def __init__(self, root_dir = None, aug_transform=None,
img_transform=None, label_transform=None, configer=None):
self.img_list, self.json_list, self.mask_list = self.__list_dirs(root_dir)
self.configer = configer
self.aug_transform = aug_transform
self.img_transform = img_transform
self.label_transform = label_transform
self.pose_data_utilizer = PoseDataUtilizer(configer)
def __init__(self, root_dir = None, base_transform=None,
input_transform=None, label_transform=None,
split="train", configer=None):
self.img_list, self.json_list, self.mask_list = self.__list_dirs(root_dir)
self.split = split
self.configer = configer
self.base_transform = base_transform
self.input_transform = input_transform
self.label_transform = label_transform
self.pose_utilizer = PoseDataUtilizer(configer)
def __init__(self,
root_dir,
base_transform=None,
input_transform=None,
heatmap_transform=None,
split="train",
configer=None):
(self.img_list, self.json_list) = self.__list_dirs(root_dir)
self.split = split
self.configer = configer
self.base_transform = base_transform
self.input_transform = input_transform
self.heatmap_transform = heatmap_transform
self.pose_utilizer = PoseDataUtilizer(configer)
def __init__(self, root_dir, base_transform=None,
input_transform=None, heatmap_transform=None,
split="train", configer=None):
(self.img_list, self.json_list) = self.__list_dirs(root_dir)
self.split = split
self.configer = configer
self.base_transform = base_transform
self.input_transform = input_transform
self.heatmap_transform = heatmap_transform
self.pose_utilizer = PoseDataUtilizer(configer)
def __init__(self, root_dir = None, base_transform=None,
input_transform=None, label_transform=None,
split="train", configer=None):
self.img_list, self.json_list, self.mask_list = self.__list_dirs(root_dir)
self.split = split
self.configer = configer
self.base_transform = base_transform
self.input_transform = input_transform
self.label_transform = label_transform
self.pose_utilizer = PoseDataUtilizer(configer)
class FAFaiLoader(data.Dataset):
def __init__(self,
root_dir=None,
base_transform=None,
input_transform=None,
label_transform=None,
split="train",
configer=None):
self.img_list, self.json_list = self.__list_dirs(root_dir)
self.split = split
self.configer = configer
self.base_transform = base_transform
self.input_transform = input_transform
self.label_transform = label_transform
self.pose_utilizer = PoseDataUtilizer(configer)
def __getitem__(self, index):
img = Image.open(self.img_list[index]).convert('RGB')
kpts, cat = self.__read_json_file(self.json_list[index])
if self.base_transform is not None:
img, _, kpts, center = self.base_transform(img,
mask=None,
kpt=kpts,
center=None)
width, height = img.size
heat_mask = np.zeros((width // self.configer.get('network', 'stride'),
height // self.configer.get('network', 'stride'),
self.configer.get('network', 'heatmap_out')),
dtype=np.uint8)
heat_mask[:, :, self.configer.get('mask', cat)] = 1
heatmap = self.__generate_heatmap(kpts=kpts, mask=heat_mask)
vec_mask = np.ones((width // self.configer.get('network', 'stride'),
height // self.configer.get('network', 'stride'),
self.configer.get('network', 'paf_out')),
dtype=np.uint8)
vec_mask = self.__get_vecmask(kpts, cat, vec_mask)
vecmap = self.pose_utilizer.generate_paf(kpts=kpts, mask=vec_mask)
if self.input_transform is not None:
img = self.input_transform(img)
if self.label_transform is not None:
heatmap = self.label_transform(heatmap)
vecmap = self.label_transform(vecmap)
heat_mask = self.label_transform(heat_mask)
vec_mask = self.label_transform(vec_mask)
return img, heatmap, heat_mask, vecmap, vec_mask
def __len__(self):
return len(self.img_list)
def __read_json_file(self, json_file):
"""
filename: JSON file
return: three list: key_points list, centers list and scales list.
