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DARTset.py
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DARTset.py
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import os
import pickle
import cv2
import imageio
import numpy as np
import torch
from manotorch.manolayer import ManoLayer
from pytorch3d.io import load_obj
from DARTset_utils import (aa_to_rotmat, fit_ortho_param, ortho_project,
plot_hand, rotmat_to_aa)
RAW_IMAGE_SIZE = 512
BG_IMAGE_SIZE = 384
DATA_ROOT = "./data"
class DARTset():
def __init__(self, data_split="train", use_full_wrist=True, load_wo_background=False):
self.name = "DARTset"
self.data_split = data_split
self.root = os.path.join(DATA_ROOT, self.name, self.data_split)
self.load_wo_background = load_wo_background
self.raw_img_size = RAW_IMAGE_SIZE
self.img_size = RAW_IMAGE_SIZE if load_wo_background else BG_IMAGE_SIZE
self.use_full_wrist = use_full_wrist
self.MANO_pose_mean = ManoLayer(joint_rot_mode="axisang",
use_pca=False,
mano_assets_root="assets/mano_v1_2",
center_idx=0,
flat_hand_mean=False).th_hands_mean.numpy().reshape(-1)
obj_filename = os.path.join('./assets/hand_mesh.obj')
_, faces, _ = load_obj(
obj_filename,
device="cpu",
load_textures=False,
)
self.reorder_idx = [0, 13, 14, 15, 20, 1, 2, 3, 16, 4, 5, 6, 17, 10, 11, 12, 19, 7, 8, 9, 18]
self.hand_faces = faces[0].numpy()
self.load_dataset()
def load_dataset(self):
self.image_paths = []
self.raw_mano_param = []
self.joints_3d = []
self.verts_3d_paths = []
self.joints_2d = []
image_parts = [
r for r in os.listdir(self.root)
if os.path.isdir(os.path.join(self.root, r)) and "verts" not in r and "wbg" not in r
]
image_parts = sorted(image_parts)
for imgs_dir in image_parts:
imgs_path = os.path.join(self.root, imgs_dir)
data_record = pickle.load(open(os.path.join(self.root, f"part_{imgs_dir}.pkl"), "rb"))
for k in range(len(data_record["pose"])):
self.image_paths.append(os.path.join(imgs_path, data_record["img"][k]))
self.raw_mano_param.append(data_record["pose"][k].astype(np.float32))
self.joints_3d.append(data_record["joint3d"][k].astype(np.float32))
self.joints_2d.append(data_record["joint2d"][k].astype(np.float32))
verts_3d_path = os.path.join(imgs_path + "_verts", data_record["img"][k].replace(".png", ".pkl"))
self.verts_3d_paths.append(verts_3d_path)
self.sample_idxs = list(range(len(self.image_paths)))
def __len__(self):
return len(self.image_paths)
def __getitem__(self, idx):
return {
"image": self.get_image(idx),
"joints_3d": self.get_joints_3d(idx),
"joints_2d": self.get_joints_2d(idx),
"joints_uvd": self.get_joints_uvd(idx),
"verts_uvd": self.get_verts_uvd(idx),
"ortho_intr": self.get_ortho_intr(idx),
"sides": self.get_sides(idx),
"mano_pose": self.get_mano_pose(idx),
"image_mask": self.get_image_mask(idx),
}
def get_joints_3d(self, idx):
joints = self.joints_3d[idx].copy()
# * Transfer from UNITY coordinate system
joints[:, 1:] = -joints[:, 1:]
joints = joints[self.reorder_idx]
joints = joints - joints[9] + np.array(
[0, 0, 0.5]) # * We use ortho projection, so we need to shift the center of the hand to the origin
return joints
def get_verts_3d(self, idx):
verts = pickle.load(open(self.verts_3d_paths[idx], "rb"))
# * Transfer from UNITY coordinate system
verts[:, 1:] = -verts[:, 1:]
verts = verts + self.get_joints_3d(idx)[5]
