin_Mean_Shift(device=device)
k_inv_dot_xy1 = get_coordinate_map(device)
instance_parameter_loss = InstanceParameterLoss(k_inv_dot_xy1)
h, w = 192, 256
with torch.no_grad():
image = cv2.imread(cfg.image_path)
raw_h, raw_w, _ = image.shape
# the network is trained with 192*256 and the intrinsic parameter is set as ScanNet
image = cv2.resize(image, (w, h))
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = Image.fromarray(image)
image = transforms(image)
image = image.to(device).unsqueeze(0)
# forward pass
logit, embedding, _, _, param = network(image)
prob = torch.sigmoid(logit[0])
# infer per pixel depth using per pixel plane parameter, currently Q_loss need a dummy gt_depth as input
_, _, per_pixel_depth = Q_loss(param, k_inv_dot_xy1,
torch.ones_like(logit))
# fast mean shift
segmentation, sampled_segmentation, sample_param = bin_mean_shift.test_forward(
prob, embedding[0], param, mask_threshold=0.1)
# since GT plane segmentation is somewhat noise, the boundary of plane in GT is not well aligned,
# we thus use avg_pool_2d to smooth the segmentation results
b = segmentation.t().view(1, -1, h, w)
pooling_b = torch.nn.functional.avg_pool2d(b, (7, 7),
stride=1,
padding=(3, 3))
b = pooling_b.view(-1, h * w).t()
segmentation = b
# infer instance depth
instance_loss, instance_depth, instance_abs_disntace, instance_parameter = instance_parameter_loss(
segmentation, sampled_segmentation, sample_param,
torch.ones_like(logit), torch.ones_like(logit), False)
# return cluster results
predict_segmentation = segmentation.cpu().numpy().argmax(axis=1)
# mask out non planar region
predict_segmentation[prob.cpu().numpy().reshape(-1) <= 0.1] = 20
predict_segmentation = predict_segmentation.reshape(h, w)
# visualization and evaluation
image = tensor_to_image(image.cpu()[0])
mask = (prob > 0.1).float().cpu().numpy().reshape(h, w)
depth = instance_depth.cpu().numpy()[0, 0].reshape(h, w)
per_pixel_depth = per_pixel_depth.cpu().numpy()[0, 0].reshape(h, w)
# use per pixel depth for non planar region
depth_clean = depth * (predict_segmentation !=
20) + 0 * (predict_segmentation == 20)
depth = depth * (predict_segmentation !=
20) + per_pixel_depth * (predict_segmentation == 20)
Image.fromarray(depth * 1000).convert('I').resize(
(raw_w, raw_h)).save(cfg.image_path.replace('.jpg', '_depth.png'))
Image.fromarray(depth_clean * 1000).convert('I').resize(
(raw_w,
raw_h)).save(cfg.image_path.replace('.jpg', '_depth_clean.png'))
# np.save("510.npy", Image.fromarray(depth*1000).convert('I'))
# change non planar to zero, so non planar region use the black color
predict_segmentation += 1
predict_segmentation[predict_segmentation == 21] = 0
pred_seg = cv2.resize(
np.stack([
colors[predict_segmentation, 0],
colors[predict_segmentation, 1], colors[predict_segmentation,
2]
],
axis=2), (w, h))
# blend image
blend_pred = (pred_seg * 0.7 + image * 0.3).astype(np.uint8)
mask = cv2.resize((mask * 255).astype(np.uint8), (w, h))
mask = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)
# visualize depth map as PlaneNet
depth = 255 - np.clip(depth / 5 * 255, 0, 255).astype(np.uint8)
depth = cv2.cvtColor(cv2.resize(depth, (w, h)), cv2.COLOR_GRAY2BGR)
image = np.concatenate((image, pred_seg, blend_pred, mask, depth),
axis=1)
cv2.imwrite(cfg.image_path.replace('.jpg', '_out.jpg'), image)
def eval(_run, _log):
cfg = edict(_run.config)
torch.manual_seed(cfg.seed)
np.random.seed(cfg.seed)
random.seed(cfg.seed)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
