def evaluate(args):
assert torch.cuda.is_available(), 'CUDA is not available.'
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
print ('The image is {:}'.format(args.image))
print ('The model is {:}'.format(args.model))
snapshot = Path(args.model)
assert snapshot.exists(), 'The model path {:} does not exist'
print ('The face bounding box is {:}'.format(args.face))
assert len(args.face) == 4, 'Invalid face input : {:}'.format(args.face)
snapshot = torch.load(snapshot)
# General Data Argumentation
mean_fill = tuple( [int(x*255) for x in [0.485, 0.456, 0.406] ] )
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
param = snapshot['args']
import pdb; pdb.set_trace()
eval_transform = transforms.Compose([transforms.PreCrop(param.pre_crop_expand), transforms.TrainScale2WH((param.crop_width, param.crop_height)), transforms.ToTensor(), normalize])
model_config = load_configure(param.model_config, None)
dataset = Dataset(eval_transform, param.sigma, model_config.downsample, param.heatmap_type, param.data_indicator)
dataset.reset(param.num_pts)
net = obtain_model(model_config, param.num_pts + 1)
net = net.cuda()
weights = remove_module_dict(snapshot['detector'])
net.load_state_dict(weights)
print ('Prepare input data')
[image, _, _, _, _, _, cropped_size], meta = dataset.prepare_input(args.image, args.face)
inputs = image.unsqueeze(0).cuda()
# network forward
with torch.no_grad():
batch_heatmaps, batch_locs, batch_scos = net(inputs)
# obtain the locations on the image in the orignial size
cpu = torch.device('cpu')
np_batch_locs, np_batch_scos, cropped_size = batch_locs.to(cpu).numpy(), batch_scos.to(cpu).numpy(), cropped_size.numpy()
locations, scores = np_batch_locs[0,:-1,:], np.expand_dims(np_batch_scos[0,:-1], -1)
scale_h, scale_w = cropped_size[0] * 1. / inputs.size(-2) , cropped_size[1] * 1. / inputs.size(-1)
locations[:, 0], locations[:, 1] = locations[:, 0] * scale_w + cropped_size[2], locations[:, 1] * scale_h + cropped_size[3]
prediction = np.concatenate((locations, scores), axis=1).transpose(1,0)
print ('the coordinates for {:} facial landmarks:'.format(param.num_pts))
for i in range(param.num_pts):
point = prediction[:, i]
print ('the {:02d}/{:02d}-th point : ({:.1f}, {:.1f}), score = {:.2f}'.format(i, param.num_pts, float(point[0]), float(point[1]), float(point[2])))
if args.save:
resize = 512
image = draw_image_by_points(args.image, prediction, 2, (255, 0, 0), args.face, resize)
image.save(args.save)
print ('save the visualization results into {:}'.format(args.save))
else:
print ('ignore the visualization procedure')
from utils import get_model_infos
from config_utils import load_configure
model_path = "/mnt/be6f1b04-2ec3-4579-a72a-8a5451087c69/Repos/landmark-detection/SBR/sbr_model.pth"
print('The model is {:}'.format(model_path))
snapshot = Path(model_path)
assert snapshot.exists(), 'The model path {:} does not exist'
snapshot = torch.load(snapshot)
mean_fill = tuple([int(x * 255) for x in [0.485, 0.456, 0.406]])
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
param = snapshot['args']
eval_transform = transforms.Compose([
transforms.PreCrop(param.pre_crop_expand),
transforms.TrainScale2WH((param.crop_width, param.crop_height)),
transforms.ToTensor(), normalize
])
model_config = load_configure(param.model_config, None)
dataset = Dataset(eval_transform, param.sigma, model_config.downsample,
param.heatmap_type, param.data_indicator)
dataset.reset(param.num_pts)
net = obtain_model(model_config, param.num_pts + 1)
net = net.cuda()
#import pdb; pdb.set_trace()
try:
weights = remove_module_dict(snapshot['detector'])
except:
weights = remove_module_dict(snapshot['state_dict'])
def evaluate(args):
assert torch.cuda.is_available(), 'CUDA is not available.'
