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main.py
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import numpy as np
import matplotlib.pyplot as plt
import tkinter
import os
import cv2
import pickle
import csv
#from visulize_with_out_head import Vis
from visualize import Vis
from scipy.optimize import least_squares
import time
from scipy.sparse import lil_matrix
import subprocess
from create_project import create_project
#=========================Create Folders for project
configVariables = create_project()
from EditVideoRunOpandDLC import getCameraParams, concatVideos, undistortVideos,trimVideos, runDeepLabCut,runOpenPose, Parse_Openpose, checkerBoardUndistort
from Filters import smoothOpenPose, kalman, butterFilt
from ops import SBA,toCsv,vec2skewMat,inverseH,R_t2H,get_RT_mtx,video_loader,get_TransMat,triangulate,triangulateFlex,triangulateTest,aruco_detect,charuco_detect
subject = configVariables[0]
cam_names = configVariables[1]
base_Cam_Index = configVariables[2]
Len_of_frame = configVariables[3]
start_frame = configVariables[4]
include_DLC = configVariables[5]
include_OpenPoseFace = configVariables[6]
include_OpenPoseHands = configVariables[7]
include_OpenPoseSkeleton = configVariables[8]
baseFilePath = configVariables[13]
baseProjectPath = configVariables[12]
calibrateCameras = configVariables[10]
useCheckerboardVid = configVariables[9]
num_of_cameras = int(configVariables[14])
##################needs update the variables here###########################
rawData = baseFilePath+'/Raw'
checkerVideoFolder = rawData+'/Checkerboard'
rawVideoFolder = rawData+'/RawGoProData'
calibrationFilePath = baseProjectPath +'/'+subject+'/Calibration'
cameraParamsFilePath = calibrationFilePath +'/CameraParams'
calibVideoFilepath = calibrationFilePath +'/CalibrationVideos'
interfilepath = baseFilePath + '/Intermediate'
videoOutputFilepath = interfilepath+'/VideoOutput'
openposeRawFilepath = interfilepath + '/OpenPoseRaw'
DLCfilepath = interfilepath + '/DeepLabCut'
undistortedFilepath = interfilepath + '/Undistorted'
combinedFilepath = interfilepath+'/CombinedVideo'
processedFilePath = baseFilePath +'/Processed'
trimFilepath = interfilepath +'/Trimmed'
openposeOutputFilepath = interfilepath + '/OpenPoseOutput'
if calibrateCameras:
getCameraParams(calibrationFilePath)
#===========================Concat,Undistort and Trim Videos
#concatVideos(rawVideoFolder,combinedFilepath)
undistortVideos(combinedFilepath,cameraParamsFilePath,undistortedFilepath)
if useCheckerboardVid == True:
if not os.path.exists(interfilepath + '/CheckerboardUndistorted'):
os.mkdir(interfilepath + '/CheckerboardUndistorted')
checkerUndistortFilepath = interfilepath + '/CheckerboardUndistorted'
checkerBoardUndistort(checkerVideoFolder,checkerUndistortFilepath)
trimVideos(undistortedFilepath,trimFilepath)
