trying to dockerizemediapipe/Dockerfile at master · google/mediapipe · GitHub jetson-containers/Dockerfile.tensorflow at master · dusty-nv/jetson-containers · GitHub
FROM nvcr.io/nvidia/l4t-base:r32.4.4
RUN apt-get update -y
RUN apt install -y wget curl git
RUN apt upgrade -y
# RUN cd ~ && mkdir bazel && cd bazel && wget https://github.com/bazelbuild/bazel/releases/download/3.4.0/bazel-3.4.0-dist.zip && sudo apt-get install build-essential openjdk-8-jdk python zip unzip && unzip bazel-3.4.0-dist.zip && env EXTRA_BAZEL_ARGS="--host_javabase=@local_jdk//:jdk" bash ./compile.sh && cp ~/bazel/output/bazel /usr/local/bin/
WORKDIR /io
WORKDIR /mediapipe
ENV DEBIAN_FRONTEND=noninteractive
ARG HDF5_DIR="/usr/lib/aarch64-linux-gnu/hdf5/serial/"
ARG MAKEFLAGS=-j8
RUN apt-get update && apt-get install -y --no-install-recommends \
build-essential \
ca-certificates \
curl \
ffmpeg \
git \
wget \
unzip \
python3-dev \
python3-opencv \
python3-pip \
libopencv-core-dev \
libopencv-highgui-dev \
libopencv-imgproc-dev \
libopencv-video-dev \
libopencv-calib3d-dev \
libopencv-features2d-dev \
software-properties-common && \
add-apt-repository -y ppa:openjdk-r/ppa && \
apt-get update && apt-get install -y openjdk-8-jdk && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
RUN pip3 install --upgrade setuptools
RUN pip3 install wheel
RUN pip3 install future
RUN apt update -y
RUN apt-get install libegl1-mesa-dev libgles2-mesa-dev libhdf5-serial-dev hdf5-tools libhdf5-dev zlib1g-dev zip libjpeg8-dev liblapack-dev libblas-dev gfortran -y
RUN pip3 install -U numpy==1.16.1 future==0.18.2 mock==3.0.5 h5py==2.10.0 keras_preprocessing==1.1.1 keras_applications==1.0.8 gast==0.2.2 futures protobuf pybind11
RUN pip3 install six
RUN pip3 install setuptools Cython wheel
RUN pip3 install numpy --verbose
RUN pip3 install h5py==2.10.0 --verbose
RUN pip3 install future==0.18.2 mock==3.0.5 h5py==2.10.0 keras_preprocessing==1.1.1 keras_applications==1.0.8 gast==0.2.2 futures protobuf pybind11 --verbose
#RUN pip3 install --pre --extra-index-url https://developer.download.nvidia.com/compute/redist/jp/v44 tensorflow
ARG TENSORFLOW_URL=https://developer.download.nvidia.com/compute/redist/jp/v44/tensorflow/tensorflow-1.15.3+nv20.9-cp36-cp36m-linux_aarch64.whl
ARG TENSORFLOW_WHL=tensorflow-1.15.3+nv20.9-cp36-cp36m-linux_aarch64.whl
RUN wget --quiet --show-progress --progress=bar:force:noscroll --no-check-certificate ${TENSORFLOW_URL} -O ${TENSORFLOW_WHL} && \
pip3 install ${TENSORFLOW_WHL} --verbose && \
rm ${TENSORFLOW_WHL}
RUN pip3 install tf_slim
#RUN ln -s /usr/bin/python3 /usr/bin/python
# Install bazel
ARG BAZEL_VERSION=3.7.1
RUN mkdir /bazel && cd /bazel && wget https://github.com/bazelbuild/bazel/releases/download/3.7.1/bazel-3.7.1-dist.zip && apt-get install build-essential openjdk-8-jdk python zip unzip && unzip bazel-3.7.1-dist.zip && env EXTRA_BAZEL_ARGS="--host_javabase=@local_jdk//:jdk" bash ./compile.sh && cp /bazel/output/bazel /usr/local/bin/
ENV PATH="/usr/local/cuda/bin:${PATH}"
ENV LD_LIBRARY_PATH="/usr/local/cuda/lib64:${LD_LIBRARY_PATH}"
RUN echo "$PATH" && echo "$LD_LIBRARY_PATH"
RUN pip3 install pycuda --verbose
COPY . /mediapipe/
#RUN bazel build -c opt --define MEDIAPIPE_DISABLE_GPU=1 mediapipe/examples/desktop/demo:object_detection_tensorflow_demo
RUN bazel build -c opt --copt -DMESA_EGL_NO_X11_HEADERS --copt -DEGL_NO_X11 mediapipe/examples/desktop/hand_tracking:hand_tracking_gpu --experimental_repo_remote_exec
after some test it seems that therew are issues buil;ding with bazel with given tensorflow inside the container
Thanks Andrey1984 for the details.
