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pose2sim/Pose2Sim/synchronization.py

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#!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
#########################################
## SYNCHRONIZE CAMERAS ##
#########################################
Post-synchronize your cameras in case they are not natively synchronized.
For each camera, computes mean vertical speed for the chosen keypoints,
and find the time offset for which their correlation is highest.
Depending on the analysed motion, all keypoints can be taken into account,
or a list of them, or the right or left side.
All frames can be considered, or only those around a specific time (typically,
the time when there is a single participant in the scene performing a clear vertical motion).
Has also been successfully tested for synchronizing random walkswith random walks.
If synchronization results are not satisfying, they can be reset to the original
state and tried again with different parameters.
INPUTS:
- json files from each camera folders
- a Config.toml file
- a skeleton model
OUTPUTS:
- synchronized json files for each camera
'''
## INIT
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
from anytree import RenderTree
from anytree.importer import DictImporter
import logging
from Pose2Sim.common import sort_stringlist_by_last_number
from Pose2Sim.skeletons import *
## AUTHORSHIP INFORMATION
__author__ = "David Pagnon, HunMin Kim"
__copyright__ = "Copyright 2021, Pose2Sim"
__credits__ = ["David Pagnon"]
__license__ = "BSD 3-Clause License"
__version__ = "0.8.2"
__maintainer__ = "David Pagnon"
__email__ = "contact@david-pagnon.com"
__status__ = "Development"
# FUNCTIONS
def convert_json2pandas(json_dir, min_conf=0.6, frame_range=[]):
'''
Convert JSON files in a directory to a pandas DataFrame.
INPUTS:
- json_dir: str. The directory path containing the JSON files.
- min_conf: float. Drop values if confidence is below min_conf.
- frame_range: select files within frame_range.
OUTPUTS:
- df_json_coords: dataframe. Extracted coordinates in a pandas dataframe.
'''
nb_coord = 25 # int(len(json_data)/3)
json_files_names = fnmatch.filter(os.listdir(os.path.join(json_dir)), '*.json') # modified ( 'json' to '*.json' )
json_files_names = sort_stringlist_by_last_number(json_files_names)
if len(frame_range) == 2:
json_files_names = np.array(json_files_names)[range(frame_range[0], frame_range[1])].tolist()
json_files_path = [os.path.join(json_dir, j_f) for j_f in json_files_names]
json_coords = []
for j_p in json_files_path:
with open(j_p) as j_f:
try:
json_data = json.load(j_f)['people'][0]['pose_keypoints_2d']
# remove points with low confidence
json_data = np.array([[json_data[3*i],json_data[3*i+1],json_data[3*i+2]] if json_data[3*i+2]>min_conf else [0.,0.,0.] for i in range(nb_coord)]).ravel().tolist()
except:
# print(f'No person found in {os.path.basename(json_dir)}, frame {i}')
json_data = [np.nan] * 25*3
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.
INPUTS:
- df: dataframe. The DataFrame from which columns will be dropped.
- col_nb: int. The column number to drop.
OUTPUTS:
- dataframe: 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 vert_speed(df, axis='y'):
'''
Calculate the vertical speed of a DataFrame along a specified axis.
INPUTS:
- df: dataframe. DataFrame of 2D coordinates.
- axis: str. The axis along which to calculate speed. 'x', 'y', or 'z', default is 'y'.
OUTPUTS:
- df_vert_speed: DataFrame of 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_zeros_nans(col, kind):
'''
Interpolate missing points (of value nan)
INPUTS:
- col: pandas column of coordinates
- kind: 'linear', 'slinear', 'quadratic', 'cubic'. Default 'cubic'
OUTPUTS:
- col_interp: interpolated pandas column
'''
mask = ~(np.isnan(col) | col.eq(0)) # true where nans or zeros
idx_good = np.where(mask)[0]
try:
f_interp = interpolate.interp1d(idx_good, col[idx_good], kind=kind, bounds_error=False)
col_interp = np.where(mask, col, f_interp(col.index))
return col_interp
except:
# print('No good values to interpolate')
return col
def time_lagged_cross_corr(camx, camy, lag_range, show=True):
'''
Compute the time-lagged cross-correlation between two pandas series.
INPUTS:
- camx: pandas series. The first time series (coordinates of reference camera).
- camy: pandas series. The second time series (camera to compare).
- lag_range: int or list. The range of frames for which to compute cross-correlation.
- show: bool. If True, display the cross-correlation plot.
OUTPUTS:
- offset: int. The time offset for which the correlation is highest.
- max_corr: float. The maximum correlation value.
