Synchronize multi cams based on keypoints speed. (#76) @rlagnsals

@rlagnsals 

* synchronization

* Auto Synchronization

* Auto Synchronization

* Update README.md

* Update README.md

* Update README.md

* Update README.md

* Update README.md

* Update README.md

* Auto Synchronization

* Update synchronize_cams.py

* Auto Synchronization

* Delete Pose2Sim/S00_Demo_Session/Config.toml

* Add files via upload

* Update Config.toml

Pose2Sim/Pose2Sim.py

@@ -265,43 +265,43 @@ def synchronization(config=None):
     or a the directory path of a trial, participant, or session,
     or the function can be called without an argument, in which case it the config directory is the current one.
     '''   
-
-    raise NotImplementedError('This has not been integrated yet. \nPlease read README.md for further explanation')
     
-    # #TODO
+    # Import the function
-    # # Determine the level at which the function is called (session:3, participant:2, trial:1)
+    from Pose2Sim.synchronize_cams import synchronize_cams_all
-    # level, config_dicts = read_config_files(config)
 
-    # if type(config)==dict:
+    # Determine the level at which the function is called (session:3, participant:2, trial:1)
-    #     config_dict = config_dicts[0]
+    level, config_dicts = read_config_files(config)
-    #     if config_dict.get('project').get('project_dir') == None:
-    #         raise ValueError('Please specify the project directory in config_dict:\n \
-    #                          config_dict.get("project").update({"project_dir":"<YOUR_TRIAL_DIRECTORY>"})')
 
-    # # Set up logging
+    if type(config)==dict:
-    # session_dir = os.path.realpath(os.path.join(config_dicts[0].get('project').get('project_dir'), '..', '..'))
+        config_dict = config_dicts[0]
-    # setup_logging(session_dir)    
+        if config_dict.get('project').get('project_dir') == None:
+            raise ValueError('Please specify the project directory in config_dict:\n \
+                             config_dict.get("project").update({"project_dir":"<YOUR_TRIAL_DIRECTORY>"})')
 
-    # # Batch process all trials
+    # Set up logging
-    # for config_dict in config_dicts:
+    session_dir = os.path.realpath(os.path.join(config_dicts[0].get('project').get('project_dir'), '..', '..'))
-    #     start = time.time()
+    setup_logging(session_dir)    
-    #     currentDateAndTime = datetime.now()
-    #     project_dir = os.path.realpath(config_dict.get('project').get('project_dir'))
-    #     seq_name = os.path.basename(project_dir)
-    #     frame_range = config_dict.get('project').get('frame_range')
-    #     frames = ["all frames" if frame_range == [] else f"frames {frame_range[0]} to {frame_range[1]}"][0]
 
-    #     logging.info("\n\n---------------------------------------------------------------------")
+    # Batch process all trials
-    #     logging.info("Camera synchronization")
+    for config_dict in config_dicts:
-    #     logging.info(f"On {currentDateAndTime.strftime('%A %d. %B %Y, %H:%M:%S')}")
+        start = time.time()
-    #     logging.info("---------------------------------------------------------------------")
+        currentDateAndTime = datetime.now()
-    #     logging.info(f"\nProject directory: {project_dir}")
+        project_dir = os.path.realpath(config_dict.get('project').get('project_dir'))
+        seq_name = os.path.basename(project_dir)
+        frame_range = config_dict.get('project').get('frame_range')
+        frames = ["all frames" if frame_range == [] else f"frames {frame_range[0]} to {frame_range[1]}"][0]
+
+        logging.info("\n\n---------------------------------------------------------------------")
+        logging.info("Camera synchronization")
+        logging.info(f"On {currentDateAndTime.strftime('%A %d. %B %Y, %H:%M:%S')}")
+        logging.info("---------------------------------------------------------------------")
+        logging.info(f"\nProject directory: {project_dir}")
         
-    #     synchronize_cams_all(config_dict)
+        synchronize_cams_all(config_dict)
     