checkpoint_dir = os.path.join('experiments', str(_run._id), 'checkpoints')
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
# build network
network = UNet(cfg.model)
if not cfg.resume_dir == 'None':
model_dict = torch.load(cfg.resume_dir)
network.load_state_dict(model_dict)
# load nets into gpu
if cfg.num_gpus > 1 and torch.cuda.is_available():
network = torch.nn.DataParallel(network)
network.to(device)
network.eval()
bin_mean_shift = Bin_Mean_Shift()
instance_parameter_loss = InstanceParameterLoss()
h, w = 192, 256
with torch.no_grad():
for i in range(1):
image = cv2.imread(cfg.input_image)
# the network is trained with 192*256 and the intrinsic parameter is set as ScanNet
image = cv2.resize(image, (w, h))
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = Image.fromarray(image)
#
image = transforms(image)
image = image.cuda().unsqueeze(0)
# forward pass
logit, embedding, _, _, param = network(image)
prob = torch.sigmoid(logit[0])
# infer per pixel depth using per pixel plane parameter, currently Q_loss need a dummy gt_depth as input
_, _, per_pixel_depth = Q_loss(param, k_inv_dot_xy1,
torch.ones_like(logit))
# fast mean shift
segmentation, sampled_segmentation, sample_param = bin_mean_shift.test_forward(
prob, embedding[0], param, mask_threshold=0.1)
# since GT plane segmentation is somewhat noise, the boundary of plane in GT is not well aligned,
# we thus use avg_pool_2d to smooth the segmentation results
b = segmentation.t().view(1, -1, h, w)
pooling_b = torch.nn.functional.avg_pool2d(b, (7, 7),
stride=1,
padding=(3, 3))
b = pooling_b.view(-1, h * w).t()
segmentation = b
# infer instance depth
instance_loss, instance_depth, instance_abs_disntace, instance_parameter = \
instance_parameter_loss(segmentation, sampled_segmentation, sample_param,
torch.ones_like(logit), torch.ones_like(logit), False)
# return cluster results
predict_segmentation = segmentation.cpu().numpy().argmax(axis=1)
# mask out non planar region
predict_segmentation[prob.cpu().numpy().reshape(-1) <= 0.1] = 20
predict_segmentation = predict_segmentation.reshape(h, w)
# visualization and evaluation
image = tensor_to_image(image.cpu()[0])
mask = (prob > 0.1).float().cpu().numpy().reshape(h, w)
depth = instance_depth.cpu().numpy()[0, 0].reshape(h, w)
per_pixel_depth = per_pixel_depth.cpu().numpy()[0, 0].reshape(h, w)
# use per pixel depth for non planar region
depth = depth * (predict_segmentation != 20) + per_pixel_depth * (
predict_segmentation == 20)
# change non planar to zero, so non planar region use the black color
predict_segmentation += 1
predict_segmentation[predict_segmentation == 21] = 0
pred_seg = cv2.resize(
np.stack([
colors[predict_segmentation,
0], colors[predict_segmentation, 1],
colors[predict_segmentation, 2]
],
axis=2), (w, h))
# blend image
blend_pred = (pred_seg * 0.7 + image * 0.3).astype(np.uint8)
mask = cv2.resize((mask * 255).astype(np.uint8), (w, h))
mask = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)
# visualize depth map as PlaneNet
depth = 255 - np.clip(depth / 5 * 255, 0, 255).astype(np.uint8)
depth = cv2.cvtColor(cv2.resize(depth, (w, h)), cv2.COLOR_GRAY2BGR)
image = np.concatenate((image, pred_seg, blend_pred, mask, depth),
axis=1)
cv2.imshow('image', image)
cv2.waitKey(0)
def eval(_run, _log):
cfg = edict(_run.config)
torch.manual_seed(cfg.seed)
np.random.seed(cfg.seed)
random.seed(cfg.seed)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
checkpoint_dir = os.path.join('experiments', str(_run._id), 'checkpoints')