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
model_name = os.path.split(args.model)[-1]
onnx_name = os.path.splitext(model_name)[0] + ".onnx"
print('The model is {:}'.format(args.model))
print('Model name is {:} \nOutput onnx file is {:}'.format(
model_name, onnx_name))
snapshot = Path(args.model)
assert snapshot.exists(), 'The model does not exist {:}'
#print('Output onnx file is {:}'.format(onnx_name))
snapshot = torch.load(snapshot)
# General Data Argumentation
mean_fill = tuple([int(x * 255) for x in [0.485, 0.456, 0.406]])
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
param = snapshot['args']
print(param)
eval_transform = transforms.Compose([
transforms.PreCrop(param.pre_crop_expand),
transforms.TrainScale2WH((param.crop_width, param.crop_height)),
transforms.ToTensor(), normalize
])
model_config = load_configure(param.model_config, None)
print(model_config)
dataset = Dataset(eval_transform, param.sigma, model_config.downsample,
param.heatmap_type, param.data_indicator)
dataset.reset(param.num_pts)
net = obtain_model(model_config, param.num_pts + 1)
net = net
weights = remove_module_dict(snapshot['state_dict'])
nu_weights = {}
for key, val in weights.items():
nu_weights[key.split('detector.')[-1]] = val
print(key.split('detector.')[-1])
weights = nu_weights
net.load_state_dict(weights)
input_name = ['image_in']
output_name = ['locs', 'scors', 'crap']
im = cv2.imread('Menpo51220/val/0000018.jpg')
imshape = im.shape
face = [0, 0, imshape[0], imshape[1]]
[image, _, _, _, _, _,
cropped_size], meta = dataset.prepare_input('Menpo51220/val/0000018.jpg',
face)
dummy_input = torch.randn(1,
3,
256,
256,
requires_grad=True,
dtype=torch.float32)
input(dummy_input.dtype)
#input('imcrap')
inputs = image.unsqueeze(0)
out_in = inputs.data.numpy()
with open('pick.pick', 'wb') as crap:
pickle.dump(out_in, crap)
with torch.no_grad():
batch_locs, batch_scos, heatmap = net(inputs)
torch.onnx.export(net.cuda(),
dummy_input.cuda(),
onnx_name,
verbose=True,
input_names=input_name,
output_names=output_name,
export_params=True)
print(batch_locs)
print(batch_scos)
print(heatmap)
cpu = torch.device('cpu')
np_batch_locs, np_batch_scos, cropped_size = batch_locs.to(
cpu).numpy(), batch_scos.to(cpu).numpy(), cropped_size.numpy()
locations = np_batch_locs[:-1, :]
scores = np.expand_dims(np_batch_scos[:-1], -1)
scale_h, scale_w = cropped_size[0] * 1. / inputs.size(
-2), cropped_size[1] * 1. / inputs.size(-1)
locations[:,
0], locations[:,
1] = locations[:,
0] * scale_w, locations[:,
1] * scale_h
prediction = np.concatenate((locations, scores), axis=1).transpose(1, 0)
pred_pts = np.transpose(prediction, [1, 0])
pred_pts = pred_pts[:, :-1]
#print(pred_pts)
sim = draw_pts(im, pred_pts=pred_pts, get_l1e=False)
cv2.imwrite('py_0.jpg', sim)
def main(args):
assert torch.cuda.is_available(), 'CUDA is not available.'
