def _make_model(self):
# prepare network
self.logger.info("Creating graph and optimizer...")
model = get_pose_net(self.backbone, False, self.jointnum)
model = DataParallel(model).cuda()
model.load_state_dict(torch.load(self.modelpath)['network'])
single_pytorch_model = model.module
single_pytorch_model.eval()
self.model = single_pytorch_model
def _make_model(self):
model_path = os.path.join(cfg.model_dir, 'snapshot_%d.pth.tar' % self.test_epoch)
assert os.path.exists(model_path), 'Cannot find model at ' + model_path
self.logger.info('Load checkpoint from {}'.format(model_path))
# prepare network
self.logger.info("Creating graph...")
model = get_model(self.vertex_num, self.joint_num, 'test')
model = DataParallel(model).cuda()
ckpt = torch.load(model_path)
model.load_state_dict(ckpt['network'], strict=False)
model.eval()
self.model = model
def _make_model(self, test_epoch):
self.test_epoch = test_epoch
model_path = os.path.join(cfg.model_dir,
'snapshot_%d.pth.tar' % self.test_epoch)
assert os.path.exists(model_path), 'Cannot find model at ' + model_path
# self.logger.info('Load checkpoint from {}'.format(model_path))
# prepare network
# self.logger.info("Creating graph...")
model = get_pose_net(self.backbone, False, self.joint_num)
model = DataParallel(model).cuda()
ckpt = torch.load(model_path)
model.load_state_dict(ckpt['network'])
model.eval()
self.model = model
class SSRunner(object):
def __init__(self, config):
self.config = config
# Data
self.dataset_ss_train, _, self.dataset_ss_val = DatasetUtil.get_dataset_by_type(
DatasetUtil.dataset_type_ss,
self.config.ss_size,
is_balance=self.config.is_balance_data,
data_root=self.config.data_root_path,
train_label_path=self.config.label_path,
max_size=self.config.max_size)
self.data_loader_ss_train = DataLoader(self.dataset_ss_train,
self.config.ss_batch_size,
True,
num_workers=16,
drop_last=True)
self.data_loader_ss_val = DataLoader(self.dataset_ss_val,
self.config.ss_batch_size,
False,
num_workers=16,
drop_last=True)
# Model
self.net = self.config.Net(num_classes=self.config.ss_num_classes,
output_stride=self.config.output_stride,
arch=self.config.arch)
if self.config.only_train_ss:
self.net = BalancedDataParallel(0, self.net, dim=0).cuda()
else:
self.net = DataParallel(self.net).cuda()
pass
cudnn.benchmark = True
# Optimize
self.optimizer = optim.SGD(params=[
{
'params': self.net.module.model.backbone.parameters(),
'lr': self.config.ss_lr
},
{
'params': self.net.module.model.classifier.parameters(),
'lr': self.config.ss_lr * 10
},
],
lr=self.config.ss_lr,
momentum=0.9,
weight_decay=1e-4)
self.scheduler = optim.lr_scheduler.MultiStepLR(
self.optimizer, milestones=self.config.ss_milestones, gamma=0.1)
# Loss
self.ce_loss = nn.CrossEntropyLoss(ignore_index=255,
reduction='mean').cuda()
pass
def train_ss(self, start_epoch=0, model_file_name=None):
if model_file_name is not None:
Tools.print("Load model form {}".format(model_file_name),
txt_path=self.config.ss_save_result_txt)
self.load_model(model_file_name)
pass
# self.eval_ss(epoch=0)
best_iou = 0.0
for epoch in range(start_epoch, self.config.ss_epoch_num):
Tools.print()
Tools.print('Epoch:{:2d}, lr={:.6f} lr2={:.6f}'.format(
epoch, self.optimizer.param_groups[0]['lr'],
self.optimizer.param_groups[1]['lr']),
txt_path=self.config.ss_save_result_txt)
###########################################################################
# 1 训练模型
all_loss = 0.0
self.net.train()
if self.config.is_balance_data:
self.dataset_ss_train.reset()
pass
for i, (inputs,
labels) in tqdm(enumerate(self.data_loader_ss_train),
total=len(self.data_loader_ss_train)):
inputs, labels = inputs.float().cuda(), labels.long().cuda()
self.optimizer.zero_grad()
result = self.net(inputs)
loss = self.ce_loss(result, labels)
loss.backward()
self.optimizer.step()
all_loss += loss.item()
if (i + 1) % (len(self.data_loader_ss_train) // 10) == 0:
score = self.eval_ss(epoch=epoch)
mean_iou = score["Mean IoU"]
if mean_iou > best_iou:
best_iou = mean_iou
save_file_name = Tools.new_dir(
os.path.join(
self.config.ss_model_dir,
"ss_{}_{}_{}.pth".format(epoch, i, best_iou)))
