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utilsChecker.py
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utilsChecker.py
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import sys
sys.path.append("./mmpose") # utilities in child directory
import cv2
import numpy as np
import pandas as pd
import os
import glob
import pickle
import json
import subprocess
import urllib.request
import shutil
import utilsDataman
import requests
import ffmpeg
import matplotlib.pyplot as plt
from scipy.ndimage import gaussian_filter1d
from scipy.signal import gaussian, sosfiltfilt, butter, find_peaks
from scipy.interpolate import pchip_interpolate
from scipy.spatial.transform import Rotation
import scipy.linalg
from itertools import combinations
import copy
from utilsCameraPy3 import Camera, nview_linear_triangulations
from utils import getOpenPoseMarkerNames, getOpenPoseFaceMarkers
from utils import numpy2TRC, rewriteVideos, delete_multiple_element,loadCameraParameters
from utilsAPI import getAPIURL
from utilsAuth import getToken
API_TOKEN = getToken()
API_URL = getAPIURL()
# %%
def download_file(url, file_name):
with urllib.request.urlopen(url) as response, open(file_name, 'wb') as out_file:
shutil.copyfileobj(response, out_file)
# %%
def getVideoLength(filename):
result = subprocess.run(
["ffprobe", "-v", "error", "-show_entries", "format=duration",
"-of", "default=noprint_wrappers=1:nokey=1", filename],
stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
return float(result.stdout)
# %%
def video2Images(videoPath, nImages=12, tSingleImage=None, filePrefix='output', skipIfRun=True, outputFolder='default'):
# Pops images out of a video.
# If tSingleImage is defined (time, not frame number), only one image will be popped
if outputFolder == 'default':
outputFolder = os.path.dirname(videoPath)
# already written out?
if not os.path.exists(os.path.join(outputFolder, filePrefix + '_0.jpg')) or not skipIfRun:
if tSingleImage is not None: # pop single image at time value
CMD = ('ffmpeg -loglevel error -skip_frame nokey -y -ss ' + str(tSingleImage) + ' -i ' + videoPath +
" -qmin 1 -q:v 1 -frames:v 1 -vf select='-eq(pict_type\,I)' " +
os.path.join(outputFolder,filePrefix + '0.png'))
os.system(CMD)
outImagePath = os.path.join(outputFolder,filePrefix + '0.png')
else: # pop multiple images from video
lengthVideo = getVideoLength(videoPath)
timeImageSamples = np.linspace(1,lengthVideo-1,nImages) # disregard first and last second
for iFrame,t_image in enumerate(timeImageSamples):
CMD = ('ffmpeg -loglevel error -skip_frame nokey -ss ' + str(t_image) + ' -i ' + videoPath +
" -qmin 1 -q:v 1 -frames:v 1 -vf select='-eq(pict_type\,I)' " +
os.path.join(outputFolder,filePrefix) + '_' + str(iFrame) + '.jpg')
os.system(CMD)
outImagePath = os.path.join(outputFolder,filePrefix) + '0.jpg'
return outImagePath
# %%
def calcIntrinsics(folderName, CheckerBoardParams=None, filenames=['*.jpg'],
imageScaleFactor=1, visualize=False, saveFileName=None):
if CheckerBoardParams is None:
# number of black to black corners and side length (cm)
CheckerBoardParams = {'dimensions': (6,9), 'squareSize': 2.71}
if '*' in filenames[0]:
imageFiles = glob.glob(folderName + '/' + filenames[0])
else:
imageFiles = [] ;
for fName in filenames:
imageFiles.append(folderName + '/' + fName)
# stop the iteration when specified
# accuracy, epsilon, is reached or
# specified number of iterations are completed.
criteria = (cv2.TERM_CRITERIA_EPS +
cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
# Vector for 3D points
threedpoints = []
# Vector for 2D points
twodpoints = []
# 3D points real world coordinates
# objectp3d = generate3Dgrid(CheckerBoardParams)
# Load images in for calibration
for iImage, pathName in enumerate(imageFiles):
image = cv2.imread(pathName)
if imageScaleFactor != 1:
dim = (int(imageScaleFactor*image.shape[1]),int(imageScaleFactor*image.shape[0]))
image = cv2.resize(image,dim,interpolation=cv2.INTER_AREA)
imageSize = np.reshape(np.asarray(np.shape(image)[0:2]).astype(np.float64),(2,1)) # This all to be able to copy camera param dictionary
grayColor = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
print(pathName + ' used for intrinsics calibration.')
