def __init__(self,
use_cache=False,
mini_factor=None,
class_name='mobile_phone',
use_annots=True,
scale_factor=1.2):
"""
Args:
filter_no_contact: remove data where hand not in contact with object
filter_thresh: min distance between hand and object to consider contact (mm)
"""
super().__init__()
self.all_queries = [
BaseQueries.images, BaseQueries.joints3d, BaseQueries.sides,
BaseQueries.objpoints3d
]
trans_queries = get_trans_queries(self.all_queries)
self.all_queries.extend(trans_queries)
self.scale_factor = scale_factor
self.split = 'all'
class_dict = {
'mobile_phone': ['o{}'.format(idx) for idx in range(6, 11)],
'ball': ['o{}'.format(idx) for idx in range(31, 36)],
'light_bulb': ['o{}'.format(idx) for idx in range(16, 21)],
'marker': ['o{}'.format(idx) for idx in range(36, 41)],
'can': ['o{}'.format(idx) for idx in range(21, 26)],
'remote_control': ['o{}'.format(idx) for idx in range(46, 51)],
'cups': ['o{}'.format(idx) for idx in range(41, 46)]
}
if class_name not in class_dict:
raise ValueError('{} should be in {}'.format(
class_name, class_dict.keys()))
self.sessions = ['s{}'.format(idx) for idx in range(1, 12)]
self.class_name = class_name
self.class_keys = class_dict[class_name]
# Set cache path
self.use_cache = use_cache
self.cache_folder = os.path.join('data', 'cache', 'core50')
os.makedirs(self.cache_folder, exist_ok=True)
self.name = 'core50'
self.mini_factor = mini_factor
self.root = '/sequoia/data2/dataset/handatasets/Core50'
self.annot_root = os.path.join(self.root, 'core50_350x350_Annot')
self.rgb_root = os.path.join(self.root, 'core50_350x350')
self.depth_root = os.path.join(self.root, 'core50_350x350_DepthMap')
self.load_dataset()
print('Got {} samples for class {}'.format(len(self.image_names),
class_name))
# get paired links as neighboured joints
self.links = [(0, 1)]
# Values from https://github.com/OpenKinect/libfreenect2/issues/41
self.depth_intrinsic = [[365.456, 0, 254.878], [0, 365.456, 205.395],
[0, 0, 1]]
self.color_intrinsic = [[1060.707250708333, 0, 956.354471815484],
[1058.608326305465, 0, 518.9784429882449],
[0, 0, 1]]
def __init__(self,
split='train',
joint_nb=21,
scale_factor=2.2,
version=1,
side='right'):
"""
Args:
filter_no_contact: remove data where hand not in contact with object
filter_thresh: min distance between hand and object to consider contact (mm)
"""
super().__init__()
super().__init__()
self.all_queries = [
BaseQueries.images, BaseQueries.joints3d, BaseQueries.sides,
BaseQueries.objpoints3d
]
trans_queries = get_trans_queries(self.all_queries)
self.side = side
self.all_queries.extend(trans_queries)
self.scale_factor = scale_factor
self.version = version
self.root = os.path.join(
'/sequoia/data2/dataset/handatasets/yanaimages/v{}'.format(
self.version))
self.name = 'yanademo_v{}'.format(self.version)
self.joint_nb = joint_nb
self.split = split
self.load_dataset()
print('Got {} samples for split {}'.format(len(self.image_names),
self.split))
# get paired links as neighboured joints
self.links = [(0, 1, 2, 3, 4), (0, 5, 6, 7, 8), (0, 9, 10, 11, 12),
(0, 13, 14, 15, 16), (0, 17, 18, 19, 20)]
# Load normalization bone values
mean_file = 'bones_synthgrasps_root_wrist.pkl'
with open(
os.path.join(
'/sequoia/data1/yhasson/code/pose_3d/handobjectdatasets/misc',
'stats', mean_file), 'rb') as p_f:
grasp_data = pickle.load(p_f)
self.mano_means = grasp_data['means']
def __init__(
self,
split="train",
split_type="subjects",
original_subject_split=True,
joint_nb=21,
