![]() ![]() Mp_face_mesh = mp.solutions.face_mesh Face Mesh and Motion Capture with MediaPipe Mp_drawing_styles = mp.solutions.drawing_styles Next, we finish the setup with some Python imports and variable declarations: import cv2 Then, open up the notebook.ipynb Jupyter notebook to follow along with the code, (note that the mp_env virtual environment will need to be added to Jupyter as a kernel). To do this, navigate into the directory in which you would like to clone the project folder and execute the following commands in a command prompt/terminal: If you wish to follow along with this tutorial exactly, you can skip the above step of installing MediaPipe directly and instead pip install from the requirements file in the associated GitHub. To install MediaPipe for Python, simply pip install it to your desired environment: pip install mediapipe MediaPipe is available for C++, Android, and more but, in this tutorial, we will be working only with Python. If you just want to follow along without having to do this, you can instead open up this Colab notebook and move on to the next section. Let's take a look at how to employ some of these APIs in Python now! Getting Startedīefore we can get to working with MediaPipe itself, we'll need to perform some preliminary installations / imports. 3D Object Detection and Pose Estimation.We'll even see how this data can be used to power tools like facial motion capture in Blender! Let's dive in. In this tutorial, we'll learn how to use some of MediaPipe's Python APIs to accomplish foundational Computer Vision tasks in just a few lines of code, including facial tracking and pose extraction. These models, along with their excessively easy-to-use APIs, in turn streamline the development process and reduce project lifetime for many applications that rely on Computer Vision. ![]() MediaPipe provides cornerstone Machine Learning models for common tasks like hand tracking, therefore removing the same developmental bottleneck that exists for a host of Machine Learning applications. ![]() Examples of Artificial Intelligence applications - A Disney-character selfie filter (left, source) and vehicular object detection (right, source) To address this problem, Google invented MediaPipe. Given that building something like a hand tracking model is time-consuming and resource-intensive, a developmental bottleneck exists in the creation of all applications that rely on hand tracking. For example, both gestural navigation and sign language detectors rely on the ability of a program to identify and track human hands. A wide range of potential Machine Learning applications today rely on several fundamental baseline Machine Learning tasks. ![]()
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