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You will learn to:
Process the image data.
Interpret and analyze body landmarks from MediaPipe Pose.
Perform pose classification using XGBoost.
Create, save, and load a machine learning model.
Create a basic Flask application.
Skills
Machine Learning Fundamentals
Data Science
Image Manipulation
Prerequisites
Basic understanding of Python
Basic understanding of image processing
Intermediate knowledge of machine learning
Technologies
Python
XGBoost
MediaPipe
Project Description
Pose landmarks are like special markers on the human body that show how it’s positioned. These markers represent important body parts like hands, legs, and joints. They’re really important for understanding and recognizing human movements and gestures. To help with landmark identification, deep learning, and computer vision are used. They’re like special tools that let us see and understand these pose landmarks.
This project will leverage the landmark detection on an image-based yoga dataset to build a yoga pose detector. For landmark detection, we’ll use the MediaPipe framework from Google. MediaPipe Pose is built on BlazePose, which is lightweight and fast and doesn’t need a lot of computer power. BlazePose identifies 33 landmarks on the body in real time. MediaPipe framework is a collection of pretrained models for object detection, segmentation, pose detection, face reading, and hand gesture detection, to name a few.
In this project, we’ll extract landmarks data of different yoga pose images via MediaPipe Pose and then use the XGBoost classifier to build a classifier that classifies among the five yoga poses. The lightweight MediaPipe Pose model will allow us to do everything while consuming limited resources.
Project Tasks
1
Introduction
Task 0: Get Started
Task 1: Import Libraries
2
Landmark Detection with Mediapipe Pose
Task 2: Landmark Detection with MediaPipe
Task 3: Visualize MediaPipe’s Landmarks
3
Processing the Dataset
Task 4: Split the Dataset
Task 5: Verify the Split
Task 6: Store the Image Data as Text
Task 7: Call the Function
Task 8: Load and Preprocess the Data
4
Classification Model
Task 9: Build and Train the Model
Task 10: Test the Classifier
Task 11: Save the Model
Task 12: Use the Model
5
Flask Application
Task 13: Create the Application’s Frontend
Task 14: Implement the Image Upload Functionality
Task 15: Complete the Image Uploading Process
Task 16: Display Pose Detection Results
Congratulations!
Relevant Courses
Use the following content to review prerequisites or explore specific concepts in detail.