'''
if isinstance(lag_range, int):
lag_range = [-lag_range, lag_range]
pearson_r = [camx.corr(camy.shift(lag)) for lag in range(lag_range[0], lag_range[1])]
offset = int(np.floor(len(pearson_r)/2)-np.argmax(pearson_r))
if not np.isnan(pearson_r).all():
max_corr = np.nanmax(pearson_r)
if show:
f, ax = plt.subplots(2,1)
# speed
camx.plot(ax=ax[0], label = f'ref cam')
camy.plot(ax=ax[0], label = f'compared cam')
ax[0].set(xlabel='Frame', ylabel='Speed (px/frame)')
ax[0].legend()
# time lagged cross-correlation
ax[1].plot(list(range(lag_range[0], lag_range[1])), pearson_r)
ax[1].axvline(np.ceil(len(pearson_r)/2) + lag_range[0],color='k',linestyle='--')
ax[1].axvline(np.argmax(pearson_r) + lag_range[0],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[1].set(title=f'Offset = {offset} frames', xlabel='Offset (frames)',ylabel='Pearson r')
plt.legend()
f.tight_layout()
plt.show()
else:
max_corr = 0
offset = 0
if show:
# print('No good values to interpolate')
pass
return offset, max_corr
def apply_offset(json_dir, offset_cam):
'''
Apply an offset to the json files in a directory.
If offset_cam is positive, the first "offset_cam" frames are temporarily
trimmed (json files become json.del files).
If offset_cam is negative, "offset_cam" new frames are padded with empty
json files (del_*.json).
INPUTS:
- json_dir: str. The directory path containing the JSON files.
- offset_cam: int. The frame offset to apply.
OUTPUTS:
- Trimmed or padded files in the directory.
'''
json_files_names = fnmatch.filter(os.listdir(os.path.join(json_dir)), '*.json')
json_files_names = sort_stringlist_by_last_number(json_files_names)
json_files_path = [os.path.join(json_dir, j_f) for j_f in json_files_names]
if offset_cam > 0: # trim first "offset_cam" frames
[os.rename(f, f+'.del') for f in json_files_path[:offset_cam]]
elif offset_cam < 0: # pad with "offset_cam" new frames
for i in range(-offset_cam):
with open(os.path.join(json_dir, f'del_{i:06}_0.json'), 'w') as f:
f.write('{"version":1.3,"people":[]}')
def reset_offset(json_dir):
'''
Reset offset by renaming .json.del files to .json
and removing the del_*.json files
INPUTS:
- json_dir: str. The directory path containing the JSON files.
OUTPUT:
- Renamed files in the directory.
'''
# padded files
padded_files_names = fnmatch.filter(os.listdir(os.path.join(json_dir)), 'del_*.json')
padded_files_path = [os.path.join(json_dir, f) for f in padded_files_names]
[os.remove(f) for f in padded_files_path]
# trimmed files
trimmed_files_names = fnmatch.filter(os.listdir(os.path.join(json_dir)), '*.json.del')
trimmed_files_path = [os.path.join(json_dir, f) for f in trimmed_files_names]
[os.rename(f, f[:-4]) for f in trimmed_files_path]
def synchronize_cams_all(config_dict):
'''
Post-synchronize your cameras in case they are not natively synchronized.
For each camera, computes mean vertical speed for the chosen keypoints,
and find the time offset for which their correlation is highest.
Depending on the analysed motion, all keypoints can be taken into account,
or a list of them, or the right or left side.
All frames can be considered, or only those around a specific time (typically,
the time when there is a single participant in the scene performing a clear vertical motion).
Has also been successfully tested for synchronizing random walkswith random walks.
If synchronization results are not satisfying, it can be reset to the original
state and tried again with different parameters.
INPUTS:
- json files from each camera folders
- a Config.toml file
- a skeleton model
OUTPUTS:
- synchronized json files for each camera
'''
# 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'))
pose_model = config_dict.get('pose').get('pose_model')
fps = config_dict.get('project').get('frame_rate')
reset_sync = config_dict.get('synchronization').get('reset_sync')
display_sync_plots = config_dict.get('synchronization').get('display_sync_plots')
keypoints_to_consider = config_dict.get('synchronization').get('keypoints_to_consider')
approx_time_maxspeed = config_dict.get('synchronization').get('approx_time_maxspeed')
lag_range = 500 # frames
min_conf = 0.4
filter_order = 4
filter_cutoff = 6
# Retrieve keypoints from model
try: # from skeletons.py
model = eval(pose_model)
except:
try: # from Config.toml
model = DictImporter().import_(config_dict.get('pose').get(pose_model))
if model.id == 'None':
model.id = None
except:
raise NameError('Model not found in skeletons.py nor in Config.toml')
keypoints_ids = [node.id for _, _, node in RenderTree(model) if node.id!=None]
keypoints_names = [node.name for _, _, node in RenderTree(model) if node.id!=None]
# List json files
pose_listdirs_names = next(os.walk(pose_dir))[1]
pose_listdirs_names = sort_stringlist_by_last_number(pose_listdirs_names)
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
nb_frames_per_cam = [len(fnmatch.filter(os.listdir(os.path.join(json_dir)), '*.json')) for json_dir in json_dirs]
cam_nb = len(json_dirs)
cam_list = list(range(cam_nb))
# Reset previous synchronization attempts
if reset_sync:
logging.info('Resetting synchronization...')
[reset_offset(json_dir) for json_dir in json_dirs]
logging.info('Synchronization reset.')
