reproj_from_trc_calib.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-


'''
    ##################################################
    ## Reproject 3D points on camera planes         ##
    ##################################################
    
    Reproject 3D points from a trc file to the camera planes determined by a 
    toml calibration file, to the DeepLabCut (default), MMpose, or 
    OpenPose format.

    The order or the markers depends on the markerset chosen markerset--it is the same as in the trc file if unspecified.
    You can change the marker order in CONSTANTS if you need to.

    New: Moving cameras and zooming cameras are now supported.
    
    Usage: 
    from Pose2Sim.Utilities import reproj_from_trc_calib; reproj_from_trc_calib.reproj_from_trc_calib_func(r'<input_trc_file>', r'<input_calib_file>', '<output_format>', r'<output_file_root>')
    python -m reproj_from_trc_calib -t input_trc_file -c input_calib_file -odm
    python -m reproj_from_trc_calib -t input_trc_file -c input_calib_file -odm --markerset halpe26
    python -m reproj_from_trc_calib -t input_trc_file -c input_calib_file --openpose --deeplabcut --mmpose --undistort
    python -m reproj_from_trc_calib -t input_trc_file -c input_calib_file -d -o output_file_root
'''


## INIT
import os
import pandas as pd
import numpy as np
import toml
import cv2
import json
import re
import hashlib
from copy import deepcopy
import argparse


## AUTHORSHIP INFORMATION
__author__ = "David Pagnon"
__copyright__ = "Copyright 2021, Pose2Sim"
__credits__ = ["David Pagnon"]
__license__ = "BSD 3-Clause License"
__version__ = "0.9.4"
__maintainer__ = "David Pagnon"
__email__ = "contact@david-pagnon.com"
__status__ = "Development"


# CONSTANTS
halpe26_markers = ['NOSB', 'LEYE', 'REYE', 'LEAR', 'REAR', 'shoulder_l', 'shoulder_r', 'elb_l', 'elb_r', 'wrist_l', 'wrist_r', 'hip_l', 'hip_r', 'knee_l', 'knee_r', 'ankle_l', 'ankle_r', 'THD', 'C7', 'SACR', 'MTP1_L', 'MTP1_R', 'MTP5_L', 'MTP5_R', 'HEEL_L', 'HEEL_R']
halpeplus_markers = ['NOSB', 'shoulder_l', 'shoulder_r', 'elb_l', 'elb_r', 'wrist_l', 'wrist_r', 'hip_l', 'hip_r', 'knee_l', 'knee_r', 'ankle_l', 'ankle_r', 'THD', 'C7', 'SACR', 'MTP1_L', 'MTP1_R', 'MTP5_L', 'MTP5_R', 'HEEL_L', 'HEEL_R', 'LHPE', 'RHPE', 'LHPI', 'RHPI', 'TOE_L', 'TOE_R', 'T10', 'UA_L', 'UA_R', 'LA_L', 'LA_R', 'UL_L', 'UL_R', 'LL_L', 'LL_R']
biocvplus_markers = ['ACROM_R', 'ACROM_L', 'C7', 'T10', 'CLAV', 'XIP_PROC', 'UA_R', 'ELB_LAT_R', 'ELB_MED_R', 'LA_R', 'WRI_LAT_R', 'WRI_MED_R', 'HAND_R', 'UA_L', 'ELB_LAT_L', 'ELB_MED_L', 'LA_L', 'WRI_LAT_L', 'WRI_MED_L', 'HAND_L', 'ASIS_R', 'ASIS_L', 'PSIS_R', 'PSIS_L', 'ILCREST_R', 'ILCREST_L', 'UL_R', 'KNEE_LAT_R', 'KNEE_MED_R', 'LL_R', 'MAL_LAT_R', 'MAL_MED_R', 'HEEL_R', 'MTP1_R', 'MTP5_R', 'TOE_R', 'UL_L', 'KNEE_LAT_L', 'KNEE_MED_L', 'LL_L', 'MAL_LAT_L', 'MAL_MED_L', 'HEEL_L', 'MTP1_L', 'MTP5_L', 'TOE_L', 'THD', 'NOSB']


## FUNCTIONS
def str_to_id(string, length=12):
    '''
    Convert a string to an integer id
    '''

    # return ''.join([str(abs(ord(char) - 96)) for char in string])

    hash_int = int(hashlib.md5(string.encode()).hexdigest(), 16) 
    return hash_int % (10 ** length)  # Trim to desired length


def computeP(calib_file, undistort=False):
    '''
    Compute projection matrices from toml calibration file.
    Zooming or moving cameras are handled.
    
