OpenCV

OpenCV (Open Source Computer Vision Library: http://opencv.org) is an open-source library that includes several hundreds of computer vision algorithms. The document describes the so-called OpenCV 2.x API, which is essentially a C++ API, as opposed to the C-based OpenCV 1.x API.

OpenCV has a modular structure, which means that the package includes several shared or static libraries. The following modules are available:

• Core functionality (core) - a compact module defining basic data structures, including the dense multi-dimensional array Mat and basic functions used by all other modules.
• Image Processing (imgproc) - an image processing module that includes linear and non-linear image filtering, geometrical image transformations (resize, affine and perspective warping, generic table-based remapping), color space conversion, histograms, and so on.
• Video Analysis (video) - a video analysis module that includes motion estimation, background subtraction, and object tracking algorithms.
• Camera Calibration and 3D Reconstruction (calib3d) - basic multiple-view geometry algorithms, single and stereo camera calibration, object pose estimation, stereo correspondence algorithms, and elements of 3D reconstruction.
• 2D Features Framework (features2d) - salient feature detectors, descriptors, and descriptor matchers.
• Object Detection (objdetect) - detection of objects and instances of the predefined classes (for example, faces, eyes, mugs, people, cars, and so on).
• High-level GUI (highgui) - an easy-to-use interface to simple UI capabilities.
• Video I/O (videoio) - an easy-to-use interface to video capturing and video codecs.
• ... some other helper modules, such as FLANN and Google test wrappers, Python bindings, and others.

MediaPipe

MediaPipe is a customizable machine learning solutions framework developed by Google. It is an open-source and cross-platform framework, and it is very lightweight. MediaPipe comes with some pre-trained ML solutions such as face detection, pose estimation, hand recognition, object detection, etc.

Configuration Options

max_num_hands: Maximum number of hands to detect.

min_detection_confidence: Minimum confidence value ([0.0, 1.0]) from the hand detection model for the detection to be considered successful.

min_tracking_confidence: Minimum confidence value ([0.0, 1.0]) from the landmark-tracking model for the hand landmarks to be considered tracked successfully, or otherwise hand detection will be invoked automatically on the next input image.

static_image_mode: If set to false, the solution treats the input images as a video stream. If set to true, hand detection runs on every input image, ideal for processing a batch of static, possibly unrelated, images.