torch.save(self.net.state_dict(), save_file_name)
Tools.print("Save Model to {}".format(save_file_name),
txt_path=self.config.ss_save_result_txt)
Tools.print()
pass
pass
self.scheduler.step()
###########################################################################
Tools.print("[E:{:3d}/{:3d}] ss loss:{:.4f}".format(
epoch, self.config.ss_epoch_num,
all_loss / len(self.data_loader_ss_train)),
txt_path=self.config.ss_save_result_txt)
###########################################################################
# 2 保存模型
if epoch % self.config.ss_save_epoch_freq == 0:
Tools.print()
save_file_name = Tools.new_dir(
os.path.join(self.config.ss_model_dir,
"ss_{}.pth".format(epoch)))
torch.save(self.net.state_dict(), save_file_name)
Tools.print("Save Model to {}".format(save_file_name),
txt_path=self.config.ss_save_result_txt)
Tools.print()
pass
###########################################################################
###########################################################################
# 3 评估模型
if epoch % self.config.ss_eval_epoch_freq == 0:
score = self.eval_ss(epoch=epoch)
pass
###########################################################################
pass
# Final Save
Tools.print()
save_file_name = Tools.new_dir(
os.path.join(self.config.ss_model_dir,
"ss_final_{}.pth".format(self.config.ss_epoch_num)))
torch.save(self.net.state_dict(), save_file_name)
Tools.print("Save Model to {}".format(save_file_name),
txt_path=self.config.ss_save_result_txt)
Tools.print()
self.eval_ss(epoch=self.config.ss_epoch_num)
pass
def eval_ss(self, epoch=0, model_file_name=None):
if model_file_name is not None:
Tools.print("Load model form {}".format(model_file_name),
txt_path=self.config.ss_save_result_txt)
self.load_model(model_file_name)
pass
self.net.eval()
metrics = StreamSegMetrics(self.config.ss_num_classes)
with torch.no_grad():
for i, (inputs,
labels) in tqdm(enumerate(self.data_loader_ss_val),
total=len(self.data_loader_ss_val)):
inputs = inputs.float().cuda()
labels = labels.long().cuda()
outputs = self.net(inputs)
preds = outputs.detach().max(dim=1)[1].cpu().numpy()
targets = labels.cpu().numpy()
metrics.update(targets, preds)
pass
pass
score = metrics.get_results()
Tools.print("{} {}".format(epoch, metrics.to_str(score)),
txt_path=self.config.ss_save_result_txt)
return score
def inference_ss(self,
model_file_name=None,
data_loader=None,
save_path=None):
if model_file_name is not None:
Tools.print("Load model form {}".format(model_file_name),
txt_path=self.config.ss_save_result_txt)
self.load_model(model_file_name)
pass
final_save_path = Tools.new_dir("{}_final".format(save_path))
self.net.eval()
metrics = StreamSegMetrics(self.config.ss_num_classes)
with torch.no_grad():
for i, (inputs, labels,
image_info_list) in tqdm(enumerate(data_loader),
total=len(data_loader)):
assert len(image_info_list) == 1
# 标签
max_size = 1000
size = Image.open(image_info_list[0]).size
basename = os.path.basename(image_info_list[0])
final_name = os.path.join(final_save_path,
basename.replace(".JPEG", ".png"))
if os.path.exists(final_name):
continue
if size[0] < max_size and size[1] < max_size:
targets = F.interpolate(torch.unsqueeze(
labels[0].float().cuda(), dim=0),
size=(size[1], size[0]),
mode="nearest").detach().cpu()
else:
targets = F.interpolate(torch.unsqueeze(labels[0].float(),
dim=0),
size=(size[1], size[0]),
mode="nearest")
targets = targets[0].long().numpy()
# 预测
outputs = 0
for input_index, input_one in enumerate(inputs):
output_one = self.net(input_one.float().cuda())
if size[0] < max_size and size[1] < max_size:
outputs += F.interpolate(
output_one,
size=(size[1], size[0]),
mode="bilinear",
align_corners=False).detach().cpu()
else:
outputs += F.interpolate(output_one.detach().cpu(),
size=(size[1], size[0]),
mode="bilinear",
align_corners=False)
pass
pass
outputs = outputs / len(inputs)
preds = outputs.max(dim=1)[1].numpy()
# 计算
metrics.update(targets, preds)
if save_path:
Image.open(image_info_list[0]).save(
os.path.join(save_path, basename))
DataUtil.gray_to_color(
np.asarray(targets[0], dtype=np.uint8)).save(
os.path.join(save_path,
