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feat: Complete Rust port of WiFi-DensePose with modular crates

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Major changes:
- Organized Python v1 implementation into v1/ subdirectory
- Created Rust workspace with 9 modular crates:
  - wifi-densepose-core: Core types, traits, errors
  - wifi-densepose-signal: CSI processing, phase sanitization, FFT
  - wifi-densepose-nn: Neural network inference (ONNX/Candle/tch)
  - wifi-densepose-api: Axum-based REST/WebSocket API
  - wifi-densepose-db: SQLx database layer
  - wifi-densepose-config: Configuration management
  - wifi-densepose-hardware: Hardware abstraction
  - wifi-densepose-wasm: WebAssembly bindings
  - wifi-densepose-cli: Command-line interface

Documentation:
- ADR-001: Workspace structure
- ADR-002: Signal processing library selection
- ADR-003: Neural network inference strategy
- DDD domain model with bounded contexts

Testing:
- 69 tests passing across all crates
- Signal processing: 45 tests
- Neural networks: 21 tests
- Core: 3 doc tests

Performance targets:
- 10x faster CSI processing (~0.5ms vs ~5ms)
- 5x lower memory usage (~100MB vs ~500MB)
- WASM support for browser deployment
This commit is contained in:
Claude
2026-01-13 03:11:16 +00:00
parent 5101504b72
commit 6ed69a3d48
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rust-port/wifi-densepose-rs/crates/wifi-densepose-core/src/error.rs Normal file
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//! Error types for the WiFi-DensePose system.
//!
//! This module provides comprehensive error handling using [`thiserror`] for
//! automatic `Display` and `Error` trait implementations.
//!
//! # Error Hierarchy
//!
//! - [`CoreError`]: Top-level error type that encompasses all subsystem errors
//! - [`SignalError`]: Errors related to CSI signal processing
//! - [`InferenceError`]: Errors from neural network inference
//! - [`StorageError`]: Errors from data persistence operations
//!
//! # Example
//!
//! ```rust
//! use wifi_densepose_core::error::{CoreError, SignalError};
//!
//! fn process_signal() -> Result<(), CoreError> {
//! // Signal processing that might fail
//! Err(SignalError::InvalidSubcarrierCount { expected: 256, actual: 128 }.into())
//! }
//! ```
use thiserror::Error;
/// A specialized `Result` type for core operations.
pub type CoreResult<T> = Result<T, CoreError>;
/// Top-level error type for the WiFi-DensePose system.
///
/// This enum encompasses all possible errors that can occur within the core
/// system, providing a unified error type for the entire crate.
#[derive(Error, Debug)]
#[non_exhaustive]
pub enum CoreError {
/// Signal processing error
#[error("Signal processing error: {0}")]
Signal(#[from] SignalError),
/// Neural network inference error
#[error("Inference error: {0}")]
Inference(#[from] InferenceError),
/// Data storage error
#[error("Storage error: {0}")]
Storage(#[from] StorageError),
/// Configuration error
#[error("Configuration error: {message}")]
Configuration {
/// Description of the configuration error
message: String,
},
/// Validation error for input data
#[error("Validation error: {message}")]
Validation {
/// Description of what validation failed
message: String,
},
/// Resource not found
#[error("Resource not found: {resource_type} with id '{id}'")]
NotFound {
/// Type of resource that was not found
resource_type: &'static str,
/// Identifier of the missing resource
id: String,
},
/// Operation timed out
#[error("Operation timed out after {duration_ms}ms: {operation}")]
Timeout {
/// The operation that timed out
operation: String,
/// Duration in milliseconds before timeout
duration_ms: u64,
},
/// Invalid state for the requested operation
#[error("Invalid state: expected {expected}, found {actual}")]
InvalidState {
/// Expected state
expected: String,
/// Actual state
actual: String,
},
/// Internal error (should not happen in normal operation)
#[error("Internal error: {message}")]
Internal {
/// Description of the internal error
message: String,
},
}
impl CoreError {
/// Creates a new configuration error.
#[must_use]
pub fn configuration(message: impl Into<String>) -> Self {
Self::Configuration {
message: message.into(),
}
}
/// Creates a new validation error.
#[must_use]
pub fn validation(message: impl Into<String>) -> Self {
Self::Validation {
message: message.into(),
}
}
/// Creates a new not found error.
