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

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@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
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HunMinKim 2024-03-16 04:26:49 +09:00 committed by GitHub
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58
Pose2Sim/Pose2Sim.py
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@ -266,42 +266,42 @@ def synchronization(config=None):
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')
# Import the function
from Pose2Sim.synchronize_cams import synchronize_cams_all
# #TODO
# # Determine the level at which the function is called (session:3, participant:2, trial:1)
# level, config_dicts = read_config_files(config)
# Determine the level at which the function is called (session:3, participant:2, trial:1)
level, config_dicts = read_config_files(config)
# if type(config)==dict:
# config_dict = config_dicts[0]
# 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>"})')
if type(config)==dict:
config_dict = config_dicts[0]
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
# session_dir = os.path.realpath(os.path.join(config_dicts[0].get('project').get('project_dir'), '..', '..'))
# setup_logging(session_dir)
# Set up logging
session_dir = os.path.realpath(os.path.join(config_dicts[0].get('project').get('project_dir'), '..', '..'))
setup_logging(session_dir)
# # Batch process all trials
# for config_dict in config_dicts:
# start = time.time()
# 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]
# Batch process all trials
for config_dict in config_dicts:
start = time.time()
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---------------------------------------------------------------------")
# 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}")
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()
# elapsed = end-start
# logging.info(f'Synchronization took {time.strftime("%Hh%Mm%Ss", time.gmtime(elapsed))}.')
end = time.time()
elapsed = end-start
logging.info(f'Synchronization took {time.strftime("%Hh%Mm%Ss", time.gmtime(elapsed))}.')
def personAssociation(config=None):

14
Pose2Sim/S00_Demo_Session/Config.toml
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@ -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.
cut_off_frequency = 10 # cut-off frequency for a 4th order low-pass Butterworth filter
# Vertical speeds (on X, Y, or Z axis, or 2D speeds)
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
speed_kind = 'y' # 'y' showed best performance.
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
weights_kpt = [1] # Only taken into account if you have several keypoints (Currently only one keypoint is supported).
[personAssociation]

448
Pose2Sim/synchronize_cams.py Normal file
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@ -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)

15
README.md
View File

@ -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).
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