use_cache=False,
mini_factor=None,
use_objects=True,
remove_objects=None, # !! overriden for now
test_object="juice_bottle",
filter_no_contact=True,
filter_thresh=10,
topology=None,
filter_object=None,
override_scale=False,
):
"""
Args:
topology: if 0, juice_bottle, salt, liquid_soap, if 1 milk
"""
super().__init__()
self.all_queries = [
BaseQueries.images,
BaseQueries.joints2d,
BaseQueries.joints3d,
BaseQueries.sides,
BaseQueries.camintrs,
BaseQueries.meta,
]
self.use_objects = use_objects
self.filter_no_contact = filter_no_contact
self.filter_thresh = filter_thresh
self.override_scale = override_scale
if self.use_objects:
self.all_queries.append(BaseQueries.objverts3d)
self.all_queries.append(BaseQueries.objpoints2d)
self.all_queries.append(BaseQueries.objfaces)
if self.use_objects: # Overriding
self.remove_objects = False
else:
self.remove_objects = False
self.topology = topology
self.test_object = test_object
trans_queries = get_trans_queries(self.all_queries)
self.all_queries.extend(trans_queries)
# Set cache path
self.use_cache = use_cache
self.cache_folder = os.path.join("data", "cache", "fhb")
os.makedirs(self.cache_folder, exist_ok=True)
self.cam_extr = np.array(
[
[0.999988496304, -0.00468848412856, 0.000982563360594, 25.7],
[0.00469115935266, 0.999985218048, -0.00273845880292, 1.22],
[-0.000969709653873, 0.00274303671904, 0.99999576807, 3.902],
[0, 0, 0, 1],
]
)
self.cam_intr = np.array(
[
[1395.749023, 0, 935.732544],
[0, 1395.749268, 540.681030],
[0, 0, 1],
]
)
self.reorder_idx = np.array(
[
0,
1,
6,
7,
8,
2,
9,
10,
11,
3,
12,
13,
14,
4,
15,
16,
17,
5,
18,
19,
20,
]
)
self.name = "fhb"
self.joint_nb = joint_nb
self.mini_factor = mini_factor
split_opts = ["action", "objects", "subjects"]
self.subjects = [
"Subject_1",
"Subject_2",
"Subject_3",
"Subject_4",
"Subject_5",
"Subject_6",
]
if split_type not in split_opts:
raise ValueError(
"Split for dataset {} should be in {}, got {}".format(
self.name, split_opts, split_type
)
)
self.split_type = split_type
self.original_subject_split = original_subject_split
self.root = "./datasymlinks/fhbhands"
self.info_root = os.path.join(self.root, "Subjects_info")
self.info_split = os.path.join(
self.root, "data_split_action_recognition.txt"
)
self.rgb_root = os.path.join(self.root, "Video_files_480")
self.skeleton_root = os.path.join(self.root, "Hand_pose_annotation_v1")
self.filter_object = filter_object
# Get file prefixes for images and annotations
self.split = split
self.rgb_template = "color_{:04d}.jpeg"
# Joints are numbered from tip to base, we want opposite
self.idxs = [
0,
4,
3,
2,
1,
8,
7,
6,
5,
12,
11,
10,
9,
16,
15,
14,
13,
20,
19,
]
self.load_dataset()
print(
"Got {} samples for split {}".format(
len(self.image_names), self.split
)
)
# get paired links as neighboured joints
self.links = [
(0, 1, 2, 3, 4),
(0, 5, 6, 7, 8),
(0, 9, 10, 11, 12),
(0, 13, 14, 15, 16),
(0, 17, 18, 19, 20),
]
def __init__(self,
split='train',
root='/sequoia/data2/dataset/handatasets/stereohands',
joint_nb=21,
use_cache=False,
gt_detections=False):
# Set cache path
self.split = split
self.use_cache = use_cache
self.cache_folder = os.path.join('data', 'cache', 'stereohands')
os.makedirs(self.cache_folder, exist_ok=True)
self.gt_detections = gt_detections
self.root = root
self.joint_nb = joint_nb
self.all_queries = [
BaseQueries.manoidxs, BaseQueries.images, BaseQueries.joints2d,
BaseQueries.joints3d, BaseQueries.sides
]
trans_queries = get_trans_queries(self.all_queries)