-    #     end = time.time()
+        end = time.time()
-    #     elapsed = end-start 
+        elapsed = end-start 
-    #     logging.info(f'Synchronization took {time.strftime("%Hh%Mm%Ss", time.gmtime(elapsed))}.')
+        logging.info(f'Synchronization took {time.strftime("%Hh%Mm%Ss", time.gmtime(elapsed))}.')
     
     
 def personAssociation(config=None):

Pose2Sim/S00_Demo_Session/Config.toml

@@ -20,7 +20,7 @@
 [project]
 # multi_person = false # true for trials with multiple participants. If false, only the main person in scene is analyzed (and it run much faster). 
 nb_persons_to_detect = 2 # checked only if multi_person is selected
-frame_rate = 60 # fps
+frame_rate = 120 # fps
 frame_range = [] # For example [10,300], or [] for all frames
 ## N.B.: If you want a time range instead, use frame_range = time_range * frame_rate
 ## For example if you want to analyze from 0.1 to 2 seconds with a 60 fps frame rate, 
@@ -104,15 +104,9 @@ openpose_path = '' # only checked if OpenPose is used
 [synchronization]
 # COMING SOON! 
 reset_sync = true # Recalculate synchronization even if already done
-frames = [2850,3490] # Frames to use for synchronization, should point to a moment with fast motion.
+speed_kind = 'y' # 'y' showed best performance.
-cut_off_frequency = 10 # cut-off frequency for a 4th order low-pass Butterworth filter
+id_kpt = [10] # number from keypoint name in skeleton.py. RWrist' BLAZEPOSE 16, BODY_25B 10, BODY_25 4 ; 'LWrist' BLAZEPOSE 15, BODY_25B 9, BODY_25 7
-# Vertical speeds (on X, Y, or Z axis, or 2D speeds)
+weights_kpt = [1] # Only taken into account if you have several keypoints (Currently only one keypoint is supported).
-speed_kind = 'y' # 'x', 'y', 'z', or '2D'
-vmax = 20 # px/s
-cam1_nb = 4
-cam2_nb = 3
-id_kpt = [9,10] # Pour plus tard aller chercher numéro depuis keypoint name dans skeleton.py. 'RWrist' BLAZEPOSE 16, BODY_25B 10, BODY_25 4 ; 'LWrist' BLAZEPOSE 15, BODY_25B 9, BODY_25 7
-weights_kpt = [1,1] # Pris en compte uniquement si on a plusieurs keypoints
 
 
 [personAssociation]