#==========================Run deeplabcut and parse through output
if include_DLC == True:
runDeepLabCut(trimFilepath,DLCfilepath)
#==========================Run OpenPose and parse through output
if include_OpenPoseFace == True or include_OpenPoseHands ==True or include_OpenPoseSkeleton == True:
runOpenPose(undistortedFilepath,videoOutputFilepath,openposeRawFilepath)
points_inFrame = Parse_Openpose(openposeRawFilepath,openposeOutputFilepath)
smoothOpenPose(openposeOutputFilepath)
#points_inFrame =67
butterFilt(openposeOutputFilepath)
#========================Get source video
if useCheckerboardVid == True:
SourceVideoFolder = baseFilePath + '/Intermediate/CheckerboardUndistorted'
else:
SourceVideoFolder = trimFilepath
fullVideoFolder = trimFilepath #Always need to use this
#======================== Set up names for videos
cam1 = cam_names[0]
cam2 = cam_names[1]
if num_of_cameras ==2:
Source_video_List = [[cam1+'.mp4',cam1],[cam2+'.mp4',cam2]]
if num_of_cameras ==3:
cam3 = cam_names[2]
Source_video_List= [[cam1+'.mp4',cam1],[cam2+'.mp4',cam2],[cam3+'.mp4',cam3]]
if num_of_cameras ==4:
cam3 = cam_names[2]
cam4 = cam_names[3]
Source_video_List= [[cam1+'.mp4',cam1],[cam2+'.mp4',cam2],[cam3+'.mp4',cam3],[cam4+'.mp4',cam4]]
#=====================Get files for dlc and openpose data
rootOPFolder = openposeOutputFilepath+'/'
rootDLCFolder = DLCfilepath +'/DLCnpy'
if num_of_cameras ==2:
Pixel_coord_FIlE_List = [[rootOPFolder+'KalmanOP_'+cam1+'.npy',cam1],
[rootOPFolder+'KalmanOP_'+cam2+'.npy',cam2]]
Pixel_coord_FIlE_List_include_DLC = [[rootOPFolder+'KalmanOP_'+cam1+'.npy',rootDLCFolder+'dlc_'+cam1+'.npy',cam1],
[rootOPFolder+'KalmanOP_'+cam2+'.npy',rootDLCFolder+'dlc_'+cam2+'.npy',cam2]]
if num_of_cameras ==3:
Pixel_coord_FIlE_List = [[rootOPFolder+'KalmanOP_'+cam1+'.npy',cam1],
[rootOPFolder+'KalmanOP_'+cam2+'.npy',cam2],
[rootOPFolder+'KalmanOP_'+cam3+'.npy',cam3]]
Pixel_coord_FIlE_List_include_DLC = [[rootOPFolder+'KalmanOP_'+cam1+'.npy',rootDLCFolder+'dlc_'+cam1+'.npy',cam1],
[rootOPFolder+'KalmanOP_'+cam2+'.npy',rootDLCFolder+'dlc_'+cam1+'.npy',cam2],
[rootOPFolder+'KalmanOP_'+cam3+'.npy',rootDLCFolder+'dlc_'+cam3+'.npy',cam3]]
if num_of_cameras ==4:
Pixel_coord_FIlE_List = [[rootOPFolder+'KalmanOP_'+cam1+'.npy',cam1],
[rootOPFolder+'KalmanOP_'+cam2+'.npy',cam2],
[rootOPFolder+'KalmanOP_'+cam3+'.npy',cam3],
[rootOPFolder+'KalmanOP_'+cam4+'.npy',cam4]]
Pixel_coord_FIlE_List_include_DLC = [[rootOPFolder+'KalmanOP_'+cam1+'.npy',rootDLCFolder+'dlc_'+cam1+'.npy',cam1],
[rootOPFolder+'KalmanOP_'+cam2+'.npy',rootDLCFolder+'dlc_'+cam2+'.npy',cam2],
[rootOPFolder+'KalmanOP_'+cam3+'.npy',rootDLCFolder+'dlc_'+cam3+'.npy',cam3],
[rootOPFolder+'KalmanOP_'+cam4+'.npy',rootDLCFolder+'dlc_'+cam4+'.npy',cam4]]
if Len_of_frame == '-1':
frameLengthAllCam = [] #create variable to stoe frame length
for dir in [fullVideoFolder]:
for video in os.listdir(dir):
vidcap = cv2.VideoCapture(os.path.join(dir,video))
frameLengthOneCam = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT))
frameLengthAllCam.append(frameLengthOneCam)
minVideo = frameLengthAllCam.index(min(frameLengthAllCam))
shortestVideo = frameLengthAllCam[minVideo]
Len_of_frame = shortestVideo
if num_of_cameras == 2:
base_cam = {cam_names[0]:0,cam_names[1]:1}
if num_of_cameras == 3:
base_cam = {cam_names[0]:0,cam_names[1]:1, cam_names[2]:2}
if num_of_cameras == 4:
base_cam = {cam_names[0]:0,cam_names[1]:1,cam_names[2]:2,cam_names[3]:3}
#==================load image from videos
for path in Source_video_List:
video_resolution = video_loader(path[0],path[1])
#==================load pixel data to a dictionary
pixelCoord = {}
if include_DLC:
for path in Pixel_coord_FIlE_List_include_DLC:
skeleton = np.load(path[0],allow_pickle = True)[start_frame:start_frame+Len_of_frame,:,:]
ball = np.load(path[1],allow_pickle = True)[start_frame:start_frame+Len_of_frame,:]
ball = ball.astype(float)
ball = ball.reshape((-1,3,3))
pixelC = np.concatenate((skeleton,ball),axis=-2)
pixelCoord[path[-1]] = pixelC
#pixelCoord[path[-1]] = pixelCoord[path[-1]][start_frame:start_frame+Len_of_frame,:,:]
else:
for path in Pixel_coord_FIlE_List:
pixelCoord[path[1]] = np.load(path[0],allow_pickle = True)
pixelCoord[path[1]] = pixelCoord[path[1]][start_frame:start_frame+Len_of_frame,:,:]
#================== calibrate the cameras
#==================load charucoboard pixel data
def calibration(calibrate_frame):
"""
calibrate all cameras using charuco board
input variable:
calibrate_frame:number of frames avaiavle in the calibration folder, all camera should have the same number of calibration frames
return:
A tuple of all (3X4) projecion matrix
"""
def Charuco_Corners_filter(allIds,allCorners):
"""
input should be corners and ids of all cameras. pick the ids with shortest length and then recollect the corners.
"""
def dict_builder(ids, corners):
"""
create a dictionary that each Id holds its corresponding pixel coordinates
"""
if len(ids) != len(corners):
raise Exceptions('ids must match corners')
ids = ids.reshape((-1,))
ret = {}
ind = 0
for id in ids:
ret[id] = corners[ind]
ind += 1
return ret
A_allIds,B_allIds,C_allIds,D_allIds = allIds
A_Corners,B_Corners,C_Corners,D_Corners = allCorners
A_len,B_len,C_len,D_len = len(A_allIds),len(B_allIds),len(C_allIds),len(D_allIds)
A_Corners,B_Corners,C_Corners,D_Corners = A_Corners.reshape((A_len,2)),B_Corners.reshape((B_len,2)),C_Corners.reshape((C_len,2)),D_Corners.reshape((D_len,2))
A_dict,B_dict,C_dict,D_dict = dict_builder(A_allIds,A_Corners),dict_builder(B_allIds,B_Corners),dict_builder(C_allIds,C_Corners),dict_builder(D_allIds,D_Corners)
#in the case of 4 camera and the shortest length of ids is 20. the final result of this funciton should be four (20,2) array.
