mediapipe/docs/getting_started/troubleshooting.md

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Troubleshooting

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Missing Python binary path

The error message:

ERROR: An error occurred during the fetch of repository 'local_execution_config_python':
  Traceback (most recent call last):
       File "/sandbox_path/external/org_tensorflow/third_party/py/python_configure.bzl", line 208
               get_python_bin(repository_ctx)
    ...
Repository command failed

usually indicates that Bazel fails to find the local Python binary. To solve this issue, please first find where the python binary is and then add --action_env PYTHON_BIN_PATH=<path to python binary> to the Bazel command. For example, you can switch to use the system default python3 binary by the following command:

bazel build -c opt \
  --define MEDIAPIPE_DISABLE_GPU=1 \
  --action_env PYTHON_BIN_PATH=$(which python3) \
  mediapipe/examples/desktop/hello_world

Missing necessary Python packages

The error message:

ImportError: No module named numpy
Is numpy installed?

usually indicates that certain Python packages are not installed. Please run pip install or pip3 install depending on your Python binary version to install those packages.

Fail to fetch remote dependency repositories

The error message:

ERROR: An error occurred during the fetch of repository 'org_tensorflow':
   java.io.IOException: Error downloading [https://mirror.bazel.build/github.com/tensorflow/tensorflow/archive/77e9ffb9b2bfb1a4f7056e62d84039626923e328.tar.gz, https://github.com/tensorflow/tensorflow/archive/77e9ffb9b2bfb1a4f7056e62d84039626923e328.tar.gz] to /sandbox_path/external/org_tensorflow/77e9ffb9b2bfb1a4f7056e62d84039626923e328.tar.gz: Tried to reconnect at offset 9,944,151 but server didn't support it

or

WARNING: Download from https://storage.googleapis.com/mirror.tensorflow.org/github.com/bazelbuild/rules_swift/releases/download/0.12.1/rules_swift.0.12.1.tar.gz failed: class java.net.ConnectException Connection timed out (Connection timed out)

usually indicates that Bazel fails to download necessary dependency repositories that MediaPipe needs. MedaiPipe has several dependency repositories that are hosted by Google sites. In some regions, you may need to set up a network proxy or use a VPN to access those resources. You may also need to append --host_jvm_args "-DsocksProxyHost=<ip address> -DsocksProxyPort=<port number>" to the Bazel command. See this GitHub issue for more details.

If you believe that it's not a network issue, another possibility is that some resources could be temporarily unavailable, please run bazel clean --expunge and retry it later. If it's still not working, please file a GitHub issue with the detailed error message.

Incorrect MediaPipe OpenCV config

The error message:

error: undefined reference to 'cv::String::deallocate()'
error: undefined reference to 'cv::String::allocate(unsigned long)'
error: undefined reference to 'cv::VideoCapture::VideoCapture(cv::String const&)'
...
error: undefined reference to 'cv::putText(cv::InputOutputArray const&, cv::String const&, cv::Point, int, double, cv::Scalar, int, int, bool)'

usually indicates that OpenCV is not properly configured for MediaPipe. Please take a look at the "Install OpenCV and FFmpeg" sections in Installation to see how to modify MediaPipe's WORKSPACE and linux_opencv/macos_opencv/windows_opencv.BUILD files for your local opencv libraries. This GitHub issue may also help.

Python pip install failure

The error message:

ERROR: Could not find a version that satisfies the requirement mediapipe
ERROR: No matching distribution found for mediapipe

after running pip install mediapipe usually indicates that there is no qualified MediaPipe Python for your system. Please note that MediaPipe Python PyPI officially supports the 64-bit version of Python 3.7 to 3.10 on the following OS:

• x86_64 Linux
• x86_64 macOS 10.15+
• amd64 Windows

If the OS is currently supported and you still see this error, please make sure that both the Python and pip binary are for Python 3.7 to 3.10. Otherwise, please consider building the MediaPipe Python package locally by following the instructions here.

Python DLL load failure on Windows

The error message:

ImportError: DLL load failed: The specified module could not be found

usually indicates that the local Windows system is missing Visual C++ redistributable packages and/or Visual C++ runtime DLLs. This can be solved by either installing the official vc_redist.x64.exe or installing the "msvc-runtime" Python package by running

$ python -m pip install msvc-runtime

Please note that the "msvc-runtime" Python package is not released or maintained by Microsoft.