"""
fp = open(json_file)
node = json.load(fp)
kpts = list()
for object in node['objects']:
kpts.append(object['keypoints'])
fp.close()
return kpts, node['category']
def __get_vecmask(self, kpts, cat, vec_mask):
for i in range(len(self.configer.get('details', 'limb_seq'))):
pair = [x - 1 for x in self.configer.get('details', 'limb_seq')[i]]
if pair[0] not in self.configer.get(
'mask', cat) or pair[1] not in self.configer.get(
'mask', cat):
vec_mask[:, :, [i * 2, i * 2 + 1]] = 0
return vec_mask
def __generate_heatmap(self, kpts=None, mask=None):
height = self.configer.get('data', 'input_size')[1]
width = self.configer.get('data', 'input_size')[0]
stride = self.configer.get('network', 'stride')
num_keypoints = self.configer.get('data', 'num_keypoints')
sigma = self.configer.get('heatmap', 'sigma')
method = self.configer.get('heatmap', 'method')
heatmap = np.zeros(
(height // stride, width // stride, num_keypoints + 1),
dtype=np.float32)
start = stride / 2.0 - 0.5
num_objects = len(kpts)
for i in range(num_objects):
for j in range(num_keypoints):
if kpts[i][j][2] != 1:
continue
x = kpts[i][j][0]
y = kpts[i][j][1]
for h in range(height // stride):
for w in range(width // stride):
xx = start + w * stride
yy = start + h * stride
dis = 0
if method == 'gaussian':
dis = ((xx - x) * (xx - x) + (yy - y) *
(yy - y)) / 2.0 / sigma / sigma
elif method == 'laplace':
dis = math.sqrt((xx - x) * (xx - x) + (yy - y) *
(yy - y)) / 2.0 / sigma
else:
Log.error(
'Method: {} is not valid.'.format(method))
exit(1)
if dis > 4.6052:
continue
# Use max operator?
heatmap[h][w][j] = max(math.exp(-dis),
heatmap[h][w][j])
if heatmap[h][w][j] > 1:
heatmap[h][w][j] = 1
heatmap[:, :, num_keypoints] = 1.0 - np.max(heatmap[:, :, :-1], axis=2)
if mask is not None:
heatmap = heatmap * mask
return heatmap
def __list_dirs(self, root_dir):
img_list = list()
json_list = list()
image_dir = os.path.join(root_dir, 'image')
json_dir = os.path.join(root_dir, 'json')
for file_name in os.listdir(image_dir):
image_name = file_name.split('.')[0]
img_list.append(os.path.join(image_dir, file_name))
json_path = os.path.join(json_dir, '{}.json'.format(image_name))
json_list.append(json_path)
if not os.path.exists(json_path):
Log.error('Json Path: {} not exists.'.format(json_path))
exit(1)
return img_list, json_list
class CPMDataLoader(data.Dataset):
def __init__(self,
root_dir,
aug_transform=None,
img_transform=None,
label_transform=None,
configer=None):
(self.img_list, self.json_list) = self.__list_dirs(root_dir)
self.configer = configer
self.aug_transform = aug_transform
self.img_transform = img_transform
self.label_transform = label_transform
self.pose_data_utilizer = PoseDataUtilizer(configer)
def __getitem__(self, index):
img = ImageHelper.pil_open_rgb(self.img_list[index])
kpts, bboxes = self.__read_json_file(self.json_list[index])
if self.aug_transform is not None:
img, kpts, bboxes = self.aug_transform(img,
kpts=kpts,
bboxes=bboxes)
heatmap = self.pose_data_utilizer.generate_heatmap(kpts=kpts)
if self.img_transform is not None:
img = self.img_transform(img)
if self.label_transform is not None:
heatmap = self.label_transform(heatmap)
return img, heatmap
def __len__(self):
return len(self.img_list)
def __read_json_file(self, json_file):
"""
filename: JSON file
return: three list: key_points list, centers list and scales list.