it is quite difficult with docker.@AastaLLL , you may try executing the single command below to see the Hello World sample at jetson:
docker run -it --rm --net=host -e DISPLAY=$DISPLAY --ipc=host --privileged -v /tmp/.X11-unix/:/tmp/.X11-unix/ -v /tmp/argus_socket:/tmp/argus_socket --cap-add SYS_PTRACE iad.ocir.io/idso6d7wodhe/jetson-mediapipe:latest /bin/bash -c '.
GLOG_logtostderr=1 bazel run --copt -DMESA_EGL_NO_X11_HEADERS --copt -DEGL_NO_X11 mediapipe/examples/desktop/hello_world:hello_world --experimental_repo_remote_exec'
reference issues
Try below step.
sudo systemctl daemon-reload
sudo systemctl restart docker
Export DISPLAY=:
xhost +
sudo docker run --net=host --runtime nvidia --rm --ipc=host -v /tmp/.X11-unix/:/tmp/.X11-unix/ -v /tmp/argus_socket:/tmp/argus_socket --cap-add SYS_PTRACE -e DISPLAY=$DISPLAY -it nvcr.io/nvidia/l4t-base:r32.2.1
A basic sample will help someone with the same interest to save lots of time.
@AastaLLL
docker run -it --rm --net=host -e DISPLAY=$DISPLAY --ipc=host --privileged -v /tmp/.X11-unix/:/tmp/.X11-unix/ -v /tmp/argus_socket:/tmp/argus_socket --cap-add SYS_PTRACE iad.ocir.io/idso6d7wodhe/jetson-mediapipe:latest /bin/bash -c '.
GLOG_logtostderr=1 bazel run --copt -DMESA_EGL_NO_X11_HEADERS --copt -DEGL_NO_X11 mediapipe/examples/desktop/hello_world:hello_world --experimental_repo_remote_exec'
However, for non dockerized use case the entire mediapipe environment needs to be set up s per the instruction steps from the github repository
Hi,
Do you mean the repository from the official mediapipe?
Thanks.
@AastaLLL
A curated list of awesome MediaPipe related code examples, libraries and software - GitHub - mgyong/awesome-mediapipe: A curated list of awesome MediaPipe related code examples, libraries and software
However, I will try to see if we could add the reference link to the main repo above somehow.
Hi,
Thanks for the information.
Thanks.