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
prepare_seed(args.rand_seed)
logstr = 'seed-{:}-time-{:}'.format(args.rand_seed, time_for_file())
logger = Logger(args.save_path, logstr)
logger.log('Main Function with logger : {:}'.format(logger))
logger.log('Arguments : -------------------------------')
for name, value in args._get_kwargs():
logger.log('{:16} : {:}'.format(name, value))
logger.log("Python version : {}".format(sys.version.replace('\n', ' ')))
logger.log("Pillow version : {}".format(PIL.__version__))
logger.log("PyTorch version : {}".format(torch.__version__))
logger.log("cuDNN version : {}".format(torch.backends.cudnn.version()))
# General Data Argumentation
mean_fill = tuple([int(x * 255) for x in [0.485, 0.456, 0.406]])
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
assert args.arg_flip == False, 'The flip is : {}, rotate is {}'.format(
args.arg_flip, args.rotate_max)
train_transform = [transforms.PreCrop(args.pre_crop_expand)]
train_transform += [
transforms.TrainScale2WH((args.crop_width, args.crop_height))
]
train_transform += [
transforms.AugScale(args.scale_prob, args.scale_min, args.scale_max)
]
#if args.arg_flip:
# train_transform += [transforms.AugHorizontalFlip()]
if args.rotate_max:
train_transform += [transforms.AugRotate(args.rotate_max)]
train_transform += [
transforms.AugCrop(args.crop_width, args.crop_height,
args.crop_perturb_max, mean_fill)
]
train_transform += [transforms.ToTensor(), normalize]
train_transform = transforms.Compose(train_transform)
eval_transform = transforms.Compose([
transforms.PreCrop(args.pre_crop_expand),
transforms.TrainScale2WH((args.crop_width, args.crop_height)),
transforms.ToTensor(), normalize
])
assert (
args.scale_min + args.scale_max
) / 2 == args.scale_eval, 'The scale is not ok : {},{} vs {}'.format(
args.scale_min, args.scale_max, args.scale_eval)
# Model Configure Load
model_config = load_configure(args.model_config, logger)
args.sigma = args.sigma * args.scale_eval
logger.log('Real Sigma : {:}'.format(args.sigma))
# Training Dataset
train_data = VDataset(train_transform, args.sigma, model_config.downsample,
args.heatmap_type, args.data_indicator,
args.video_parser)
train_data.load_list(args.train_lists, args.num_pts, True)
train_loader = torch.utils.data.DataLoader(train_data,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True)
# Evaluation Dataloader
eval_loaders = []
if args.eval_vlists is not None:
for eval_vlist in args.eval_vlists:
eval_vdata = IDataset(eval_transform, args.sigma,
model_config.downsample, args.heatmap_type,
args.data_indicator)
eval_vdata.load_list(eval_vlist, args.num_pts, True)
eval_vloader = torch.utils.data.DataLoader(
eval_vdata,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
eval_loaders.append((eval_vloader, True))
if args.eval_ilists is not None:
for eval_ilist in args.eval_ilists:
eval_idata = IDataset(eval_transform, args.sigma,
model_config.downsample, args.heatmap_type,
args.data_indicator)
eval_idata.load_list(eval_ilist, args.num_pts, True)
eval_iloader = torch.utils.data.DataLoader(
eval_idata,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
eval_loaders.append((eval_iloader, False))
# Define network
lk_config = load_configure(args.lk_config, logger)
logger.log('model configure : {:}'.format(model_config))
logger.log('LK configure : {:}'.format(lk_config))
net = obtain_model(model_config, lk_config, args.num_pts + 1)
assert model_config.downsample == net.downsample, 'downsample is not correct : {} vs {}'.format(
model_config.downsample, net.downsample)
logger.log("=> network :\n {}".format(net))
logger.log('Training-data : {:}'.format(train_data))
for i, eval_loader in enumerate(eval_loaders):
eval_loader, is_video = eval_loader
logger.log('The [{:2d}/{:2d}]-th testing-data [{:}] = {:}'.format(
i, len(eval_loaders), 'video' if is_video else 'image',
eval_loader.dataset))
logger.log('arguments : {:}'.format(args))
opt_config = load_configure(args.opt_config, logger)
if hasattr(net, 'specify_parameter'):
net_param_dict = net.specify_parameter(opt_config.LR, opt_config.Decay)
else:
net_param_dict = net.parameters()
optimizer, scheduler, criterion = obtain_optimizer(net_param_dict,
opt_config, logger)
logger.log('criterion : {:}'.format(criterion))
net, criterion = net.cuda(), criterion.cuda()
net = torch.nn.DataParallel(net)
last_info = logger.last_info()
if last_info.exists():
logger.log("=> loading checkpoint of the last-info '{:}' start".format(
last_info))
last_info = torch.load(last_info)
start_epoch = last_info['epoch'] + 1
checkpoint = torch.load(last_info['last_checkpoint'])
assert last_info['epoch'] == checkpoint[
'epoch'], 'Last-Info is not right {:} vs {:}'.format(
last_info, checkpoint['epoch'])
net.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
scheduler.load_state_dict(checkpoint['scheduler'])
logger.log("=> load-ok checkpoint '{:}' (epoch {:}) done".format(
logger.last_info(), checkpoint['epoch']))
elif args.init_model is not None:
init_model = Path(args.init_model)
assert init_model.exists(), 'init-model {:} does not exist'.format(
init_model)
checkpoint = torch.load(init_model)
checkpoint = remove_module_dict(checkpoint['state_dict'], True)
net.module.detector.load_state_dict(checkpoint)
logger.log("=> initialize the detector : {:}".format(init_model))
start_epoch = 0
else:
logger.log("=> do not find the last-info file : {:}".format(last_info))
start_epoch = 0
detector = torch.nn.DataParallel(net.module.detector)
eval_results = eval_all(args, eval_loaders, detector, criterion,
'start-eval', logger, opt_config)
if args.eval_once:
logger.log("=> only evaluate the model once")
logger.close()
return
# Main Training and Evaluation Loop
start_time = time.time()
epoch_time = AverageMeter()
for epoch in range(start_epoch, opt_config.epochs):
scheduler.step()
need_time = convert_secs2time(
epoch_time.avg * (opt_config.epochs - epoch), True)
epoch_str = 'epoch-{:03d}-{:03d}'.format(epoch, opt_config.epochs)
LRs = scheduler.get_lr()
logger.log(
'\n==>>{:s} [{:s}], [{:s}], LR : [{:.5f} ~ {:.5f}], Config : {:}'.
format(time_string(), epoch_str, need_time, min(LRs), max(LRs),
opt_config))
# train for one epoch
train_loss = train(args, train_loader, net, criterion, optimizer,
epoch_str, logger, opt_config, lk_config,
epoch >= lk_config.start)
# log the results
logger.log('==>>{:s} Train [{:}] Average Loss = {:.6f}'.format(
time_string(), epoch_str, train_loss))
# remember best prec@1 and save checkpoint
save_path = save_checkpoint(
{
'epoch': epoch,
'args': deepcopy(args),
'arch': model_config.arch,
'state_dict': net.state_dict(),
'detector': detector.state_dict(),
'scheduler': scheduler.state_dict(),
'optimizer': optimizer.state_dict(),
},
logger.path('model') /
'{:}-{:}.pth'.format(model_config.arch, epoch_str), logger)
last_info = save_checkpoint(
{
'epoch': epoch,
'last_checkpoint': save_path,
}, logger.last_info(), logger)
eval_results = eval_all(args, eval_loaders, detector, criterion,
epoch_str, logger, opt_config)
# measure elapsed time
epoch_time.update(time.time() - start_time)
start_time = time.time()
logger.close()
#import caffe2.python.onnx as onnx
import onnx
from pathlib import Path
import sys
lib_dir = (Path(__file__).parent / '..' / 'lib').resolve()
if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
assert sys.version_info.major == 3, 'Please upgrade from {:} to Python 3.x'.format(sys.version_info)
from datasets import GeneralDataset as Dataset
from xvision import transforms
print('ineval')
# Load the ONNX model
mean_fill = tuple([int(x * 255) for x in [0.485, 0.456, 0.406]])
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
model = onnx.load("cpm_vgg16-epoch-008-050.onnx")
pre_crop_expand=0.2
# Check that the IR is well formed
onnx.checker.check_model(model)
# Print a human readable representation of the graph
print(onnx.helper.printable_graph(model.graph))
#rep = backend.prepare(model, device="CUDA:0")
eval_transform = transforms.Compose(
[transforms.PreCrop(pre_crop_expand), transforms.TrainScale2WH((256, 256)),
transforms.ToTensor(), normalize])
dataset = Dataset(eval_transform, 4.0, 8, 'gaussian', '300W-68')
def evaluate(args):
assert torch.cuda.is_available(), 'CUDA is not available.'