Native method not found

The error message:

java.lang.UnsatisfiedLinkError: No implementation found for void com.google.wick.Wick.nativeWick

usually indicates that a needed native library, such as /libwickjni.so has not been loaded or has not been included in the dependencies of the app or cannot be found for some reason. Note that Java requires every native library to be explicitly loaded using the function System.loadLibrary.

No registered calculator found

The error message:

No registered object with name: OurNewCalculator; Unable to find Calculator "OurNewCalculator"

usually indicates that OurNewCalculator is referenced by name in a CalculatorGraphConfig but that the library target for OurNewCalculator has not been linked to the application binary. When a new calculator is added to a calculator graph, that calculator must also be added as a build dependency of the applications using the calculator graph.

This error is caught at runtime because calculator graphs reference their calculators by name through the field CalculatorGraphConfig::Node:calculator. When the library for a calculator is linked into an application binary, the calculator is automatically registered by name through the REGISTER_CALCULATOR macro using the registration.h library. Note that REGISTER_CALCULATOR can register a calculator with a namespace prefix, identical to its C++ namespace. In this case, the calculator graph must also use the same namespace prefix.

Out Of Memory error

Exhausting memory can be a symptom of too many packets accumulating inside a running MediaPipe graph. This can occur for a number of reasons, such as:

1. Some calculators in the graph simply can't keep pace with the arrival of packets from a realtime input stream such as a video camera.
2. Some calculators are waiting for packets that will never arrive.

For problem (1), it may be necessary to drop some old packets in older to process the more recent packets. For some hints, see: How to process realtime input streams.

For problem (2), it could be that one input stream is lacking packets for some reason. A device or a calculator may be misconfigured or may produce packets only sporadically. This can cause downstream calculators to wait for many packets that will never arrive, which in turn causes packets to accumulate on some of their input streams. MediaPipe addresses this sort of problem using "timestamp bounds". For some hints see: How to process realtime input streams.

The MediaPipe setting CalculatorGraphConfig::max_queue_size limits the number of packets enqueued on any input stream by throttling inputs to the graph. For realtime input streams, the number of packets queued at an input stream should almost always be zero or one. If this is not the case, you may see the following warning message:

Resolved a deadlock by increasing max_queue_size of input stream

Also, the setting CalculatorGraphConfig::report_deadlock can be set to cause graph run to fail and surface the deadlock as an error, such that max_queue_size to acts as a memory usage limit.

Graph hangs

Many applications will call CalculatorGraph::CloseAllPacketSources and CalculatorGraph::WaitUntilDone to finish or suspend execution of a MediaPipe graph. The objective here is to allow any pending calculators or packets to complete processing, and then to shutdown the graph. If all goes well, every stream in the graph will reach Timestamp::Done, and every calculator will reach CalculatorBase::Close, and then CalculatorGraph::WaitUntilDone will complete successfully.

If some calculators or streams cannot reach state Timestamp::Done or CalculatorBase::Close, then the method CalculatorGraph::Cancel can be called to terminate the graph run without waiting for all pending calculators and packets to complete.

Output timing is uneven

Some realtime MediaPipe graphs produce a series of video frames for viewing as a video effect or as a video diagnostic. Sometimes, a MediaPipe graph will produce these frames in clusters, for example when several output frames are extrapolated from the same cluster of input frames. If the outputs are presented as they are produced, some output frames are immediately replaced by later frames in the same cluster, which makes the results hard to see and evaluate visually. In cases like this, the output visualization can be improved by presenting the frames at even intervals in real time.

MediaPipe addresses this use case by mapping timestamps to points in real time. Each timestamp indicates a time in microseconds, and a calculator such as LiveClockSyncCalculator can delay the output of packets to match their timestamps. This sort of calculator adjusts the timing of outputs such that:

1. The time between outputs corresponds to the time between timestamps as closely as possible.
2. Outputs are produced with the smallest delay possible.

CalculatorGraph lags behind inputs

For many realtime MediaPipe graphs, low latency is an objective. MediaPipe supports "pipelined" style parallel processing in order to begin processing of each packet as early as possible. Normally the lowest possible latency is the total time required by each calculator along a "critical path" of successive calculators. The latency of the a MediaPipe graph could be worse than the ideal due to delays introduced to display frames a even intervals as described in Output timing is uneven.

If some of the calculators in the graph cannot keep pace with the realtime input streams, then latency will continue to increase, and it becomes necessary to drop some input packets. The recommended technique is to use the MediaPipe calculators designed specifically for this purpose such as FlowLimiterCalculator as described in How to process realtime input streams.