Pose2Sim/synchronize_cams.py

@@ -0,0 +1,448 @@
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from scipy import signal
+from scipy import interpolate
+import json
+import os
+import fnmatch
+import pickle as pk
+import re
+
+
+'''
+    #########################################
+    ## Synchronize cameras                 ##
+    #########################################
+
+    Steps undergone in this script
+    0. Converting json files to pandas dataframe
+    1. Computing speeds (vertical)
+    2. Plotting paired correlations of speeds from one camera viewpoint to another (work on one single keypoint, or on all keypoints, or on a weighted selection of keypoints)
+    3. 
+    Ideally, this should be done automatically for all views, checking pairs 2 by 2 with the highest correlation coefficient, 
+    and ask for confirmation before deleting the frames in question (actually renamed .json.del - reset_sync option in Config.toml).
+'''
+
+
+############
+# FUNCTIONS#
+############
+
+def convert_json2csv(json_dir):
+    """
+    Convert JSON files in a directory to a pandas DataFrame.
+
+    Args:
+        json_dir (str): The directory path containing the JSON files.
+
+    Returns:
+        pandas.DataFrame: A DataFrame containing the coordinates extracted from the JSON files.
+    """
+    json_files_names = fnmatch.filter(os.listdir(os.path.join(json_dir)), '*.json') # modified ( 'json' to '*.json' )
+    json_files_names.sort(key=lambda name: int(re.search(r'(\d+)_keypoints\.json', name).group(1)))
+    json_files_path = [os.path.join(json_dir, j_f) for j_f in json_files_names]
+    json_coords = []
+    for i, j_p in enumerate(json_files_path):
+        # if i in range(frames)
+            with open(j_p) as j_f:
+                try:
+                    json_data = json.load(j_f)['people'][0]['pose_keypoints_2d']
+                except:
+                    print(f'No person found in {os.path.basename(json_dir)}, frame {i}')
+                    json_data = [0]*75
+            json_coords.append(json_data)
+    df_json_coords = pd.DataFrame(json_coords)
+    return df_json_coords
+
+def drop_col(df, col_nb):
+    """
+    Drops every nth column from a DataFrame.
+
+    Parameters:
+    df (pandas.DataFrame): The DataFrame from which columns will be dropped.
+    col_nb (int): The column number to drop.
+
+    Returns:
+    pandas.DataFrame: The DataFrame with dropped columns.
+    """
+
+    idx_col = list(range(col_nb-1, df.shape[1], col_nb)) 
+    df_dropped = df.drop(idx_col, axis=1)
+    df_dropped.columns = range(df_dropped.columns.size)
+    return df_dropped
+
+def speed_vert(df, axis='y'):
+    """
+    Calculate the vertical speed of a DataFrame along a specified axis.
+
+    Parameters:
+    df (DataFrame): The input DataFrame.
+    axis (str): The axis along which to calculate the speed. Default is 'y'.
+
+    Returns:
+    DataFrame: The DataFrame containing the vertical speed values.
+    """
+    axis_dict = {'x':0, 'y':1, 'z':2}
+    df_diff = df.diff()
+    df_diff = df_diff.fillna(df_diff.iloc[1]*2)
+    df_vert_speed = pd.DataFrame([df_diff.loc[:, 2*k + axis_dict[axis]] for k in range(int(df_diff.shape[1] / 2))]).T # modified ( df_diff.shape[1]*2 to df_diff.shape[1] / 2 )
+    df_vert_speed.columns = np.arange(len(df_vert_speed.columns))
+    return df_vert_speed
+
+
+def interpolate_nans(col, kind):
+    '''
+    Interpolate missing points (of value nan)
+
+    INPUTS
+    - col pandas column of coordinates
+    - kind 'linear', 'slinear', 'quadratic', 'cubic'. Default 'cubic'
+
+    OUTPUT
+    - col_interp interpolated pandas column
+    '''
+    
+    idx = col.index
+    idx_good = np.where(np.isfinite(col))[0] #index of non zeros
+    if len(idx_good) == 10: return col
+    # idx_notgood = np.delete(np.arange(len(col)), idx_good)
+
+    if not kind: # 'linear', 'slinear', 'quadratic', 'cubic'
+        f_interp = interpolate.interp1d(idx_good, col[idx_good], kind='cubic', bounds_error=False)