#[must_use]
pub fn not_found(resource_type: &'static str, id: impl Into<String>) -> Self {
Self::NotFound {
resource_type,
id: id.into(),
}
}
/// Creates a new timeout error.
#[must_use]
pub fn timeout(operation: impl Into<String>, duration_ms: u64) -> Self {
Self::Timeout {
operation: operation.into(),
duration_ms,
}
}
/// Creates a new invalid state error.
#[must_use]
pub fn invalid_state(expected: impl Into<String>, actual: impl Into<String>) -> Self {
Self::InvalidState {
expected: expected.into(),
actual: actual.into(),
}
}
/// Creates a new internal error.
#[must_use]
pub fn internal(message: impl Into<String>) -> Self {
Self::Internal {
message: message.into(),
}
}
/// Returns `true` if this error is recoverable.
#[must_use]
pub fn is_recoverable(&self) -> bool {
match self {
Self::Signal(e) => e.is_recoverable(),
Self::Inference(e) => e.is_recoverable(),
Self::Storage(e) => e.is_recoverable(),
Self::Timeout { .. } => true,
Self::NotFound { .. }
| Self::Configuration { .. }
| Self::Validation { .. }
| Self::InvalidState { .. }
| Self::Internal { .. } => false,
}
}
}
/// Errors related to CSI signal processing.
#[derive(Error, Debug)]
#[non_exhaustive]
pub enum SignalError {
/// Invalid number of subcarriers in CSI data
#[error("Invalid subcarrier count: expected {expected}, got {actual}")]
InvalidSubcarrierCount {
/// Expected number of subcarriers
expected: usize,
/// Actual number of subcarriers received
actual: usize,
},
/// Invalid antenna configuration
#[error("Invalid antenna configuration: {message}")]
InvalidAntennaConfig {
/// Description of the configuration error
message: String,
},
/// Signal amplitude out of valid range
#[error("Signal amplitude {value} out of range [{min}, {max}]")]
AmplitudeOutOfRange {
/// The invalid amplitude value
value: f64,
/// Minimum valid amplitude
min: f64,
/// Maximum valid amplitude
max: f64,
},
/// Phase unwrapping failed
#[error("Phase unwrapping failed: {reason}")]
PhaseUnwrapFailed {
/// Reason for the failure
reason: String,
},
/// FFT operation failed
#[error("FFT operation failed: {message}")]
FftFailed {
/// Description of the FFT error
message: String,
},
/// Filter design or application error
#[error("Filter error: {message}")]
FilterError {
/// Description of the filter error
message: String,
},
/// Insufficient samples for processing
#[error("Insufficient samples: need at least {required}, got {available}")]
InsufficientSamples {
/// Minimum required samples
required: usize,
/// Available samples
available: usize,
},
/// Signal quality too low for reliable processing
#[error("Signal quality too low: SNR {snr_db:.2} dB below threshold {threshold_db:.2} dB")]
LowSignalQuality {
/// Measured SNR in dB
snr_db: f64,
/// Required minimum SNR in dB
threshold_db: f64,
},
/// Timestamp synchronization error
#[error("Timestamp synchronization error: {message}")]
TimestampSync {
/// Description of the sync error
message: String,
},
/// Invalid frequency band
#[error("Invalid frequency band: {band}")]
InvalidFrequencyBand {
/// The invalid band identifier
band: String,
},
}
impl SignalError {
/// Returns `true` if this error is recoverable.
#[must_use]
pub const fn is_recoverable(&self) -> bool {
match self {
Self::LowSignalQuality { .. }
| Self::InsufficientSamples { .. }
| Self::TimestampSync { .. }
| Self::PhaseUnwrapFailed { .. }
| Self::FftFailed { .. } => true,
Self::InvalidSubcarrierCount { .. }
| Self::InvalidAntennaConfig { .. }
| Self::AmplitudeOutOfRange { .. }
| Self::FilterError { .. }
| Self::InvalidFrequencyBand { .. } => false,
}
}
}
/// Errors related to neural network inference.