my_list = [A_len,B_len,C_len,D_len]
val, idx = min((val, idx) for (idx, val) in enumerate(my_list))#return the shortest length and its index(camera indx)
#ta,tb = min((2.1,22),(2,-1000000))
#print('test',ta,tb)
print(idx,':',val)
A,B,C,D = np.zeros((val,2)),np.zeros((val,2)),np.zeros((val,2)),np.zeros((val,2))
target_ids = allIds[idx].reshape((-1,))
c = 0
for i in target_ids:
A[c],B[c],C[c],D[c] = A_dict[i].reshape((2)),B_dict[i].reshape((2)),C_dict[i].reshape((2)),D_dict[i].reshape((2))
c+= 1
A,B,C,D = A.reshape((1,val,2)),B.reshape((1,val,2)),C.reshape((1,val,2)),D.reshape((1,val,2))
num_of_points = val
return A,B,C,D,num_of_points
#CamA
K_CamA,A_dist,A_rvecs,A_tvecs,A_corners, A_allIds = charuco_detect(baseFilePath+'/Calibration/'+cam1+'_Calibration/*jpg',cam1,video_resolution)#this function in ops line 437
A_corners = np.array(A_corners)
tvec_CamA,rvec_CamA = A_tvecs,A_rvecs#tvec/rvec generated 1 per image in the calibration folder, so we only need one of them
RoMat_A, _ = cv2.Rodrigues(rvec_CamA)#convert (3,1) rvec to (3,3) rotation matrix
H_CamA = R_t2H(RoMat_A,tvec_CamA)#this funciton in ops line 87, combine rotation matrix and tvec into a Homogeniens transformation matrix
#CamB
K_CamB,B_dist,B_rvecs,B_tvecs,B_corners,B_allIds = charuco_detect(baseFilePath+'/Calibration/'+cam2+'_Calibration/*jpg',cam2,video_resolution)
B_corners = np.array(B_corners)
tvec_CamB,rvec_CamB = B_tvecs,B_rvecs
RoMat_B, _ = cv2.Rodrigues(rvec_CamB) #convert
H_CamB = R_t2H(RoMat_B,tvec_CamB)
#CamC
K_CamC,C_dist,C_rvecs,C_tvecs,C_corners,C_allIds = charuco_detect(baseFilePath+'/Calibration/'+cam3+'_Calibration/*jpg',cam3,video_resolution)
C_corners = np.array(C_corners)
tvec_CamC,rvec_CamC = C_tvecs[1],C_rvecs[1]
RoMat_C, _ = cv2.Rodrigues(rvec_CamC) #convert
H_CamC = R_t2H(RoMat_C,tvec_CamC)
#CamD
K_CamD,D_dist,D_rvecs,D_tvecs,D_corners,D_allIds = charuco_detect(baseFilePath+'/Calibration/'+cam4+'_Calibration/*jpg',cam4,video_resolution)
D_corners = np.array(D_corners)
tvec_CamD,rvec_CamD = D_tvecs[1],D_rvecs[1]
RoMat_D, _ = cv2.Rodrigues(rvec_CamD) #convert
H_CamD = R_t2H(RoMat_D,tvec_CamD)
(A_allIds,B_allIds,C_allIds,D_allIds) = np.array(A_allIds),np.array(B_allIds),np.array(C_allIds),np.array(D_allIds)
(A_Corners,B_Corners,C_Corners,D_Corners) = np.array(A_corners),np.array(B_corners),np.array(C_corners),np.array(D_corners)
allIds = (A_allIds[0],B_allIds[0],C_allIds[0],D_allIds[0])
allCorners = (A_Corners[0],B_Corners[0],C_Corners[0],D_Corners[0])
#-======filtered pixel coordinates
A_corners,B_corners,C_corners,D_corners,calibration_points = Charuco_Corners_filter(allIds,allCorners)
if optimize_projection_matrix:
if num_of_cameras == 2:
MA,MB = get_TransMat(H_CamA,H_CamB)
PA,PB = np.dot(K_CamA,MA),np.dot(K_CamB,MB)
Proj_points = np.stack((A_corners,B_corners),axis = 2)#project points format should be (frame,#_of_keypoints,#_of_views,3), in this case (2,24,2,2)
Proj_Mat = np.stack((PA,PB),axis=0)