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
# build network
network = UNet(cfg.model)
if not cfg.resume_dir == 'None':
model_dict = torch.load(cfg.resume_dir)
network.load_state_dict(model_dict)
# load nets into gpu
if cfg.num_gpus > 1 and torch.cuda.is_available():
network = torch.nn.DataParallel(network)
network.to(device)
network.eval()
bin_mean_shift = Bin_Mean_Shift()
instance_parameter_loss = InstanceParameterLoss()
h, w = 192, 256
f = open(cfg.dataset_csv, 'r')
lines = f.readlines()
f.close()
lines.pop(0) # header
with torch.no_grad():
#for i, image_path in enumerate(tqdm(files)):
for line in tqdm(lines):
splits = line.split(',')
image_path = splits[0]
image_name = image_path.split('/')[-1]
image_path = os.path.join(cfg.image_path, image_name)
#image = cv2.imread(cfg.input_image)
image = cv2.imread(image_path)
oh, ow, _ = image.shape
# the network is trained with 192*256 and the intrinsic parameter is set as ScanNet
image = cv2.resize(image, (w, h))
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = Image.fromarray(image)
#
image = transforms(image)
image = image.cuda().unsqueeze(0)
# forward pass
logit, embedding, _, _, param = network(image)
prob = torch.sigmoid(logit[0])
# infer per pixel depth using per pixel plane parameter, currently Q_loss need a dummy gt_depth as input
_, _, per_pixel_depth = Q_loss(param, k_inv_dot_xy1, torch.ones_like(logit))
# fast mean shift
segmentation, sampled_segmentation, sample_param = bin_mean_shift.test_forward(prob, embedding[0], param, mask_threshold=0.1)
# since GT plane segmentation is somewhat noise, the boundary of plane in GT is not well aligned,
# we thus use avg_pool_2d to smooth the segmentation results
b = segmentation.t().view(1, -1, h, w)
pooling_b = torch.nn.functional.avg_pool2d(b, (7, 7), stride=1, padding=(3, 3))
b = pooling_b.view(-1, h*w).t()
segmentation = b
# infer instance depth
instance_loss, instance_depth, instance_abs_disntace, instance_parameter = \
instance_parameter_loss(segmentation, sampled_segmentation, sample_param,
torch.ones_like(logit), torch.ones_like(logit), False)
# return cluster results
predict_segmentation = segmentation.cpu().numpy().argmax(axis=1)
#import pdb; pdb.set_trace()
# mask out non planar region
predict_segmentation[prob.cpu().numpy().reshape(-1) <= 0.1] = 20
predict_segmentation = predict_segmentation.reshape(h, w)
# visualization and evaluation
image = tensor_to_image(image.cpu()[0])
mask = (prob > 0.1).float().cpu().numpy().reshape(h, w)
depth = instance_depth.cpu().numpy()[0, 0].reshape(h, w)
per_pixel_depth = per_pixel_depth.cpu().numpy()[0, 0].reshape(h, w)
# use per pixel depth for non planar region
depth = depth * (predict_segmentation != 20) + per_pixel_depth * (predict_segmentation == 20)
# change non planar to zero, so non planar region use the black color
predict_segmentation += 1
predict_segmentation[predict_segmentation==21] = 0
num_ins = predict_segmentation.max()
seg_path = os.path.join(cfg.output_path, 'seg')
bboxes = []
for j in range(1, num_ins + 1):
m = (predict_segmentation == j).astype(np.float)
m = m * 255.0
m = m.astype(np.uint8)
m = cv2.resize(m, (ow, oh), interpolation=cv2.INTER_NEAREST)
x, y = np.where(m >= 127)
if len(x) == 0:
continue
min_x = np.min(x)
max_x = np.max(x)
min_y = np.min(y)
max_y = np.max(y)
bbox = [min_x, min_y, max_x, max_y, 1.0]
bboxes.append(bbox)
#tqdm.write("{} {}".format(ow, oh))
#tqdm.write(str(bboxes))
file_obj = open(os.path.join(cfg.output_path, image_name.split('.')[0] + '.pkl'), 'wb')