# Find the chess board corners
# If desired number of corners are
# found in the image then ret = true
# ret, corners = cv2.findChessboardCorners(
# grayColor, CheckerBoardParams['dimensions'],
# cv2.CALIB_CB_ADAPTIVE_THRESH
# + cv2.CALIB_CB_FAST_CHECK +
# cv2.CALIB_CB_NORMALIZE_IMAGE)
ret,corners,meta = cv2.findChessboardCornersSBWithMeta( grayColor, CheckerBoardParams['dimensions'],
cv2.CALIB_CB_EXHAUSTIVE +
cv2.CALIB_CB_ACCURACY +
cv2.CALIB_CB_LARGER)
# If desired number of corners can be detected then,
# refine the pixel coordinates and display
# them on the images of checker board
if ret == True:
# 3D points real world coordinates
checkerCopy = copy.copy(CheckerBoardParams)
checkerCopy['dimensions'] = meta.shape[::-1] # reverses order so width is first
objectp3d = generate3Dgrid(checkerCopy)
threedpoints.append(objectp3d)
# Refining pixel coordinates
# for given 2d points.
# corners2 = cv2.cornerSubPix(
# grayColor, corners, (11, 11), (-1, -1), criteria)
corners2 = corners/imageScaleFactor # Don't need subpixel refinement with findChessboardCornersSBWithMeta
twodpoints.append(corners2)
# Draw and display the corners
image = cv2.drawChessboardCorners(image,
meta.shape[::-1],
corners2, ret)
#findAspectRatio
ar = imageSize[1]/imageSize[0]
# cv2.namedWindow("img", cv2.WINDOW_NORMAL)
cv2.resize(image,(int(600*ar),600))
# Save intrinsic images
imageSaveDir = os.path.join(folderName,'IntrinsicCheckerboards')
if not os.path.exists(imageSaveDir):
os.mkdir(imageSaveDir)
cv2.imwrite(os.path.join(imageSaveDir,'intrinsicCheckerboard' + str(iImage) + '.jpg'), image)
if visualize:
print('Press enter or close image to continue')
cv2.imshow('img', image)
cv2.waitKey(0)
cv2.destroyAllWindows()
if ret == False:
print("Couldn't find checkerboard in " + pathName)
if len(twodpoints) < .5*len(imageFiles):
print('Checkerboard not detected in at least half of intrinsic images. Re-record video.')
return None
# Perform camera calibration by
# passing the value of above found out 3D points (threedpoints)
# and its corresponding pixel coordinates of the
# detected corners (twodpoints)
ret, matrix, distortion, r_vecs, t_vecs = cv2.calibrateCamera(
threedpoints, twodpoints, grayColor.shape[::-1], None, None)
CamParams = {'distortion':distortion,'intrinsicMat':matrix,'imageSize':imageSize}
if saveFileName is not None:
saveCameraParameters(saveFileName,CamParams)
return CamParams
# %%
def computeAverageIntrinsics(session_path,trialIDs,CheckerBoardParams,nImages=25):
CamParamList = []
camModels = []
for trial_id in trialIDs:
resp = requests.get(API_URL + "trials/{}/".format(trial_id),
headers = {"Authorization": "Token {}".format(API_TOKEN)})
trial = resp.json()
camModels.append(trial['videos'][0]['parameters']['model'])
trial_name = trial['name']
if trial_name == 'null':
trial_name = trial_id
# Make directory (folder for trialname, intrinsics also saved there)
video_dir = os.path.join(session_path,trial_name)
os.makedirs(video_dir, exist_ok=True)
video_path = os.path.join(video_dir,trial_name + ".mov")
# Download video if not done
if not os.path.exists(video_path):
download_file(trial["videos"][0]["video"], video_path)
if not os.path.exists(os.path.join(video_dir,'cameraIntrinsics.pickle')):
# Compute intrinsics from images popped out of intrinsic video.
video2Images(video_path,filePrefix=trial_name,nImages=nImages)
CamParams = calcIntrinsics(os.path.join(session_path,trial_name), CheckerBoardParams=CheckerBoardParams,
filenames=['*.jpg'],
saveFileName=os.path.join(video_dir,'cameraIntrinsics.pickle'),
visualize = False)
if CamParams is None:
saveCameraParameters(os.path.join(video_dir,'cameraIntrinsics.pickle'),CamParams)
else:
CamParams = loadCameraParameters(os.path.join(video_dir,'cameraIntrinsics.pickle'))
if CamParams is not None:
CamParamList.append(CamParams)
# Compute results
intComp = {}
intComp['fx'] = [c['intrinsicMat'][0,0] for c in CamParamList]
intComp['fy'] = [c['intrinsicMat'][1,1] for c in CamParamList]
intComp['cx'] = [c['intrinsicMat'][0,2] for c in CamParamList]
intComp['cy'] = [c['intrinsicMat'][1,2] for c in CamParamList]
intComp['d'] = np.asarray([c['distortion'] for c in CamParamList])
intCompNames = list(intComp.keys())
for v in intCompNames:
intComp[v + '_u'] = np.mean(intComp[v],axis=0)
intComp[v + '_std'] = np.std(intComp[v],axis=0)
intComp[v + '_stdPerc'] = np.divide(intComp[v + '_std'],intComp[v + '_u']) * 100
phoneModel = camModels[0]
if any([camModel != phoneModel for camModel in camModels]):
raise Exception('You are averaging intrinsics across different phone models.')