BA_points2D = np.stack((A_corners,B_corners),axis = 0)
input_param = np.hstack((PA.ravel(),PB.ravel()))
elif num_of_cameras == 3:
MA,MB,MC = get_TransMat(H_CamA,H_CamB,H_CamC)
PA,PB,PC = np.dot(K_CamA,MA),np.dot(K_CamB,MB),np.dot(K_CamC,MC)
Proj_points = np.stack((A_corners,B_corners,C_corners),axis = 2)#project points format should be (frame,#_of_keypoints,#_of_views,3), in this case (2,24,2,2)
Proj_Mat = np.stack((PA,PB,PC),axis=0)
BA_points2D = np.stack((A_corners,B_corners,C_corners),axis = 0)
input_param = np.hstack((PA.ravel(),PB.ravel(),PC.ravel()))
elif num_of_cameras == 4:
MA,MB,MC,MD = get_TransMat(H_CamA,H_CamB,H_CamC,H_Cam_D)
PA,PB,PC = np.dot(K_CamA,MA),np.dot(K_CamB,MB),np.dot(K_CamC,MC),np.dot(K_CamD,MD)
Proj_points = np.stack((A_corners,B_corners,C_corners,D_corners),axis = 2)#project points format should be (frame,#_of_keypoints,#_of_views,3), in this case (2,24,2,2)
Proj_Mat = np.stack((PA,PB,PC,PD),axis=0)
BA_points2D = np.stack((A_corners,B_corners,C_corners,D_corners),axis = 0)
input_param = np.hstack((PA.ravel(),PB.ravel(),PC.ravel(),PD.ravel()))
coords = triangulate(Proj_points,Proj_Mat).solveA()#tranigulate points
coords = coords[:,:,:-1]
input_points = coords.reshape((-1,))
ba_input = np.hstack((input_points,input_param))
VIS_cam_List = [0]*calibrate_frame*calibration_points #always assume camera is the principle view
refined_points,refined_Pmat = SBA(calibrate_frame,BA_points2D,ba_input,VIS_cam_List,calibration_points,'A')
l = len(refined_Pmat)//num_of_cameras
if num_of_cameras == 2:
PA,PB = (refined_Pmat[:l].reshape((3,4)),refined_Pmat[l:].reshape((3,4)))
return (PA,PB)
elif num_of_cameras == 3:
PA,PB,PC = (refined_Pmat[:l].reshape((3,4)),refined_Pmat[l:2*l].reshape((3,4),refined_Pmat[2*l:].reshape((3,4)))
return (PA,PB,PC)
elif num_of_cameras == 4:
PA,PB,PC,PC = (refined_Pmat[:l].reshape((3,4)),refined_Pmat[l:2*l].reshape((3,4),refined_Pmat[2*l:3*l].reshape((3,4),refined_Pmat[3*l:4*l].reshape((3,4)))
return (PA,PB,PC,PD)
def run(ProjectMatix):
#=================load skeleton 2d keypoints
pixelCoord = {}
if include_ball:
for path in Pixel_coord_FIlE_List_include_ball:
skeleton = np.load(path[0],allow_pickle = True)[start_frame:start_frame+Len_of_frame,:,:]
ball = np.load(path[1],allow_pickle = True)[start_frame:start_frame+Len_of_frame,:]
ball = ball.astype(float)
ball = ball.reshape((-1,3,3))
pixelC = np.concatenate((skeleton,ball),axis=-2)
pixelCoord[path[-1]] = pixelC
#pixelCoord[path[-1]] = pixelCoord[path[-1]][start_frame:start_frame+Len_of_frame,:,:]
else:
for path in Pixel_coord_FIlE_List:
pixelCoord[path[1]] = np.load(path[0],allow_pickle = True)
pixelCoord[path[1]] = pixelCoord[path[1]][start_frame:start_frame+Len_of_frame,:,:]
#==================concatnate data
if num_of_cameras == 2:
if base_Cam_Index == 'A':
Proj_points = np.stack((pixelCoord['CamA'],pixelCoord['CamB']),axis = 2)
Proj_Mat = np.stack((ProjectMatix[0],ProjectMatix[1]),axis=0)