"""
json_dict = JsonHelper.load_file(json_file)
kpts = list()
bboxes = list()
for object in json_dict['objects']:
kpts.append(object['keypoints'])
if 'bbox' in object:
bboxes.append(object['bbox'])
return kpts, bboxes
def __list_dirs(self, root_dir):
img_list = list()
json_list = list()
image_dir = os.path.join(root_dir, 'image')
json_dir = os.path.join(root_dir, 'json')
img_extension = os.listdir(image_dir)[0].split('.')[-1]
for file_name in os.listdir(json_dir):
image_name = '.'.join(file_name.split('.')[:-1])
img_list.append(
os.path.join(image_dir, '{}.{}'.format(image_name,
img_extension)))
json_path = os.path.join(json_dir, file_name)
json_list.append(json_path)
if not os.path.exists(json_path):
Log.error('Json Path: {} not exists.'.format(json_path))
exit(0)
return img_list, json_list
class OPDataLoader(data.Dataset):
def __init__(self, root_dir = None, aug_transform=None,
img_transform=None, label_transform=None, configer=None):
self.img_list, self.json_list, self.mask_list = self.__list_dirs(root_dir)
self.configer = configer
self.aug_transform = aug_transform
self.img_transform = img_transform
self.label_transform = label_transform
self.pose_data_utilizer = PoseDataUtilizer(configer)
def __getitem__(self, index):
img = ImageHelper.pil_open_rgb(self.img_list[index])
if os.path.exists(self.mask_list[index]):
maskmap = ImageHelper.pil_open_p(self.mask_list[index])
else:
maskmap = ImageHelper.np2img(np.ones((img.size[1], img.size[0]), dtype=np.uint8))
kpts, bboxes = self.__read_json_file(self.json_list[index])
if self.aug_transform is not None and len(bboxes) > 0:
img, maskmap, kpts, bboxes = self.aug_transform(img, mask=maskmap, kpts=kpts, bboxes=bboxes)
elif self.aug_transform is not None:
img, maskmap, kpts = self.aug_transform(img, mask=maskmap, kpts=kpts)
width, height = maskmap.size
maskmap = ImageHelper.resize(maskmap, (width // self.configer.get('network', 'stride'),
height // self.configer.get('network', 'stride')), Image.NEAREST)
maskmap = np.expand_dims(np.array(maskmap, dtype=np.float32), axis=2)
heatmap = self.pose_data_utilizer.generate_heatmap(kpts=kpts, mask=maskmap)
vecmap = self.pose_data_utilizer.generate_paf(kpts=kpts, mask=maskmap)
if self.img_transform is not None:
img = self.img_transform(img)
if self.label_transform is not None:
heatmap = self.label_transform(heatmap)
vecmap = self.label_transform(vecmap)
maskmap = self.label_transform(maskmap)
return img, heatmap, maskmap, vecmap
def __len__(self):
return len(self.img_list)
def __read_json_file(self, json_file):
"""
filename: JSON file
return: three list: key_points list, centers list and scales list.
"""
json_dict = JsonHelper.load_file(json_file)
kpts = list()
bboxes = list()
for object in json_dict['objects']:
kpts.append(object['keypoints'])
if 'bbox' in object:
bboxes.append(object['bbox'])
return kpts, bboxes
def __list_dirs(self, root_dir):
img_list = list()
json_list = list()
mask_list = list()
image_dir = os.path.join(root_dir, 'image')
json_dir = os.path.join(root_dir, 'json')
mask_dir = os.path.join(root_dir, 'mask')
img_extension = os.listdir(image_dir)[0].split('.')[-1]
for file_name in os.listdir(json_dir):
image_name = '.'.join(file_name.split('.')[:-1])
img_list.append(os.path.join(image_dir, '{}.{}'.format(image_name, img_extension)))
mask_path = os.path.join(mask_dir, '{}.png'.format(image_name))
mask_list.append(mask_path)
json_path = os.path.join(json_dir, file_name)
json_list.append(json_path)
if not os.path.exists(json_path):
Log.error('Json Path: {} not exists.'.format(json_path))
exit(1)
return img_list, json_list, mask_list
class OPCocoLoader(data.Dataset):
def __init__(self, root_dir = None, base_transform=None,
input_transform=None, label_transform=None,
split="train", configer=None):
self.img_list, self.json_list, self.mask_list = self.__list_dirs(root_dir)
self.split = split
self.configer = configer
self.base_transform = base_transform
self.input_transform = input_transform
self.label_transform = label_transform
self.pose_utilizer = PoseDataUtilizer(configer)
def __getitem__(self, index):
img = Image.open(self.img_list[index]).convert('RGB')
maskmap = Image.open(self.mask_list[index]).convert('P')
kpts, center, scale = self.__read_json_file(self.json_list[index])
if self.base_transform is not None:
img, maskmap, kpts, center = self.base_transform(img, mask=maskmap, kpt=kpts, center=center)
width, height = maskmap.size
maskmap = maskmap.resize((width // self.configer.get('network', 'stride'),
height // self.configer.get('network', 'stride')), Image.NEAREST)
maskmap = np.expand_dims(np.array(maskmap, dtype=np.float32), axis=2)
heatmap = self.pose_utilizer.generate_heatmap(kpts=kpts, mask=maskmap)
vecmap = self.pose_utilizer.generate_paf(kpts=kpts, mask=maskmap)
if self.input_transform is not None:
img = self.input_transform(img)
if self.label_transform is not None:
heatmap = self.label_transform(heatmap)
vecmap = self.label_transform(vecmap)
maskmap = self.label_transform(maskmap)
return img, heatmap, maskmap, vecmap
def __len__(self):
return len(self.img_list)
def __read_json_file(self, json_file):
"""
filename: JSON file
return: three list: key_points list, centers list and scales list.