#[derive(Error, Debug)]
#[non_exhaustive]
pub enum InferenceError {
/// Model file not found or could not be loaded
#[error("Failed to load model from '{path}': {reason}")]
ModelLoadFailed {
/// Path to the model file
path: String,
/// Reason for the failure
reason: String,
},
/// Input tensor shape mismatch
#[error("Input shape mismatch: expected {expected:?}, got {actual:?}")]
InputShapeMismatch {
/// Expected tensor shape
expected: Vec<usize>,
/// Actual tensor shape
actual: Vec<usize>,
},
/// Output tensor shape mismatch
#[error("Output shape mismatch: expected {expected:?}, got {actual:?}")]
OutputShapeMismatch {
/// Expected tensor shape
expected: Vec<usize>,
/// Actual tensor shape
actual: Vec<usize>,
},
/// CUDA/GPU error
#[error("GPU error: {message}")]
GpuError {
/// Description of the GPU error
message: String,
},
/// Model inference failed
#[error("Inference failed: {message}")]
InferenceFailed {
/// Description of the failure
message: String,
},
/// Model not initialized
#[error("Model not initialized: {name}")]
ModelNotInitialized {
/// Name of the uninitialized model
name: String,
},
/// Unsupported model format
#[error("Unsupported model format: {format}")]
UnsupportedFormat {
/// The unsupported format
format: String,
},
/// Quantization error
#[error("Quantization error: {message}")]
QuantizationError {
/// Description of the quantization error
message: String,
},
/// Batch size error
#[error("Invalid batch size: {size}, maximum is {max_size}")]
InvalidBatchSize {
/// The invalid batch size
size: usize,
/// Maximum allowed batch size
max_size: usize,
},
}
impl InferenceError {
/// Returns `true` if this error is recoverable.
#[must_use]
pub const fn is_recoverable(&self) -> bool {
match self {
Self::GpuError { .. } | Self::InferenceFailed { .. } => true,
Self::ModelLoadFailed { .. }
| Self::InputShapeMismatch { .. }
| Self::OutputShapeMismatch { .. }
| Self::ModelNotInitialized { .. }
| Self::UnsupportedFormat { .. }
| Self::QuantizationError { .. }
| Self::InvalidBatchSize { .. } => false,
}
}
}
/// Errors related to data storage and persistence.
#[derive(Error, Debug)]
#[non_exhaustive]
pub enum StorageError {
/// Database connection failed
#[error("Database connection failed: {message}")]
ConnectionFailed {
/// Description of the connection error
message: String,
},
/// Query execution failed
#[error("Query failed: {query_type} - {message}")]
QueryFailed {
/// Type of query that failed
query_type: String,
/// Error message
message: String,
},
/// Record not found
#[error("Record not found: {table}.{id}")]
RecordNotFound {
/// Table name
table: String,
/// Record identifier
id: String,
},
/// Duplicate key violation
#[error("Duplicate key in {table}: {key}")]
DuplicateKey {
/// Table name
table: String,
/// The duplicate key
key: String,
},
/// Transaction error
#[error("Transaction error: {message}")]
TransactionError {
/// Description of the transaction error
message: String,
},
/// Serialization/deserialization error
#[error("Serialization error: {message}")]
SerializationError {
/// Description of the serialization error
message: String,
},
/// Cache error
#[error("Cache error: {message}")]
CacheError {
/// Description of the cache error
message: String,
},
/// Migration error
#[error("Migration error: {message}")]
MigrationError {
/// Description of the migration error
message: String,
},
/// Storage capacity exceeded
#[error("Storage capacity exceeded: {current} / {limit} bytes")]
CapacityExceeded {
/// Current storage usage
current: u64,
/// Storage limit
limit: u64,
},
}
impl StorageError {
/// Returns `true` if this error is recoverable.
#[must_use]
pub const fn is_recoverable(&self) -> bool {
match self {
Self::ConnectionFailed { .. }
| Self::QueryFailed { .. }
| Self::TransactionError { .. }
| Self::CacheError { .. } => true,
Self::RecordNotFound { .. }
| Self::DuplicateKey { .. }
| Self::SerializationError { .. }
| Self::MigrationError { .. }
| Self::CapacityExceeded { .. } => false,
}
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_core_error_display() {
let err = CoreError::configuration("Invalid threshold value");
assert!(err.to_string().contains("Configuration error"));
assert!(err.to_string().contains("Invalid threshold"));
}
#[test]
fn test_signal_error_recoverable() {
let recoverable = SignalError::LowSignalQuality {
snr_db: 5.0,
threshold_db: 10.0,
};
assert!(recoverable.is_recoverable());
let non_recoverable = SignalError::InvalidSubcarrierCount {
expected: 256,
actual: 128,
};
assert!(!non_recoverable.is_recoverable());
}
#[test]
fn test_error_conversion() {
let signal_err = SignalError::InvalidSubcarrierCount {
expected: 256,
actual: 128,
};
let core_err: CoreError = signal_err.into();
assert!(matches!(core_err, CoreError::Signal(_)));
}
#[test]
fn test_not_found_error() {
let err = CoreError::not_found("CsiFrame", "frame_123");
assert!(err.to_string().contains("CsiFrame"));
assert!(err.to_string().contains("frame_123"));
}
#[test]
fn test_timeout_error() {
let err = CoreError::timeout("inference", 5000);
assert!(err.to_string().contains("5000ms"));
assert!(err.to_string().contains("inference"));
}
}

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//! # WiFi-DensePose Core
//!
