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import os
from tqdm import tqdm
import numpy as np
import pickle as pkl
import utils.rotation_conversions as geometry
import torch
from .dataset import Dataset
# from torch.utils.data import Dataset
action2motion_joints = [8, 1, 2, 3, 4, 5, 6, 7, 0, 9, 10, 11, 12, 13, 14, 21, 24, 38]
def get_z(cam_s, cam_pos, joints, img_size, flength):
"""
Solves for the depth offset of the model to approx. orth with persp camera.
"""
# Translate the model itself: Solve the best z that maps to orth_proj points
joints_orth_target = (cam_s * (joints[:, :2] + cam_pos) + 1) * 0.5 * img_size
height3d = np.linalg.norm(np.max(joints[:, :2], axis=0) - np.min(joints[:, :2], axis=0))
height2d = np.linalg.norm(np.max(joints_orth_target, axis=0) - np.min(joints_orth_target, axis=0))
tz = np.array(flength * (height3d / height2d))
return float(tz)
def get_trans_from_vibe(vibe, index, use_z=True):
alltrans = []
for t in range(vibe["joints3d"][index].shape[0]):
# Convert crop cam to orig cam
# No need! Because `convert_crop_cam_to_orig_img` from demoutils of vibe
# does this already for us :)
# Its format is: [sx, sy, tx, ty]
cam_orig = vibe["orig_cam"][index][t]
x = cam_orig[2]
y = cam_orig[3]
if use_z:
z = get_z(cam_s=cam_orig[0], # TODO: There are two scales instead of 1.
cam_pos=cam_orig[2:4],
joints=vibe['joints3d'][index][t],
img_size=540,
flength=500)
# z = 500 / (0.5 * 480 * cam_orig[0])
else:
z = 0
trans = [x, y, z]
alltrans.append(trans)
alltrans = np.array(alltrans)
return alltrans - alltrans[0]
class UESTC(Dataset):
dataname = "uestc"
def __init__(self, datapath="dataset/uestc", method_name="vibe", view="all", **kargs):
self.datapath = datapath
self.method_name = method_name
self.view = view
super().__init__(**kargs)
# Load pre-computed #frames data
with open(os.path.join(datapath, 'info', 'num_frames_min.txt'), 'r') as f:
num_frames_video = np.asarray([int(s) for s in f.read().splitlines()])
# Out of 118 subjects -> 51 training, 67 in test
all_subjects = np.arange(1, 119)
self._tr_subjects = [
1, 2, 6, 12, 13, 16, 21, 24, 28, 29, 30, 31, 33, 35, 39, 41, 42, 45, 47, 50,
52, 54, 55, 57, 59, 61, 63, 64, 67, 69, 70, 71, 73, 77, 81, 84, 86, 87, 88,
90, 91, 93, 96, 99, 102, 103, 104, 107, 108, 112, 113]
self._test_subjects = [s for s in all_subjects if s not in self._tr_subjects]
# Load names of 25600 videos
with open(os.path.join(datapath, 'info', 'names.txt'), 'r') as f:
videos = f.read().splitlines()
self._videos = videos
if self.method_name == "vibe":
vibe_data_path = os.path.join(datapath, "vibe_cache_refined.pkl")
vibe_data = pkl.load(open(vibe_data_path, "rb"))
self._pose = vibe_data["pose"]
num_frames_method = [p.shape[0] for p in self._pose]
globpath = os.path.join(datapath, "globtrans_usez.pkl")
if os.path.exists(globpath):
self._globtrans = pkl.load(open(globpath, "rb"))
else:
self._globtrans = []
for index in tqdm(range(len(self._pose))):
self._globtrans.append(get_trans_from_vibe(vibe_data, index, use_z=True))
pkl.dump(self._globtrans, open("globtrans_usez.pkl", "wb"))
self._joints = vibe_data["joints3d"]
self._jointsIx = action2motion_joints
else:
raise ValueError("This method name is not recognized.")