    INPUT:
    - calib_file: calibration .toml file.
    - undistort: boolean
    
    OUTPUT:
    - P: projection matrix as list of arrays
    '''
    
    K, R, T, Kh, H = [], [], [], [], []
    P = []
    
    calib = toml.load(calib_file)
    for cam in list(calib.keys()):
        if cam != 'metadata':
            S = np.array(calib[cam]['size'])
            K = np.array(calib[cam]['matrix'])

            if len(K.shape) == 2: # static camera
                if undistort:
                    dist = np.array(calib[cam]['distortions'])
                    optim_K = cv2.getOptimalNewCameraMatrix(K, dist, [int(s) for s in S], 1, [int(s) for s in S])[0]
                    Kh = np.block([optim_K, np.zeros(3).reshape(3,1)])
                else:
                    Kh = np.block([K, np.zeros(3).reshape(3,1)])
            elif len(K.shape) == 3: # zooming camera
                if undistort:
                    dist = np.array(calib[cam]['distortions'])
                    optim_K = [cv2.getOptimalNewCameraMatrix(K[f], dist, [int(s) for s in S], 1, [int(s) for s in S])[0] for f in range(len(K))]
                    Kh = [np.block([optim_K[f], np.zeros(3).reshape(3,1)]) for f in range(len(K))]
                else:
                    Kh = [np.block([K[f], np.zeros(3).reshape(3,1)]) for f in range(len(K))]

            R = np.array(calib[cam]['rotation'])
            T = np.array(calib[cam]['translation'])
            if len(R.shape) == 1: # static camera
                R_mat, _ = cv2.Rodrigues(np.array(calib[cam]['rotation']))
                H = np.block([[R_mat,T.reshape(3,1)], [np.zeros(3), 1 ]])
            elif len(R.shape) == 2: # moving camera
                R_mat = [cv2.Rodrigues(R[f])[0] for f in range(len(R))]
                H = [np.block([[R_mat[f],T[f].reshape(3,1)], [np.zeros(3), 1 ]]) for f in range(len(R))]
                
            if len(K.shape) == 2 and len(R.shape)==1: # static camera
                P.append([Kh @ H])
            elif len(K.shape) == 3 and len(R.shape)==1: # zooming camera
                P.append([Kh[f] @ H for f in range(len(K))])
            elif len(K.shape) == 2 and len(R.shape)==2: # moving camera
                P.append([Kh @ H[f] for f in range(len(R))])
            elif len(K.shape) == 3 and len(R.shape)==2: # zooming and moving camera
                P.append([Kh[f] @ H[f] for f in range(len(K))])

    return np.array(P)
    
    
def retrieve_calib_params(calib_file):
    '''
    Compute projection matrices from toml calibration file.
    Zooming or moving cameras are handled.
    
    INPUT:
    - calib_file: calibration .toml file.
    
    OUTPUT:
    - S: (h,w) vectors as list of 2x1 arrays
    - K: intrinsic matrices as list of 3x3 arrays
    - dist: distortion vectors as list of 4x1 arrays
    - optim_K: intrinsic matrices for undistorting points as list of 3x3 arrays
    - R: rotation rodrigue vectors as list of 3x1 arrays
    - T: translation vectors as list of 3x1 arrays
    '''
    
    calib = toml.load(calib_file)

    S, K, dist, optim_K, R, T = [], [], [], [], [], []
    for c, cam in enumerate(calib.keys()):
        if cam != 'metadata':
            S.append(np.array(calib[cam]['size']))
            K.append(np.array(calib[cam]['matrix']))
            dist.append(np.array(calib[cam]['distortions']))

            if len(K[c].shape) == 2: # static camera
                optim_K.append(cv2.getOptimalNewCameraMatrix(K[c], dist[c], [int(s) for s in S[c]], 1, [int(s) for s in S[c]])[0])
            elif len(K[c].shape) == 3: # zooming camera
                optim_K.append([cv2.getOptimalNewCameraMatrix(K[c][f], dist[c], [int(s) for s in S[c]], 1, [int(s) for s in S[c]])[0] for f in range(len(K[c]))])
            
            R.append(np.array(calib[cam]['rotation']))
            T.append(np.array(calib[cam]['translation']))

    calib_params = {'S': S, 'K': K, 'dist': dist, 'optim_K': optim_K, 'R': R, 'T': T}
            
    return calib_params


def reprojection(P_all, Q):
    '''
    Reprojects 3D point on all cameras.
    
