def load_dataset(self):
cfg = self.cfg
file_name = os.path.join(self.cfg["project_path"], cfg["dataset"])
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
base = str(self.cfg["project_path"])
im_path = sample[0][0]
if isinstance(im_path, str):
im_path = robust_split_path(im_path)
else:
im_path = [s.strip() for s in im_path]
item.im_path = os.path.join(base, *im_path)
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
coords = [joint[1:] for joint in joints]
coords = arr(coords)
item.coords = coords
item.joints = [joints]
item.joint_id = [arr(joint_id)]
# print(item.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"])
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
im_path = sample["image"]
if isinstance(im_path, str):
im_path = robust_split_path(im_path)
item.im_path = os.path.join(*im_path)
item.im_size = sample["size"]
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"])
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
im_path = sample[0][0]
if isinstance(im_path, str):
im_path = robust_split_path(im_path)
else:
im_path = [s.strip() for s in im_path]
item.im_path = os.path.join(*im_path)
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 = os.path.join(*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