pickle.dump(bboxes, file_obj)
file_obj.close()
pred_seg = cv2.resize(np.stack([colors[predict_segmentation, 0],
colors[predict_segmentation, 1],
colors[predict_segmentation, 2]], axis=2), (w, h))
# blend image
blend_pred = (pred_seg * 0.7 + image * 0.3).astype(np.uint8)
mask = cv2.resize((mask * 255).astype(np.uint8), (w, h))
mask = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)
# visualize depth map as PlaneNet
depth = 255 - np.clip(depth / 5 * 255, 0, 255).astype(np.uint8)
depth = cv2.cvtColor(cv2.resize(depth, (w, h)), cv2.COLOR_GRAY2BGR)
image = np.concatenate((image, pred_seg, blend_pred, mask, depth), axis=1)
mask_path = os.path.join(cfg.output_path, 'mask')
def predict(_run, _log):
cfg = edict(_run.config)
torch.manual_seed(cfg.seed)
np.random.seed(cfg.seed)
random.seed(cfg.seed)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# build network
network = UNet(cfg.model)
if not (cfg.resume_dir == 'None'):
model_dict = torch.load(cfg.resume_dir,
map_location=lambda storage, loc: storage)
network.load_state_dict(model_dict)
# load nets into gpu
if cfg.num_gpus > 1 and torch.cuda.is_available():
network = torch.nn.DataParallel(network)
network.to(device)
network.eval()
transforms = tf.Compose([
tf.ToTensor(),
tf.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
bin_mean_shift = Bin_Mean_Shift(device=device)
k_inv_dot_xy1 = get_coordinate_map(device)
instance_parameter_loss = InstanceParameterLoss(k_inv_dot_xy1)
h, w = 192, 256
focal_length = 517.97
offset_x = 320
offset_y = 240
K = [[focal_length, 0, offset_x], [0, focal_length, offset_y], [0, 0, 1]]
K_inv = np.linalg.inv(np.array(K))
K_inv_dot_xy_1 = np.zeros((3, h, w))
for y in range(h):
for x in range(w):
yy = float(y) / h * 480
xx = float(x) / w * 640
ray = np.dot(K_inv, np.array([xx, yy, 1]).reshape(3, 1))
K_inv_dot_xy_1[:, y, x] = ray[:, 0]
with torch.no_grad():
cam = cv2.VideoCapture(0)
i = 0
while i < 1:
input('Press Enter to capture Image')
start_time_1 = time.time()
return_value, image = cam.read()
i += 1
#del(cam)
cam.release()
#image = cv2.imread("cam_images/opencv0.png")
# the network is trained with 192*256 and the intrinsic parameter is set as ScanNet
image = cv2.resize(image, (w, h))
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = Image.fromarray(image)
image = transforms(image)
image = image.to(device).unsqueeze(0)
# forward pass
logit, embedding, _, _, param = network(image)
prob = torch.sigmoid(logit[0])
# infer per pixel depth using per pixel plane parameter, currently Q_loss need a dummy gt_depth as input
_, _, per_pixel_depth = Q_loss(param, k_inv_dot_xy1,
torch.ones_like(logit))
# fast mean shift
segmentation, sampled_segmentation, sample_param = bin_mean_shift.test_forward(
prob, embedding[0], param, mask_threshold=0.1)
print("segmentation- ", segmentation.shape)
# since GT plane segmentation is somewhat noise, the boundary of plane in GT is not well aligned,
# we thus use avg_pool_2d to smooth the segmentation results
b = segmentation.t().view(1, -1, h, w)
pooling_b = torch.nn.functional.avg_pool2d(b, (7, 7),
stride=1,
padding=(3, 3))
b = pooling_b.view(-1, h * w).t()
segmentation = b
print("segmentation-boundary -", segmentation.shape)
# infer instance depth
instance_loss, instance_depth, instance_abs_disntace, instance_parameter = instance_parameter_loss(
segmentation, sampled_segmentation, sample_param,
torch.ones_like(logit), torch.ones_like(logit), False)
# return cluster results
segmentation = segmentation.cpu().numpy().argmax(axis=1)