    INPUTS:
    - P_all: list of arrays. Projection matrix for all cameras
    - Q: array of triangulated point (x,y,z,1.)

    OUTPUTS:
    - x_calc, y_calc: list of coordinates of point reprojected on all cameras
    '''
    
    x_calc, y_calc = [], []
    for c in range(len(P_all)):  
        P_cam = P_all[c]
        x_calc.append(P_cam[0] @ Q / (P_cam[2] @ Q))
        y_calc.append(P_cam[1] @ Q / (P_cam[2] @ Q))
        
    return x_calc, y_calc
    

def df_from_trc(trc_path):
    '''
    Retrieve header and data from trc path.
    '''

    # DataRate	CameraRate	NumFrames	NumMarkers	Units	OrigDataRate	OrigDataStartFrame	OrigNumFrames
    df_header = pd.read_csv(trc_path, sep="\t", skiprows=1, header=None, nrows=2, encoding="ISO-8859-1")
    header = dict(zip(df_header.iloc[0].tolist(), df_header.iloc[1].tolist()))
    
    # Label1_X  Label1_Y    Label1_Z    Label2_X    Label2_Y
    df_lab = pd.read_csv(trc_path, sep="\t", skiprows=3, nrows=1)
    labels = df_lab.columns.tolist()[2:-1:3]
    labels_XYZ = np.array([[labels[i]+'_X', labels[i]+'_Y', labels[i]+'_Z'] for i in range(len(labels))], dtype='object').flatten()
    labels_FTXYZ = np.concatenate((['Frame#','Time'], labels_XYZ))
    
    data = pd.read_csv(trc_path, sep="\t", skiprows=5, index_col=False, header=None, names=labels_FTXYZ)
    
    return header, data


def yup2zup(Q):
    '''
    Turns Y-up system coordinates into Z-up coordinates

    INPUT:
    - Q: pandas dataframe
    N 3D points as columns, ie 3*N columns in Z-up system coordinates
    and frame number as rows

    OUTPUT:
    - Q: pandas dataframe with N 3D points in Y-up system coordinates
    '''
    
    # X->Y, Y->Z, Z->X
    cols = list(Q.columns)
    cols = np.array([[cols[i*3+2],cols[i*3],cols[i*3+1]] for i in range(int(len(cols)/3))]).flatten()
    Q = Q[cols]

    return Q


def dataset_to_openpose(coords_df, openpose_path_root, marker_list=['NOSB', 'shoulder_l', 'shoulder_r', 'elb_l', 'elb_r', 'wrist_l', 'wrist_r', 'hip_l', 'hip_r', 'knee_l', 'knee_r', 'ankle_l', 'ankle_r', 'THD', 'C7', 'SACR', 'MTP1_L', 'MTP1_R', 'MTP5_L', 'MTP5_R', 'HEEL_L', 'HEEL_R', 'LHPE', 'RHPE', 'LHPI', 'RHPI', 'TOE_L', 'TOE_R', 'T10', 'UA_L', 'UA_R', 'LA_L', 'LA_R', 'UL_L', 'UL_R', 'LL_L', 'LL_R']):
    '''
    Write 2D labels to OpenPose format.