//! Core types, traits, and utilities for the WiFi-DensePose pose estimation system.
//!
//! This crate provides the foundational building blocks used throughout the
//! WiFi-DensePose ecosystem, including:
//!
//! - **Core Data Types**: [`CsiFrame`], [`ProcessedSignal`], [`PoseEstimate`],
//! [`PersonPose`], and [`Keypoint`] for representing `WiFi` CSI data and pose
//! estimation results.
//!
//! - **Error Types**: Comprehensive error handling via the [`error`] module,
//! with specific error types for different subsystems.
//!
//! - **Traits**: Core abstractions like [`SignalProcessor`], [`NeuralInference`],
//! and [`DataStore`] that define the contracts for signal processing, neural
//! network inference, and data persistence.
//!
//! - **Utilities**: Common helper functions and types used across the codebase.
//!
//! ## Feature Flags
//!
//! - `std` (default): Enable standard library support
//! - `serde`: Enable serialization/deserialization via serde
//! - `async`: Enable async trait definitions
//!
//! ## Example
//!
//! ```rust
//! use wifi_densepose_core::{CsiFrame, Keypoint, KeypointType, Confidence};
//!
//! // Create a keypoint with high confidence
//! let keypoint = Keypoint::new(
//! KeypointType::Nose,
//! 0.5,
//! 0.3,
//! Confidence::new(0.95).unwrap(),
//! );
//!
//! assert!(keypoint.is_visible());
//! ```
#![cfg_attr(not(feature = "std"), no_std)]
#![forbid(unsafe_code)]
#[cfg(not(feature = "std"))]
extern crate alloc;
pub mod error;
pub mod traits;
pub mod types;
pub mod utils;
// Re-export commonly used types at the crate root
pub use error::{CoreError, CoreResult, SignalError, InferenceError, StorageError};
pub use traits::{SignalProcessor, NeuralInference, DataStore};
pub use types::{
// CSI types
CsiFrame, CsiMetadata, AntennaConfig,
// Signal types
ProcessedSignal, SignalFeatures, FrequencyBand,
// Pose types
PoseEstimate, PersonPose, Keypoint, KeypointType,
// Common types
Confidence, Timestamp, FrameId, DeviceId,
// Bounding box
BoundingBox,
};
/// Crate version
pub const VERSION: &str = env!("CARGO_PKG_VERSION");
/// Maximum number of keypoints per person (COCO format)
pub const MAX_KEYPOINTS: usize = 17;
/// Maximum number of subcarriers typically used in `WiFi` CSI
pub const MAX_SUBCARRIERS: usize = 256;
/// Default confidence threshold for keypoint visibility
pub const DEFAULT_CONFIDENCE_THRESHOLD: f32 = 0.5;
/// Prelude module for convenient imports.
///
/// Convenient re-exports of commonly used types and traits.
///
/// ```rust
/// use wifi_densepose_core::prelude::*;
/// ```
pub mod prelude {
pub use crate::error::{CoreError, CoreResult};
pub use crate::traits::{DataStore, NeuralInference, SignalProcessor};
pub use crate::types::{
AntennaConfig, BoundingBox, Confidence, CsiFrame, CsiMetadata, DeviceId, FrameId,
FrequencyBand, Keypoint, KeypointType, PersonPose, PoseEstimate, ProcessedSignal,
SignalFeatures, Timestamp,
};
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_version_is_valid() {
assert!(!VERSION.is_empty());
}
#[test]
fn test_constants() {
assert_eq!(MAX_KEYPOINTS, 17);
assert!(MAX_SUBCARRIERS > 0);
assert!(DEFAULT_CONFIDENCE_THRESHOLD > 0.0);
assert!(DEFAULT_CONFIDENCE_THRESHOLD < 1.0);
}
}

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//! Core trait definitions for the WiFi-DensePose system.
