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pose2sim/Pose2Sim/common.py
davidpagnon b31215c8c0 integration of pose estimation
2024-07-10 10:12:57 +02:00

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#!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
###########################################################################
## OTHER SHARED UTILITIES ##
###########################################################################
Functions shared between modules, and other utilities
'''
## INIT
import toml
import json
import numpy as np
import re
import cv2
import c3d
import sys
import matplotlib as mpl
mpl.use('qt5agg')
mpl.rc('figure', max_open_warning=0)
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as NavigationToolbar
from PyQt5.QtWidgets import QMainWindow, QApplication, QWidget, QTabWidget, QVBoxLayout
import warnings
warnings.filterwarnings("ignore", category=UserWarning, module="c3d")
## AUTHORSHIP INFORMATION
__author__ = "David Pagnon"
__copyright__ = "Copyright 2021, Maya-Mocap"
__credits__ = ["David Pagnon"]
__license__ = "BSD 3-Clause License"
__version__ = "0.9.4"
__maintainer__ = "David Pagnon"
__email__ = "contact@david-pagnon.com"
__status__ = "Development"
## FUNCTIONS
def common_items_in_list(list1, list2):
'''
Do two lists have any items in common at the same index?
Returns True or False
'''
for i, j in enumerate(list1):
if j == list2[i]:
return True
return False
def bounding_boxes(js_file, margin_percent=0.1, around='extremities'):
'''
Compute the bounding boxes of the people in the json file.
Either around the extremities (with a margin)
or around the center of the person (with a margin).
INPUTS:
- js_file: json file
- margin_percent: margin around the person
- around: 'extremities' or 'center'
OUTPUT:
- bounding_boxes: list of bounding boxes [x_min, y_min, x_max, y_max]
'''
bounding_boxes = []
with open(js_file, 'r') as json_f:
js = json.load(json_f)
for people in range(len(js['people'])):
if len(js['people'][people]['pose_keypoints_2d']) < 3: continue
else:
x = js['people'][people]['pose_keypoints_2d'][0::3]
y = js['people'][people]['pose_keypoints_2d'][1::3]
x_min, x_max = min(x), max(x)
y_min, y_max = min(y), max(y)
if around == 'extremities':
dx = (x_max - x_min) * margin_percent
dy = (y_max - y_min) * margin_percent
bounding_boxes.append([x_min-dx, y_min-dy, x_max+dx, y_max+dy])
elif around == 'center':
x_mean, y_mean = np.mean(x), np.mean(y)
x_size = (x_max - x_min) * (1 + margin_percent)
y_size = (y_max - y_min) * (1 + margin_percent)
bounding_boxes.append([x_mean - x_size/2, y_mean - y_size/2, x_mean + x_size/2, y_mean + y_size/2])
return bounding_boxes
def retrieve_calib_params(calib_file):
'''
Compute projection matrices from toml calibration file.
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
- inv_K: inverse intrinsic matrices as list of 3x3 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, inv_K, R, R_mat, 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']))
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])
inv_K.append(np.linalg.inv(K[c]))
R.append(np.array(calib[cam]['rotation']))
R_mat.append(cv2.Rodrigues(R[c])[0])
T.append(np.array(calib[cam]['translation']))
calib_params = {'S': S, 'K': K, 'dist': dist, 'inv_K': inv_K, 'optim_K': optim_K, 'R': R, 'R_mat': R_mat, 'T': T}
return calib_params
def computeP(calib_file, undistort=False):
'''
Compute projection matrices from toml calibration file.
INPUT:
- calib_file: calibration .toml file.
- undistort: boolean
OUTPUT:
- P: projection matrix as list of arrays
'''
calib = toml.load(calib_file)
P = []
for cam in list(calib.keys()):
if cam != 'metadata':
K = np.array(calib[cam]['matrix'])
if undistort:
S = np.array(calib[cam]['size'])
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)])
R, _ = cv2.Rodrigues(np.array(calib[cam]['rotation']))
T = np.array(calib[cam]['translation'])
H = np.block([[R,T.reshape(3,1)], [np.zeros(3), 1 ]])
P.append(Kh @ H)
return P
def weighted_triangulation(P_all,x_all,y_all,likelihood_all):
'''
Triangulation with direct linear transform,
weighted with likelihood of joint pose estimation.
INPUTS:
- P_all: list of arrays. Projection matrices of all cameras
- x_all,y_all: x, y 2D coordinates to triangulate
- likelihood_all: likelihood of joint pose estimation
OUTPUT:
- Q: array of triangulated point (x,y,z,1.)
