off-the shelf multiple view multi-person pose estimation:

In this work we address the problem of estimating multiple 3D human poses from a set of calibrated cameras. Estimating 3D human poses from multiple views has several compelling properties: humans can be projected into a joint global coordinate space, sets of cameras can cover a much larger area than a single camera could, and ambiguities, occlusions and motion blurs can be resolved by other views. We propose a bottom-up approach where we first triangulate geometrically reasonable 2D joint pairs from which we then build a sparse graph with edges between close-by joints of the same type and edges between joints that form limbs. Partitioning this graph yields a set of 3D human pose estimations. Our approach is robust and can handle situations where each camera only has a small field of view with people only partially visible. We obtain competitive results for single person 3D pose estimation and state-of-the art results for multi-person 3D pose estimation.

import cv2
from poseestimation import model
from mvpose import settings, pose
from mvpose.geometry.camera import ProjectiveCamera

# pose estimation model
pe = model.PoseEstimator()

# the system uses [mm] as world coordinates. Thus, if your calibrated
# cameras use another meaure you need to provide the appropriate scale.
# For example, assuming the cameras are calibrated in [m] we need to
# scale as follows:
params = settings.get_settings(scale_to_mm=1000)

imread = lambda p: cv2.cvtColor(cv2.imread(p), cv2.COLOR_BGR2RGB))

# load 'n' frames 
Im = [
    imread('/path/cam1.jpg'),
    imread('/path/cam2.jpg'),
    ...,
    imread('/path/camn.jpg')
]
heatmaps, pafs = pe.predict_pafs_and_heatmaps(Im)

# prepare cameras
# K := {3x3} camera calibration matrix
# rvec := {1x3} rodrigues vector for rotation
# tvec := {1x3} translation vector
# distCoef := {1x5} distortion coefficient (can be [0, 0, 0, 0, 0])
# w,h := width/height if images
Calib = [
    ProjectiveCamera(K1, rvec1, tvec1, distCoef1, w, h),
    ProjectiveCamera(K2, rvec2, tvec2, distCoef2, w, h),
    ...,
    ProjectiveCamera(Kn, rvecn, tvecn, distCoefn, w, h)
]

detections = pose.estimate(Calib, heatmaps, pafs, settings=params)

print('number of detected people: ', len(detections))

# each detection consists of 17 joints (MSCOCO-style) that either represent a 
# 3D location or 'None', in case of no detection of the joint.

The software is developed using Python 3.6 and we recommend to use Anaconda Python. The following additional (non-standard) libraries are needed:

scikit image: image manipulation

conda install -c anaconda scikit-image 

google or-tools: for solving optimization problems

pip install --user --upgrade ortools

poseestimation: for the part affinity fields

pip install git+https://github.com/jutanke/easy_multi_person_pose_estimation

numba: enables high performance functions in Python using NumPy

conda install -c numba numba

c3d: for reading 3d data

pip install c3d

pak: for loading some of the datasets

pip install git+https://github.com/justayak/pppr.git
pip install git+https://github.com/justayak/pak.git

opencv3.X: for common computer vision tasks (reproject 3d points, etc) We recommend to compile your own version of OpenCV for your python setup. At the cmake-stage you can choose whatever additional flags you like (cuda, opencvcontrib, etc.) -- however, keep the ones written below as they are. You might need to install additional packages (e.g. ffmpeg, libpng, etc.) before you are able to successfully build OpenCV (check out the OpenCV documentation for your OS for further instructions). (Remarks: The version of OpenCV does not matter too much as long as it is 3.X. However, the version bundled with anaconda (I tested with cv2 version 3.1) is apparently not compiled against ffmpeg so you cannot load video files. if you want to be on the 'safe' side compile it on your own!)

git clone https://github.com/opencv/opencv.git
cd opencv && git checkout 3.4.0
mkdir build && cd build
cmake -DBUILD_opencv_java=OFF \ 
    -DBUILD_TESTS=OFF -DBUILD_PERF_TESTS=OFF -DCMAKE_BUILD_TYPE=RELEASE \
    -DCMAKE_INSTALL_PREFIX=$(python3 -c "import sys; print(sys.prefix)") \
    -DPYTHON3_EXECUTABLE=$(which python3) \
    -DPYTHON3_INCLUDE_DIR=$(python3 -c "from distutils.sysconfig import get_python_inc; print(get_python_inc())") \
    -DPYTHON3_PACKAGES_PATH=$(python3 -c "from distutils.sysconfig import get_python_lib; print(get_python_lib())") .. 
make -j4
make install

cselect: select colors

pip install git+https://github.com/jutanke/cselect.git