# output averaged parameters
CamParamsAverage = {}
params = list(CamParamList[0].keys())
for param in params:
CamParamsAverage[param] = np.mean(np.asarray([c[param] for c in CamParamList]),axis=0)
return CamParamsAverage, CamParamList, intComp, phoneModel
# %%
def generate3Dgrid(CheckerBoardParams):
# 3D points real world coordinates. Assuming z=0
objectp3d = np.zeros((1, CheckerBoardParams['dimensions'][0]
* CheckerBoardParams['dimensions'][1],
3), np.float32)
objectp3d[0, :, :2] = np.mgrid[0:CheckerBoardParams['dimensions'][0],
0:CheckerBoardParams['dimensions'][1]].T.reshape(-1, 2)
objectp3d = objectp3d * CheckerBoardParams['squareSize']
return objectp3d
# %%
def saveCameraParameters(filename,CameraParams):
if not os.path.exists(os.path.dirname(filename)):
os.makedirs(os.path.dirname(filename),exist_ok=True)
open_file = open(filename, "wb")
pickle.dump(CameraParams, open_file)
open_file.close()
return True
#%%
def getVideoRotation(videoPath):
meta = ffmpeg.probe(videoPath)
try:
rotation = meta['format']['tags']['com.apple.quicktime.video-orientation']
except:
# For AVI (after we rewrite video), no rotation paramter, so just using h and w.
# For now this is ok, we don't need leaning right/left for this, just need to know
# how to orient the pose estimation resolution parameters.
try:
if meta['format']['format_name'] == 'avi':
if meta['streams'][0]['height']>meta['streams'][0]['width']:
rotation = 90
else:
rotation = 0
else:
raise Exception('no rotation info')
except:
rotation = 90 # upright is 90, and intrinsics were captured in that orientation
return int(rotation)
#%%
def rotateIntrinsics(CamParams,videoPath):
rotation = getVideoRotation(videoPath)
# upright is 90, which is how intrinsics recorded so no rotation needed
if rotation !=90:
originalCamParams = copy.deepcopy(CamParams)
fx = originalCamParams['intrinsicMat'][0,0]
fy = originalCamParams['intrinsicMat'][1,1]
px = originalCamParams['intrinsicMat'][0,2]
py = originalCamParams['intrinsicMat'][1,2]
lx = originalCamParams['imageSize'][1]
ly = originalCamParams['imageSize'][0]
if rotation == 0: # leaning left
# Flip image size
CamParams['imageSize'] = np.flipud(CamParams['imageSize'])
# Flip focal lengths
CamParams['intrinsicMat'][0,0] = fy
CamParams['intrinsicMat'][1,1] = fx
# Change principle point - from upper left
CamParams['intrinsicMat'][0,2] = py
CamParams['intrinsicMat'][1,2] = lx-px
elif rotation == 180: # leaning right
# Flip image size
CamParams['imageSize'] = np.flipud(CamParams['imageSize'])
# Flip focal lengths
CamParams['intrinsicMat'][0,0] = fy
CamParams['intrinsicMat'][1,1] = fx
# Change principle point - from upper left
CamParams['intrinsicMat'][0,2] = ly-py
CamParams['intrinsicMat'][1,2] = px
elif rotation == 270: # upside down
# Change principle point - from upper left
CamParams['intrinsicMat'][0,2] = lx-px
CamParams['intrinsicMat'][1,2] = ly-py
return CamParams
# %%
def calcExtrinsics(imageFileName, CameraParams, CheckerBoardParams,
imageScaleFactor=1,visualize=False,
imageUpsampleFactor=1,useSecondExtrinsicsSolution=False):
# Camera parameters is a dictionary with intrinsics
# stop the iteration when specified
# accuracy, epsilon, is reached or
# specified number of iterations are completed.
criteria = (cv2.TERM_CRITERIA_EPS +
cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
# Vector for 3D points
threedpoints = []
# Vector for 2D points
twodpoints = []
# 3D points real world coordinates. Assuming z=0
objectp3d = generate3Dgrid(CheckerBoardParams)
# Load and resize image - remember calibration image res needs to be same as all processing
image = cv2.imread(imageFileName)
if imageScaleFactor != 1:
dim = (int(imageScaleFactor*image.shape[1]),int(imageScaleFactor*image.shape[0]))
image = cv2.resize(image,dim,interpolation=cv2.INTER_AREA)
if imageUpsampleFactor != 1:
dim = (int(imageUpsampleFactor*image.shape[1]),int(imageUpsampleFactor*image.shape[0]))
imageUpsampled = cv2.resize(image,dim,interpolation=cv2.INTER_AREA)
else:
imageUpsampled = image
# Find the chess board corners
# If desired number of corners are
# found in the image then ret = true
#TODO need to add a timeout to the findChessboardCorners function
grayColor = cv2.cvtColor(imageUpsampled, cv2.COLOR_BGR2GRAY)
## Contrast TESTING - openCV does thresholding already, but this may be a bit helpful for bumping contrast
# grayColor = grayColor.astype('float64')
# cv2.imshow('Grayscale', grayColor.astype('uint8'))
# savePath = os.path.join(os.path.dirname(imageFileName),'extrinsicGray.jpg')
# cv2.imwrite(savePath,grayColor)
# grayContrast = np.power(grayColor,2)
# grayContrast = grayContrast/(np.max(grayContrast)/255)
# # plt.figure()
# # plt.imshow(grayContrast, cmap='gray')
# # cv2.imshow('ContrastEnhanced', grayContrast.astype('uint8'))
# savePath = os.path.join(os.path.dirname(imageFileName),'extrinsicGrayContrastEnhanced.jpg')
# cv2.imwrite(savePath,grayContrast)
# grayContrast = grayContrast.astype('uint8')
# grayColor = grayColor.astype('uint8')
## End contrast Testing
## Testing settings - slow and don't help
# ret, corners = cv2.findChessboardCorners(
# grayContrast, CheckerBoardParams['dimensions'],
# cv2.CALIB_CB_ADAPTIVE_THRESH
# + cv2.CALIB_CB_FAST_CHECK +
# cv2.CALIB_CB_NORMALIZE_IMAGE)
# Note I tried findChessboardCornersSB here, but it didn't find chessboard as reliably
ret, corners = cv2.findChessboardCorners(
grayColor, CheckerBoardParams['dimensions'],
cv2.CALIB_CB_ADAPTIVE_THRESH)
# If desired number of corners can be detected then,
# refine the pixel coordinates and display
# them on the images of checker board
if ret == True:
# 3D points real world coordinates
threedpoints.append(objectp3d)