+    else:
+        f_interp = interpolate.interp1d(idx_good, col[idx_good], kind=kind, bounds_error=False) # modified
+    col_interp = np.where(np.isfinite(col), col, f_interp(idx)) #replace nans with interpolated values
+    col_interp = np.where(np.isfinite(col_interp), col_interp, np.nanmean(col_interp)) #replace remaining nans
+
+    return col_interp #, idx_notgood
+
+
+def find_highest_wrist_position(df_coords, wrist_index):
+    """
+    Find the frame with the highest wrist position in a list of coordinate DataFrames.
+    Highest wrist position frame use for finding the fastest frame.
+    
+    Args:
+    df_coords (list): List of coordinate DataFrames.
+    wrist_index (int): The index of the wrist in the keypoint list.
+    
+    Returns:
+    list: The index of the frame with the highest wrist position.
+    """
+
+    start_frames = []
+    min_y_coords = []
+    for df in df_coords:
+        # Wrist y-coordinate column index (2n where n is the keypoint index)
+        # Assuming wrist_index is a list and we want to use the first element
+        y_col_index = wrist_index[0] * 2 + 1
+        
+        # Replace 0 with NaN to avoid considering them and find the index of the lowest y-coordinate value
+        min_y_coord = df.iloc[:, y_col_index].replace(0, np.nan).min()
+        min_y_index = df.iloc[:, y_col_index].replace(0, np.nan).idxmin()
+        if min_y_coord <= 100: # if the wrist is too high, it is likely to be an outlier
+            print("The wrist is too high. Please check the data for outliers.")
+
+        start_frames.append(min_y_index)
+        min_y_coords.append(min_y_coord)
+
+    return start_frames, min_y_coords
+
+def find_motion_end(df_coords, wrist_index, start_frame, lowest_y, fps):
+    """
+    Find the frame where hands down movement ends.
+    Hands down movement is defined as the time when the wrist moves down from the highest position.
+
+    Args:
+    df_coord (DataFrame): The coordinate DataFrame of the reference camera.
+    wrist_index (int): The index of the wrist in the keypoint list.
+    start_frame (int): The frame where the hands down movement starts.
+    fps (int): The frame rate of the cameras in Hz.
+
+    Returns:
+    int: The index of the frame where hands down movement ends.
+    """
+    y_col_index = wrist_index * 2 + 1
+    wrist_y_values = df_coords.iloc[:, y_col_index].values # wrist y-coordinates
+    highest_y_value = lowest_y
+    highest_y_index = start_frame
+
+    # Find the highest y-coordinate value and its index
+    for i in range(highest_y_index + 1, len(wrist_y_values)):
+        if wrist_y_values[i] - highest_y_value >= 50:
+            start_increase_index = i
+            break
+    else:
+        raise ValueError("The wrist does not move down.")
+    
+    start = start_increase_index - start_frame
+    time = (start + fps) / fps
+
+    return time
+
+def find_fastest_frame(df_speed_list):
+    """
+    Find the frame with the highest speed in a list of speed DataFrames.
+    Fastest frame should locate in after highest wrist position frame.
+    
+    Args:
+    df_speed_list (list): List of speed DataFrames.
+    df_speed (DataFrame): The speed DataFrame of the reference camera.
+    fps (int): The frame rate of the cameras in Hz.
+    lag_time (float): The time lag in seconds.
+
+    Returns:
+    int: The index of the frame with the highest speed.
+    """
+
+    for speed_series in df_speed_list:
+        max_speed = speed_series.abs().max()
+        max_speed_index = speed_series.abs().idxmax()
+    
+    if max_speed < 10:
+        print(" !!Warning!! : The maximum speed is likely to be not representative of the actual movement. Consider increasing the time parameter in Config.toml.")
+    return max_speed_index, max_speed
+
+
+def plot_time_lagged_cross_corr(camx, camy, ax, fps, lag_time, camx_max_speed_index, camy_max_speed_index):
+    """
+    Calculate and plot the max correlation between two cameras with a time lag.
