The MediaPipe perception pipeline is called aGraph. Let us take the example of the first solution, Hands. We feed a stream of images as input which comes out with hand landmarks rendered on the images. The flow chart below represents the MP (Abbr. MediaPipe) hand solution graph. FIGURE 2...
Object tracking subgraph uses a BoxTracker calculator which is a generic tracking library, also used in Mediapipe's 2D Object Detection and Tracking. The tracking runs every frame and when a new detection is available, it consolidates the detection and tracking results. The tracker tracks the box...
The Objectron 3D object detection and tracking pipeline is implemented as a MediaPipe graph, which internally uses a detection subgraph and a tracking subgraph. The detection subgraph performs ML inference only once every few frames to reduce computation load, and decodes the output tensor to a ...
we train a palm detector instead of a hand detector, since estimating bounding boxes of rigid objects like palms and fists is significantly simpler than detecting hands with articulated fingers. In addition, as palms are smaller objects, the non-maximum suppression algorithm works well even for two...
For Face Detection, it uses BlazeFast, which, as the name suggests, is extremely fast and lightweight, and optimized for mobile GPU inference. The Face Detection outputs a cropped region from the video frame. We then run the 3D-Landmark model on the cropped area. Implementation ...
The proposed model uses a Mask R-CNN algorithm that detects multi-object recognition and overlapping objects. Moreover, MediaPipe is used to estimate the worker's body parts as well as the posture. For the experiment of the proposed model, 560 images were collected d...
uses WebAssembly (WASM) to run C++ code in-browser. You don't need to know C++, but it helps to know that C++ makes you take out your own garbage. If you don't free up unused memory, you will find that your web page uses more and more memory over time. It can have performance...
// of ODR if this header includes any actual code that uses MtlResources. mutable std::unique_ptr<MtlResources> mtl_resources_;#if MEDIAPIPE_USE_WEBGPU mutable wgpu::Device webgpu_device_; mutable wgpu::Texture webgpu_texture2d_; #endif // MEDIAPIPE_USE_WEBGPU ...
downloadImageFromUrl( url='https://github.com/cvzone/cvzone/blob/master/Results/shapes.png?raw=true') # Perform edge detection using the Canny algorithm imgCanny = cv2.Canny(imgShapes, 50, 150) # Dilate the edges to strengthen the detected contours imgDilated = cv2.dilate(imgCanny, np....
""" This function is used to implement our Drowsy detection algorithm.Args: frame: (np.array) Input frame matrix. thresholds: (dict) Contains the two threshold values WAIT_TIME and EAR_THRESH.Returns: The processed frame and a boolean flag to ...