basename.replace(".JPEG", "_l.png")))
DataUtil.gray_to_color(np.asarray(
preds[0], dtype=np.uint8)).save(
os.path.join(save_path,
basename.replace(".JPEG", ".png")))
Image.fromarray(np.asarray(
preds[0], dtype=np.uint8)).save(final_name)
pass
pass
pass
score = metrics.get_results()
Tools.print("{}".format(metrics.to_str(score)),
txt_path=self.config.ss_save_result_txt)
return score
def load_model(self, model_file_name):
Tools.print("Load model form {}".format(model_file_name),
txt_path=self.config.ss_save_result_txt)
checkpoint = torch.load(model_file_name)
if len(os.environ["CUDA_VISIBLE_DEVICES"].split(",")) == 1:
# checkpoint = {key.replace("module.", ""): checkpoint[key] for key in checkpoint}
pass
self.net.load_state_dict(checkpoint, strict=True)
Tools.print("Restore from {}".format(model_file_name),
txt_path=self.config.ss_save_result_txt)
pass
def stat(self):
stat(self.net, (3, self.config.ss_size, self.config.ss_size))
pass
pass
'R_Ankle', 'L_Hip', 'L_Knee', 'L_Ankle', 'Pelvis', 'Spine',
'Head', 'R_Hand', 'L_Hand', 'R_Toe', 'L_Toe')
flip_pairs = ((2, 5), (3, 6), (4, 7), (8, 11), (9, 12), (10, 13), (17, 18),
(19, 20))
skeleton = ((0, 16), (16, 1), (1, 15), (15, 14), (14, 8), (14, 11), (8, 9),
(9, 10), (10, 19), (11, 12), (12, 13), (13, 20), (1, 2), (2, 3),
(3, 4), (4, 17), (1, 5), (5, 6), (6, 7), (7, 18))
# snapshot load
model_path = './snapshot_%d.pth.tar' % int(args.test_epoch)
assert osp.exists(model_path), 'Cannot find model at ' + model_path
print('Load checkpoint from {}'.format(model_path))
model = get_pose_net(cfg, False, joint_num)
model = DataParallel(model).cuda()
ckpt = torch.load(model_path)
model.load_state_dict(ckpt['network'])
model.eval()
# prepare input image
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=cfg.pixel_mean, std=cfg.pixel_std)
])
img_path = 'input.jpg'
original_img = cv2.imread(img_path)
original_img_height, original_img_width = original_img.shape[:2]
# prepare bbox
bbox_list = [
[139.41, 102.25, 222.39, 241.57],\
[287.17, 61.52, 74.88, 165.61],\
align_joint_idx = [8, 12, 16, 20, 21]
non_rigid_joint_idx = [3, 21]
hand_model_path = '../data/hand_model'
mesh = mesh.Mesh(osp.join(hand_model_path, 'hand.obj'))
mesh.load_skeleton(osp.join(hand_model_path, 'skeleton.txt'),
joint_num) # joint set is defined in here
mesh.load_skinning_weight(osp.join(hand_model_path, 'skinning_weight.txt'))
mesh.load_local_pose(osp.join(hand_model_path, 'local_pose.txt'))
mesh.load_global_pose(osp.join(hand_model_path, 'global_pose.txt'))
mesh.load_global_pose_inv(osp.join(hand_model_path, 'global_pose_inv.txt'))
# load pre-trained DeepHandMesh
model_path = './snapshot_' + args.test_epoch + '.pth.tar'
assert os.path.exists(model_path), 'Cannot find model at ' + model_path
model = get_model('test', mesh, root_joint_idx, align_joint_idx,
non_rigid_joint_idx)
model = DataParallel(model).cuda()
ckpt = torch.load(model_path)
model.load_state_dict(ckpt['network'], strict=False)
model.eval()
# forward and save output
joint_euler = torch.zeros((1, joint_num, 3)).float().cuda()
joint_euler[0, 8, 2] = math.pi / 2
joint_euler[0, 12, 2] = math.pi / 2
joint_euler[0, 16, 2] = math.pi / 2
joint_euler[0, 20, 2] = math.pi / 2
with torch.no_grad():
out = model.module.decode(joint_euler)
mesh.save_obj(out['mesh_out_refined'][0].cpu().numpy(), None, 'mesh.obj')
def train(args, pt_dir, chkpt_path, trainloader, devloader, writer, logger, hp,
hp_str):
model = get_SLOCountNet(hp).cuda()
print("FOV: {}", model.get_fov(hp.features.n_fft))
model_parameters = filter(lambda p: p.requires_grad, model.parameters())
params = sum([np.prod(p.size()) for p in model_parameters])
print("N_parameters : {}".format(params))
model = DataParallel(model)
if hp.train.optimizer == 'adam':
optimizer = torch.optim.Adam(model.parameters(), lr=hp.train.adam)
else:
raise Exception("%s optimizer not supported" % hp.train.optimizer)
epoch = 0
best_loss = np.inf
if chkpt_path is not None:
logger.info("Resuming from checkpoint: %s" % chkpt_path)
checkpoint = torch.load(chkpt_path)
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
epoch = checkpoint['step']