    INPUTS:
    - coords_df: pandas dataframe with 2D labels. E.g.: all_dfs = pd.read_csv(dlc_labels_path, header = [0,1,2,3], index_col=0)
    - openpose_path_root: path to save the json files (frame number will be appended)
    - marker_list: list of markers in the order provided by the dataset. E.g. for Halpeplus: ['NOSB', 'shoulder_l', 'shoulder_r', 'elb_l', 'elb_r', 'wrist_l', 'wrist_r', 'hip_l', 'hip_r', 'knee_l', 'knee_r', 'ankle_l', 'ankle_r', 'THD', 'C7', 'SACR', 'MTP1_L', 'MTP1_R', 'MTP5_L', 'MTP5_R', 'HEEL_L', 'HEEL_R', 'LHPE', 'RHPE', 'LHPI', 'RHPI', 'TOE_L', 'TOE_R', 'T10', 'UA_L', 'UA_R', 'LA_L', 'LA_R', 'UL_L', 'UL_R', 'LL_L', 'LL_R']

    OUTPUTS:
    - coordinates written in the openpose json format (one per frame)
    '''
    
    #prepare json files
    json_dict = {'version':1.3, 'people':[]}
    json_dict['people'] = [{'person_id':[-1], 
                    'pose_keypoints_2d': np.zeros(len(marker_list)*3), 
                    'face_keypoints_2d': [], 
                    'hand_left_keypoints_2d':[], 
                    'hand_right_keypoints_2d':[], 
                    'pose_keypoints_3d':[], 
                    'face_keypoints_3d':[], 
                    'hand_left_keypoints_3d':[], 
                    'hand_right_keypoints_3d':[]}]
    
    # write one json file per camera and per frame
    persons = list(set(['_'.join(item.split('_')[:5]) for item in coords_df.columns.levels[1]]))
    for frame in range(len(coords_df)):
        for person in persons:
            json_dict_copy = deepcopy(json_dict)
            coords = coords_df.iloc[frame, coords_df.columns.get_level_values(1)==person]
            # store 2D keypoints and respect model keypoint order
            coords_list = []
            for marker in marker_list:
                coords_mk = coords.loc[coords.index.get_level_values(2)==marker]
                coords_list += [0.0, 0.0, 0] if np.isnan(coords_mk).any() else coords_mk.tolist()+[1]
            json_dict_copy['people'][0]['pose_keypoints_2d'] = coords_list

        # write json file
        json_file = os.path.join(os.path.dirname(openpose_path_root), f'{os.path.splitext(os.path.basename(openpose_path_root))[0]}_{frame:04d}.json')
        with open(json_file, 'w') as js_f:
            js_f.write(json.dumps(json_dict_copy))


def dataset_to_mmpose2d(coords_df, mmpose_json_file, img_size, markerset='custom', marker_list=['NOSB', 'shoulder_l', 'shoulder_r', 'elb_l', 'elb_r', 'wrist_l', 'wrist_r', 'hip_l', 'hip_r', 'knee_l', 'knee_r', 'ank_l', 'ankle_r', 'THD', 'CY', 'SACR', 'MTP1_L', 'MTP1_R', 'MTP5_L', 'MTP5_R', 'HEEL_L', 'HEEL_R', 'LHPE', 'RHPE', 'LHPI', 'RHPI', 'TOE_L', 'TOE_R', 'T10', 'UA_L', 'UA_R', 'LA_L', 'LA_R', 'UL_L', 'UL_R', 'LL_L', 'LL_R']):
    '''
    Export 2D labels to MMPose format.

    INPUTS:
    - coords_df: pandas dataframe with 2D labels. E.g.: all_dfs = pd.read_csv(dlc_labels_path, header = [0,1,2,3]), index_col=0)
    - mmpose_json_file: path to save the json file
    - img_size: image size [width, height]
    - markerset: name of the markerset. E.g.: 'halpe26', 'halpeplus', 'biocvplus'
    - marker_list: list of markers from inverse kinematics and/or SMPL mesh. E.g.: ['ankle_l', 'NOSB',]

    OUTPUTS:
    - labels2d_json: saved json file
    '''

    labels2d_json_data = {}
    labels2d_json_data['info'] = {'description': f'Bedlam Pose {markerset}', 
                                    'url': 'https://github.com/davidpagnon/bedlam_pose', 
                                    'version': '0.1', 
                                    'year': 2024, 
                                    'contributor': 'David Pagnon', 
                                    'date_created': '2024/08/14'}
    labels2d_json_data['licenses'] = [{'url': 'https://bedlam.is.tue.mpg.de/license.html', 'id': 1, 'name': 'Non-commercial scientific research purposes'},
                                        {'url': 'https://creativecommons.org/licenses/by/4.0/deed.en', 'id': 2, 'name': 'Attribution License'}]
    labels2d_json_data['images'] = []
    labels2d_json_data['annotations'] = []
    labels2d_json_data['categories'] = [{'id': 1, 'name': 'person'}]
    padding = 0.05