elif base_Cam_Index == 'B':
Proj_points = np.stack((pixelCoord['CamB'],pixelCoord['CamA']),axis = 2)
Proj_Mat = np.stack((ProjectMatix[1],ProjectMatix[0]),axis=0)
elif num_of_cameras == 3:
if base_Cam_Index == 'A':
Proj_points = np.stack((pixelCoord['CamA'],pixelCoord['CamB'],pixelCoord['CamC']),axis = 2)
Proj_Mat = np.stack((ProjectMatix[0],ProjectMatix[1],ProjectMatix[2]),axis=0)
elif base_Cam_Index == 'B':
Proj_points = np.stack((pixelCoord['CamB'],pixelCoord['CamA'],pixelCoord['CamC']),axis = 2)
Proj_Mat = np.stack((ProjectMatix[1],ProjectMatix[0],ProjectMatix[2]),axis=0)
elif base_Cam_Index == 'C':
Proj_points = np.stack((pixelCoord['CamC'],pixelCoord['CamA'],pixelCoord['CamB']),axis = 2)
Proj_Mat = np.stack((ProjectMatix[2],ProjectMatix[0],ProjectMatix[1]),axis=0)
elif num_of_cameras == 4:
if base_Cam_Index == 'A':
Proj_points = np.stack((pixelCoord['CamA'],pixelCoord['CamB'],pixelCoord['CamC'],pixelCoord['CamD']),axis = 2)
Proj_Mat = np.stack((ProjectMatix[0],ProjectMatix[1],ProjectMatix[2],ProjectMatix[3]),axis=0)
elif base_Cam_Index == 'B':
Proj_points = np.stack((pixelCoord['CamB'],pixelCoord['CamA'],pixelCoord['CamC'],pixelCoord['CamD']),axis = 2)
Proj_Mat = np.stack((ProjectMatix[1],ProjectMatix[0],ProjectMatix[2],ProjectMatix[3]),axis=0)
elif base_Cam_Index == 'C':
Proj_points = np.stack((pixelCoord['CamC'],pixelCoord['CamA'],pixelCoord['CamB'],pixelCoord['CamD']),axis = 2)
Proj_Mat = np.stack((ProjectMatix[2],ProjectMatix[0],ProjectMatix[1],ProjectMatix[3]),axis=0)
elif base_Cam_Index == 'D':
Proj_points = np.stack((pixelCoord['CamD'],pixelCoord['CamA'],pixelCoord['CamB'],pixelCoord['CamC']),axis = 2)
Proj_Mat = np.stack((ProjectMatix[3],ProjectMatix[0],ProjectMatix[1],ProjectMatix[2]),axis=0)
coords = triangulate(Proj_points,Proj_Mat).solveA()#tranigulate points
coords = coords[:,:,:-1]
np.save(baseFilePath+'/Processed/DataPoints3D.npy',coords)
print('3D coordinates saved')
return coords
# input_points = coords.reshape((-1,))
# ba_input = np.hstack((input_points,input_param))
# print("optimization started")
# #C,M = SBA(Len_of_frame,Proj_Mat,BA_points2D,ba_input,VIS_cam_List)
# print('coords shape',coords.shape)
# np.save(processedFilePath+'reconstructed'+'.npy',coords)
# #np.save(SAVE_FOLDER+'out_optimized.npy',C)
# print('save sussesful')
if __name__ == "__main__":
ProjectMatix = calibration(2)
coords = run(ProjectMatix)
if num_of_cameras == 3:
Vis(SourceVideoFolder+'/'+Source_video_List[0][0],SourceVideoFolder+'/'+Source_video_List[1][0],SourceVideoFolder+'/'+Source_video_List[2][0],coords).display()
elif num_of_cameras == 2:
Vis(SourceVideoFolder+'/'+Source_video_List[1][0],SourceVideoFolder+'/'+Source_video_List[0][0],None,coords).display()
elif num_of_cameras == 4:
Vis(SourceVideoFolder+'/'+Source_video_List[0][0],SourceVideoFolder+'/'+Source_video_List[1][0],SourceVideoFolder+'/'+Source_video_List[2][0],SourceVideoFolder+'/'+Source_video_List[3][0],coords).display()