# will use new given hparams.
if hp_str != checkpoint['hp_str']:
logger.warning("New hparams is different from checkpoint.")
else:
logger.info("Starting new training run")
try:
for epoch in range(epoch, hp.train.n_epochs):
vad_scores = Binarymetrics.BinaryMeter() # activity scores
vod_scores = Binarymetrics.BinaryMeter() # overlap scores
count_scores = Binarymetrics.MultiMeter() # Countnet scores
model.train()
tot_loss = 0
with tqdm(trainloader) as t:
t.set_description("Epoch: {}".format(epoch))
for count, batch in enumerate(trainloader):
features, labels = batch
features = features.cuda()
labels = labels.cuda()
preds = model(features)
loss = criterion(preds, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# compute proper metrics for VAD
loss = loss.item()
if loss > 1e8 or math.isnan(loss): # check if exploded
logger.error("Loss exploded to %.02f at step %d!" %
(loss, epoch))
raise Exception("Loss exploded")
VADpreds = torch.sum(torch.exp(preds[:, 1:5, :]),
dim=1).unsqueeze(1)
VADlabels = torch.sum(labels[:, 1:5, :],
dim=1).unsqueeze(1)
vad_scores.update(VADpreds, VADlabels)
VODpreds = torch.sum(torch.exp(preds[:, 2:5, :]),
dim=1).unsqueeze(1)
VODlabels = torch.sum(labels[:, 2:5, :],
dim=1).unsqueeze(1)
vod_scores.update(VODpreds, VODlabels)
count_scores.update(
torch.argmax(torch.exp(preds), 1).unsqueeze(1),
torch.argmax(labels, 1).unsqueeze(1))
tot_loss += loss
vad_fa = vad_scores.get_fa().item()
vad_miss = vad_scores.get_miss().item()
vad_precision = vad_scores.get_precision().item()
vad_recall = vad_scores.get_recall().item()
vad_matt = vad_scores.get_matt().item()
vad_f1 = vad_scores.get_f1().item()
vad_tp = vad_scores.tp.item()
vad_tn = vad_scores.tn.item()
vad_fp = vad_scores.fp.item()
vad_fn = vad_scores.fn.item()
vod_fa = vod_scores.get_fa().item()
vod_miss = vod_scores.get_miss().item()
vod_precision = vod_scores.get_precision().item()
vod_recall = vod_scores.get_recall().item()
vod_matt = vod_scores.get_matt().item()
vod_f1 = vod_scores.get_f1().item()
vod_tp = vod_scores.tp.item()
vod_tn = vod_scores.tn.item()
vod_fp = vod_scores.fp.item()
vod_fn = vod_scores.fn.item()
count_fa = count_scores.get_accuracy().item()
count_miss = count_scores.get_miss().item()
count_precision = count_scores.get_precision().item()
count_recall = count_scores.get_recall().item()
count_matt = count_scores.get_matt().item()
count_f1 = count_scores.get_f1().item()
count_tp = count_scores.get_tp().item()
count_tn = count_scores.get_tn().item()
count_fp = count_scores.get_fp().item()
count_fn = count_scores.get_fn().item()
t.set_postfix(loss=tot_loss / (count + 1),
vad_miss=vad_miss,
vad_fa=vad_fa,
vad_prec=vad_precision,
vad_recall=vad_recall,
vad_matt=vad_matt,
vad_f1=vad_f1,
vod_miss=vod_miss,
vod_fa=vod_fa,
vod_prec=vod_precision,
vod_recall=vod_recall,
vod_matt=vod_matt,
vod_f1=vod_f1,
count_miss=count_miss,
count_fa=count_fa,
count_prec=count_precision,
count_recall=count_recall,
count_matt=count_matt,
count_f1=count_f1)
t.update()
writer.log_metrics("train_vad", loss, vad_fa, vad_miss, vad_recall,
vad_precision, vad_f1, vad_matt, vad_tp, vad_tn,
vad_fp, vad_fn, epoch)
writer.log_metrics("train_vod", loss, vod_fa, vod_miss, vod_recall,