if not self.use_full_wrist:
verts = verts[:778]
verts = verts.astype(np.float32)
return verts
def get_joints_2d(self, idx):
joints_2d = self.joints_2d[idx].copy()[self.reorder_idx]
joints_2d = joints_2d / self.raw_img_size * self.img_size
return joints_2d
def get_image_path(self, idx):
return self.image_paths[idx]
def get_ortho_intr(self, idx):
ortho_cam = fit_ortho_param(self.get_joints_3d(idx), self.get_joints_2d(idx))
return ortho_cam
def get_image(self, idx):
path = self.image_paths[idx]
if self.load_wo_background:
img = np.array(imageio.imread(path, pilmode="RGBA"), dtype=np.uint8)
img = img[:, :, :3]
else:
path = os.path.join(*path.split("/")[:-2], path.split("/")[-2] + "_wbg", path.split("/")[-1])
img = cv2.imread(path)[..., ::-1]
return img
def get_image_mask(self, idx):
path = self.image_paths[idx]
image = np.array(imageio.imread(path, pilmode="RGBA"), dtype=np.uint8)
image = cv2.resize(image, dsize=(self.img_size, self.img_size))
return (image[:, :, 3] >= 128).astype(np.float32) * 255.0
def get_joints_uvd(self, idx):
uv = self.get_joints_2d(idx)
d = self.get_joints_3d(idx)[:, 2:] # (21, 1)
uvd = np.concatenate((uv, d), axis=1)
return uvd
def get_verts_uvd(self, idx):
v3d = self.get_verts_3d(idx)
ortho_cam = self.get_ortho_intr(idx)
ortho_proj_verts = ortho_project(v3d, ortho_cam)
d = v3d[:, 2:]
uvd = np.concatenate((ortho_proj_verts, d), axis=1)
return uvd
def get_raw_mano_param(self, idx):
return self.raw_mano_param[idx].copy()
def get_mano_pose(self, idx):
pose = self.get_raw_mano_param(idx) # [16, 3]
# * Transfer from UNITY coordinate system
unity2cam = np.array([[1, 0, 0], [0, -1, 0], [0, 0, -1]]).astype(np.float32)
root = rotmat_to_aa(unity2cam @ aa_to_rotmat(pose[0]))[None]
new_pose = np.concatenate([root.reshape(-1), pose[1:].reshape(-1) + self.MANO_pose_mean], axis=0) # [48]
return new_pose.astype(np.float32)
def get_mano_shape(self, idx):
return np.zeros((10), dtype=np.float32)
def get_sides(self, idx):
return "right"
if __name__ == "__main__":
dart_set = DARTset(data_split="test")
for i in range(len(dart_set)):
output = dart_set[i]
image = output["image"]
mask = (output["image_mask"]).astype(np.uint8)
joints_2d = output["joints_2d"]
joints_3d = output["joints_3d"]
joints_uvd = output["joints_uvd"]
verts_uvd = output["verts_uvd"]
ortho_intr = output["ortho_intr"]
mano_pose = output["mano_pose"]
mano = ManoLayer(joint_rot_mode="axisang",
use_pca=False,
mano_assets_root="assets/mano_v1_2",
center_idx=9,
flat_hand_mean=True)
mano_joints = mano(torch.tensor(mano_pose).unsqueeze(0)).joints.numpy()[0]
mano_2d = ortho_project(mano_joints, ortho_intr)
proj_2d = ortho_project(joints_3d, ortho_intr)
frame_1 = image.copy()
mask = mask[:, :, None]
mask = np.concatenate([mask, mask * 0, mask * 0], axis=2)
frame_2 = cv2.addWeighted(frame_1, 0.5, mask, 0.5, 0)
all_2d_opt = {"ortho_proj": proj_2d, "gt": joints_2d, "uv": joints_uvd[:, :2], "mano_2d": mano_2d}
plot_hand(frame_1, all_2d_opt["uv"], linewidth=1)
plot_hand(frame_1, all_2d_opt["gt"], linewidth=2)
plot_hand(frame_1, all_2d_opt["ortho_proj"], linewidth=1)
plot_hand(frame_1, all_2d_opt["mano_2d"], linewidth=1)
img_list = [image, frame_1, frame_2]
comb_image = np.hstack(img_list)
comb_image = cv2.cvtColor(comb_image, cv2.COLOR_BGR2RGB)
cv2.imshow("comb_image", comb_image)
cv2.waitKey(0)