# Refining pixel coordinates
# for given 2d points.
corners2 = cv2.cornerSubPix(
grayColor, corners, (11, 11), (-1, -1), criteria) / imageUpsampleFactor
twodpoints.append(corners2)
# For testing: Draw and display the corners
# image = cv2.drawChessboardCorners(image,
# CheckerBoardParams['dimensions'],
# corners2, ret)
# Draw small dots instead
# Choose dot size based on size of squares in pixels
circleSize = 1
squareSize = np.linalg.norm((corners2[1,0,:] - corners2[0,0,:]).squeeze())
if squareSize >12:
circleSize = 2
for iPoint in range(corners2.shape[0]):
thisPt = corners2[iPoint,:,:].squeeze()
cv2.circle(image, tuple(thisPt.astype(int)), circleSize, (255,255,0), 2)
#cv2.imshow('img', image)
#cv2.waitKey(0)
#cv2.destroyAllWindows()
if ret == False:
print('No checkerboard detected. Will skip cam in triangulation.')
return None
# Find position and rotation of camera in board frame.
# ret, rvec, tvec = cv2.solvePnP(objectp3d, corners2,
# CameraParams['intrinsicMat'],
# CameraParams['distortion'])
# This function gives two possible solutions.
# It helps with the ambiguous cases with small checkerboards (appears like
# left handed coord system). Unfortunately, there isn't a clear way to
# choose the correct solution. It is the nature of the solvePnP problem
# with a bit of 2D point noise.
rets, rvecs, tvecs, reprojError = cv2.solvePnPGeneric(
objectp3d, corners2, CameraParams['intrinsicMat'],
CameraParams['distortion'], flags=cv2.SOLVEPNP_IPPE)
rvec = rvecs[1]
tvec = tvecs[1]
if rets < 1 or np.max(rvec) == 0 or np.max(tvec) == 0:
print('solvePnPGeneric failed. Use SolvePnPRansac')
# Note: can input extrinsics guess if we generally know where they are.
# Add to lists to look like solvePnPRansac results
rvecs = []
tvecs = []
ret, rvec, tvec, inliers = cv2.solvePnPRansac(
objectp3d, corners2, CameraParams['intrinsicMat'],
CameraParams['distortion'])
if ret is True:
rets = 1
rvecs.append(rvec)
tvecs.append(tvec)
else:
print('Extrinsic calculation failed. Will skip cam in triangulation.')
return None
# Select which extrinsics solution to use
extrinsicsSolutionToUse = 0
if useSecondExtrinsicsSolution:
extrinsicsSolutionToUse = 1
topLevelExtrinsicImageFolder = os.path.abspath(
os.path.join(os.path.dirname(imageFileName),
'../../../../CalibrationImages'))
if not os.path.exists(topLevelExtrinsicImageFolder):
os.makedirs(topLevelExtrinsicImageFolder,exist_ok=True)
for iRet,rvec,tvec in zip(range(rets),rvecs,tvecs):
theseCameraParams = copy.deepcopy(CameraParams)
# Show reprojections
img_points, _ = cv2.projectPoints(objectp3d, rvec, tvec,
CameraParams['intrinsicMat'],
CameraParams['distortion'])
# Plot reprojected points
# for c in img_points.squeeze():
# cv2.circle(image, tuple(c.astype(int)), 2, (0, 255, 0), 2)
# Show object coordinate system
imageCopy = copy.deepcopy(image)
imageWithFrame = cv2.drawFrameAxes(
imageCopy, CameraParams['intrinsicMat'],
CameraParams['distortion'], rvec, tvec, 200, 4)