//!
//! This module defines the fundamental abstractions used throughout the system,
//! enabling a modular and testable architecture.
//!
//! # Traits
//!
//! - [`SignalProcessor`]: Process raw CSI frames into neural network-ready tensors
//! - [`NeuralInference`]: Run pose estimation inference on processed signals
//! - [`DataStore`]: Persist and retrieve CSI data and pose estimates
//!
//! # Design Philosophy
//!
//! These traits are designed with the following principles:
//!
//! 1. **Single Responsibility**: Each trait handles one concern
//! 2. **Testability**: All traits can be easily mocked for unit testing
//! 3. **Async-Ready**: Async versions available with the `async` feature
//! 4. **Error Handling**: Consistent use of `Result` types with domain errors
use crate::error::{CoreResult, InferenceError, SignalError, StorageError};
use crate::types::{CsiFrame, FrameId, PoseEstimate, ProcessedSignal, Timestamp};
/// Configuration for signal processing.
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(serde::Deserialize, serde::Serialize))]
pub struct SignalProcessorConfig {
/// Number of frames to buffer before processing
pub buffer_size: usize,
/// Sampling rate in Hz
pub sample_rate_hz: f64,
/// Whether to apply noise filtering
pub apply_noise_filter: bool,
/// Noise filter cutoff frequency in Hz
pub filter_cutoff_hz: f64,
/// Whether to normalize amplitudes
pub normalize_amplitude: bool,
/// Whether to unwrap phases
pub unwrap_phase: bool,
/// Window function for spectral analysis
pub window_function: WindowFunction,
}
impl Default for SignalProcessorConfig {
fn default() -> Self {
Self {
buffer_size: 64,
sample_rate_hz: 1000.0,
apply_noise_filter: true,
filter_cutoff_hz: 50.0,
normalize_amplitude: true,
unwrap_phase: true,
window_function: WindowFunction::Hann,
}
}
}
/// Window functions for spectral analysis.
#[derive(Debug, Clone, Copy, PartialEq, Eq, Default)]
#[cfg_attr(feature = "serde", derive(serde::Deserialize, serde::Serialize))]
pub enum WindowFunction {
/// Rectangular window (no windowing)
Rectangular,
/// Hann window
#[default]
Hann,
/// Hamming window
Hamming,
/// Blackman window
Blackman,
/// Kaiser window
Kaiser,
}
/// Signal processor for converting raw CSI frames into processed signals.
///
/// Implementations of this trait handle:
/// - Buffering and aggregating CSI frames
/// - Noise filtering and signal conditioning
/// - Phase unwrapping and amplitude normalization
/// - Feature extraction
///
/// # Example
///
/// ```ignore
/// use wifi_densepose_core::{SignalProcessor, CsiFrame};
///
/// fn process_frames(processor: &mut impl SignalProcessor, frames: Vec<CsiFrame>) {
/// for frame in frames {
/// if let Err(e) = processor.push_frame(frame) {
/// eprintln!("Failed to push frame: {}", e);
/// }
/// }
///
/// if let Some(signal) = processor.try_process() {
/// println!("Processed signal with {} time steps", signal.num_time_steps());
/// }
/// }
/// ```
pub trait SignalProcessor: Send + Sync {
/// Returns the current configuration.
fn config(&self) -> &SignalProcessorConfig;
/// Updates the configuration.
///
/// # Errors
///
/// Returns an error if the configuration is invalid.
fn set_config(&mut self, config: SignalProcessorConfig) -> Result<(), SignalError>;
/// Pushes a new CSI frame into the processing buffer.
///
/// # Errors
///
/// Returns an error if the frame is invalid or the buffer is full.
fn push_frame(&mut self, frame: CsiFrame) -> Result<(), SignalError>;
/// Attempts to process the buffered frames.
///
/// Returns `None` if insufficient frames are buffered.
/// Returns `Some(ProcessedSignal)` on successful processing.
///
/// # Errors
///
/// Returns an error if processing fails.
fn try_process(&mut self) -> Result<Option<ProcessedSignal>, SignalError>;
/// Forces processing of whatever frames are buffered.
///
/// # Errors
///
/// Returns an error if no frames are buffered or processing fails.
fn force_process(&mut self) -> Result<ProcessedSignal, SignalError>;
/// Returns the number of frames currently buffered.
fn buffered_frame_count(&self) -> usize;
/// Clears the frame buffer.
fn clear_buffer(&mut self);
/// Resets the processor to its initial state.
fn reset(&mut self);
}
/// Configuration for neural network inference.
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(serde::Deserialize, serde::Serialize))]
pub struct InferenceConfig {
/// Path to the model file
pub model_path: String,
/// Device to run inference on
pub device: InferenceDevice,
/// Maximum batch size
pub max_batch_size: usize,
/// Number of threads for CPU inference
pub num_threads: usize,
/// Confidence threshold for detections
pub confidence_threshold: f32,
/// Non-maximum suppression threshold
pub nms_threshold: f32,
/// Whether to use half precision (FP16)
pub use_fp16: bool,
}
impl Default for InferenceConfig {
fn default() -> Self {
Self {
model_path: String::new(),
device: InferenceDevice::Cpu,
max_batch_size: 8,
num_threads: 4,
confidence_threshold: 0.5,
nms_threshold: 0.45,
use_fp16: false,
}
}
}
/// Device for running neural network inference.
#[derive(Debug, Clone, Copy, PartialEq, Eq, Default)]
#[cfg_attr(feature = "serde", derive(serde::Deserialize, serde::Serialize))]
pub enum InferenceDevice {
/// CPU inference
#[default]
Cpu,
/// CUDA GPU inference
Cuda {
/// GPU device index
device_id: usize,
},
/// TensorRT accelerated inference
TensorRt {
/// GPU device index
device_id: usize,
},
/// CoreML (Apple Silicon)
CoreMl,
/// WebGPU for browser environments
WebGpu,
}
/// Neural network inference engine for pose estimation.
///
/// Implementations of this trait handle:
/// - Loading and managing neural network models
/// - Running inference on processed signals
/// - Post-processing outputs into pose estimates
///
/// # Example
///
/// ```ignore
/// use wifi_densepose_core::{NeuralInference, ProcessedSignal};
///
/// async fn estimate_pose(
/// engine: &impl NeuralInference,
/// signal: ProcessedSignal,
/// ) -> Result<PoseEstimate, InferenceError> {
/// engine.infer(signal).await
/// }
/// ```
pub trait NeuralInference: Send + Sync {
/// Returns the current configuration.
fn config(&self) -> &InferenceConfig;
/// Returns `true` if the model is loaded and ready.
fn is_ready(&self) -> bool;
/// Returns the model version string.
fn model_version(&self) -> &str;
/// Loads the model from the configured path.
///
/// # Errors
///
/// Returns an error if the model cannot be loaded.
fn load_model(&mut self) -> Result<(), InferenceError>;
/// Unloads the current model to free resources.
fn unload_model(&mut self);
/// Runs inference on a single processed signal.
///
/// # Errors
///
/// Returns an error if inference fails.
fn infer(&self, signal: &ProcessedSignal) -> Result<PoseEstimate, InferenceError>;
/// Runs inference on a batch of processed signals.
///
/// # Errors
///
/// Returns an error if inference fails.
fn infer_batch(&self, signals: &[ProcessedSignal])
-> Result<Vec<PoseEstimate>, InferenceError>;
/// Warms up the model by running a dummy inference.
///
/// # Errors
///
/// Returns an error if warmup fails.
fn warmup(&mut self) -> Result<(), InferenceError>;
/// Returns performance statistics.
fn stats(&self) -> InferenceStats;
}
/// Performance statistics for neural network inference.
#[derive(Debug, Clone, Default)]
pub struct InferenceStats {
/// Total number of inferences performed
pub total_inferences: u64,
/// Average inference latency in milliseconds
pub avg_latency_ms: f64,
/// 95th percentile latency in milliseconds
pub p95_latency_ms: f64,
/// Maximum latency in milliseconds
pub max_latency_ms: f64,
/// Inferences per second throughput
pub throughput: f64,
/// GPU memory usage in bytes (if applicable)
pub gpu_memory_bytes: Option<u64>,
}
/// Query options for data store operations.
#[derive(Debug, Clone, Default)]
pub struct QueryOptions {
/// Maximum number of results to return
pub limit: Option<usize>,
/// Number of results to skip
pub offset: Option<usize>,
/// Start time filter (inclusive)
pub start_time: Option<Timestamp>,
/// End time filter (inclusive)
pub end_time: Option<Timestamp>,
/// Device ID filter
pub device_id: Option<String>,
/// Sort order
pub sort_order: SortOrder,
}
/// Sort order for query results.