"""
fp = open(json_file)
node = json.load(fp)
kpts = list()
centers = list()
scales = list()
for object in node['persons']:
kpts.append(object['keypoints'])
centers.append(object['pos_center'])
scales.append(object['scale'])
fp.close()
return kpts, centers, scales
def __list_dirs(self, root_dir):
img_list = list()
json_list = list()
mask_list = list()
image_dir = os.path.join(root_dir, 'image')
json_dir = os.path.join(root_dir, 'json')
mask_dir = os.path.join(root_dir, 'mask')
for file_name in os.listdir(image_dir):
image_name = file_name.split('.')[0]
img_list.append(os.path.join(image_dir, file_name))
mask_path = os.path.join(mask_dir, '{}.png'.format(image_name))
mask_list.append(mask_path)
json_path = os.path.join(json_dir, '{}.json'.format(image_name))
json_list.append(json_path)
if not os.path.exists(json_path):
Log.error('Json Path: {} not exists.'.format(json_path))
exit(1)
return img_list, json_list, mask_list
class CPMLaneLoader(data.Dataset):
def __init__(self, root_dir, base_transform=None,
input_transform=None, heatmap_transform=None,
split="train", configer=None):
(self.img_list, self.json_list) = self.__list_dirs(root_dir)
self.split = split
self.configer = configer
self.base_transform = base_transform
self.input_transform = input_transform
self.heatmap_transform = heatmap_transform
self.pose_utilizer = PoseDataUtilizer(configer)
def __getitem__(self, index):
img = Image.open(self.img_list[index]).convert('RGB')
kpts = self.__get_kpts(self.json_list[index])
mask = None
center = None
if self.base_transform is not None:
img, _, mask, kpts, center = self.base_transform(img, mask=mask, kpt=kpts, center=center)
heatmap = self.pose_utilizer.generate_heatmap(kpts=kpts, mask=mask)
if self.input_transform is not None:
img = self.input_transform(img)
if self.heatmap_transform is not None:
heatmap = self.heatmap_transform(heatmap)
return img, heatmap
def __len__(self):
return len(self.img_list)
def __get_kpts(self, json_path):
kpt = list()
fp = open(json_path)
node = json.load(fp)
for item in node['objects']:
# if 'arrow' not in item['label'].split('_'):
# continue
# if item['label'] != 'arrow_straight':
# continue
for points in item['polygon']:
ind = list()
ind.append(list([points[0], points[1], 1]))
kpt.append(ind)
return kpt
def __list_dirs(self, root_dir):
img_list = list()
json_list = list()
image_dir = os.path.join(root_dir, 'image')
json_dir = os.path.join(root_dir, 'json')
for file_name in os.listdir(image_dir):
image_name = file_name.split('.')[0]
img_list.append(os.path.join(image_dir, file_name))
json_path = os.path.join(json_dir, '{}.json'.format(image_name))
json_list.append(json_path)
if not os.path.exists(json_path):
Log.error('Json Path: {} not exists.'.format(json_path))
exit(0)
return img_list, json_list
class CPMLaneLoader(data.Dataset):
def __init__(self,
root_dir,
base_transform=None,
input_transform=None,
heatmap_transform=None,
split="train",
configer=None):
(self.img_list, self.json_list) = self.__list_dirs(root_dir)
self.split = split
self.configer = configer
self.base_transform = base_transform
self.input_transform = input_transform
self.heatmap_transform = heatmap_transform
self.pose_utilizer = PoseDataUtilizer(configer)
def __getitem__(self, index):
img = Image.open(self.img_list[index]).convert('RGB')
kpts = self.__get_kpts(self.json_list[index])
mask = None
center = None
if self.base_transform is not None:
img, _, mask, kpts, center = self.base_transform(img,
mask=mask,
kpt=kpts,
center=center)
heatmap = self.pose_utilizer.generate_heatmap(kpts=kpts, mask=mask)
if self.input_transform is not None:
img = self.input_transform(img)
if self.heatmap_transform is not None:
heatmap = self.heatmap_transform(heatmap)
return img, heatmap