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
print('The image is {:}'.format(args.image))
print('The model is {:}'.format(args.model))
snapshot = Path(args.model)
assert snapshot.exists(), 'The model path {:} does not exist'
snapshot = torch.load(snapshot)
# General Data Argumentation
mean_fill = tuple([int(x * 255) for x in [0.485, 0.456, 0.406]])
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
param = snapshot['args']
eval_transform = transforms.Compose([
transforms.PreCrop(param.pre_crop_expand),
transforms.TrainScale2WH((param.crop_width, param.crop_height)),
transforms.ToTensor(), normalize
])
model_config = load_configure(param.model_config, None)
dataset = Dataset(eval_transform, param.sigma, model_config.downsample,
param.heatmap_type, param.data_indicator)
dataset.reset(param.num_pts)
net = obtain_model(model_config, param.num_pts + 1)
net = net.cuda()
weights = remove_module_dict(snapshot['state_dict'])
nu_weights = {}
for key, val in weights.items():
nu_weights[key.split('detector.')[-1]] = val
print(key.split('detector.')[-1])
weights = nu_weights
net.load_state_dict(weights)
print('Prepare input data')
l1 = []
record_writer = Collection_engine.produce_generator()
total_images = len(images)
for im_ind, aimage in enumerate(images):
progressbar(im_ind, total_images)
pts_name = os.path.splitext(aimage)[0] + '.pts'
pts_full = _pts_path_ + pts_name
gtpts = get_pts(pts_full, 90)
aim = _image_path + aimage
args.image = aim
im = cv2.imread(aim)
imshape = im.shape
args.face = [0, 0, imshape[0], imshape[1]]
[image, _, _, _, _, _,
cropped_size], meta = dataset.prepare_input(args.image, args.face)
inputs = image.unsqueeze(0).cuda()
# network forward
with torch.no_grad():
batch_heatmaps, batch_locs, batch_scos = net(inputs)
# obtain the locations on the image in the orignial size
cpu = torch.device('cpu')
np_batch_locs, np_batch_scos, cropped_size = batch_locs.to(
cpu).numpy(), batch_scos.to(cpu).numpy(), cropped_size.numpy()
locations, scores = np_batch_locs[0, :-1, :], np.expand_dims(
np_batch_scos[0, :-1], -1)
scale_h, scale_w = cropped_size[0] * 1. / inputs.size(
-2), cropped_size[1] * 1. / inputs.size(-1)
locations[:,
0], locations[:,
1] = locations[:, 0] * scale_w + cropped_size[
2], locations[:,
1] * scale_h + cropped_size[3]
prediction = np.concatenate((locations, scores),
axis=1).transpose(1, 0)
#print ('the coordinates for {:} facial landmarks:'.format(param.num_pts))
for i in range(param.num_pts):
point = prediction[:, i]
#print ('the {:02d}/{:02d}-th point : ({:.1f}, {:.1f}), score = {:.2f}'.format(i, param.num_pts, float(point[0]), float(point[1]), float(point[2])))
if args.save:
args.save = _output_path + aimage
resize = 512
#image = draw_image_by_points(args.image, prediction, 2, (255, 0, 0), args.face, resize)
#sim, l1e =draw_pts(im, gt_pts=gtpts, pred_pts=prediction, get_l1e=True)
#print(np.mean(l1e))
#l1.append(np.mean(l1e))
pred_pts = np.transpose(prediction, [1, 0])
pred_pts = pred_pts[:, :-1]
record_writer.consume_data(im,
gt_pts=gtpts,
pred_pts=pred_pts,
name=aimage)
#cv2.imwrite(_output_path+aimage, sim)
#image.save(args.save)
#print ('save the visualization results into {:}'.format(args.save))
else:
print('ignore the visualization procedure')
record_writer.post_process()
record_writer.generate_output(output_path=_output_path,
epochs=50,
name='Supervision By Registration')
def evaluate(args):
assert torch.cuda.is_available(), 'CUDA is not available.'