    # for each image
    persons = list(set(['_'.join(item.split('_')[:5]) for item in coords_df.columns.levels[1]]))
    for i in range(len(coords_df)):
        file_name = coords_df.index[i][0]
        w, h = int(img_size[0]), int(img_size[1])
        file_id = str_to_id(file_name, length=12)

        labels2d_json_data['images'] += [{'file_name': file_name, 
                                            'height': h, 
                                            'width': w, 
                                            'id': file_id, 
                                            'license': 1}]
        
        # for each person
        for person in persons:

            # store 2D keypoints and respect model keypoint order
            coords = coords_df.iloc[i, coords_df.columns.get_level_values(1)==person]
            coords_list = []
            for marker in marker_list:
                # visibility: 2 visible, 1 occluded, 0 out of frame
                coords_mk = coords.loc[coords.index.get_level_values(2)==marker]
                coords_list += coords_mk.tolist()+[2] if not np.isnan(coords_mk).any() else [0.0, 0.0, 0]
            
            num_keypoints = len(marker_list)

            # bbox
            x_coords = coords.loc[coords.index.get_level_values(3)=='x']
            y_coords = coords.loc[coords.index.get_level_values(3)=='y']
            min_x, min_y, max_x, max_y = np.nanmin(x_coords), np.nanmin(y_coords), np.nanmax(x_coords), np.nanmax(y_coords)
            bbox_width = np.round(max_x - min_x, decimals=1)
            bbox_height = np.round(max_y - min_y, decimals=1)
            # bbox = [min_x, min_y, max_x, max_y]
            bbox = [min_x, min_y, bbox_width, bbox_height] # coco format
            # add padding
            bbox = [max(0, min_x-bbox_width*padding),
                    max(0, min_y-bbox_height*padding),
                    bbox_width*(1+padding*2) if max_x+bbox_width*padding < w else bbox_width*(1+padding),
                    bbox_height*(1+padding*2) if max_y+bbox_height*padding < h else bbox_height*(1+padding)]

            ann_id = str_to_id(person+file_name) 
            category_id = 1
            segmentation = [[min_x, min_y, min_x, max_y, max_x, max_y, max_x, min_y]] # no segmentation
            area = np.round(bbox_width * bbox_height, decimals=1)
            iscrowd = 0 # each annotation represents one single person
            
            if not np.isnan(bbox).any():
                labels2d_json_data['annotations'] += [{ 'keypoints': coords_list, 
                                                        'num_keypoints': num_keypoints, 
                                                        'bbox': bbox, 
                                                        'id': ann_id, 
                                                        'image_id': file_id, 
                                                        'category_id': category_id,
                                                        'segmentation': segmentation,
                                                        'area': area,
                                                        'iscrowd': iscrowd}]
    
    with open(mmpose_json_file, 'w') as f:
        json.dump(labels2d_json_data, f)


def reproj_from_trc_calib_func(**args):
    '''
    Reproject 3D points from a trc file to the camera planes determined by a 
    toml calibration file, to the DeepLabCut (default), MMpose, or 
    OpenPose format.

    The order or the markers depends on the markerset chosen markerset--it is the same as in the trc file if unspecified.
    You can change the marker order in CONSTANTS if you need to.

    New: Moving cameras and zooming cameras are now supported.
    
    Usage: 
    from Pose2Sim.Utilities import reproj_from_trc_calib; reproj_from_trc_calib.reproj_from_trc_calib_func(r'<input_trc_file>', r'<input_calib_file>', '<output_format>', r'<output_file_root>')
    python -m reproj_from_trc_calib -t input_trc_file -c input_calib_file -odm
    python -m reproj_from_trc_calib -t input_trc_file -c input_calib_file --openpose --deeplabcut --mmpose --undistort
    python -m reproj_from_trc_calib -t input_trc_file -c input_calib_file -d -o output_file_root
    '''