1 Like
I tried to set full path for cuda.h and cuda_runtime.h, but im still getting glog errors:
ERROR: /home/nvidia/mediapipe/mediapipe/examples/desktop/BUILD:59:11: C++ compilation of rule ‘//mediapipe/examples/desktop:demo_run_graph_main_gpu’ failed (Exit 1)define virtual_includes/default_glog_headers/glog/logging.h:510:23: note: in expansion of macro ‘COMPACT_GOOGLE_LOG_INFO’define ## severity.stream()define virtual_includes/default_glog_headers/glog/logging.h:510:23: note: in expansion of macro ‘COMPACT_GOOGLE_LOG_INFO’define ## severity.stream()define virtual_includes/default_glog_headers/glog/logging.h:510:23: note: in expansion of macro ‘COMPACT_GOOGLE_LOG_INFO’define ## severity.stream()define virtual_includes/default_glog_headers/glog/logging.h:510:23: note: in expansion of macro ‘COMPACT_GOOGLE_LOG_INFO’define ## severity.stream()define virtual_includes/default_glog_headers/glog/logging.h:510:23: note: in expansion of macro ‘COMPACT_GOOGLE_LOG_INFO’define ## severity.stream()define virtual_includes/default_glog_headers/glog/logging.h:510:23: note: in expansion of macro ‘COMPACT_GOOGLE_LOG_INFO’define ## severity.stream()define virtual_includes/default_glog_headers/glog/logging.h:510:23: note: in expansion of macro ‘COMPACT_GOOGLE_LOG_ERROR’define ## severity.stream()define virtual_includes/default_glog_headers/glog/logging.h:510:23: note: in expansion of macro ‘COMPACT_GOOGLE_LOG_INFO’define ## severity.stream()
I have a question for the moderators, is it possible to run anything other than the examples given in the mediapipe , I was able to compile my personal code using the mediapipe hands-api, is it possible to run on the Jetson-nano.
I’m able to use the Jetson nano to run my own programs using mediapipe. Ofc, my code is based on the examples they provide and is C++ based though and that runs more or less out of the box using the GPU. Getting python examples to run on the GPU instead of CPU requires some work modifying build configs. I tried briefly but without much success.
Thanks for the prompt, I was able to build the mediapipe using bazelisk. Now when I run my Python file, It’s showing no module named mediapipe.
https://github.com/google/mediapipe/issues/1982
it seems that you might need to install mediapipe python module
Hey,Python hand landmark tracking fails with GPU · Issue #1273 · google/mediapipe · GitHub , Mediapipe python interface is not working on GPU · Issue #1651 · google/mediapipe · GitHub in regards to make the Python interface works on the GPU and GitHub - jiuqiant/mediapipe_python_aarch64 for building MediaPipe for the Jetson AGX Xavier.MEDIAPIPE_GPU_HAND.md (5.1 KB)Tensorrt engine instead of tflite for faster inference on nvidia xavier. · Issue #723 · google/mediapipe · GitHub , but I couldn’t make the proposed solution work.
2 Likes
Mediapipe is working fine on my nano using images and videos but won’t work with my csi camera ! I tried streaming using opencv and jetson.utils but it won’t ! any help ?
@ahmedgr914
diff -u /home/nvidia/mediapipe/mediapipe/examples/desktop/orig/demo_run_graph_main_gpu.cc /home/nvidia/mediapipe/mediapipe/examples/desktop/demo_run_graph_main_gpu.cc
--- /home/nvidia/mediapipe/mediapipe/examples/desktop/orig/demo_run_graph_main_gpu.cc 2021-04-21 16:31:39.941459722 -0400
+++ /home/nvidia/mediapipe/mediapipe/examples/desktop/demo_run_graph_main_gpu.cc2021-04-21 15:03:30.674384288 -0400
@@ -68,10 +68,11 @@
LOG(INFO) << "Initialize the camera or load the video.";
cv::VideoCapture capture;
const bool load_video = !absl::GetFlag(FLAGS_input_video_path).empty();
- if (load_video) {
+ const char* gst = "nvarguscamerasrc ! video/x-raw(memory:NVMM), width=1280, height=720, framerate=30/1 ! nvvidconv ! video/x-raw,format=BGRx ! videoconvert ! video/x-raw, format=BGR ! appsink";
+if (load_video) {
capture.open(absl::GetFlag(FLAGS_input_video_path));
} else {
- capture.open(0);
+ capture.open(gst, cv::CAP_GSTREAMER);
}
RET_CHECK(capture.isOpened());
1 Like
after creating the new demo_run_graph_main_gpu.cc I should redo the installation right ?
was you able to run with CSI after redoing the build of the example?
Thank you for this. I had looked at a lot of these issue pages, but a lot of the information was scattered all over the place, so I must have missed a step somewhere when I tried it before.
1 Like