self.all_queries.extend(trans_queries)
self.name = 'stereohands'
self.manoidxs = list(range(1, 21))
# Get file prefixes for images and annotations
self.intr = np.array([[822.79041, 0, 318.47345],
[0, 822.79041, 250.31296], [0, 0, 1]])
self.rgb_folder = os.path.join(root, "images")
self.label_folder = os.path.join(root, "labels")
self.right_template = 'BB_right_{}.png'
self.left_template = 'BB_left_{}.png'
# get paired links as neighboured joints
self.links = [(0, 1, 2, 3, 4), (0, 5, 6, 7, 8), (0, 9, 10, 11, 12),
(0, 13, 14, 15, 16), (0, 17, 18, 19, 20)]
if split == 'train':
self.sequences = [
'B2Counting', 'B2Random', 'B3Counting', 'B3Random',
'B4Counting', 'B4Random', 'B5Counting', 'B5Random',
'B6Counting', 'B6Random'
]
elif split == 'test':
self.sequences = ['B1Counting', 'B1Random']
elif split == 'val':
self.sequences = ['B2Counting', 'B2Random']
elif split == 'train_val':
self.sequences = [
'B3Counting', 'B3Random', 'B4Counting', 'B4Random',
'B5Counting', 'B5Random', 'B6Counting', 'B6Random'
]
elif split == 'all':
self.sequences = [
'B1Counting', 'B1Random', 'B2Counting', 'B2Random',
'B3Counting', 'B3Random', 'B4Counting', 'B4Random',
'B5Counting', 'B5Random', 'B6Counting', 'B6Random'
]
else:
raise ValueError('split {} not in [train|test|val|train_val|all]')
self.split = split
self.center_path = os.path.join(root, "detections",
'centers_{}.txt'.format(self.split))
self.scale_path = os.path.join(root, "detections",
'scales_{}.txt'.format(self.split))
self.bbox_path = os.path.join(root, "detections",
'bboxes_{}.txt'.format(self.split))
self.load_dataset()
# Load normalization bone values
mean_file = 'bones_synthgrasps_root_palm.pkl'
with open(
os.path.join(
'/sequoia/data1/yhasson/code/pose_3d/handobjectdatasets/misc',
'stats', mean_file), 'rb') as p_f:
grasp_data = pickle.load(p_f)
self.mano_means = grasp_data['means']
def __init__(
self,
split="train",
root=None,
joint_nb=21,
mini_factor=None,
use_cache=False,
root_palm=False,
mode="obj",
segment=False,
override_scale=False,
use_external_points=True,
apply_obj_transform=True,
segmented_depth=True,
shapenet_root="datasymlinks/ShapeNetCore.v2",
obman_root="datasymlinks/obman",
):
# Set cache path
self.split = split
obman_root = os.path.join(obman_root, split)
self.override_scale = override_scale # Use fixed scale
self.root_palm = root_palm
self.mode = mode
self.segment = segment
self.apply_obj_transform = apply_obj_transform
self.segmented_depth = segmented_depth
self.use_external_points = use_external_points
if mode == "all" and not self.override_scale:
self.all_queries = [
BaseQueries.images,
BaseQueries.joints2d,
BaseQueries.joints3d,
BaseQueries.sides,
BaseQueries.segms,
BaseQueries.verts3d,
BaseQueries.hand_pcas,
BaseQueries.hand_poses,
BaseQueries.camintrs,
BaseQueries.depth,
]
if use_external_points:
self.all_queries.append(BaseQueries.objpoints3d)
else:
self.all_queries.append(BaseQueries.objverts3d)
self.all_queries.append(BaseQueries.objfaces)
self.rgb_folder = os.path.join(obman_root, "rgb")
elif mode == "obj" or (self.mode == "all" and self.override_scale):
self.all_queries = [BaseQueries.images, BaseQueries.camintrs]
if use_external_points:
self.all_queries.append(BaseQueries.objpoints3d)
else:
self.all_queries.append(BaseQueries.objpoints3d)
self.all_queries.append(BaseQueries.objverts3d)
self.all_queries.append(BaseQueries.objfaces)
if mode == "obj":
self.rgb_folder = os.path.join(obman_root, "rgb_obj")
else:
self.rgb_folder = os.path.join(obman_root, "rgb")
elif mode == "hand":
self.all_queries = [
BaseQueries.images,
BaseQueries.joints2d,
BaseQueries.joints3d,
BaseQueries.sides,
BaseQueries.segms,
BaseQueries.verts3d,
BaseQueries.hand_pcas,
BaseQueries.hand_poses,
BaseQueries.camintrs,
BaseQueries.depth,
]
self.rgb_folder = os.path.join(obman_root, "rgb_hand")
else:
raise ValueError(
"Mode should be in [all|obj|hand], got {}".format(mode))
trans_queries = get_trans_queries(self.all_queries)
self.all_queries.extend(trans_queries)
# Cache information
self.use_cache = use_cache
self.name = "obman"
self.cache_folder = os.path.join("data", "cache", self.name)
os.makedirs(self.cache_folder, exist_ok=True)
self.mini_factor = mini_factor
self.cam_intr = np.array([[480.0, 0.0, 128.0], [0.0, 480.0, 128.0],
[0.0, 0.0, 1.0]]).astype(np.float32)
self.cam_extr = np.array([
[1.0, 0.0, 0.0, 0.0],
[0.0, -1.0, 0.0, 0.0],
[0.0, 0.0, -1.0, 0.0],
]).astype(np.float32)
self.joint_nb = joint_nb
self.segm_folder = os.path.join(obman_root, "segm")
self.prefix_template = "{:08d}"
self.meta_folder = os.path.join(obman_root, "meta")
self.coord2d_folder = os.path.join(obman_root, "coords2d")
# Define links on skeleton
self.links = [
(0, 1, 2, 3, 4),
(0, 5, 6, 7, 8),
(0, 9, 10, 11, 12),
(0, 13, 14, 15, 16),
(0, 17, 18, 19, 20),
]
# Object info
self.shapenet_template = os.path.join(
shapenet_root, "{}/{}/models/model_normalized.pkl")
self.load_dataset()
def __init__(
self,
split='train',
split_type='subjects',
original_subject_split=True,
joint_nb=21,
use_cache=False,
mini_factor=None,
use_objects=True,
remove_objects=None, # !! overriden for now
test_object='juice_bottle',
filter_no_contact=True,
filter_thresh=10,
topology=None,
filter_object=None,
override_scale=False):
"""
Args:
topology: if 0, juice_bottle, salt, liquid_soap, if 1 milk
"""
super().__init__()
self.all_queries = [
BaseQueries.images, BaseQueries.joints2d, BaseQueries.joints3d,
BaseQueries.sides, BaseQueries.camintrs, BaseQueries.meta
]
self.use_objects = use_objects
self.filter_no_contact = filter_no_contact
self.filter_thresh = filter_thresh
self.override_scale = override_scale
if self.use_objects:
self.all_queries.append(BaseQueries.objverts3d)
self.all_queries.append(BaseQueries.objpoints2d)
self.all_queries.append(BaseQueries.objfaces)
if self.use_objects: ## Overriding
self.remove_objects = False
else:
self.remove_objects = False
self.topology = topology
self.test_object = test_object
trans_queries = get_trans_queries(self.all_queries)
self.all_queries.extend(trans_queries)
# Set cache path
self.use_cache = use_cache
self.cache_folder = os.path.join('data', 'cache', 'fhb')
os.makedirs(self.cache_folder, exist_ok=True)
self.cam_extr = np.array(
[[0.999988496304, -0.00468848412856, 0.000982563360594, 25.7],
[0.00469115935266, 0.999985218048, -0.00273845880292, 1.22],
[-0.000969709653873, 0.00274303671904, 0.99999576807,
3.902], [0, 0, 0, 1]])
self.cam_intr = np.array([[1395.749023, 0, 935.732544],
[0, 1395.749268, 540.681030], [0, 0, 1]])
self.reorder_idx = np.array([
0, 1, 6, 7, 8, 2, 9, 10, 11, 3, 12, 13, 14, 4, 15, 16, 17, 5, 18,
19, 20
])
self.name = 'fhb'
self.joint_nb = joint_nb
self.mini_factor = mini_factor
split_opts = ['action', 'objects', 'subjects']
self.subjects = [
'Subject_1', 'Subject_2', 'Subject_3', 'Subject_4', 'Subject_5',
'Subject_6'
]
if split_type not in split_opts:
raise ValueError('Split for dataset {} should be in {}, got {}'.
