list index out of range

[abirbenaissa]

Hello, i just cloned the repo to use it for the pre processing of the KIT dataset but i faced this issue that i didn't know how to solve, if any one knows how please help ? Traceback (most recent call last): File "C:\Users\TRETEC\Downloads\Internship documents\Complextext2animation\src\data.py", line 622, in eval(args.dataset)(args.path2data, preProcess_flag=True) File "C:\Users\TRETEC\Downloads\Internship documents\Complextext2animation\src\data.py", line 317, in init for filename in tup[0][2]: IndexError: list index out of range

and the code : class KITMocap(RawData): def init(self, path2data, preProcess_flag=False): super(KITMocap, self).init()

load skeleton

    base_path = ''

    # Uncomment this when running in DGX everytime

    self._SKELPATH = base_path + 'dataProcessing/skeleton.p'
    self._MMMSKELPATH = base_path + 'skeleton.xml'
    self._MMMSAMPLEPATH = base_path + 'dataProcessing/00001_mmm.xml'

    os.makedirs(Path(self._SKELPATH).parent, exist_ok=True)
    # get the skeleton and permutation
    self.skel, self.permutation, self.new_joints = self.get_skeletonNpermutation()

    # save skeleton
    torch.save(self.skel, open(self._SKELPATH, 'wb'))

    if preProcess_flag:
        self.preProcess(path2data)
        self.preProcess_axs(path2data)

    # Reading data
    data = []
    # for tup in os.walk(path2data):
    tup = [f for f in os.walk(path2data)]
    for filename in tup[0][2]:(----------LINE 317 -------)
        if Path(filename).suffix == '.xml':
            annotpath = Path(
                tup[0][0])/(filename.split('_')[0] + '_annotations.json')
            annot = json.load(open(annotpath, 'r'))
            meta_path = Path(tup[0][0])/(filename.split('_')[0] + '_meta.json')
            meta_annot = json.load(open(meta_path, 'r'))
            quatpath = filename.split('_')[0] + '_quat.csv'
            axpath = filename.split('_')[0] + '_ax.csv'
            fkepath = filename.split('_')[0] + '_rifke.fke'
            rifkepath = filename.split('_')[0] + '_rifke.csv'
            if annot:
                kount = 0
                for description in annot:
                    data.append([(Path(tup[0][0])/filename).as_posix(),
                                 description,
                                 (Path(tup[0][0])/quatpath).as_posix(),
                                 (Path(tup[0][0])/axpath).as_posix(),
                                 (Path(tup[0][0])/fkepath).as_posix(),
                                 (Path(tup[0][0])/rifkepath).as_posix(),
                                 meta_annot['annotation_perplexities'][kount]
                                 ])
                    kount += 1

            else:
                data.append([(Path(tup[0][0])/filename).as_posix(),
                             '',
                             (Path(tup[0][0])/quatpath).as_posix(),
                             (Path(tup[0][0])/axpath).as_posix(),
                             (Path(tup[0][0])/fkepath).as_posix(),
                             (Path(tup[0][0])/rifkepath).as_posix(),
                             ''
                             ])

    self.df = pd.DataFrame(data=data, columns=[
                           'euler', 'descriptions', 'quaternion', 'axis-angle', 'fke', 'rifke', 'perplexity'])

    self.columns = pd.read_csv(
        self.df.iloc[0].quaternion, index_col=0).columns
    joints = [col[:-3] for col in self.columns]
    self.joints = []
    self.columns_dict = {}
    start = 0
    for joint in joints:
        if not self.joints:
            self.joints.append(joint)
            end = 1
        elif self.joints[-1] == joint:
            end += 1
        else:
            self.columns_dict.update(
                {self.joints[-1]: self.columns[start:end]})
            self.joints.append(joint)
            start = end
            end = end + 1
    self.columns_dict.update({self.joints[-1]: self.columns[start:end]})

