mediapipe
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mediapipe | Google Test | |
---|---|---|
49 | 67 | |
25,405 | 33,041 | |
2.1% | 2.6% | |
9.9 | 8.3 | |
4 days ago | 7 days ago | |
C++ | C++ | |
Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
mediapipe
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Mediapipe openpose Controlnet model for SD
mediapipe/docs/solutions/pose.md at master · google/mediapipe · GitHub
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MEDIAPIPE on-device diffusion plugins for conditioned text-to-image generation
Today, we announce MediaPipe diffusion plugins, which enable controllable text-to-image generation to be run on-device. Expanding upon our prior work on GPU inference for on-device large generative models, we introduce new low-cost solutions for controllable text-to-image generation that can be plugged into existing diffusion models and their Low-Rank Adaptation (LoRA) variants.
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Running a TensorFlow object detector model and drawing boxes around objects at 60 FPS - all in React Native / JavaScript!
You can just grab the TFLite version! https://github.com/google/mediapipe/blob/master/docs/solutions/models.md
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OpenAI came after our domain because we use GPT in it
I believe Google already released transformers under an apache 2 license with a patent grant:
https://github.com/google/mediapipe/blob/master/mediapipe/mo...
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Open source Background Remover: Remove Background from images and video using AI
I was going to say that I like the MediaPipe Selfie Segmentation model for doing this sort of thing in a web page, but I've just noticed (when getting the GitHub link[1]) that Google have marked the code as legacy[2] ... no idea if the new solution is better/easier to use[3].
For what it's worth, my CodePen using the old model is here: https://codepen.io/kaliedarik/pen/PopBxBM
[1] - https://github.com/google/mediapipe/blob/master/docs/solutio...
[2] - "Attention: Thank you for your interest in MediaPipe Solutions. As of April 4, 2023, this solution was upgraded to a new MediaPipe Solution."
[3] - https://developers.google.com/mediapipe/solutions/vision/ima...
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[P] Pattern recognition
I have used mediapipe very successfully in multiple projects and it's very easy to get running. You can choose from many different vision tasks including hand landmarks ( https://github.com/google/mediapipe/blob/master/docs/solutions/hands.md )
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Getting face feature pose statistics
I found MediaPipe's Face Mesh and was impressed with how simple it was to get going, but it just gives you the landmark points and I've not gone any further yet.
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New ControlNet Face Model
We've trained ControlNet on a subset of the LAION-Face dataset using modified output from MediaPipe's face mesh annotator to provide a new level of control when generating images of faces.
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Trained an ML model using TensorFlow.js to classify American Sign Language (ASL) alphabets on browser. We are creating an open-source platform and would love to receive your feedback on our project.
Medipaipe library link: https://mediapipe.dev/
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mediapipe VS daisykit - a user suggested alternative
2 projects | 24 Mar 2023
Google Test
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Creating k-NN with C++ (from Scratch)
cmake_minimum_required(VERSION 3.5) project(knn_cpp CXX) include(FetchContent) FetchContent_Declare( googletest GIT_REPOSITORY https://github.com/google/googletest.git GIT_TAG release-1.11.0 ) FetchContent_MakeAvailable(googletest) FetchContent_Declare(matplotplusplus GIT_REPOSITORY https://github.com/alandefreitas/matplotplusplus GIT_TAG origin/master) FetchContent_GetProperties(matplotplusplus) if(NOT matplotplusplus_POPULATED) FetchContent_Populate(matplotplusplus) add_subdirectory(${matplotplusplus_SOURCE_DIR} ${matplotplusplus_BINARY_DIR} EXCLUDE_FROM_ALL) endif() function(knn_cpp_test TEST_NAME TEST_SOURCE) add_executable(${TEST_NAME} ${TEST_SOURCE}) target_link_libraries(${TEST_NAME} PUBLIC matplot) aux_source_directory(${CMAKE_CURRENT_SOURCE_DIR}/../lib LIB_SOURCES) target_link_libraries(${TEST_NAME} PRIVATE gtest gtest_main gmock gmock_main) target_include_directories(${TEST_NAME} PRIVATE ${CMAKE_CURRENT_SOURCE_DIR} ${CMAKE_CURRENT_SOURCE_DIR}/../) target_sources(${TEST_NAME} PRIVATE ${LIB_SOURCES} ) include(GoogleTest) gtest_discover_tests(${TEST_NAME}) endfunction() knn_cpp_test(LinearAlgebraTest la_test.cc) knn_cpp_test(KnnTest knn_test.cc) knn_cpp_test(UtilsTest utils_test.cc)
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Starting with C
Okay, time to start unit tests!!! We will use Unity Test Framework to do unit testing. It is one of widely used testing frameworks alongside with Check, Google Test etc. Just downloading source code, and putting it to the project folder is enough to make it work (that is also why it is portable).