# Create zoomed version.
ht = image.shape[0]
wd = image.shape[1]
bufferVal = 0.05 * np.mean([ht,wd])
topEdge = int(np.max([np.squeeze(np.min(img_points,axis=0))[1]-bufferVal,0]))
leftEdge = int(np.max([np.squeeze(np.min(img_points,axis=0))[0]-bufferVal,0]))
bottomEdge = int(np.min([np.squeeze(np.max(img_points,axis=0))[1]+bufferVal,ht]))
rightEdge = int(np.min([np.squeeze(np.max(img_points,axis=0))[0]+bufferVal,wd]))
# imageCopy2 = copy.deepcopy(imageWithFrame)
imageCropped = imageCopy[topEdge:bottomEdge,leftEdge:rightEdge,:]
# Save extrinsics picture with axis
imageSize = np.shape(image)[0:2]
#findAspectRatio
ar = imageSize[1]/imageSize[0]
# cv2.namedWindow("axis", cv2.WINDOW_NORMAL)
cv2.resize(imageWithFrame,(600,int(np.round(600*ar))))
# save crop image to local camera file
savePath2 = os.path.join(os.path.dirname(imageFileName),
'extrinsicCalib_soln{}.jpg'.format(iRet))
cv2.imwrite(savePath2,imageCropped)
if visualize:
print('Close image window to continue')
cv2.imshow('axis', image)
cv2.waitKey()
cv2.destroyAllWindows()
R_worldFromCamera = cv2.Rodrigues(rvec)[0]
theseCameraParams['rotation'] = R_worldFromCamera
theseCameraParams['translation'] = tvec
theseCameraParams['rotation_EulerAngles'] = rvec
# save extrinsics parameters to video folder
# will save the selected parameters in Camera folder in main
saveExtPath = os.path.join(
os.path.dirname(imageFileName),
'cameraIntrinsicsExtrinsics_soln{}.pickle'.format(iRet))
saveCameraParameters(saveExtPath,theseCameraParams)
# save images to top level folder and return correct extrinsics
camName = os.path.split(os.path.abspath(
os.path.join(os.path.dirname(imageFileName), '../../')))[1]
if iRet == extrinsicsSolutionToUse:
fullCamName = camName
CameraParamsToUse = copy.deepcopy(theseCameraParams)
else:
fullCamName = 'altSoln_{}'.format(camName)
savePath = os.path.join(topLevelExtrinsicImageFolder, 'extrinsicCalib_'
+ fullCamName + '.jpg')
cv2.imwrite(savePath,imageCropped)
return CameraParamsToUse
# %%
def calcExtrinsicsFromVideo(videoPath, CamParams, CheckerBoardParams,
visualize=False, imageUpsampleFactor=2,
useSecondExtrinsicsSolution=False):
# Get video parameters.
vidLength = getVideoLength(videoPath)
videoDir, videoName = os.path.split(videoPath)
# Pick end of video as only sample point. For some reason, won't output
# video with t close to vidLength, so we count down til it does.
tSampPts = [np.round(vidLength-0.3, decimals=1)]
upsampleIters = 0
for iTime,t in enumerate(tSampPts):
# Pop an image.
imagePath = os.path.join(videoDir, 'extrinsicImage0.png')
if os.path.exists(imagePath):
os.remove(imagePath)
while not os.path.exists(imagePath) and t>=0:
video2Images(videoPath, nImages=1, tSingleImage=t, filePrefix='extrinsicImage', skipIfRun=False)
t -= 0.2
# Default to beginning if can't find a keyframe.
if not os.path.exists(imagePath):
video2Images(videoPath, nImages=1, tSingleImage=0.01, filePrefix='extrinsicImage', skipIfRun=False)
# Throw error if it can't find a keyframe.
if not os.path.exists(imagePath):
exception = 'No calibration image could be extracted for at least one camera. Verify your setup and try again. Visit https://www.opencap.ai/best-pratices to learn more about camera calibration and https://www.opencap.ai/troubleshooting for potential causes for a failed calibration.'
raise Exception(exception, exception)
# Try to find the checkerboard; return None if you can't find it.
CamParamsTemp = calcExtrinsics(
os.path.join(videoDir, 'extrinsicImage0.png'),
CamParams, CheckerBoardParams, visualize=visualize,
imageUpsampleFactor=imageUpsampleFactor,
useSecondExtrinsicsSolution=useSecondExtrinsicsSolution)
while iTime == 0 and CamParamsTemp is None and upsampleIters < 3:
if imageUpsampleFactor > 1:
imageUpsampleFactor = 1
elif imageUpsampleFactor == 1:
imageUpsampleFactor = .5
elif imageUpsampleFactor < 1:
imageUpsampleFactor = 1
CamParamsTemp = calcExtrinsics(
os.path.join(videoDir, 'extrinsicImage0.png'),
CamParams, CheckerBoardParams, visualize=visualize,
imageUpsampleFactor=imageUpsampleFactor,
useSecondExtrinsicsSolution=useSecondExtrinsicsSolution)
upsampleIters += 1
if CamParamsTemp is not None:
# If checkerboard was found, exit.
CamParams = CamParamsTemp.copy()
return CamParams
# If made it through but didn't return camera params, throw an error.
exception = 'The checkerboard was not detected by at least one camera. Verify your setup and try again. Visit https://www.opencap.ai/best-pratices to learn more about camera calibration and https://www.opencap.ai/troubleshooting for potential causes for a failed calibration.'