format(self.name, split_opts, split_type))
self.split_type = split_type
self.original_subject_split = original_subject_split
self.root = '/sequoia/data2/dataset/handatasets/fhb'
self.info_root = os.path.join(self.root, 'Subjects_info')
self.info_split = os.path.join(self.root,
'data_split_action_recognition.txt')
self.rgb_root = os.path.join(self.root, 'process_yana', 'videos_480')
self.skeleton_root = os.path.join(self.root, 'Hand_pose_annotation_v1')
self.filter_object = filter_object
# Get file prefixes for images and annotations
self.split = split
self.rgb_template = 'color_{:04d}.jpeg'
# Joints are numbered from tip to base, we want opposite
self.idxs = [
0, 4, 3, 2, 1, 8, 7, 6, 5, 12, 11, 10, 9, 16, 15, 14, 13, 20, 19
]
self.load_dataset()
print('Got {} samples for split {}'.format(
len(self.image_names), self.split))
# get paired links as neighboured joints
self.links = [(0, 1, 2, 3, 4), (0, 5, 6, 7, 8), (0, 9, 10, 11, 12),
(0, 13, 14, 15, 16), (0, 17, 18, 19, 20)]
# Load normalization bone values
mean_file = 'bones_synthgrasps_root_wrist.pkl'
with open(
os.path.join(
'/sequoia/data1/yhasson/code/pose_3d/handobjectdatasets/misc',
'stats', mean_file), 'rb') as p_f:
grasp_data = pickle.load(p_f)
self.mano_means = grasp_data['means']
def __init__(
self,
split="train",
root="/sequoia/data2/dataset/handatasets/stereohands",
joint_nb=21,
use_cache=False,
gt_detections=False,
):
# Set cache path
self.split = split
self.use_cache = use_cache
self.cache_folder = os.path.join("data", "cache", "stereohands")
os.makedirs(self.cache_folder, exist_ok=True)
self.gt_detections = gt_detections
self.root = root
self.joint_nb = joint_nb
self.all_queries = [
BaseQueries.manoidxs,
BaseQueries.images,
BaseQueries.joints2d,
BaseQueries.joints3d,
BaseQueries.sides,
]
trans_queries = get_trans_queries(self.all_queries)
self.all_queries.extend(trans_queries)
self.name = "stereohands"
self.manoidxs = list(range(1, 21))
# Get file prefixes for images and annotations
self.intr = np.array([[822.79041, 0, 318.47345],
[0, 822.79041, 250.31296], [0, 0, 1]])
self.rgb_folder = os.path.join(root, "images")
self.label_folder = os.path.join(root, "labels")
self.right_template = "BB_right_{}.png"
self.left_template = "BB_left_{}.png"
# get paired links as neighboured joints
self.links = [
(0, 1, 2, 3, 4),
(0, 5, 6, 7, 8),
(0, 9, 10, 11, 12),
(0, 13, 14, 15, 16),
(0, 17, 18, 19, 20),
]
if split == "train":
self.sequences = [
"B2Counting",
"B2Random",
"B3Counting",
"B3Random",
"B4Counting",
"B4Random",
"B5Counting",
"B5Random",
"B6Counting",
"B6Random",
]
elif split == "test":
self.sequences = ["B1Counting", "B1Random"]
elif split == "val":
self.sequences = ["B2Counting", "B2Random"]
elif split == "train_val":
self.sequences = [
"B3Counting",
"B3Random",
"B4Counting",
"B4Random",
"B5Counting",
"B5Random",
"B6Counting",
"B6Random",
]
elif split == "all":
self.sequences = [
"B1Counting",
"B1Random",
"B2Counting",
"B2Random",
"B3Counting",
"B3Random",
"B4Counting",
"B4Random",
"B5Counting",
"B5Random",
"B6Counting",
"B6Random",
]
else:
raise ValueError("split {} not in [train|test|val|train_val|all]")
self.split = split
self.center_path = os.path.join(root, "detections",
"centers_{}.txt".format(self.split))
self.scale_path = os.path.join(root, "detections",
"scales_{}.txt".format(self.split))
self.bbox_path = os.path.join(root, "detections",
"bboxes_{}.txt".format(self.split))
self.load_dataset()