num_frames_video = np.minimum(num_frames_video, num_frames_method)
num_frames_video = num_frames_video.astype(int)
self._num_frames_in_video = [x for x in num_frames_video]
N = len(videos)
self._actions = np.zeros(N, dtype=int)
for ind in range(N):
self._actions[ind] = self.parse_action(videos[ind])
self._actions = [x for x in self._actions]
total_num_actions = 40
self.num_actions = total_num_actions
keep_actions = np.arange(0, total_num_actions)
self._action_to_label = {x: i for i, x in enumerate(keep_actions)}
self._label_to_action = {i: x for i, x in enumerate(keep_actions)}
self.num_classes = len(keep_actions)
self._train = []
self._test = []
self.info_actions = []
def get_rotation(view):
theta = - view * np.pi/4
axis = torch.tensor([0, 1, 0], dtype=torch.float)
axisangle = theta*axis
matrix = geometry.axis_angle_to_matrix(axisangle)
return matrix
# 0 is identity if needed
rotations = {key: get_rotation(key) for key in [0, 1, 2, 3, 4, 5, 6, 7]}
for index, video in enumerate(tqdm(videos, desc='Preparing UESTC data..')):
act, view, subject, side = self._get_action_view_subject_side(video)
self.info_actions.append({"action": act,
"view": view,
"subject": subject,
"side": side})
if self.view == "frontview":
if side != 1:
continue
# rotate to front view
if side != 1:
# don't take the view 8 in side 2
if view == 8:
continue
rotation = rotations[view]
global_matrix = geometry.axis_angle_to_matrix(torch.from_numpy(self._pose[index][:, :3]))
# rotate the global pose
self._pose[index][:, :3] = geometry.matrix_to_axis_angle(rotation @ global_matrix).numpy()
# rotate the joints
self._joints[index] = self._joints[index] @ rotation.T.numpy()
self._globtrans[index] = (self._globtrans[index] @ rotation.T.numpy())
# add the global translation to the joints
self._joints[index] = self._joints[index] + self._globtrans[index][:, None]
if subject in self._tr_subjects:
self._train.append(index)
elif subject in self._test_subjects:
self._test.append(index)
else:
raise ValueError("This subject doesn't belong to any set.")
# if index > 200:
# break
# Select only sequences which have a minimum number of frames
if self.num_frames > 0:
threshold = self.num_frames*3/4
else:
threshold = 0
method_extracted_ix = np.where(num_frames_video >= threshold)[0].tolist()
self._train = list(set(self._train) & set(method_extracted_ix))
# keep the test set without modification
self._test = list(set(self._test))
action_classes_file = os.path.join(datapath, "info/action_classes.txt")
with open(action_classes_file, 'r') as f:
self._action_classes = np.array(f.read().splitlines())
# with open(processd_path, 'wb') as file:
# pkl.dump(xxx, file)
def _load_joints3D(self, ind, frame_ix):
if len(self._joints[ind]) == 0:
raise ValueError(
f"Cannot load index {ind} in _load_joints3D function.")
if self._jointsIx is not None:
joints3D = self._joints[ind][frame_ix][:, self._jointsIx]
else:
joints3D = self._joints[ind][frame_ix]
return joints3D
def _load_rotvec(self, ind, frame_ix):
# 72 dim smpl
pose = self._pose[ind][frame_ix, :].reshape(-1, 24, 3)
return pose
def _get_action_view_subject_side(self, videopath):
# TODO: Can be moved to tools.py
spl = videopath.split('_')
action = int(spl[0][1:])
view = int(spl[1][1:])
subject = int(spl[2][1:])
side = int(spl[3][1:])
return action, view, subject, side
def _get_videopath(self, action, view, subject, side):
# Unused function
return 'a{:d}_d{:d}_p{:03d}_c{:d}_color.avi'.format(
action, view, subject, side)
def parse_action(self, path, return_int=True):
# Override parent method
info, _, _, _ = self._get_action_view_subject_side(path)
if return_int:
return int(info)
else:
return info
if __name__ == "__main__":
dataset = UESTC()
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