print("segmentation-shape -", segmentation.shape)
# mask out non planar region
segmentation[prob.cpu().numpy().reshape(-1) <= 0.1] = 20
segmentation = segmentation.reshape(h, w)
print("segments-reshape - ", segmentation.shape)
# visualization and evaluation
#one
image = tensor_to_image(image.cpu()[0])
mask = (prob > 0.1).float().cpu().numpy().reshape(h, w)
depth = instance_depth.cpu().numpy()[0, 0].reshape(h, w)
per_pixel_depth = per_pixel_depth.cpu().numpy()[0, 0].reshape(h, w)
# use per pixel depth for non planar region
depth = depth * (segmentation != 20) + per_pixel_depth * (segmentation
== 20)
# change non planar to zero, so non planar region use the black color
segmentation += 1
segmentation[segmentation == 21] = 0
print("segmentation-zero - ", segmentation.shape)
# two
pred_seg = cv2.resize(
np.stack([
colors[segmentation, 0], colors[segmentation, 1],
colors[segmentation, 2]
],
axis=2), (w, h))
# blend image
# three
blend_pred = (pred_seg * 0.4 + image * 0.6).astype(np.uint8)
#four
mask = cv2.resize((mask * 255).astype(np.uint8), (w, h))
mask = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)
# visualize depth map as PlaneNet
#five
depth = 255 - np.clip(depth / 5 * 255, 0, 255).astype(np.uint8)
depth = cv2.cvtColor(cv2.resize(depth, (w, h)), cv2.COLOR_GRAY2BGR)
print("image -", image.shape)
print("pred_seg -", pred_seg.shape)
print("blend_pred- ", blend_pred.shape)
print("mask -", mask.shape)
print("depth -", depth.shape)
image_c = np.concatenate((image, pred_seg, blend_pred, mask, depth),
axis=1)
imageFilename = str(index) + '_model_texture.png'
cv2.imwrite(folder + '/' + imageFilename, image_c)
print("segmentation-", segmentation.shape)
print("depth-", depth.shape)
cv2.imwrite("segmentation.jpg", segmentation)
# create face from segmentation
faces = []
for y in range(h - 1):
for x in range(w - 1):
segmentIndex = segmentation[y, x]
# ignore non planar region
if segmentIndex == 0:
continue
# add face if three pixel has same segmentatioin
depths = [depth[y][x], depth[y + 1][x], depth[y + 1][x + 1]]
if segmentation[y + 1, x] == segmentIndex and segmentation[
y + 1, x + 1] == segmentIndex and np.array(
depths).min() > 0 and np.array(depths).max() < 10:
faces.append((x, y, x, y + 1, x + 1, y + 1))
depths = [depth[y][x], depth[y][x + 1], depth[y + 1][x + 1]]
if segmentation[y][x + 1] == segmentIndex and segmentation[y + 1][
x + 1] == segmentIndex and np.array(
depths).min() > 0 and np.array(depths).max() < 10:
faces.append((x, y, x + 1, y + 1, x + 1, y))
with open(folder + '/' + str(index) + '_model.ply', 'w') as f:
header = """ply
format ascii 1.0
comment VCGLIB generated
comment TextureFile """
header += imageFilename
header += """
element vertex """
header += str(h * w)
header += """
property float x
property float y
property float z
property uchar red { start of vertex color }
property uchar green
property uchar blue
element face """
header += str(len(faces))
header += """
property list uchar int vertex_indices
property list uchar float texcoord
end_header
"""
f.write(header)
for y in range(h):
for x in range(w):
segmentIndex = segmentation[y][x]
if segmentIndex == 20:
f.write("0.0 0.0 0.0\n")
continue
ray = K_inv_dot_xy_1[:, y, x]
X, Y, Z = ray * depth[y, x]
R, G, B = image[y, x]
f.write(
str(X) + ' ' + str(Y) + ' ' + str(Z) + ' ' + str(R) + ' ' +
str(G) + ' ' + str(B) + '\n')
for face in faces:
f.write('3 ')
for c in range(3):
f.write(str(face[c * 2 + 1] * w + face[c * 2]) + ' ')
f.write('6 ')
for c in range(3):
f.write(
