Mediapipe Versions Save

Merged https://github.com/google/mediapipe/pull/1969, https://github.com/google/mediapipe/pull/2039, https://github.com/google/mediapipe/pull/2101, https://github.com/google/mediapipe/pull/2136, https://github.com/google/mediapipe/pull/2154, https://github.com/google/mediapipe/pull/2201, https://github.com/google/mediapipe/pull/2291, https://github.com/google/mediapipe/pull/2292, https://github.com/google/mediapipe/pull/2347, https://github.com/google/mediapipe/pull/2350, https://github.com/google/mediapipe/pull/2450, https://github.com/google/mediapipe/pull/2462, and https://github.com/google/mediapipe/pull/2464.

A huge thank you to all contributors: @Abduttayyeb @brettkoonce @chr0nikler  @daniel13520cs @gabrielsanchez @GantMan @GzuPark  @homuler @magamig @PeterPocsi @TomHsiao1260  @yuripourre 

• MediaPipe Pose now outputs "segmentation_mask" when enable_segmentation is set.
• MediaPipe Python FaceMesh, Hands, Pose, and Holistic have new drawing styles. Please refer to the documentation and the Colab examples for more details.
• MediaPipe now offers Android FaceMesh and Hands solution APIs (currently in Alpha). The maven artifacts are available in Google's Maven Repository. To try the example apps, please import the solution example Android Studio project on either Linux, macOS, and Windows.

• MediaPipe Pose (and Holistic) now also outputs estimated real-world 3D coordinates of pose landmarks (in meters with the origin at center of hips)
• MediaPipe Face Detection now supports a "model_selection" option to switch between short-range and full-range models

• Released MediaPipe Selfie Segmentation
  • JavaScript Solution API w/ a CodePen example
  • Python Solution API w/ a Colab example
  • Android, iOS and desktop example apps

• Better pose landmark accuracy with the updated pose detection and pose landmark model
• Added a lite and a heavy version of the pose landmark model, in addition to the existing full version (also updated and improved)
• Added a model_complexity config option in MediaPipe Pose and MediaPipe Holistic Solution APIs to select across the 3 model versions
• Removed the upper_body_only option in MediaPipe Pose and MediaPipe Holistic, as the standard model now already handles upper-body-only use cases well

• Move up minimum bazel version to 3.7.2, and encourage the use of Bazelisk in Installation.
• Move up TensorFlow dependency.
• MediaPipe Objectron desktop/C++ example.

• MediaPipe Pose
  • New pose landmark model with Z coordinates (BlazePose GHUM 3D), generated from synthetic data
  • Added tutorials and Colabs for Pose Classification
• MediaPipe Objectron
  • Added Python Solution API
• MediaPipe Face Detection
  • Added Python Solution API and JavaScript Solution API
• MediaPipe Face Mesh
  • Face Geometry Module (code) now supports face-detection input (to generate a face geometry from a detection), in addition to the existing support for face-landmark input
• Image: a new multi-backend image data container:
  • Covering both CPU and GPU storage
  • Currently used in ImageToTensorCalculator and Face Detection Module
  • Companion utility ToImageCalculator and FromImageCalculator

• Released experimental new framework APIs
• Updated MediaPipe Objectron to support simultaneous detection of multiple objects
• Bug fixes
  • Fixed a crash in the MediaPipe Iris iOS example app
  • Fixed memory leak in Android example apps (in ExternalTextureConverter)
  • Fixed a file read mode issue in pose_landmark_model_loader.pbtxt.

• MediaPipe Holistic for real-time simultaneous tracking of human pose, face and hand landmarks
  • Google AI blog post
• MediaPipe in Python
  • Ready-to-use Python Solution APIs for MediaPipe Face Mesh, Hands, Pose and Holistic
  • Try it in MediaPipe on Google Colab
• MediaPipe in JavaScript
  • Ready-to-use JavaScript Solution APIs for MediaPipe Face Mesh, Hands, Pose and Holistic
  • Try it in MediaPipe on CodePen
• Switch build from C++14 to C++17

• MediaPipe in Python
  • Released Python Solution API for MediaPipe Face Mesh and MediaPipe Hands
  • Updated Python Solution API for MediaPipe Pose
  • Also released usage examples as Google Colabs
• MediaPipe Objectron
  • Released a faster two-stage pipeline
  • Added support for more object classes: shoe, chair, cup and camera
  • Released training dataset, (to be) announced in a Google AI Blog post
• New Tensor class
  • Released as a multi-dimensional tensor data container, supporting multiple backends like CPU, Metal buffer, GL buffer and GL texture 2D
  • Added new Tensor-based pre-processing, inference and post-processing calculators in mediapipe/calculators/tensor, branched from existing calculators in mediapipe/calculators/tflite
  • Most of the (sub-)graph in mediapipe/modules have been updated to use Tensor and the associated calculators. The plan is to fully migrate all in the repo (and deprecate mediapipe/calculators/tflite by end of 2020.
• MediaPipe Hands
  • Refactored graphs to depend on the new palm_detection and hand_landmark module.
  • Improved model speed for both palm detection and hand landmark.
  • Extended the main hand tracking example apps to support multiple hands, to replace the separate multi-hand tracking examples.
• MediaPipe Face Detection
  • Refactored graphs to depend on the face_detection module.