vod_precision, vod_f1, vod_matt, vod_tp, vod_tn,
vod_fp, vod_fn, epoch)
writer.log_metrics("train_count", loss, count_fa, count_miss,
count_recall, count_precision, count_f1,
count_matt, count_tp, count_tn, count_fp,
count_fn, epoch)
# end epoch save model and validate it
val_loss = validate(hp, model, devloader, writer, epoch)
if hp.train.save_best == 0:
save_path = os.path.join(pt_dir, 'chkpt_%d.pt' % epoch)
torch.save(
{
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'step': epoch,
'hp_str': hp_str,
}, save_path)
logger.info("Saved checkpoint to: %s" % save_path)
else:
if val_loss < best_loss: # save only when best
best_loss = val_loss
save_path = os.path.join(pt_dir, 'chkpt_%d.pt' % epoch)
torch.save(
{
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'step': epoch,
'hp_str': hp_str,
}, save_path)
logger.info("Saved checkpoint to: %s" % save_path)
return best_loss
except Exception as e:
logger.info("Exiting due to exception: %s" % e)
traceback.print_exc()
class RootNet(object):
def __init__(self, weightsPath, principal_points=None, focal=(1500, 1500)):
"""
:param weightsPath:
:param principal_points:
:param focal:
"""
self.focal = focal
self.principal_points = principal_points
self.net = get_pose_net(cfg, False)
self.net = DataParallel(self.net).cuda()
weigths = torch.load(weightsPath)
self.net.load_state_dict(weigths['network'])
self.net.eval()
self.transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=cfg.pixel_mean, std=cfg.pixel_std)
])
def estimate(self, bboxes, image, tracking=False):
"""
:param bboxes:
:param image:
:return:
"""
if self.principal_points is None:
self.principal_points = [image.shape[1] / 2, image.shape[0] / 2]
output = []
for bbox in bboxes:
bbox_xywh = convertToXYWH(bbox[0], bbox[1], bbox[2], bbox[3])
bbox_root = process_bbox(bbox_xywh, image.shape[1], image.shape[0])
img, img2bb_trans = generate_patch_image(image, bbox_root, False,
0.0)
img = self.transform(img).cuda()[None, :, :, :]
k_value = np.array([
math.sqrt(cfg.bbox_real[0] * cfg.bbox_real[1] * self.focal[0] *
self.focal[1] / (bbox_root[2] * bbox_root[3]))
]).astype(np.float32)
k_value = torch.FloatTensor([k_value]).cuda()[None, :]
# forward
with torch.no_grad():
root_3d = self.net(
img, k_value) # x,y: pixel, z: root-relative depth (mm)
root_3d = root_3d[0].cpu().numpy()
# inverse affine transform (restore the crop and resize)
root_3d[0] = root_3d[0] / cfg.output_shape[1] * cfg.input_shape[1]
root_3d[1] = root_3d[1] / cfg.output_shape[0] * cfg.input_shape[0]
root_3d_xy1 = np.concatenate(
(root_3d[:2], np.ones_like(root_3d[:1])))
img2bb_trans_001 = np.concatenate(
(img2bb_trans, np.array([0, 0, 1]).reshape(1, 3)))
root_3d[:2] = np.dot(np.linalg.inv(img2bb_trans_001),
root_3d_xy1)[:2]
# get 3D coordinates for bbox
root_3d = pixel2cam(root_3d[None, :], self.focal,
self.principal_points)
if tracking:
pid = bbox[-1]
output.append([bbox, root_3d, pid])
else:
output.append([bbox, root_3d])
return output
def load_model(path='rootnet/rootnet_snapshot_18.pth.tar'):
model = DataParallel(get_pose_net()).cuda()
model.load_state_dict(torch.load(path)['network'])
model.eval()
return model
def execute(args):
try:
logger.info('人物深度処理開始: {0}', args.img_dir, decoration=MLogger.DECORATION_BOX)
if not os.path.exists(args.img_dir):
logger.error("指定された処理用ディレクトリが存在しません。: {0}", args.img_dir, decoration=MLogger.DECORATION_BOX)
return False
parser = get_parser()
argv = parser.parse_args(args=[])
if not os.path.exists(argv.model_path):
logger.error("指定された学習モデルが存在しません。: {0}", argv.model_path, decoration=MLogger.DECORATION_BOX)
return False
cudnn.benchmark = True
# snapshot load
model = get_pose_net(argv, False)
model = DataParallel(model).to('cuda')
ckpt = torch.load(argv.model_path)
model.load_state_dict(ckpt['network'])
model.eval()
focal = [1500, 1500] # x-axis, y-axis
# prepare input image
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean=argv.pixel_mean, std=argv.pixel_std)])
# 全人物分の順番別フォルダ
ordered_person_dir_pathes = sorted(glob.glob(os.path.join(args.img_dir, "ordered", "*")), key=sort_by_numeric)
frame_pattern = re.compile(r'^(frame_(\d+)\.png)')
for oidx, ordered_person_dir_path in enumerate(ordered_person_dir_pathes):
logger.info("【No.{0}】人物深度推定開始", f"{oidx:03}", decoration=MLogger.DECORATION_LINE)
frame_json_pathes = sorted(glob.glob(os.path.join(ordered_person_dir_path, "frame_*.json")), key=sort_by_numeric)
for frame_json_path in tqdm(frame_json_pathes, desc=f"No.{oidx:03} ... "):
m = frame_pattern.match(os.path.basename(frame_json_path))
if m:
frame_image_name = str(m.groups()[0])
fno_name = str(m.groups()[1])
# 該当フレームの画像パス
frame_image_path = os.path.join(args.img_dir, "frames", fno_name, frame_image_name)
if os.path.exists(frame_image_path):
frame_joints = {}
with open(frame_json_path, 'r') as f:
frame_joints = json.load(f)
width = int(frame_joints['image']['width'])
height = int(frame_joints['image']['height'])
original_img = cv2.imread(frame_image_path)
bx = float(frame_joints["bbox"]["x"])
by = float(frame_joints["bbox"]["y"])
bw = float(frame_joints["bbox"]["width"])
bh = float(frame_joints["bbox"]["height"])
# ROOT_NETで深度推定
bbox = process_bbox([bx, by, bw, bh], width, height, argv)
img, img2bb_trans = generate_patch_image(original_img, bbox, False, 0.0, argv)
img = transform(img).to('cuda')[None,:,:,:]
k_value = np.array([math.sqrt(argv.bbox_real[0] * argv.bbox_real[1] * focal[0] * focal[1] / (bbox[2] * bbox[3]))]).astype(np.float32)
k_value = torch.FloatTensor([k_value]).to('cuda')[None,:]
with torch.no_grad():
root_3d = model(img, k_value) # x,y: pixel, z: root-relative depth (mm)
img = img[0].to('cpu').numpy()
root_3d = root_3d[0].to('cpu').numpy()
root_3d[0] = root_3d[0] / argv.output_shape[0] * bbox[2] + bbox[0]
root_3d[1] = root_3d[1] / argv.output_shape[1] * bbox[3] + bbox[1]
frame_joints["root"] = {"x": float(root_3d[0]), "y": float(root_3d[1]), "z": float(root_3d[2]), \
"input": {"x": argv.input_shape[0], "y": argv.input_shape[1]}, "output": {"x": argv.output_shape[0], "y": argv.output_shape[1]}, \
"focal": {"x": focal[0], "y": focal[1]}}
with open(frame_json_path, 'w') as f:
json.dump(frame_joints, f, indent=4)
logger.info('人物深度処理終了: {0}', args.img_dir, decoration=MLogger.DECORATION_BOX)
return True
except Exception as e:
logger.critical("人物深度で予期せぬエラーが発生しました。", e, decoration=MLogger.DECORATION_BOX)
return False