str(float(face[c * 2]) / w) + ' ' +
str(1 - float(face[c * 2 + 1]) / h) + ' ')
f.write('\n')
f.close()
pass
# visuvalization
filename = 'predictions/0_model.ply'
print("3D mesh generated in ",
"--- %s seconds ---" % (time.time() - start_time_1))
print(filename)
mesh = pv.read(filename)
cpos = mesh.plot()
plotter = pv.Plotter(off_screen=True)
plotter.add_mesh(mesh)
plotter.show(screenshot="myscreenshot.png")
rimage = cv2.imread("predictions/0_model_texture.png")
cv2.imshow("image,depth,mask,segmentation -", rimage)
cv2.waitKey(0)
#closing all open windows
cv2.destroyAllWindows()
return
class Predictor():
def __init__(self):
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
self.cfg = Cfg()
# build network
self.network = UNet(self.cfg)
self.model_dict = torch.load('pretrained.pt',
map_location=lambda storage, loc: storage)
self.network.load_state_dict(self.model_dict)
self.network.to(self.device)
self.network.eval()
self.transforms = tf.Compose([
tf.ToTensor(),
tf.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
self.bin_mean_shift = Bin_Mean_Shift(device=self.device)
self.k_inv_dot_xy1 = get_coordinate_map(self.device)
self.instance_parameter_loss = InstanceParameterLoss(
self.k_inv_dot_xy1)
self.h, self.w = 192, 256
def predict(self, image_path):
with torch.no_grad():
image = cv2.imread(image_path)
# the network is trained with 192*256 and the intrinsic parameter is set as ScanNet
image = cv2.resize(image, (self.w, self.h))
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = Image.fromarray(image)
image = self.transforms(image)
image = image.to(self.device).unsqueeze(0)
# forward pass
logit, embedding, _, _, param = self.network(image)
prob = torch.sigmoid(logit[0])
# infer per pixel depth using per pixel plane parameter, currently Q_loss need a dummy gt_depth as input
_, _, per_pixel_depth = Q_loss(param, self.k_inv_dot_xy1,
torch.ones_like(logit))
# fast mean shift
segmentation, sampled_segmentation, sample_param = self.bin_mean_shift.test_forward(
prob, embedding[0], param, mask_threshold=0.1)
# since GT plane segmentation is somewhat noise, the boundary of plane in GT is not well aligned,
# we thus use avg_pool_2d to smooth the segmentation results
b = segmentation.t().view(1, -1, self.h, self.w)
pooling_b = torch.nn.functional.avg_pool2d(b, (7, 7),
stride=1,
padding=(3, 3))
b = pooling_b.view(-1, self.h * self.w).t()
segmentation = b
# infer instance depth
instance_loss, instance_depth, instance_abs_disntace, instance_parameter = self.instance_parameter_loss(
segmentation, sampled_segmentation, sample_param,
torch.ones_like(logit), torch.ones_like(logit), False)
# return cluster results
predict_segmentation = segmentation.cpu().numpy().argmax(axis=1)
# mask out non planar region
predict_segmentation[prob.cpu().numpy().reshape(-1) <= 0.1] = 20
predict_segmentation = predict_segmentation.reshape(self.h, self.w)
# visualization and evaluation
image = tensor_to_image(image.cpu()[0])
mask = (prob > 0.1).float().cpu().numpy().reshape(self.h, self.w)
depth = instance_depth.cpu().numpy()[0, 0].reshape(self.h, self.w)
per_pixel_depth = per_pixel_depth.cpu().numpy()[0, 0].reshape(
self.h, self.w)
# use per pixel depth for non planar region
depth = depth * (predict_segmentation != 20) + per_pixel_depth * (
predict_segmentation == 20)
# change non planar to zero, so non planar region use the black color
predict_segmentation += 1
predict_segmentation[predict_segmentation == 21] = 0
return depth, predict_segmentation, mask, instance_parameter