    input_trc_file = os.path.realpath(args.get('input_trc_file')) # invoked with argparse
    input_calib_file = os.path.realpath(args.get('input_calib_file'))
    openpose_output = args.get('openpose')
    deeplabcut_output = args.get('deeplabcut')
    mmpose_output = args.get('mmpose')
    markerset = args.get('markerset')
    undistort_points = args.get('undistort_points')
    output_file_root = args.get('output_file_root')
    if output_file_root == None:
        output_file_root = input_trc_file.replace('.trc', '_reproj')
    if os.path.exists(output_file_root):
        os.makedirs(output_file_root, exist_ok=True)
    if not openpose_output and not deeplabcut_output and not mmpose_output:
        raise ValueError('Output_format must be specified either "openpose" (-o), "deeplabcut" (-d), or "mmpose" (-m)')

    # Extract data from trc file
    header_trc, data_trc = df_from_trc(input_trc_file)
    data_trc_zup = pd.concat([data_trc.iloc[:,:2], yup2zup(data_trc.iloc[:,2:])], axis=1) # yup to zup system coordinates
    bodyparts = [d[:-2] for d in data_trc_zup.columns[2::3]]
    num_bodyparts = int(header_trc['NumMarkers'])
    filename = os.path.splitext(os.path.basename(input_trc_file))[0]
    
    # Extract data from calibration file
    P_all = computeP(input_calib_file, undistort=undistort_points)
    calib_params = retrieve_calib_params(input_calib_file)
    calib_params_size = [calib_params['S'][i] for i in range(len(P_all))]
    if undistort_points:
        calib_params_R_filt = [calib_params['R'][i] for i in range(len(P_all))]
        calib_params_T_filt = [calib_params['T'][i] for i in range(len(P_all))]
        calib_params_K_filt = [calib_params['K'][i] for i in range(len(P_all))]
        calib_params_dist_filt = [calib_params['dist'][i] for i in range(len(P_all))]

    # Create camera folders
    reproj_dir = os.path.realpath(output_file_root)
    cam_dirs = [os.path.join(reproj_dir, f'cam{cam+1:02d}_json') for cam in range(len(P_all))]
    if not os.path.exists(reproj_dir): os.mkdir(reproj_dir)  
    try:
        [os.mkdir(cam_dir) for cam_dir in cam_dirs]
    except:
        pass

    # header preparation
    num_frames = [len(data_trc) if P_all.shape[1]==1 else min(P_all.shape[1], len(data_trc))][0]
    columns_iterables = [['DavidPagnon'], ['person0'], bodyparts, ['x','y']]
    columns_h5 = pd.MultiIndex.from_product(columns_iterables, names=['scorer', 'individuals', 'bodyparts', 'coords'])
    rows_iterables = [[os.path.join(os.path.splitext(input_trc_file)[0],f'img_{i:03d}.jpg') for i in range(num_frames)]]
    rows_h5 = pd.MultiIndex.from_product(rows_iterables)
    data_h5 = pd.DataFrame(np.nan, index=rows_h5, columns=columns_h5)

    # Reproject 3D points on all cameras
    data_proj = [deepcopy(data_h5) for cam in range(len(P_all))] # copy data_h5 as many times as there are cameras
    Q = data_trc_zup.iloc[:,2:]
    for frame in range(num_frames):
        coords = [[] for cam in range(len(P_all))]
        P_all_frame = [P_all[cam][0] if P_all.shape[1]==1 else P_all[cam][frame] for cam in range(len(P_all))]
        for keypoint in range(num_bodyparts):
            q = np.append(Q.iloc[frame,3*keypoint:3*keypoint+3], 1)
            if undistort_points:
                coords_2D_all = [cv2.projectPoints(np.array(q[:-1]), calib_params_R_filt[i], calib_params_T_filt[i], calib_params_K_filt[i], calib_params_dist_filt[i])[0] for i in range(len(P_all))]
                x_all = [coords_2D_all[i][0,0,0] for i in range(len(P_all_frame))]
                y_all = [coords_2D_all[i][0,0,1] for i in range(len(P_all_frame))]
            else:
                x_all, y_all = reprojection(P_all_frame, q)
            # Store with one single decimal
            x_all = np.round(np.array(x_all), decimals=1)
            y_all = np.round(np.array(y_all), decimals=1)