def _get_df(self):
    return self.df

def _get_f(self):
    return 100

def _get_skeleton(self):
    return self.skel

@property
def rifke_dict(self):
    return {'fid_l': np.array([14, 15]),
            'fid_r': np.array([19, 20]),
            'sdr_l': 6,
            'sdr_r': 9,
            'hip_l': 12,
            'hip_r': 17}

def preProcess(self, path2data):
    print('Preprocessing KIT Data')
    for tup in os.walk(path2data):
        for filename in (tup[2]):
            if Path(filename).suffix == '.xml':
                filepath = Path(tup[0])/filename
                quatpath = filename.split('_')[0] + '_quat.csv'
                quatpath = (Path(tup[0])/quatpath).as_posix()
                xyz_data, skel, joints, root_pos, rotations = self.mmm2quat(
                    filepath)
                # create quat dataframe
                root_pos = root_pos.squeeze(0)
                rotations = rotations.contiguous().view(
                    rotations.shape[1], -1)
                quats = torch.cat([root_pos, rotations], dim=-1).numpy()
                columns = ['root_tx', 'root_ty', 'root_tz'] + \
                          ['{}_{}'.format(joint, axis) for joint in joints for axis in [
                              'rw', 'rx', 'ry', 'rz']]
                df = pd.DataFrame(data=quats, columns=columns)
                df.to_csv(quatpath)
                filename_fke = str(Path(quatpath).parent) + \
                    '/' + filename.split('_')[0] + '_fke.csv'
                print(filename_fke)
                filename_rifke = str(
                    Path(quatpath).parent) + '/' + filename.split('_')[0] + '_rifke.csv'
                self.quat2fke(df, filename_fke, filename_rifke)

def preProcess_axs(self, path2data):
    print('Preprocessing KIT Data axis angles')
    for tup in os.walk(path2data):
        for filename in (tup[2]):
            if Path(filename).suffix == '.xml':
                filepath = Path(tup[0])/filename
                axpath = filename.split('_')[0] + '_ax.csv'
                axpath = (Path(tup[0])/axpath).as_posix()
                # xyz_data, skel, joints, root_pos, rotations = self.mmm2quat(
                #     filepath)
                joints, root_pos, rotations = self.mmm2axis(filepath)
                # create quat dataframe
                root_pos = root_pos.squeeze(0)
                rotations = rotations.contiguous().view(
                    rotations.shape[1], -1)
                quats = torch.cat([root_pos, rotations], dim=-1).numpy()
                columns = ['root_tx', 'root_ty', 'root_tz'] + \
                          ['{}_{}'.format(joint, axis) for joint in joints for axis in [
                              'rx', 'ry', 'rz']]
                df = pd.DataFrame(data=quats, columns=columns)
                df.to_csv(axpath)

def mat2amc(self, data, filename):
    lines = ["#!OML:ASF H:",
             ":FULLY-SPECIFIED",
             ":DEGREES"]
    for count, row in enumerate(data):
        start = 0
        lines.append('{}'.format(count+1))
        for joint in self.joints:
            end = start + len(self.columns_dict[joint])
            format_str = '{} ' * (len(self.columns_dict[joint]) + 1)
            format_str = format_str[:-1]  # remove the extra space
            lines.append(format_str.format(
                *([joint] + list(row[start:end]))))
            start = end
    lines = '\n'.join(lines) + '\n'

    os.makedirs(filename.parent, exist_ok=True)
    with open(filename, 'w') as fp:
        fp.writelines(lines)

def get_new_parents(self, parents, joints_left, joints_right, joints):
    permutation, new_parents = permute(parents)
    joints_w_root = ['root'] + joints
    new_joints = [joints_w_root[perm] for perm in permutation]
    new_joints_idx = list(range(len(new_joints)))
    new_joints_left = []
    new_joints_right = []
    for idx, jnt in enumerate(new_joints):
        if jnt[0] == 'R':
            new_joints_right.append(idx)
        else:
            new_joints_left.append(idx)

    return permutation, new_parents, new_joints_left, new_joints_right, new_joints

# KITMocap Specific
def get_skeletonNpermutation(self):
    # make a parents_list
    parents = [-1, 3, 0, 2, 1, 8, 9, 0, 7, 1,
               6, 12, 5, 16, 17, 0, 15, 1, 14, 20, 13]
    joints_left = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
    joints_right = [13, 14, 15, 16, 17, 18, 19, 20]