def __len__(self):
return len(self.img_list)
def __get_kpts(self, json_path):
kpt = list()
fp = open(json_path)
node = json.load(fp)
for item in node['objects']:
# if 'arrow' not in item['label'].split('_'):
# continue
# if item['label'] != 'arrow_straight':
# continue
for points in item['polygon']:
ind = list()
ind.append(list([points[0], points[1], 1]))
kpt.append(ind)
return kpt
def __list_dirs(self, root_dir):
img_list = list()
json_list = list()
image_dir = os.path.join(root_dir, 'image')
json_dir = os.path.join(root_dir, 'json')
for file_name in os.listdir(image_dir):
image_name = file_name.split('.')[0]
img_list.append(os.path.join(image_dir, file_name))
json_path = os.path.join(json_dir, '{}.json'.format(image_name))
json_list.append(json_path)
if not os.path.exists(json_path):
Log.error('Json Path: {} not exists.'.format(json_path))
exit(0)
return img_list, json_list
class AECocoLoader(data.Dataset):
def __init__(self, root_dir = None, base_transform=None,
input_transform=None, label_transform=None,
split="train", configer=None):
self.img_list, self.json_list, self.mask_list = self.__list_dirs(root_dir)
self.split = split
self.configer = configer
self.base_transform = base_transform
self.input_transform = input_transform
self.label_transform = label_transform
self.pose_utilizer = PoseDataUtilizer(configer)
def __getitem__(self, index):
img = Image.open(self.img_list[index]).convert('RGB')
maskmap = Image.open(self.mask_list[index]).convert('P')
kpts, center, scale = self.__read_json_file(self.json_list[index])
if self.base_transform is not None:
img, maskmap, kpts, center = self.base_transform(img, mask=maskmap, kpt=kpts, center=center)
width, height = maskmap.size
maskmap = maskmap.resize((width // self.configer.get('network', 'stride'),
height // self.configer.get('network', 'stride')), Image.NEAREST)
maskmap = np.expand_dims(np.array(maskmap, dtype=np.float32), axis=2)
heatmap = self.pose_utilizer.generate_heatmap(kpts=kpts, mask=maskmap)
tagmap, num_objects = self.pose_utilizer.generate_tagmap(kpts=kpts)
if self.input_transform is not None:
img = self.input_transform(img)
if self.label_transform is not None:
heatmap = self.label_transform(heatmap)
maskmap = self.label_transform(maskmap)
return img, heatmap, maskmap, tagmap, num_objects
def __len__(self):
return len(self.img_list)
def __read_json_file(self, json_file):
"""
filename: JSON file
return: three list: key_points list, centers list and scales list.
"""
fp = open(json_file)
node = json.load(fp)
kpts = list()
centers = list()
scales = list()
for object in node['persons']:
kpts.append(object['keypoints'])
centers.append(object['pos_center'])
scales.append(object['scale'])
fp.close()
return kpts, centers, scales
def __list_dirs(self, root_dir):
img_list = list()
json_list = list()
mask_list = list()
image_dir = os.path.join(root_dir, 'image')
json_dir = os.path.join(root_dir, 'json')
mask_dir = os.path.join(root_dir, 'mask')
for file_name in os.listdir(image_dir):
image_name = file_name.split('.')[0]
img_list.append(os.path.join(image_dir, file_name))
mask_path = os.path.join(mask_dir, '{}.png'.format(image_name))
mask_list.append(mask_path)
json_path = os.path.join(json_dir, '{}.json'.format(image_name))
json_list.append(json_path)
if not os.path.exists(json_path):
Log.error('Json Path: {} not exists.'.format(json_path))
exit(1)
return img_list, json_list, mask_list
class FAFaiLoader(data.Dataset):
def __init__(self, root_dir = None, base_transform=None,
input_transform=None, label_transform=None,
split="train", configer=None):
self.img_list, self.json_list = self.__list_dirs(root_dir)
self.split = split
self.configer = configer
self.base_transform = base_transform
self.input_transform = input_transform
