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Getting Started |
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Attention: Thanks for your interest in MediaPipe! We have moved to https://developers.google.com/mediapipe as the primary developer documentation site for MediaPipe as of April 3, 2023.
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
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.
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. MediaPipe 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.
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.
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.
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-runtimePlease note that the "msvc-runtime" Python package is not released or maintained by Microsoft.
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.
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.
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:
- 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.
- 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.
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.
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:
- The time between outputs corresponds to the time between timestamps as closely as possible.
- Outputs are produced with the smallest delay possible.
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.
Debugging MediaPipe calculators often requires a deep understanding of the data
flow and timestamp synchronization. Incoming packets to calculators are first
buffered in input queues per stream to be synchronized by the assigned
InputStreamHandler. The InputStreamHandler job is to determine the input
packet set for a settled timestamp, which puts the calculator into a “ready”
state, followed by triggering a Calculator::Process call with the determined
packet set as input.
The DebugInputStreamHandler can be used to track incoming packets and
timestamp settlements in real-time in the application's LOG(INFO) output. It can
be assigned to specific calculators via the Calculator's input_stream_handler or
graph globally via the CalculatorGraphConfig's input_stream_handler field.
During the graph execution, incoming packets generate LOG messages which reveal the timestamp and type of the packet, followed by the current state of all input queues:
[INFO] SomeCalculator: Adding packet (ts:2, type:int) to stream INPUT_B:0:input_b
[INFO] SomeCalculator: INPUT_A:0:input_a num_packets: 0 min_ts: 2
[INFO] SomeCalculator: INPUT_B:0:input_b num_packets: 1 min_ts: 2
In addition, it enables the monitoring of timestamp settlement events (in case
the DefaultInputStreamHandler is applied). This can help to reveal an
unexpected timestamp bound increase on input streams resulting in a
Calculator::Process call with an incomplete input set resulting in empty packets
on (potentially required) input streams.
Example scenario:
node {
calculator: "SomeCalculator"
input_stream: "INPUT_A:a"
input_stream: "INPUT_B:b"
...
}
Given a calculator with two inputs, receiving an incoming packet with timestamp
1 on stream A followed by an input packet with timestamp 2 on stream B. The
timestamp bound increase to 2 on stream B with pending input packet on stream A
at timestamp 1 triggers the Calculator::Process call with an incomplete input
set for timestamp 1. In this case, the DefaultInputStreamHandler outputs:
[INFO] SomeCalculator: Filled input set at ts: 1 with MISSING packets in input streams: INPUT_B:0:input_b.
MediaPipe uses VLOG in many places to log important events for debugging
purposes, while not affecting performance if logging is not enabled.
See more about VLOG on abseil VLOG
Mind that VLOG can be spammy if you enable it globally e.g. (using --v
flag). The solution --vmodule flag that allows different levels to be set for
different source files.
In cases when --v / --vmodule cannot be used (e.g. running an Android app),
MediaPipe allows to set VLOG --v / --vmodule flags overrides for debugging
purposes which are applied when CalculatorGraph is created.
Overrides:
MEDIAPIPE_VLOG_V: define and provide value you provide for--vMEDIAPIPE_VLOG_VMODULE: define and provide value you provide for--vmodule
You can set overrides by adding:
--copt=-DMEDIAPIPE_VLOG_VMODULE=\"*calculator*=5\"
with your desired module patterns and VLOG levels (see more details for
--vmodule at abseil VLOG) to your build command.
IMPORTANT: mind that adding the above to your build command will trigger rebuild
of the whole binary including dependencies. So, considering VLOG overrides
exist for debugging purposes only, it is faster to simply modify
vlog_overrides.cc adding MEDIAPIPE_VLOG_V/VMODULE at the very top.
If you are using Clang 18 or older, you may have to disable some compiler optimizations in our CPU backend.
To disable support for avxvnniint8, add the following to you .bazelrc:
build --define=xnn_enable_avxvnniint8=false