            [coords[cam].extend([x_all[cam], y_all[cam]]) for cam in range(len(P_all_frame))]
        for cam in range(len(P_all_frame)):
            data_proj[cam].iloc[frame,:] = coords[cam]
    
    # Replace by nan when reprojection out of image
    for cam in range(len(P_all_frame)):
        x_valid = (data_proj[cam].iloc[:, ::2] >= 0) & (data_proj[cam].iloc[:, ::2] < calib_params_size[cam][0])
        y_valid = (data_proj[cam].iloc[:, 1::2] >= 0) & (data_proj[cam].iloc[:, 1::2] < calib_params_size[cam][1])
        data_proj[cam].iloc[:, ::2] = data_proj[cam].iloc[:, ::2].where(x_valid, np.nan)
        data_proj[cam].iloc[:, ::2] = np.where(y_valid==False, np.nan, data_proj[cam].iloc[:, ::2])
        data_proj[cam].iloc[:, 1::2] = data_proj[cam].iloc[:, 1::2].where(y_valid, np.nan)
        data_proj[cam].iloc[:, 1::2] = np.where(x_valid==False, np.nan, data_proj[cam].iloc[:, 1::2])

    # Marker list in the right order
    if markerset == 'halpe26':
        marker_list = halpe26_markers
    elif markerset == 'halpeplus':
        marker_list = halpeplus_markers
    elif markerset == 'biocvplus':
        marker_list = biocvplus_markers
    else:
        marker_list = list(dict.fromkeys(data_proj[cam].columns.get_level_values(2)[1:]))

    # Save as h5 and csv if DeepLabCut format
    if deeplabcut_output:
        # to h5
        h5_files = [os.path.join(cam_dir,f'{filename}_cam_{i+1:02d}_dlc.h5') for i,cam_dir in enumerate(cam_dirs)]
        [data_proj[i].to_hdf(h5_files[i], index=True, key='reprojected_points') for i in range(len(P_all))]

        # to csv
        csv_files = [os.path.join(cam_dir,f'{filename}_cam_{i+1:02d}_dlc.csv') for i,cam_dir in enumerate(cam_dirs)]
        [data_proj[i].to_csv(csv_files[i], sep=',', index=True, lineterminator='\n') for i in range(len(P_all))]

    # Save as json if Coco/MMpose format
    if mmpose_output:
        for cam, cam_dir in enumerate(cam_dirs):
            mmpose_json_file = os.path.join(cam_dir, f'{filename}_cam_{cam+1:02d}_mmpose.json')
            dataset_to_mmpose2d(data_proj[cam], mmpose_json_file, calib_params_size[cam], markerset=markerset, marker_list=marker_list)
        
    # Save as json if OpenPose format
    if openpose_output:
        for cam, cam_dir in enumerate(cam_dirs):
            openpose_path_root = os.path.join(cam_dir, f'{filename}_cam{cam+1:02d}_openpose.json')
            dataset_to_openpose(data_proj[cam], openpose_path_root, marker_list=marker_list)
            
    # Wrong format
    if not openpose_output and not deeplabcut_output and not mmpose_output:
        raise ValueError('output_format must be either "openpose" or "deeplabcut"')
    
    print(f'Reprojected points saved at {output_file_root}.')
    
    
if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('-t', '--input_trc_file', required = True, help='trc 3D coordinates input file path')
    parser.add_argument('-c', '--input_calib_file', required = True, help='toml calibration input file path')
    parser.add_argument('-o', '--openpose', required=False, action='store_true', help='output format in the openpose json format')
    parser.add_argument('-d', '--deeplabcut', required=False, action='store_true', help='output format in the deeplabcut csv and h5 formats')
    parser.add_argument('-m', '--mmpose', required=False, action='store_true', help='output format in the Coco/MMpose json format')
    parser.add_argument('-s', '--markerset', required=False, help='markerset name, e.g. halpe26, halpeplus, biocvplus')
    parser.add_argument('-u', '--undistort_points', required=False, action='store_true', help='takes distortion into account if True')
    parser.add_argument('-O', '--output_file_root', required=False, help='output file root path, without extension')
    args = vars(parser.parse_args())

    reproj_from_trc_calib_func(**args)