'''
A = np.empty((0,4))
for c in range(len(x_all)):
P_cam = P_all[c]
A = np.vstack((A, (P_cam[0] - x_all[c]*P_cam[2]) * likelihood_all[c] ))
A = np.vstack((A, (P_cam[1] - y_all[c]*P_cam[2]) * likelihood_all[c] ))
if np.shape(A)[0] >= 4:
S, U, Vt = cv2.SVDecomp(A)
V = Vt.T
Q = np.array([V[0][3]/V[3][3], V[1][3]/V[3][3], V[2][3]/V[3][3], 1])
else:
Q = np.array([np.nan,np.nan,np.nan,1])
return Q
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 euclidean_distance(q1, q2):
'''
Euclidean distance between 2 points (N-dim).
INPUTS:
- q1: list of N_dimensional coordinates of point
- q2: idem
OUTPUTS:
- euc_dist: float. Euclidian distance between q1 and q2
'''
q1 = np.array(q1)
q2 = np.array(q2)
dist = q2 - q1
if np.isnan(dist).all():
dist = np.empty_like(dist)
dist[...] = np.inf
euc_dist = np.sqrt(np.nansum( [d**2 for d in dist]))
return euc_dist
def world_to_camera_persp(r, t):
'''
Converts rotation R and translation T
from Qualisys world centered perspective
to OpenCV camera centered perspective
and inversely.
Qc = RQ+T --> Q = R-1.Qc - R-1.T
'''
r = r.T
t = - r @ t
return r, t
def rotate_cam(r, t, ang_x=0, ang_y=0, ang_z=0):
'''
Apply rotations around x, y, z in cameras coordinates
Angle in radians
'''
r,t = np.array(r), np.array(t)
if r.shape == (3,3):
rt_h = np.block([[r,t.reshape(3,1)], [np.zeros(3), 1 ]])
elif r.shape == (3,):
rt_h = np.block([[cv2.Rodrigues(r)[0],t.reshape(3,1)], [np.zeros(3), 1 ]])
r_ax_x = np.array([1,0,0, 0,np.cos(ang_x),-np.sin(ang_x), 0,np.sin(ang_x),np.cos(ang_x)]).reshape(3,3)
r_ax_y = np.array([np.cos(ang_y),0,np.sin(ang_y), 0,1,0, -np.sin(ang_y),0,np.cos(ang_y)]).reshape(3,3)
r_ax_z = np.array([np.cos(ang_z),-np.sin(ang_z),0, np.sin(ang_z),np.cos(ang_z),0, 0,0,1]).reshape(3,3)
r_ax = r_ax_z @ r_ax_y @ r_ax_x
r_ax_h = np.block([[r_ax,np.zeros(3).reshape(3,1)], [np.zeros(3), 1]])
r_ax_h__rt_h = r_ax_h @ rt_h
r = r_ax_h__rt_h[:3,:3]
t = r_ax_h__rt_h[:3,3]
return r, t
def quat2rod(quat, scalar_idx=0):
'''
Converts quaternion to Rodrigues vector
INPUT:
- quat: quaternion. np.array of size 4
- scalar_idx: index of scalar part of quaternion. Default: 0, sometimes 3
OUTPUT:
- rod: Rodrigues vector. np.array of size 3
'''
if scalar_idx == 0:
w, qx, qy, qz = np.array(quat)
if scalar_idx == 3:
qx, qy, qz, w = np.array(quat)
else:
print('Error: scalar_idx should be 0 or 3')
rodx = qx * np.tan(w/2)
rody = qy * np.tan(w/2)
rodz = qz * np.tan(w/2)
rod = np.array([rodx, rody, rodz])
return rod
def quat2mat(quat, scalar_idx=0):
'''
Converts quaternion to rotation matrix
INPUT:
- quat: quaternion. np.array of size 4
- scalar_idx: index of scalar part of quaternion. Default: 0, sometimes 3
OUTPUT:
- mat: 3x3 rotation matrix
'''
if scalar_idx == 0:
w, qx, qy, qz = np.array(quat)
elif scalar_idx == 3:
qx, qy, qz, w = np.array(quat)
else:
print('Error: scalar_idx should be 0 or 3')
r11 = 1 - 2 * (qy**2 + qz**2)
r12 = 2 * (qx*qy - qz*w)
r13 = 2 * (qx*qz + qy*w)
r21 = 2 * (qx*qy + qz*w)
r22 = 1 - 2 * (qx**2 + qz**2)
r23 = 2 * (qy*qz - qx*w)
r31 = 2 * (qx*qz - qy*w)
r32 = 2 * (qy*qz + qx*w)
r33 = 1 - 2 * (qx**2 + qy**2)
mat = np.array([r11, r12, r13, r21, r22, r23, r31, r32, r33]).reshape(3,3).T
return mat
def sort_stringlist_by_last_number(string_list):
'''
Sort a list of strings based on the last number in the string.