raise Exception(exception, exception)
return None
# %%
def isCheckerboardUpsideDown(CameraParams):
# With backwall orientation, R[1,1] will always be positive in correct orientation
# and negative if upside down
for cam in list(CameraParams.keys()):
if CameraParams[cam] is not None:
upsideDown = CameraParams[cam]['rotation'][1,1] < 0
break
#Default if no camera params (which is a garbage case anyway)
upsideDown = False
return upsideDown
# %%
def autoSelectExtrinsicSolution(sessionDir,keypoints2D,confidence,extrinsicsOptions):
keypoints2D = copy.copy(keypoints2D)
confidence = copy.copy(confidence)
camNames = list(extrinsicsOptions.keys())
optimalCalibrationDict = {}
# Order the cameras based on biggest difference between solutions. Want to start
# with these
if len(camNames)>2:
camNames = orderCamerasForAutoCalDetection(extrinsicsOptions)
# Find first pair of cameras
optimalCalibrationDict[camNames[0]], optimalCalibrationDict[camNames[1]] = computeOptimalCalibrationCombination(
keypoints2D,confidence,extrinsicsOptions,[camNames[0],camNames[1]])
# Continue for third and additional cameras
additionalCameras = []
if len(camNames)>2:
additionalCameras = camNames[2:]
for camName in additionalCameras:
_, optimalCalibrationDict[camName] = computeOptimalCalibrationCombination(
keypoints2D,confidence,extrinsicsOptions,[camNames[0],camName],
firstCamSoln=optimalCalibrationDict[camNames[0]])
# save calibrationJson to Videos
calibrationOptionsFile = os.path.join(sessionDir,'Videos','calibOptionSelections.json')
with open(calibrationOptionsFile, 'w') as f:
json.dump(optimalCalibrationDict, f)
f.close()
# Make camera params dict
CamParamDict = {}
for camName in camNames:
CamParamDict[camName] = extrinsicsOptions[camName][optimalCalibrationDict[camName]]
# Switch any cameras in local file system.
for cam,val in optimalCalibrationDict.items():
if val == 1:
saveFileName = os.path.join(sessionDir,'Videos',cam,'cameraIntrinsicsExtrinsics.pickle')
saveCameraParameters(saveFileName,extrinsicsOptions[cam][1])
return CamParamDict
# %%
def computeOptimalCalibrationCombination(keypoints2D,confidence,extrinsicsOptions,
CamNames,firstCamSoln=None):
if firstCamSoln is None:
firstCamOptions = range(len(extrinsicsOptions[CamNames[0]]))
else:
firstCamOptions = [firstCamSoln]
# Remove face markers - they are intermittent.
_, idxFaceMarkers = getOpenPoseFaceMarkers()
#find most confident frame
confidenceMat = np.minimum(confidence[CamNames[0]],confidence[CamNames[1]])
confidenceMat = np.delete(confidenceMat, idxFaceMarkers,axis=0)
iFrame = np.argmax(confidenceMat.mean(axis=1))
# put keypoints in list and delete face markers
keypointList = [np.expand_dims(np.delete(keypoints2D[CamNames[0]],idxFaceMarkers,axis=0)[:,iFrame],axis=1),
np.expand_dims(np.delete(keypoints2D[CamNames[1]],idxFaceMarkers,axis=0)[:,iFrame],axis=1)]
meanReprojectionErrors = []
combinations = []
for iCam0 in firstCamOptions:
for iCam1 in range(len(extrinsicsOptions[CamNames[1]])):
combinations.append([iCam0,iCam1])
CameraParamList = [extrinsicsOptions[CamNames[0]][iCam0],
extrinsicsOptions[CamNames[1]][iCam1]]
# triangulate
points3D,_ = triangulateMultiview(CameraParamList,keypointList)
# reproject
# Make list of camera objects
cameraObjList = []
for camParams in CameraParamList:
c = Camera()
c.set_K(camParams['intrinsicMat'])
c.set_R(camParams['rotation'])
c.set_t(np.reshape(camParams['translation'],(3,1)))
cameraObjList.append(c)
# Organize points for reprojectionError function
stackedPoints = np.stack([k[:,None,0,:] for k in keypointList])
pointsInput = []
for i in range(stackedPoints.shape[1]):
pointsInput.append(stackedPoints[:,i,0,:].T)
# Calculate combined reprojection error
reprojError = calcReprojectionError(cameraObjList,pointsInput,points3D,
normalizeError=True)
meanReprojectionErrors.append(np.mean(reprojError))
# Select solution with minimum error
idx = np.argmin(meanReprojectionErrors)
if (sorted(meanReprojectionErrors)[1]-sorted(meanReprojectionErrors)[0])/sorted(meanReprojectionErrors)[0] < .5:
# This only happens when the vector from checker board origin to camera is < a few degrees. If you offset the board
# vertically by a few feet, the solution becomes very clear again (ratio of ~7). We could throw an error.
print("Warning: not much separability between auto checker board selection options. Try moving checkerboard closer to cameras, and move it so it is not straight-on with any of the cameras.")
# Pick default to first solution when they are super close like this.
idx = 0
return combinations[idx][0], combinations[idx][1]
# %%
def orderCamerasForAutoCalDetection(extrinsicsOptions):
# dict of rotations between first and second solutions
rotDifs = []
testVec = np.array((1,0,0))
for cals in extrinsicsOptions.values():
rot = np.matmul(cals[0]['rotation'],cals[1]['rotation'])
# This will be close to 0 if the rotation was small, 2 if it is 180
rotDifs.append(1-np.dot(np.matmul(testVec,rot),testVec))
sortedCams = [cam for _, cam in sorted(zip(rotDifs, extrinsicsOptions.keys()),reverse=True)]
return sortedCams
# %%
def synchronizeVideos(CameraDirectories, trialRelativePath, pathPoseDetector,
undistortPoints=False, CamParamDict=None,
confidenceThreshold=0.3,
filtFreqs={'gait':12,'default':30},
imageBasedTracker=False, cams2Use=['all'],
poseDetector='OpenPose', trialName=None, bbox_thr=0.8,
resolutionPoseDetection='default',
visualizeKeypointAnimation=False):
markerNames = getOpenPoseMarkerNames()
