def __init__(
self,
root_dir: str,
split: str,
annotor: str = "all",
seed: int = 5,
train_ratio: float = 0.9,
):
"""Initializes the Interhand dataset class, relevant paths, meta_info jsons,
dataframes and the Joints class for remappinng interhand formatted joints to
that of AIT.
Args:
root_dir (str): Path to the directory with image samples.
split (str): set to 'train', 'val' or 'test'.
annotor (str, optional): [description]. Defaults to 'all'. Other options are
'human_annot' and 'machine_annot' .
"""
self.root_dir = root_dir
# To convert from freihand to AIT format.
self.joints = Joints()
self.seed = seed
self.train_ratio = train_ratio
self.annotor = annotor # "human_annot" and "machine_annot" possible.
self.annotation_sampling_folder = "InterHand2.6M.annotations.5.fps"
self.image_sampling_folder = "InterHand2.6M_5fps_batch0/images"
self._split = split
self.split = "train" if split in ["train", "val"] else split
(
self.image_info,
self.annotations_info,
self.camera_info,
self.joints_dict,
) = self.get_meta_info()
self.indices = self.create_train_val_split()
def __init__(
self,
root_dir: str,
split: str = "train",
seed: int = 5,
train_ratio: float = 0.9,
):
"""Initializes the MPII dataset class, relevant paths and the Joints
initializes the class for remapping of MPII formatted joints to that of AIT.
joints mapping at
https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/doc/output.md#hand-output-format
Args:
root_dir (str): Path to the directory with image samples.
split (str): To select train or test split.
"""
self.root_dir = root_dir
self.split = split
self.seed = seed
self.train_ratio = train_ratio
split_set = "train" if self.split in ["train", "val"] else split
self.image_dir_path = os.path.join(self.root_dir,
f"manual_{split_set}")
self.label_dir_path = os.path.join(self.root_dir,
f"manual_{split_set}")
self.img_names = self.get_image_names()
self.labels = self.get_labels()
self.indices = self.create_train_val_split()
# To convert from MPII to AIT format.
self.joints = Joints()
def __init__(self, root_dir: str, split: str = "train"):
self.root_dir = root_dir
self.split = split
self.joints_list, self.img_list = self.get_joints_labels_and_images()
self.img_dict = {item["id"]: item for item in self.img_list}
self.joints = Joints()
self.indices = self.create_train_val_split()
def __init__(
self, root_dir: str, split: str, seed: int = 5, train_ratio: float = 0.9
):
"""Initializes the freihand dataset class, relevant paths and the Joints
class for remapping of freihand formatted joints to that of AIT.
Args:
root_dir (str): Path to the directory with image samples.
"""
self.root_dir = root_dir
self.split = split
self.seed = seed
self.train_ratio = train_ratio
self.labels = self.get_labels()
self.scale = self.get_scale()
self.camera_param = self.get_camera_param()
self.img_names, self.img_path = self.get_image_names()
self.indices = self.create_train_val_split()
# To convert from freihand to AIT format.
self.joints = Joints()
import copy
from math import cos, pi, sin
from typing import Tuple, Union
import numpy as np
import torch
from PIL import Image
from src.data_loader.joints import Joints
from src.types import CAMERA_PARAM, JOINTS_3D, JOINTS_25D, SCALE
from src.constants import MANO_MAT
from torch.utils.data import ConcatDataset, DataLoader, Dataset, WeightedRandomSampler
from torchvision import transforms
JOINTS = Joints()
PARENT_JOINT = JOINTS.mapping.ait.wrist
CHILD_JOINT = JOINTS.mapping.ait.index_mcp
def convert_to_2_5D(K: CAMERA_PARAM,
joints_3D: JOINTS_3D) -> Tuple[JOINTS_25D, SCALE]:
"""Converts coordinates from 3D to 2.5D
Refer: https://arxiv.org/pdf/1804.09534.pdf
Args:
K (CAMERA_PARAM):3x3 Matrix with camera parameters.
joints_3D (JOINTS_3D): Original 3D coordinates unscaled.
Returns:
Tuple[JOINTS_25D, SCALE]: 2.5 D coordinates and scale information.
"""
scale = (((joints_3D[CHILD_JOINT] -
import os
from typing import Union
import cv2
import matplotlib.pyplot as plt
import numpy as np
import torch
from comet_ml import Experiment
from pytorch_lightning.loggers import comet
from src.constants import MASTER_THESIS_DIR
from src.data_loader.joints import Joints
from src.types import JOINTS_3D, JOINTS_25D
from src.utils import read_json
from torchvision import transforms
joints = Joints()
def plot_hand(
axis: plt.Axes,
coords_hand: np.array,
plot_3d: bool = False,
linewidth: str = "1",
linestyle: str = "-",
alpha: float = 1.0,
ms=1,
):
"""Makes a hand stick figure from the coordinates wither in uv plane or xyz plane on the passed axes object.
Code adapted from: https://github.com/lmb-freiburg/freihand/blob/master/utils/eval_util.py
Args: