parse_args[source]

parse_args()

def parse_args():
    parser = argparse.ArgumentParser(
        description='Reformat qtmdata so that we can train.'
    )
    parser.add_argument(
        '--data-file', 
        dest='data_file',
        help='qtm text file that has been formated in matlab', 
        type=str
    )
    parser.add_argument(
        '--output-dir',
        dest='output_dir',
        help='directory for reformated keypoint data (default: ./)',
        default='./',
        type=str
    )
    parser.add_argument(
        '--cut-frame',
        dest='cut_frame',
        help='input what frame to cut the dataframe at.',
        type=int,
    )
    parser.add_argument(
        '--camera',
        dest='camera',
        help='which misqus camera (1, 2, 3) to use in runningpose dataset',
        type=int,
        choices=range(1, 4)
    )
    
    return parser.parse_args()

TODO: Fix 2D converter

main[source]

main(args)

Loads the qtm data then removes unwanted keypoints. Then it infers new keypoints adds them.

def main(args):
    """
    Loads the qtm data then removes unwanted keypoints.
    Then it infers new keypoints adds them.
    """
    # Loads the textfiles
    labels_np = np.loadtxt('qtm_labels.txt', dtype = 'str')
    data_3D = np.loadtxt(args.data_file, dtype = 'float', delimiter= ',')

    # Reformats the data to a dataframe
    data_3D = pd.DataFrame(data_3D, index=labels_np).T

    # Remove unwanted keypoints
    data_3D = data_3D.drop(
        columns=[
            'HeadL', 'HeadR', 'Chest', 'LThighFrontLow', 'RThighFrontLow', 
            'LShinFrontHigh', 'RShinFrontHigh', 'LForefoot5', 'RForefoot5', 
            'LHeelBack', 'RHeelBack', 'LArm', 'RArm','WaistLFront', 'WaistL', 
            'WaistRFront', 'WaistR', 'LHand2', 'RHand2'
        ]
    )
    # Create "new" keypoints by finding the mean between specific keypoints
    left_elbow_3D = data_3D.loc[:, ['LElbowOut','LElbowIn']].mean(axis=1)
    right_elbow_3D = data_3D.loc[:, ['RElbowOut','RElbowIn']].mean(axis=1)

    left_wrist_3D = data_3D.loc[:, ['LWristIn','LWristOut']].mean(axis=1)
    right_wrist_3D = data_3D.loc[:, ['RWristOut','RWristIn']].mean(axis=1)

    left_knee_3D = data_3D.loc[:, ['LKneeOut','LKneeIn']].mean(axis=1)
    right_knee_3D = data_3D.loc[:, ['RKneeOut','RKneeIn']].mean(axis=1)

    left_ankle_3D = data_3D.loc[:, ['LAnkleOut','LAnkleIn']].mean(axis=1)
    right_ankle_3D = data_3D.loc[:, ['RAnkleOut','RAnkleIn']].mean(axis=1)

    # Remove the keypoints that was taken as a mean
    data_3D = data_3D.drop(
        columns=[
            'LElbowOut','LElbowIn', 'RElbowOut','RElbowIn', 
            'LWristIn','LWristOut', 'RWristIn','RWristOut', 
            'LKneeIn', 'LKneeOut','RKneeIn', 'RKneeOut',
            'LAnkleOut','LAnkleIn','RAnkleOut','RAnkleIn'
        ]
    )
    # Adds the new keypoint data to the dataframe
    data_3D['LElbow'] = left_elbow_3D
    data_3D['RElbow'] = right_elbow_3D
    data_3D['LWrist'] = left_wrist_3D
    data_3D['RWrist'] = right_wrist_3D
    data_3D['LKnee'] = left_knee_3D
    data_3D['RKnee'] = right_knee_3D
    data_3D['LAnkle'] = left_ankle_3D
    data_3D['RAnkle'] = right_ankle_3D
   
    # Remove every other frame, our videodata Miqus is 85hz and the data is 170hz
    # OBS! (3 rows corresponds to 1 frame.)
    remove = True
    for i in range(0, data_3D.shape[0]):
        if i % 3 == 0:
            remove = not(remove)

        if remove:
            data_3D = data_3D.drop(i)
    
    data_3D = data_3D.reset_index(drop=True)

    # Convert 3D world to 2D camera coordinates
    # data_2D = convert_to_2D(data_3D, args.camera-1) # args.camera-1 for cameras (0,1,2) instead of (1,2,3)

    # Creates output names that depends on the name of the data file 
    data_file_name = os.path.basename(
        os.path.normpath(args.data_file)).rsplit(".")[0]
    # out_2D = os.path.join(
    #     args.output_dir, data_file_name + '_2D_keypoints.csv')
    out_3D = os.path.join(
        args.output_dir, data_file_name + '_3D_keypoints.csv')
    
    # Save the keypoint data as csv files
    # TODO: Add reformat to 2D data i.e 3DWorld -> 3DCamera -> 2D (projection)
    # pd.DataFrame.to_csv(data_2D, path_or_buf=out_2D)
    # TODO: Check if it is better to save this as npz instead.
    pd.DataFrame.to_csv(data_3D, path_or_buf=out_3D)
    # pd.DataFrame.to_csv(data_2D, path_or_buf=out_2D)