# Create list of cameras.
if cams2Use[0] == 'all':
cameras2Use = list(CameraDirectories.keys())
else:
cameras2Use = cams2Use
cameras2Use_in = copy.deepcopy(cameras2Use)
# Initialize output lists
pointList = []
confList = []
CameraDirectories_selectedCams = {}
CamParamList_selectedCams = []
for cam in cameras2Use:
CameraDirectories_selectedCams[cam] = CameraDirectories[cam]
CamParamList_selectedCams.append(CamParamDict[cam])
# Load data.
camsToExclude = []
for camName in CameraDirectories_selectedCams:
cameraDirectory = CameraDirectories_selectedCams[camName]
videoFullPath = os.path.normpath(os.path.join(cameraDirectory,
trialRelativePath))
trialPrefix, _ = os.path.splitext(
os.path.basename(trialRelativePath))
if poseDetector == 'OpenPose':
outputPklFolder = "OutputPkl_" + resolutionPoseDetection
elif poseDetector == 'mmpose':
outputPklFolder = "OutputPkl_mmpose_" + str(bbox_thr)
openposePklDir = os.path.join(outputPklFolder, trialName)
pathOutputPkl = os.path.join(cameraDirectory, openposePklDir)
ppPklPath = os.path.join(pathOutputPkl, trialPrefix+'_rotated_pp.pkl')
key2D, confidence = loadPklVideo(
ppPklPath, videoFullPath, imageBasedTracker=imageBasedTracker,
poseDetector=poseDetector,confidenceThresholdForBB=0.3)
thisVideo = cv2.VideoCapture(videoFullPath.replace('.mov', '_rotated.avi'))
frameRate = np.round(thisVideo.get(cv2.CAP_PROP_FPS))
if key2D.shape[1] == 0 and confidence.shape[1] == 0:
camsToExclude.append(camName)
else:
pointList.append(key2D)
confList.append(confidence)
# If video is not existing, the corresponding camera should be removed.
idx_camToExclude = []
for camToExclude in camsToExclude:
cameras2Use.remove(camToExclude)
CameraDirectories_selectedCams.pop(camToExclude)
idx_camToExclude.append(cameras2Use_in.index(camToExclude))
# By removing the cameras in CamParamDict and CameraDirectories, we
# modify those dicts in main, which is needed for the next stages.
CamParamDict.pop(camToExclude)
CameraDirectories.pop(camToExclude)
delete_multiple_element(CamParamList_selectedCams, idx_camToExclude)
# Creates a web animation for each camera's keypoints. For debugging.
if visualizeKeypointAnimation:
import plotly.express as px
import plotly.io as pio
pio.renderers.default = 'browser'
for i,data in enumerate(pointList):
nPoints,nFrames,_ = data.shape
# Reshape the 3D numpy array to 2D, preserving point and frame indices
data_reshaped = np.copy(data).reshape(-1, 2)
# Create DataFrame
df = pd.DataFrame(data_reshaped, columns=['x', 'y'])
# Add columns for point number and frame number
df['Point'] = np.repeat(np.arange(nPoints), nFrames)
df['Frame'] = np.tile(np.arange(nFrames), nPoints)
# Reorder columns if needed
df = df[['Point', 'Frame', 'x', 'y']]
# Create a figure and add an animated scatter plot
fig = px.scatter(df,x='x', y='y', title="Cam " + str(i),
animation_frame='Frame',
range_x=[0, 1200], range_y=[1200,0],
color='Point', color_continuous_scale=px.colors.sequential.Viridis)
# Show the animation
fig.show()
# Synchronize keypoints.
pointList, confList, nansInOutList,startEndFrameList = synchronizeVideoKeypoints(
pointList, confList, confidenceThreshold=confidenceThreshold,
filtFreqs=filtFreqs, sampleFreq=frameRate, visualize=False,
maxShiftSteps=2*frameRate, CameraParams=CamParamList_selectedCams,
cameras2Use=cameras2Use,
CameraDirectories=CameraDirectories_selectedCams, trialName=trialName)
if undistortPoints:
if CamParamList_selectedCams is None:
raise Exception('Need to have CamParamList to undistort Images')
# nFrames = pointList[0].shape[1]
unpackedPoints = unpackKeypointList(pointList) ;
undistortedPoints = []
for points in unpackedPoints:
undistortedPoints.append(undistort2Dkeypoints(
points, CamParamList_selectedCams, useIntrinsicMatAsP=True))
pointList = repackKeypointList(undistortedPoints)
pointDir = {}
confDir = {}
nansInOutDir = {}
startEndFrames = {}
for iCam, camName in enumerate(CameraDirectories_selectedCams):
pointDir[camName] = pointList[iCam]
confDir[camName] = confList[iCam]
nansInOutDir[camName] = nansInOutList[iCam]
startEndFrames[camName] = startEndFrameList[iCam]
return pointDir, confDir, markerNames, frameRate, nansInOutDir, startEndFrames, cameras2Use
# %%
def synchronizeVideoKeypoints(keypointList, confidenceList,
confidenceThreshold=0.3,
filtFreqs = {'gait':12,'default':500},
sampleFreq=30, visualize=False, maxShiftSteps=30,
isGait=False, CameraParams = None,
cameras2Use=['none'],CameraDirectories = None,
trialName=None, trialID=''):
visualize2Dkeypoint = False # this is a visualization just for testing what filtered input data looks like
# keypointList is a mCamera length list of (nmkrs,nTimesteps,2) arrays of camera 2D keypoints
print('Synchronizing Keypoints')
