def load_dataset(self):
cfg = self.cfg
file_name = os.path.join(self.cfg.project_path, cfg.dataset)
with open(os.path.join(self.cfg.project_path, file_name), "rb") as f:
# Pickle the 'data' dictionary using the highest protocol available.
pickledata = pickle.load(f)
self.raw_data = pickledata
num_images = len(pickledata)
data = []
has_gt = True
for i in range(num_images):
sample = pickledata[i] # mlab[0, i]
item = DataItem()
item.image_id = i
item.im_path = sample["image"] # [0][0]
item.im_size = sample["size"] # sample[1][0]
if "joints" in sample.keys():
Joints = sample["joints"]
if (np.size(
np.concatenate([
Joints[person_id][:, 1:3]
for person_id in Joints.keys()
])) > 0):
item.joints = Joints
else:
has_gt = False # no animal has joints!
# item.numanimals=len(item.joints)-1 #as there are also the parts that are not per animal
else:
has_gt = False
data.append(item)
self.has_gt = has_gt
return data
def load_dataset(self):
cfg = self.cfg
file_name = os.path.join(self.cfg.project_path,cfg.dataset)
# Load Matlab file dataset annotation
mlab = sio.loadmat(file_name)
self.raw_data = mlab
mlab = mlab['dataset']
num_images = mlab.shape[1]
# print('Dataset has {} images'.format(num_images))
data = []
has_gt = True
for i in range(num_images):
sample = mlab[0, i]
item = DataItem()
item.image_id = i
item.im_path = sample[0][0]
item.im_size = sample[1][0]
if len(sample) >= 3:
joints = sample[2][0][0]
# print(sample)
joint_id = joints[:, 0]
# make sure joint ids are 0-indexed
if joint_id.size != 0:
assert((joint_id < cfg.num_joints).any())
joints[:, 0] = joint_id
item.joints = [joints]
else:
has_gt = False
#if cfg.crop:
# crop = sample[3][0] - 1
# item.crop = extend_crop(crop, cfg.crop_pad, item.im_size)
data.append(item)
self.has_gt = has_gt
return data
def load_dataset(self):
cfg = self.cfg
file_name = os.path.join(self.cfg.project_path,cfg.dataset)
if '.mat' in file_name: #legacy loader
mlab = sio.loadmat(file_name)
self.raw_data = mlab
mlab = mlab['dataset']
num_images = mlab.shape[1]
data = []
has_gt = True
for i in range(num_images):
sample = mlab[0, i]
item = DataItem()
item.image_id = i
item.im_path = sample[0][0]
item.im_size = sample[1][0]
if len(sample) >= 3:
joints = sample[2][0][0]
joint_id = joints[:, 0]
# make sure joint ids are 0-indexed
if joint_id.size != 0:
assert((joint_id < cfg.num_joints).any())
joints[:, 0] = joint_id
item.joints = [joints]
else:
has_gt = False
data.append(item)
self.has_gt = has_gt
return data
else:
print("Loading pickle data with float coordinates!")
file_name = cfg.dataset.split(".")[0] + ".pickle"
with open(os.path.join(self.cfg.project_path,file_name), 'rb') as f:
pickledata=pickle.load(f)
self.raw_data = pickledata
num_images = len(pickledata) #mlab.shape[1]
data = []
has_gt = True
for i in range(num_images):
sample = pickledata[i] #mlab[0, i]
item = DataItem()
item.image_id = i
item.im_path = sample['image'] #[0][0]
item.im_size = sample['size'] #sample[1][0]
if len(sample) >= 3:
item.num_animals=len(sample['joints'])
item.joints=[sample['joints']]
else:
has_gt = False
data.append(item)
self.has_gt = has_gt
return data