+    How it works:
+     1. Reference camera is camx and the other is camy. (Reference camera should record last. If not, the offset will be positive.)
+     2. The initial shift alppied to camy to match camx is calculated.
+     3. Additionally shift camy by max_lag frames to find the max correlation.
+    
+    Args:
+    camx (pandas.Series): The speed series of the reference camera.
+    camy (pandas.Series): The speed series of the other camera.
+    ax (matplotlib.axes.Axes): The axes to plot the correlation.
+    fps (int): The frame rate of the cameras in Hz.
+    lag_time (float): The time lag in seconds.
+    camx_max_speed_index (int): The index of the frame with the highest speed in camx.
+    camy_max_speed_index (int): The index of the frame with the highest speed in camy.
+
+    Returns:
+    int: The offset value to apply to synchronize the cameras.
+    float: The maximum correlation value.
+    """
+
+    # Initial shift of camy to match camx
+    # initial_shift = -(camy_max_speed_index - camx_max_speed_index) + fps
+    # camy = camy.shift(initial_shift).dropna()
+    
+    max_lag = int(fps * lag_time)
+    pearson_r = []
+    lags = range(-max_lag, 1)
+    
+    for lag in lags:
+        if lag < 0:
+            shifted_camy = camy.shift(lag).dropna() # shift the camy segment by lag
+
+            corr = camx.corr(shifted_camy) # calculate the correlation between the camx segment and the shifted camy segment
+        elif lag == 0:
+            corr = camx.corr(camy)
+        else:
+            continue 
+        pearson_r.append(corr)
+
+
+   # Handle NaN values in pearson_r and find the max correlation ignoring NaNs
+    pearson_r = np.array(pearson_r)
+    max_corr = np.nanmax(pearson_r)  # Use nanmax to ignore NaNs
+    offset = np.nanargmax(pearson_r) - max_lag  # Use nanargmax to find the index of the max correlation ignoring NaNs
+    # real_offset = offset + initial_shift
+    
+    # visualize
+    ax.plot(lags, pearson_r)
+    ax.axvline(offset, color='r', linestyle='--', label='Peak synchrony')
+    plt.annotate(f'Max correlation={np.round(max_corr,2)}', xy=(0.05, 0.9), xycoords='axes fraction')
+    # ax.set(title=f'Offset = {offset}{initial_shift} = {real_offset} frames', xlabel='Offset (frames)', ylabel='Pearson r')
+    ax.set(title=f'Offset = {offset} frames', xlabel='Offset (frames)', ylabel='Pearson r')
+    plt.legend()
+    
+    return offset, max_corr
+
+
+def apply_offset(offset, json_dirs, reset_sync, cam1_nb, cam2_nb):
+    """
+    Apply the offset to synchronize the cameras.
+    Offset is always applied to the second camera.
+    Offset would be always negative if the first camera is the last to start recording.
+    Delete the camy json files from initial frame to offset frame.
+
+    Args:
+    offset (int): The offset value to apply to synchronize the cameras.
+    json_dirs (list): List of directories containing the JSON files for each camera.
+    reset_sync (bool): Whether to reset the synchronization by deleting the .del files.
+    cam1_nb (int): The number of the reference camera.
+    cam2_nb (int): The number of the other camera.
+    """ 
+
+    if offset == 0:
+        print(f"Cams {cam1_nb} and {cam2_nb} are already synchronized. No offset applied.")
+        json_dir_to_offset = json_dirs[cam2_nb]
+    elif offset > 0 and not reset_sync:
+        print(f"Consider adjusting the lag time.")
+        raise ValueError(f"Are you sure the reference camera is the last to start recording?")
+    else:
+        offset = abs(offset)
+        json_dir_to_offset = json_dirs[cam2_nb]
+
+    json_files = sorted(fnmatch.filter(os.listdir(json_dir_to_offset), '*.json'), key=lambda x: int(re.findall('\d+', x)[0]))
+
+    if reset_sync:
+        del_files = fnmatch.filter(os.listdir(json_dir_to_offset), '*.del')
+        for del_file in del_files:
+            os.rename(os.path.join(json_dir_to_offset, del_file), os.path.join(json_dir_to_offset, del_file[:-4]))
+    else:
+            for i in range(offset):
+                os.rename(os.path.join(json_dir_to_offset, json_files[i]), os.path.join(json_dir_to_offset, json_files[i] + '.del'))
+
+
+
+#################
+# Main Function #
+#################
+
+def synchronize_cams_all(config_dict):
+
+    #############
+    # CONSTANTS #
+    #############
+
+    # get parameters from Config.toml
+    project_dir = config_dict.get('project').get('project_dir')
+    pose_dir = os.path.realpath(os.path.join(project_dir, 'pose'))
+    fps =  config_dict.get('project').get('frame_rate') # frame rate of the cameras (Hz)
+    reset_sync = config_dict.get('synchronization').get('reset_sync')  # Start synchronization over each time it is run
+
+    # Vertical speeds (on 'Y')
+    speed_kind = config_dict.get('synchronization').get('speed_kind') # this maybe fixed in the future
+    id_kpt =  config_dict.get('synchronization').get('id_kpt') #  get the numbers from the keypoint names in skeleton.py: 'RWrist' BLAZEPOSE 16, BODY_25B 10, BODY_25 4 ; 'LWrist' BLAZEPOSE 15, BODY_25B 9, BODY_25 7
+    weights_kpt = config_dict.get('synchronization').get('weights_kpt') # only considered if there are multiple keypoints.
+
+    ######################################
+    # 0. CONVERTING JSON FILES TO PANDAS #
+    ######################################
+
+    # Also filter, and then save the filtered data
+    pose_listdirs_names = next(os.walk(pose_dir))[1]
+    pose_listdirs_names.sort(key=lambda name: int(re.search(r'(\d+)', name).group(1)))
+    json_dirs_names = [k for k in pose_listdirs_names if 'json' in k]
+    json_dirs = [os.path.join(pose_dir, j_d) for j_d in json_dirs_names] # list of json directories in pose_dir
+
+    # keypoints coordinates
+    df_coords = []
+    for i, json_dir in enumerate(json_dirs):
+        df_coords.append(convert_json2csv(json_dir))
+        df_coords[i] = drop_col(df_coords[i],3) # drop likelihood
+
+    ## To save it and reopen it if needed
+    with open(os.path.join(pose_dir, 'coords'), 'wb') as fp:
+        pk.dump(df_coords, fp)
+    with open(os.path.join(pose_dir, 'coords'), 'rb') as fp:
+        df_coords = pk.load(fp)
+
+    #############################
+    # 1. COMPUTING SPEEDS       #
+    #############################
+
+    # Vitesse verticale    
+    df_speed = []
+    for i in range(len(json_dirs)):
+        if speed_kind == 'y':
+            df_speed.append(speed_vert(df_coords[i]))
+
+    #############################################
+    # 2. PLOTTING PAIRED CORRELATIONS OF SPEEDS #
+    #############################################
+
+    # Do this on all cam pairs
+    # Choose pair with highest correlation
+
+    # on a particular point (typically the wrist on a vertical movement)
+    # or on a selection of weighted points
+
+    # find the lowest position of the wrist
+    lowest_frames, lowest_y_coords = find_highest_wrist_position(df_coords, id_kpt)
+
+    # set reference camera
+    ref_cam_nb = 0
+    max_speeds = []
+
+    for cam_nb in range(1, len(json_dirs)):
+        # find the highest wrist position for each camera
+        camx_start_frame = lowest_frames[ref_cam_nb]
+        camy_start_frame = lowest_frames[cam_nb]
+
+        camx_lowest_y = lowest_y_coords[ref_cam_nb]
+        camy_lowest_y = lowest_y_coords[cam_nb]
+
+        camx_time = find_motion_end(df_coords[ref_cam_nb], id_kpt[0], camx_start_frame, camx_lowest_y, fps)
+        camy_time = find_motion_end(df_coords[cam_nb], id_kpt[0], camy_start_frame, camy_lowest_y, fps)
+
+        camx_end_frame = camx_start_frame + int(camx_time * fps)
+        camy_end_frame = camy_start_frame + int(camy_time * fps)
+
+        camx_segment = df_speed[ref_cam_nb].iloc[camx_start_frame:camx_end_frame+1, id_kpt[0]]
+        camy_segment = df_speed[cam_nb].iloc[camy_start_frame:camy_end_frame+1, id_kpt[0]]
+
+
+        # Find the fastest speed and the frame
+        camx_max_speed_index, camx_max_speed = find_fastest_frame([camx_segment])