# Deep copies such that the inputs do not get modified.
c_CameraParams = copy.deepcopy(CameraParams)
c_cameras2Use = copy.deepcopy(cameras2Use)
c_CameraDirectoryDict = copy.deepcopy(CameraDirectories)
# Turn Camera Dict into List
c_CameraDirectories = list(c_CameraDirectoryDict.values())
# Check if one camera has only 0s as confidence scores, which would mean
# no one has been properly identified. We want to kick out this camera
# from the synchronization and triangulation. We do that by popping out
# the corresponding data before syncing and add back 0s later.
badCameras = []
for icam, conf in enumerate(confidenceList):
if np.max(conf) == 0.0:
badCameras.append(icam)
idxBadCameras = [badCameras[i]-i for i in range(len(badCameras))]
for idxBadCamera in idxBadCameras:
print('{} kicked out of synchronization'.format(
c_cameras2Use[idxBadCamera]))
keypointList.pop(idxBadCamera)
confidenceList.pop(idxBadCamera)
c_CameraParams.pop(idxBadCamera)
c_cameras2Use.pop(idxBadCamera)
c_CameraDirectories.pop(idxBadCamera)
markerNames = getOpenPoseMarkerNames()
mkrDict = {mkr:iMkr for iMkr,mkr in enumerate(markerNames)}
# First, remove occluded markers
footMkrs = {'right':[mkrDict['RBigToe'], mkrDict['RSmallToe'], mkrDict['RHeel'],mkrDict['RAnkle']],
'left':[mkrDict['LBigToe'], mkrDict['LSmallToe'], mkrDict['LHeel'],mkrDict['LAnkle']]}
armMkrs = {'right':[mkrDict['RElbow'], mkrDict['RWrist']],
'left':[mkrDict['LElbow'], mkrDict['LWrist']]}
plt.close('all')
# Copy for visualization
keypointListUnfilt = copy.deepcopy(keypointList)
# remove occluded foot markers (uses large differences in confidence)
keypointList,confidenceList = zip(*[removeOccludedSide(keys,conf,footMkrs,confidenceThreshold,visualize=False) for keys,conf in zip(keypointList,confidenceList)])
# remove occluded arm markers
keypointList,confidenceList = zip(*[removeOccludedSide(keys,conf,armMkrs,confidenceThreshold,visualize=False) for keys,conf in zip(keypointList,confidenceList)])
# Copy for visualization
keypointListOcclusionRemoved = copy.deepcopy(keypointList)
# Don't change these. The ankle markers are used for gait detector
markers4VertVel = [mkrDict['RAnkle'], mkrDict['LAnkle']] # R&L Ankles and Heels did best. There are some issues though - like when foot marker velocity is aligned with camera ray
markers4HandPunch = [mkrDict['RWrist'], mkrDict['LWrist'],mkrDict['RShoulder'],mkrDict['LShoulder']]
markers4Ankles = [mkrDict['RAnkle'],mkrDict['LAnkle']]
# find velocity signals for synchronization
nCams = len(keypointList)
vertVelList = []
mkrSpeedList = []
handPunchVertPositionList = []
allMarkerList = []
for (keyRaw,conf) in zip(keypointList,confidenceList):
keyRaw_clean, _, _, _ = clean2Dkeypoints(keyRaw,conf,confidenceThreshold=0.3,nCams=nCams,linearInterp=True)
keyRaw_clean_smooth = smoothKeypoints(keyRaw_clean, sdKernel=3)
handPunchVertPositionList.append(getPositions(keyRaw_clean_smooth,markers4HandPunch,direction=1))
vertVelList.append(getVertVelocity(keyRaw_clean_smooth)) # doing it again b/c these settings work well for synchronization
mkrSpeedList.append(getMarkerSpeed(keyRaw_clean_smooth,markers4VertVel,confidence=conf,averageVels=False)) # doing it again b/c these settings work well for synchronization
allMarkerList.append(keyRaw_clean_smooth)
# Find indices with high confidence that overlap between cameras.
# Note: Could get creative and do camera pair syncing in the future, based
# on cameras with greatest amount of overlapping confidence.
overlapInds_clean, minConfLength_all = findOverlap(confidenceList,
markers4VertVel)
# If no overlap found, try with fewer cameras.
c_nCams = len(confidenceList)
while not np.any(overlapInds_clean) and c_nCams>2:
print("Could not find overlap with {} cameras - trying with {} cameras".format(c_nCams, c_nCams-1))
cam_list = [i for i in range(nCams)]
# All possible combination with c_nCams-1 cameras.
from itertools import combinations
combs = set(combinations(cam_list, c_nCams-1))
overlapInds_clean_combs = []