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Just in case: Debian Bookworm comes with a buggy GCC
Updating GCC (it happened to GoogleTest).
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Automatically run tests, formatters & linters with CI!
Roy's project uses Google Test, a C++ testing framework. His testing setup is similar to mine as we both keep source files in one directory and tests in another. The key difference is that I can run the tests using the Visual Studios run button. It was fairly easy to write the new tests as there were existing ones that I could reference to check the syntax!
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C++ Unit Testing Using Google Test - My Experience
The Google Test Documentation provides a primer for first-time users. The primer introduces some basic concepts and terminology, some of which I've been able to learn for this lab exercise.
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Basic C++ Unit Testing with GTest, CMake, and Submodules
> git submodule add https://github.com/google/googletest.git > git submodule update --init --recursive
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VS code + cmake + gtest?
cmake_minimum_required(VERSION 3.14) project(my_project) # GoogleTest requires at least C++14 set(CMAKE_CXX_STANDARD 14) set(CMAKE_CXX_STANDARD_REQUIRED ON) include(FetchContent) FetchContent_Declare( googletest URL https://github.com/google/googletest/archive/03597a01ee50ed33e9dfd640b249b4be3799d395.zip ) # For Windows: Prevent overriding the parent project's compiler/linker settings set(gtest_force_shared_crt ON CACHE BOOL "" FORCE) FetchContent_MakeAvailable(googletest) enable_testing() add_executable( hello_test hello_test.cpp ) target_link_libraries( hello_test GTest::gtest_main ) include(GoogleTest) gtest_discover_tests(hello_test)
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FetchContent with Multiple URLs
FetchContent\_Declare(googletestGIT\_REPOSITORY [[email protected]](mailto:[email protected]):googletest.git [https://github.com/google/googletest.git](https://github.com/google/googletest.git)GIT\_TAG release-1.12.1)FetchContent\_MakeAvailable(googletest)
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CI/CD pipelines for embedded
Not sure about CppUnit but I can speak to my previous experience using the googletest framework which compiles your tests to an executable, and since it's a very simple framework we were able to cross-compile and run directly on our device. We just had to hook up a device to the server that was running the CI so it could flash it when needed. That basically meant that our process was:
- Basic CMake question regarding subdirectories
What are some alternatives?
openpose - OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation
Catch - A modern, C++-native, test framework for unit-tests, TDD and BDD - using C++14, C++17 and later (C++11 support is in v2.x branch, and C++03 on the Catch1.x branch)
ue4-mediapipe-plugin - UE4 MediaPipe plugin
Boost.Test - The reference C++ unit testing framework (TDD, xUnit, C++03/11/14/17)
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
CppUTest - CppUTest unit testing and mocking framework for C/C++
AlphaPose - Real-Time and Accurate Full-Body Multi-Person Pose Estimation&Tracking System
CppUnit - C++ port of JUnit
BlazePose-tensorflow - A third-party Tensorflow Implementation for paper "BlazePose: On-device Real-time Body Pose tracking".
doctest - The fastest feature-rich C++11/14/17/20/23 single-header testing framework
jeelizFaceFilter - Javascript/WebGL lightweight face tracking library designed for augmented reality webcam filters. Features : multiple faces detection, rotation, mouth opening. Various integration examples are provided (Three.js, Babylon.js, FaceSwap, Canvas2D, CSS3D...).
Unity Test API - Simple Unit Testing for C