    # read a demo file to get the joints
    joints, _, _, _ = self.mmm2csv(Path(self._MMMSAMPLEPATH))

    permutation, new_parents, new_joints_left, new_joints_right, new_joints = self.get_new_parents(
        parents, joints_left, joints_right, joints)

    import xml.etree.ElementTree as ET
    tree = ET.parse(self._MMMSKELPATH)
    root = tree.getroot()

    # make an offset list
    offset_dict = self.get_offsets(root, joints)
    offset_dict.update({'root': [0, 0, 0]})

    new_offsets = [offset_dict[joint] for joint in new_joints]

    # make a Skeleton
    skel = Skeleton(new_offsets, new_parents,
                    new_joints_left, new_joints_right, new_joints)
    return skel, permutation, new_joints

# read an xml file
def mmm2csv(self, src):
    joint_names, mmm_dict = parse_motions(src.as_posix())[0]
    root_pos = np.array(mmm_dict['root_pos'],
                        dtype=float)  # * 0.001 / 0.056444
    #root_pos = root_pos[:, [1,2,0]]
    root_rot = np.array(mmm_dict['root_rot'],
                        dtype=float)  # * 180/np.pi
    #root_rot = root_rot[:, [1,2,0]]
    joint_pos = np.array(
        mmm_dict['joint_pos'], dtype=float)  # * 180/np.pi

    joint_dict = {}
    for idx, name in enumerate(joint_names):
        if name.split('_')[0][-1] != 't':
            xyz = name.split('_')[0][-1]
            joint = name.split('_')[0][:-1]
        else:
            xyz = 'y'
            joint = name.split('_')[0]
        if joint not in joint_dict:
            joint_dict[joint] = dict()
        joint_dict[joint][xyz] = joint_pos[:, idx]

    joints = []
    values = []
    for cnt, joint in enumerate(joint_dict):
        joint_vals = []
        joints.append(joint)
        for axes in ['x', 'y', 'z']:
            if axes in joint_dict[joint]:
                joint_vals.append(joint_dict[joint][axes])
            else:
                joint_vals.append(np.zeros_like(root_pos[:, 0]))
        values.append(np.stack(joint_vals, axis=1))
    values = np.stack(values, axis=0)

    return joints, root_pos, root_rot, values

def get_offsets(self, root, Joints):
    joints = root.findall('RobotNode')
    offset_dict = {}
    for joint in joints:
        matrix = joint.findall('Transform')
        if matrix:
            offset = []
            # switch y and z axis
            for row in ['row1', 'row3', 'row2']:
                Row = matrix[0].findall('Matrix4x4')[0].findall(row)
                offset.append(float(Row[0].attrib['c4']))
            joint_name = joint.attrib['name']
            if joint_name.split('_')[0][-6:] == 'egment':
                if joint_name[:-13] in Joints:
                    offset_dict[joint_name[:-13]] = offset
            else:
                if joint_name[:-6] in Joints:
                    offset_dict[joint_name[:-6]] = offset
                elif joint_name[:-7] in Joints:
                    offset_dict[joint_name[:-7]] = offset
    return offset_dict

def mmm2axis(self, path):
    joints, root_pos, root_rot, values = self.mmm2csv(path)
    # convert to quaternions
    values_quat = euler2quatbatch(values, axes='sxyz')
    root_rot_quat = euler2quatbatch(root_rot, axes='sxyz')