# Synchronize cameras
else:
# Determine frames to consider for synchronization
if isinstance(approx_time_maxspeed, list): # search around max speed
approx_frame_maxspeed = [int(fps * t) for t in approx_time_maxspeed]
nb_frames_per_cam_excludingdel = [len(fnmatch.filter(os.listdir(os.path.join(json_dir)), '*.json'))-len(fnmatch.filter(os.listdir(os.path.join(json_dir)), '*.json.del')) for json_dir in json_dirs]
search_around_frames = [[a-lag_range if a-lag_range>0 else 0, a+lag_range if a+lag_range<nb_frames_per_cam_excludingdel[i] else nb_frames_per_cam_excludingdel[i]] for i,a in enumerate(approx_frame_maxspeed)]
elif approx_time_maxspeed == 'auto': # search on the whole sequence (slower if long sequence)
search_around_frames = [[0, nb_frames_per_cam[i]] for i in range(cam_nb)]
else:
raise ValueError('approx_time_maxspeed should be a list of floats or "auto"')
# Extract, interpolate, and filter keypoint coordinates
df_coords = []
b, a = signal.butter(filter_order/2, filter_cutoff/(fps/2), 'low', analog = False)
for i, json_dir in enumerate(json_dirs):
df_coords.append(convert_json2pandas(json_dir, min_conf=min_conf, frame_range=search_around_frames[i]))
df_coords[i] = drop_col(df_coords[i],3) # drop likelihood
if keypoints_to_consider == 'right':
kpt_indices = [i for i,k in zip(keypoints_ids,keypoints_names) if k.startswith('R') or k.startswith('right')]
kpt_indices = np.sort(np.concatenate([np.array(kpt_indices)*2, np.array(kpt_indices)*2+1]))
df_coords[i] = df_coords[i][kpt_indices]
elif keypoints_to_consider == 'left':
kpt_indices = [i for i,k in zip(keypoints_ids,keypoints_names) if k.startswith('L') or k.startswith('left')]
kpt_indices = np.sort(np.concatenate([np.array(kpt_indices)*2, np.array(kpt_indices)*2+1]))
df_coords[i] = df_coords[i][kpt_indices]
elif isinstance(keypoints_to_consider, list):
kpt_indices = [i for i,k in zip(keypoints_ids,keypoints_names) if k in keypoints_to_consider]
kpt_indices = np.sort(np.concatenate([np.array(kpt_indices)*2, np.array(kpt_indices)*2+1]))
df_coords[i] = df_coords[i][kpt_indices]
elif keypoints_to_consider == 'all':
pass
else:
raise ValueError('keypoints_to_consider should be "all", "right", "left", or a list of keypoint names.\n\
If you specified keypoints, make sure that they exist in your pose_model.')
df_coords[i] = df_coords[i].apply(interpolate_zeros_nans, axis=0, args = ['linear'])
df_coords[i] = df_coords[i].bfill().ffill()
df_coords[i] = pd.DataFrame(signal.filtfilt(b, a, df_coords[i], axis=0))
# Compute sum of speeds
df_speed = []
sum_speeds = []
for i in range(cam_nb):
df_speed.append(vert_speed(df_coords[i]))
sum_speeds.append(abs(df_speed[i]).sum(axis=1))
# nb_coord = df_speed[i].shape[1]
# sum_speeds[i][ sum_speeds[i]>vmax*nb_coord ] = 0
# # Replace 0 by random values, otherwise 0 padding may lead to unreliable correlations
# sum_speeds[i].loc[sum_speeds[i] < 1] = sum_speeds[i].loc[sum_speeds[i] < 1].apply(lambda x: np.random.normal(0,1))
sum_speeds[i] = pd.DataFrame(signal.filtfilt(b, a, sum_speeds[i], axis=0)).squeeze()
# Compute offset for best synchronization:
# Highest correlation of sum of absolute speeds for each cam compared to reference cam
ref_cam_id = nb_frames_per_cam.index(min(nb_frames_per_cam)) # ref cam: least amount of frames
ref_frame_nb = len(df_coords[ref_cam_id])
lag_range = int(ref_frame_nb/2)
cam_list.pop(ref_cam_id)
offset = []
for cam_id in cam_list:
offset_cam_section, max_corr_cam = time_lagged_cross_corr(sum_speeds[ref_cam_id], sum_speeds[cam_id], lag_range, show=display_sync_plots)
offset_cam = offset_cam_section - (search_around_frames[ref_cam_id][0] - search_around_frames[cam_id][0])
if isinstance(approx_time_maxspeed, list):
logging.info(f'--> Camera {ref_cam_id} and {cam_id}: {offset_cam} frames offset ({offset_cam_section} on the selected section), correlation {round(max_corr_cam, 2)}.')
else:
logging.info(f'--> Camera {ref_cam_id} and {cam_id}: {offset_cam} frames offset, correlation {round(max_corr_cam, 2)}.')
apply_offset(json_dirs[cam_id], offset_cam)
offset.append(offset_cam)
offset.insert(ref_cam_id, 0)
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