+        camy_max_speed_index, camy_max_speed = find_fastest_frame([camy_segment])
+        max_speeds.append(camx_max_speed)
+        max_speeds.append(camy_max_speed)
+        vmax = max(max_speeds)
+
+        # Find automatically the best lag time
+        lag_time = round((camy_max_speed_index - camx_max_speed_index) / fps + 1)
+
+        # FInd the fatest frame
+        camx_start_frame = camx_max_speed_index - (fps) * (lag_time)
+        if camx_start_frame < 0:
+            camx_start_frame = 0
+        else:
+            camx_start_frame = int(camx_start_frame)
+        camy_start_frame = camy_max_speed_index - (fps) * (lag_time)
+        camx_end_frame = camx_max_speed_index + (fps) * (lag_time)
+        camy_end_frame = camy_max_speed_index + (fps) * (lag_time)
+
+        if len(id_kpt) == 1 and id_kpt[0] != 'all':
+            camx = df_speed[ref_cam_nb].iloc[camx_start_frame:camx_end_frame+1, id_kpt[0]]
+            camy = df_speed[cam_nb].iloc[camy_start_frame:camy_end_frame+1, id_kpt[0]]
+        elif id_kpt == ['all']:
+            camx = df_speed[ref_cam_nb].iloc[camx_start_frame:camx_end_frame+1].sum(axis=1)
+            camy = df_speed[cam_nb].iloc[camy_start_frame:camy_end_frame+1].sum(axis=1)
+        elif len(id_kpt) == 1 and len(id_kpt) == len(weights_kpt):
+            dict_id_weights = {i:w for i, w in zip(id_kpt, weights_kpt)}
+            camx = df_speed[ref_cam_nb] @ pd.Series(dict_id_weights).reindex(df_speed[ref_cam_nb].columns, fill_value=0)
+            camy = df_speed[cam_nb] @ pd.Series(dict_id_weights).reindex(df_speed[cam_nb].columns, fill_value=0)
+            camx = camx.iloc[camx_start_frame:camx_end_frame+1]
+            camy = camy.iloc[camy_start_frame:camy_end_frame+1]
+        else:
+            raise ValueError('wrong values for id_kpt or weights_kpt')        
+        
+        # filter the speeds
+        camx = camx.where(lambda x: (x <= vmax) & (x >= -vmax), other=np.nan)
+        camy = camy.where(lambda x: (x <= vmax) & (x >= -vmax), other=np.nan)
+
+        f, ax = plt.subplots(2,1)
+
+        # speed
+        camx.plot(ax=ax[0], label = f'cam {ref_cam_nb+1}')
+        camy.plot(ax=ax[0], label = f'cam {cam_nb+1}')
+        ax[0].set(xlabel='Frame',ylabel='Speed (pxframe)')
+        ax[0].legend()
+  
+        # time lagged cross-correlation
+        offset, max_corr = plot_time_lagged_cross_corr(camx, camy, ax[1], fps, lag_time, camx_max_speed_index, camy_max_speed_index)
+        f.tight_layout()
+        plt.show()
+        print(f'Using number{id_kpt} keypoint, synchronized camera {ref_cam_nb+1} and camera {cam_nb+1}, with an offset of {offset} and a max correlation of {max_corr}.')
+
+        # apply offset
+        apply_offset(offset, json_dirs, reset_sync, ref_cam_nb, cam_nb)
+
+

README.md

@@ -307,7 +307,20 @@ All AlphaPose models are supported (HALPE_26, HALPE_68, HALPE_136, COCO_133, COC
 > _**Cameras need to be synchronized, so that 2D points correspond to the same position across cameras.**_\
 ***N.B.:** Skip this step if your cameras are already synchronized.*
 
-If your cameras are not natively synchronized, you can use [this script](https://github.com/perfanalytics/pose2sim/blob/main/Pose2Sim/Utilities/synchronize_cams_draft.py). This is still a draft, and will be updated in the future.\
+``` python
+from Pose2Sim import Pose2Sim
+Pose2Sim.synchronization()
+```
+
+Reference camera (usally cam1) should start record at last between whole cameras.\
+Set fps, id_kpt, weight_kpt, reset_sync in Config.toml.\
+**How to get perfect sync point**
+1. Set cameras position where they can see person wrist clearly.
+2. Press record button, and what pressed last time to be reference camera.
+3. Walk to proper location( See 1 ).
+4. Raise your hands.
+5. Downward your hand fastly.
+
 Alternatively, use a flashlight or a clap to synchronize them. GoPro cameras can also be synchronized with a timecode, by GPS (outdoors) or with a remote control (slightly less reliable).
 
 </br>