self.label_transform = label_transform
self.pose_utilizer = PoseDataUtilizer(configer)
def __getitem__(self, index):
img = Image.open(self.img_list[index]).convert('RGB')
kpts, cat = self.__read_json_file(self.json_list[index])
if self.base_transform is not None:
img, _, kpts, center = self.base_transform(img, mask=None, kpt=kpts, center=None)
width, height = img.size
heat_mask = np.zeros((width // self.configer.get('network', 'stride'),
height // self.configer.get('network', 'stride'),
self.configer.get('network', 'heatmap_out')), dtype=np.uint8)
heat_mask[:, :, self.configer.get('mask', cat)] = 1
heatmap = self.__generate_heatmap(kpts=kpts, mask=heat_mask)
vec_mask = np.ones((width // self.configer.get('network', 'stride'),
height // self.configer.get('network', 'stride'),
self.configer.get('network', 'paf_out')), dtype=np.uint8)
vec_mask = self.__get_vecmask(kpts, cat, vec_mask)
vecmap = self.pose_utilizer.generate_paf(kpts=kpts, mask=vec_mask)
if self.input_transform is not None:
img = self.input_transform(img)
if self.label_transform is not None:
heatmap = self.label_transform(heatmap)
vecmap = self.label_transform(vecmap)
heat_mask = self.label_transform(heat_mask)
vec_mask = self.label_transform(vec_mask)
return img, heatmap, heat_mask, vecmap, vec_mask
def __len__(self):
return len(self.img_list)
def __read_json_file(self, json_file):
"""
filename: JSON file
return: three list: key_points list, centers list and scales list.
"""
fp = open(json_file)
node = json.load(fp)
kpts = list()
for object in node['objects']:
kpts.append(object['keypoints'])
fp.close()
return kpts, node['category']
def __get_vecmask(self, kpts, cat, vec_mask):
for i in range(len(self.configer.get('details', 'limb_seq'))):
pair = [x-1 for x in self.configer.get('details', 'limb_seq')[i]]
if pair[0] not in self.configer.get('mask', cat) or pair[1] not in self.configer.get('mask', cat):
vec_mask[:, :, [i*2, i*2+1]] = 0
return vec_mask
def __generate_heatmap(self, kpts=None, mask=None):
height = self.configer.get('data', 'input_size')[1]
width = self.configer.get('data', 'input_size')[0]
stride = self.configer.get('network', 'stride')
num_keypoints = self.configer.get('data', 'num_keypoints')
sigma = self.configer.get('heatmap', 'sigma')
method = self.configer.get('heatmap', 'method')
heatmap = np.zeros((height // stride,
width // stride,
num_keypoints + 1), dtype=np.float32)
start = stride / 2.0 - 0.5
num_objects = len(kpts)
for i in range(num_objects):
for j in range(num_keypoints):
if kpts[i][j][2] != 1:
continue
x = kpts[i][j][0]
y = kpts[i][j][1]
for h in range(height // stride):
for w in range(width // stride):
xx = start + w * stride
yy = start + h * stride
dis = 0
if method == 'gaussian':
dis = ((xx - x) * (xx - x) + (yy - y) * (yy - y)) / 2.0 / sigma / sigma
elif method == 'laplace':
dis = math.sqrt((xx - x) * (xx - x) + (yy - y) * (yy - y)) / 2.0 / sigma
else:
Log.error('Method: {} is not valid.'.format(method))
exit(1)
if dis > 4.6052:
continue
# Use max operator?
heatmap[h][w][j] = max(math.exp(-dis), heatmap[h][w][j])
if heatmap[h][w][j] > 1:
heatmap[h][w][j] = 1
heatmap[:, :, num_keypoints] = 1.0 - np.max(heatmap[:, :, :-1], axis=2)
if mask is not None:
heatmap = heatmap * mask
return heatmap
def __list_dirs(self, root_dir):
img_list = list()
json_list = list()
image_dir = os.path.join(root_dir, 'image')
json_dir = os.path.join(root_dir, 'json')
for file_name in os.listdir(image_dir):
image_name = file_name.split('.')[0]
img_list.append(os.path.join(image_dir, file_name))
json_path = os.path.join(json_dir, '{}.json'.format(image_name))
json_list.append(json_path)
if not os.path.exists(json_path):
Log.error('Json Path: {} not exists.'.format(json_path))
exit(1)
return img_list, json_list