Works if other numbers in the string, if strings after number. Ignores alphabetical order.
Example: ['json1', 'js4on2.b', 'eypoints_0000003.json', 'ajson0', 'json10']
gives: ['ajson0', 'json1', 'js4on2.b', 'eypoints_0000003.json', 'json10']
'''
def sort_by_last_number(s):
return int(re.findall(r'\d+', s)[-1])
return sorted(string_list, key=sort_by_last_number)
def natural_sort_key(s):
'''
Sorts list of strings with numbers in natural order (alphabetical and numerical)
Example: ['item_1', 'item_2', 'item_10', 'stuff_1']
'''
return [int(c) if c.isdigit() else c.lower() for c in re.split(r'(\d+)', s)]
def zup2yup(Q):
'''
Turns Z-up system coordinates into Y-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+1],cols[i*3+2],cols[i*3]] for i in range(int(len(cols)/3))]).flatten()
Q = Q[cols]
return Q
def extract_trc_data(trc_path):
'''
Extract marker names and coordinates from a trc file.
INPUTS:
- trc_path: Path to the trc file
OUTPUTS:
- marker_names: List of marker names
- marker_coords: Array of marker coordinates (n_frames, t+3*n_markers)
'''
# marker names
with open(trc_path, 'r') as file:
lines = file.readlines()
marker_names_line = lines[3]
marker_names = marker_names_line.strip().split('\t')[2::3]
# time and marker coordinates
trc_data_np = np.genfromtxt(trc_path, skip_header=5, delimiter = '\t')[:,1:]
return marker_names, trc_data_np
def create_c3d_file(c3d_path, marker_names, trc_data_np):
'''
Create a c3d file from the data extracted from a trc file.
INPUTS:
- c3d_path: Path to the c3d file
- marker_names: List of marker names
- trc_data_np: Array of marker coordinates (n_frames, t+3*n_markers)
OUTPUTS:
- c3d file
'''
# retrieve frame rate
times = trc_data_np[:,0]
frame_rate = round((len(times)-1) / (times[-1] - times[0]))
# write c3d file
writer = c3d.Writer(point_rate=frame_rate, analog_rate=0, point_scale=1.0, point_units='mm', gen_scale=-1.0)
writer.set_point_labels(marker_names)
writer.set_screen_axis(X='+Z', Y='+Y')
for frame in trc_data_np:
residuals = np.full((len(marker_names), 1), 0.0)
cameras = np.zeros((len(marker_names), 1))
coords = frame[1:].reshape(-1,3)*1000
points = np.hstack((coords, residuals, cameras))
writer.add_frames([(points, np.array([]))])
writer.set_start_frame(0)
writer._set_last_frame(len(trc_data_np)-1)
with open(c3d_path, 'wb') as handle:
writer.write(handle)
def convert_to_c3d(trc_path):
'''
Make Visual3D compatible c3d files from a trc path
INPUT:
- trc_path: string, trc file to convert
OUTPUT:
- c3d file
'''
c3d_path = trc_path.replace('.trc', '.c3d')
marker_names, trc_data_np = extract_trc_data(trc_path)
create_c3d_file(c3d_path, marker_names, trc_data_np)
return c3d_path
## CLASSES
class plotWindow():
'''
Display several figures in tabs
Taken from https://github.com/superjax/plotWindow/blob/master/plotWindow.py
USAGE:
pw = plotWindow()
f = plt.figure()
plt.plot(x1, y1)
pw.addPlot("1", f)
f = plt.figure()
plt.plot(x2, y2)
pw.addPlot("2", f)
'''
def __init__(self, parent=None):
self.app = QApplication(sys.argv)
self.MainWindow = QMainWindow()
self.MainWindow.__init__()
self.MainWindow.setWindowTitle("Multitabs figure")
self.canvases = []
self.figure_handles = []
self.toolbar_handles = []
self.tab_handles = []
self.current_window = -1
self.tabs = QTabWidget()
self.MainWindow.setCentralWidget(self.tabs)
self.MainWindow.resize(1280, 720)
self.MainWindow.show()
def addPlot(self, title, figure):
new_tab = QWidget()
layout = QVBoxLayout()
new_tab.setLayout(layout)
figure.subplots_adjust(left=0.1, right=0.99, bottom=0.1, top=0.91, wspace=0.2, hspace=0.2)
new_canvas = FigureCanvas(figure)
new_toolbar = NavigationToolbar(new_canvas, new_tab)
layout.addWidget(new_canvas)
layout.addWidget(new_toolbar)
self.tabs.addTab(new_tab, title)
self.toolbar_handles.append(new_toolbar)
self.canvases.append(new_canvas)
self.figure_handles.append(figure)
self.tab_handles.append(new_tab)
def show(self):
self.app.exec_()
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