sm-ai-pose-generator

A Rust crate to streamline the generation of AI pose data from various input formats. It provides tools for parsing, processing, and exporting pose information, facilitating integration with AI training pipelines and related applications.

Installation

Add the following to your Cargo.toml file under the [dependencies] section: toml sm-ai-pose-generator = "0.1.0" # Replace with the actual version number

Usage Examples

Here are a few examples demonstrating how to use the sm-ai-pose-generator crate:

1. Parsing Pose Data from a JSON File:

This example shows how to load pose data from a JSON file and process it. rust use sm_ai_pose_generator::pose_data::PoseData; use std::fs;

fn main() -> Result<(), Box> { let json_data = fs::read_to_string("pose_data.json")?; let pose_data: PoseData = serde_json::from_str(&json_data)?;

println!("Number of keypoints: {}", pose_data.keypoints.len());

// Perform further processing on the pose data here

Ok(())

}

2. Converting Pose Data to a Different Format:

This example demonstrates converting the loaded pose data to a different format, such as a custom binary format. rust use sm_ai_pose_generator::pose_data::PoseData; use std::fs;

fn main() -> Result<(), Box> { let json_data = fs::read_to_string("pose_data.json")?; let pose_data: PoseData = serde_json::from_str(&json_data)?;

// Convert pose data to a custom binary format (example)
let binary_data = pose_data.to_binary();

fs::write("pose_data.bin", binary_data)?;

Ok(())

}

impl PoseData { fn to_binary(&self) -> Vec { // Implement your binary conversion logic here. // This is a placeholder. A real implementation would // serialize the keypoints and other relevant data. println!("Converting to binary format (placeholder)"); Vec::new() } }

3. Filtering Pose Data Based on Confidence Scores:

This example shows how to filter pose data based on the confidence scores associated with each keypoint. rust use sm_ai_pose_generator::pose_data::PoseData; use std::fs;

fn main() -> Result<(), Box> { let json_data = fs::read_to_string("pose_data.json")?; let mut pose_data: PoseData = serde_json::from_str(&json_data)?;

// Filter keypoints with confidence scores below a threshold
pose_data.keypoints.retain(|keypoint| keypoint.confidence > 0.8);

println!("Number of keypoints after filtering: {}", pose_data.keypoints.len());

Ok(())

}

4. Creating Pose Data Programmatically:

This example demonstrates how to create PoseData instances programmatically, useful for generating synthetic data or manipulating existing data. rust use sm_ai_pose_generator::pose_data::{PoseData, Keypoint};

fn main() { let mut pose_data = PoseData::default();

// Add some keypoints
pose_data.keypoints.push(Keypoint { x: 10.0, y: 20.0, confidence: 0.9 });
pose_data.keypoints.push(Keypoint { x: 30.0, y: 40.0, confidence: 0.7 });

println!("Created pose data with {} keypoints", pose_data.keypoints.len());

}

Feature Summary

• Pose Data Representation: Provides a structured representation of pose data with keypoints and associated confidence scores.
• Data Parsing: Facilitates parsing pose data from common formats like JSON.
• Data Conversion: Supports conversion to other formats, enabling interoperability with various systems.
• Data Filtering: Offers filtering capabilities to refine pose data based on confidence levels or other criteria.
• Programmatic Creation: Allows for creating pose data instances directly in code.

License

MIT

This crate is part of the sm-ai-pose-generator ecosystem. For advanced features and enterprise-grade tools, visit: https://supermaker.ai/image/ai-pose-generator/