    # switch y and z axis
    # Note the qinv_np is very important as 2 axes are being interchanged - can be proved using basic vector equations
    root_pos = root_pos[..., [0, 2, 1]]
    values_quat = qinv_np(values_quat[..., [0, 1, 3, 2]])
    root_rot_quat = qinv_np(root_rot_quat[..., [0, 1, 3, 2]])

    rotations = np.expand_dims(np.transpose(np.concatenate((np.expand_dims(
        root_rot_quat, axis=0), values_quat), axis=0), axes=[1, 0, 2]), axis=0)
    root_pos = np.expand_dims(root_pos, axis=0)

    axis_rotations = quaternion_to_expmap(rotations)

    new_rotations = torch.from_numpy(
        axis_rotations[:, :, self.permutation, :])
    new_root_pos = torch.from_numpy(root_pos.copy())
    return self.new_joints, new_root_pos, new_rotations

def mmm2quat(self, path):
    joints, root_pos, root_rot, values = self.mmm2csv(path)

    # convert to quaternions
    values_quat = euler2quatbatch(values, axes='sxyz')
    root_rot_quat = euler2quatbatch(root_rot, axes='sxyz')

    # switch y and z axis
    # Note the qinv_np is very important as 2 axes are being interchanged - can be proved using basic vector equations
    root_pos = root_pos[..., [0, 2, 1]]
    values_quat = qinv_np(values_quat[..., [0, 1, 3, 2]])
    root_rot_quat = qinv_np(root_rot_quat[..., [0, 1, 3, 2]])

    rotations = np.expand_dims(np.transpose(np.concatenate((np.expand_dims(
        root_rot_quat, axis=0), values_quat), axis=0), axes=[1, 0, 2]), axis=0)
    root_pos = np.expand_dims(root_pos, axis=0)

    new_rotations = torch.from_numpy(rotations[:, :, self.permutation, :])
    new_root_pos = torch.from_numpy(root_pos.copy())

    xyz_data = self.skel.forward_kinematics(new_rotations, new_root_pos)[0]
    return xyz_data.numpy(), self.skel, self.new_joints, new_root_pos, new_rotations

if name == 'main': """PreProcessing""" import argparse parser = argparse.ArgumentParser() parser.add_argument('-dataset', default='KITMocap', type=str, help='dataset kind') parser.addargument('-path2data', default='./dataset/kit-mocap', type=str, help='dataset kind') args, = parser.parse_known_args() eval(args.dataset)(args.path2data, preProcess_flag=True)(-----------------LINE622----------------) print('Succesfully Preprocessed {} data'.format(args.dataset))

[anindita127]

Hi. This error usually occurs when it cannot find the data. Please make sure you have copied the dataset to the correct location.

[abirbenaissa]

Thank u for ur fast reply, but i did exactly like this " Download the KIT Motion-Language dataset and unzip the files in the $BASE/dataset/kit-mocap folder." and my repository looks like this Complextext2animation dataset kit-mocap src src dataProcessing

am i doing somthing wrong ?

[abirbenaissa]

Complextext2animation ----------dataset --------------kit-mocap ----------src --------------src -------------------dataProcessing

[anindita127]

There should be one src folder. Complextext2animation ----------dataset --------------kit-mocap ----------src --------------dataProcessing

[chenmozxh]

Thank u for ur fast reply, but i did exactly like this " Download the KIT Motion-Language dataset and unzip the files in the $BASE/dataset/kit-mocap folder." and my repository looks like this Complextext2animation dataset kit-mocap src src dataProcessing

am i doing somthing wrong ?

I encounter the same problem with you. i guess the reason is the "import package" of python is messed up, the program cannot find the existed file. did you fix the problem? i try many different ways, but all of them do not work!

[chenmozxh]

i figure out. at the root dir (which is $bas3/Complextext2motion), run "python src/dataProcessing/meanVariance.py -mask '[0]' -feats_kind rifke -dataset KITMocap -path2data dataset/kit-mocap -f_new 8"

just give it a try