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
print('The model is {:}'.format(args.model))
snapshot = Path(args.model)
assert snapshot.exists(), 'The model path {:} does not exist'
snapshot = torch.load(snapshot)
# General Data Argumentation
mean_fill = tuple([int(x * 255) for x in [0.485, 0.456, 0.406]])
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
param = snapshot['args']
eval_transform = transforms.Compose([
transforms.PreCrop(param.pre_crop_expand),
transforms.TrainScale2WH((param.crop_width, param.crop_height)),
transforms.ToTensor(), normalize
])
model_config = load_configure(param.model_config, None)
dataset = Dataset(eval_transform, param.sigma, model_config.downsample,
param.heatmap_type, param.data_indicator)
dataset.reset(param.num_pts)
net = obtain_model(model_config, param.num_pts + 1)
net = net.cuda()
weights = remove_module_dict(snapshot['state_dict'])
nu_weights = {}
for key, val in weights.items():
nu_weights[key.split('detector.')[-1]] = val
print(key.split('detector.')[-1])
weights = nu_weights
net.load_state_dict(weights)
print('Prepare input data')
images = os.listdir(args.image_path)
images = natsort.natsorted(images)
total_images = len(images)
for im_ind, aimage in enumerate(images):
progressbar(im_ind, total_images)
#aim = os.path.join(args.image_path, aimage)
aim = '0.jpg'
args.image = aim
im = cv2.imread(aim)
imshape = im.shape
print(imshape)
input('crap12')
args.face = [0, 0, imshape[0], imshape[1]]
[image, _, _, _, _, _,
cropped_size], meta = dataset.prepare_input(args.image, args.face)
inputs = image.unsqueeze(0).cuda()
scale_h, scale_w = cropped_size[0] * 1. / inputs.size(
-2), cropped_size[1] * 1. / inputs.size(-1)
print(inputs.size(-2))
print(inputs.size(-1))
print(scale_w.data.numpy())
print(scale_h.data.numpy())
print(cropped_size.data.numpy())
input('crap')
# network forward
with torch.no_grad():
batch_locs, batch_scos = net(inputs)
c_im = np.expand_dims(image.data.numpy(), 0)
c_locs, c_scors = rep.run(c_im)
# obtain the locations on the image in the orignial size
cpu = torch.device('cpu')
np_batch_locs, np_batch_scos, cropped_size = batch_locs.to(
cpu).numpy(), batch_scos.to(cpu).numpy(), cropped_size.numpy()
locations, scores = np_batch_locs[0, :-1, :], np.expand_dims(
np_batch_scos[0, :-1], -1)
locations[:, 0], locations[:, 1] = locations[:, 0] * scale_w + cropped_size[2], locations[:, 1] * scale_h + \
cropped_size[3]
prediction = np.concatenate((locations, scores),
axis=1).transpose(1, 0)
c_locations = c_locs[0, :-1, :]
c_locations[:, 0], c_locations[:, 1] = c_locations[:, 0] * scale_w + cropped_size[2], c_locations[:, 1] * scale_h + \
cropped_size[3]
c_scores = np.expand_dims(c_scors[0, :-1], -1)
c_pred_pts = np.concatenate((c_locations, c_scores),
axis=1).transpose(1, 0)
pred_pts = np.transpose(prediction, [1, 0])
pred_pts = pred_pts[:, :-1]
c_pred_pts = np.transpose(c_pred_pts, [1, 0])
c_pred_pts = c_pred_pts[:, :-1]
print(c_scors, '\n\n\n')
print(np_batch_scos)
print(c_scors - np_batch_scos)
if args.save:
json_file = os.path.splitext(aimage)[0] + '.jpg'
save_path = os.path.join(args.save, 'caf' + json_file)
save_path2 = os.path.join(args.save, 'py_' + json_file)
sim2 = draw_pts(im, pred_pts=pred_pts, get_l1e=False)
sim = draw_pts(im, pred_pts=c_pred_pts, get_l1e=False)
#print(pred_pts)
cv2.imwrite(save_path, sim)
cv2.imwrite(save_path2, sim2)
input('save1')
# image.save(args.save)
# print ('save the visualization results into {:}'.format(args.save))
else:
print('ignore the visualization procedure')