#[derive(Debug, Clone, Copy, PartialEq, Eq, Default)]
pub enum SortOrder {
/// Ascending order (oldest first)
#[default]
Ascending,
/// Descending order (newest first)
Descending,
}
/// Data storage trait for persisting and retrieving CSI data and pose estimates.
///
/// Implementations can use various backends:
/// - PostgreSQL/SQLite for relational storage
/// - Redis for caching
/// - Time-series databases for efficient temporal queries
///
/// # Example
///
/// ```ignore
/// use wifi_densepose_core::{DataStore, CsiFrame, PoseEstimate};
///
/// async fn save_and_query(
/// store: &impl DataStore,
/// frame: CsiFrame,
/// estimate: PoseEstimate,
/// ) {
/// store.store_csi_frame(&frame).await?;
/// store.store_pose_estimate(&estimate).await?;
///
/// let recent = store.get_recent_estimates(10).await?;
/// println!("Found {} recent estimates", recent.len());
/// }
/// ```
pub trait DataStore: Send + Sync {
/// Returns `true` if the store is connected and ready.
fn is_connected(&self) -> bool;
/// Stores a CSI frame.
///
/// # Errors
///
/// Returns an error if the store operation fails.
fn store_csi_frame(&self, frame: &CsiFrame) -> Result<(), StorageError>;
/// Retrieves a CSI frame by ID.
///
/// # Errors
///
/// Returns an error if the frame is not found or retrieval fails.
fn get_csi_frame(&self, id: &FrameId) -> Result<CsiFrame, StorageError>;
/// Retrieves CSI frames matching the query options.
///
/// # Errors
///
/// Returns an error if the query fails.
fn query_csi_frames(&self, options: &QueryOptions) -> Result<Vec<CsiFrame>, StorageError>;
/// Stores a pose estimate.
///
/// # Errors
///
/// Returns an error if the store operation fails.
fn store_pose_estimate(&self, estimate: &PoseEstimate) -> Result<(), StorageError>;
/// Retrieves a pose estimate by ID.
///
/// # Errors
///
/// Returns an error if the estimate is not found or retrieval fails.
fn get_pose_estimate(&self, id: &FrameId) -> Result<PoseEstimate, StorageError>;
/// Retrieves pose estimates matching the query options.
///
/// # Errors
///
/// Returns an error if the query fails.
fn query_pose_estimates(
&self,
options: &QueryOptions,
) -> Result<Vec<PoseEstimate>, StorageError>;
/// Retrieves the N most recent pose estimates.
///
/// # Errors
///
/// Returns an error if the query fails.
fn get_recent_estimates(&self, count: usize) -> Result<Vec<PoseEstimate>, StorageError>;
/// Deletes CSI frames older than the given timestamp.
///
/// # Errors
///
/// Returns an error if the deletion fails.
fn delete_csi_frames_before(&self, timestamp: &Timestamp) -> Result<u64, StorageError>;
/// Deletes pose estimates older than the given timestamp.
///
/// # Errors
///
/// Returns an error if the deletion fails.
fn delete_pose_estimates_before(&self, timestamp: &Timestamp) -> Result<u64, StorageError>;
/// Returns storage statistics.
fn stats(&self) -> StorageStats;
}
/// Storage statistics.
#[derive(Debug, Clone, Default)]
pub struct StorageStats {
/// Total number of CSI frames stored
pub csi_frame_count: u64,
/// Total number of pose estimates stored
pub pose_estimate_count: u64,
/// Total storage size in bytes
pub total_size_bytes: u64,
/// Oldest record timestamp
pub oldest_record: Option<Timestamp>,
/// Newest record timestamp
pub newest_record: Option<Timestamp>,
}
// =============================================================================
// Async Trait Definitions (with `async` feature)
// =============================================================================
#[cfg(feature = "async")]
use async_trait::async_trait;
/// Async version of [`SignalProcessor`].
#[cfg(feature = "async")]
#[async_trait]
pub trait AsyncSignalProcessor: Send + Sync {
/// Returns the current configuration.
fn config(&self) -> &SignalProcessorConfig;
/// Updates the configuration.
async fn set_config(&mut self, config: SignalProcessorConfig) -> Result<(), SignalError>;
/// Pushes a new CSI frame into the processing buffer.
async fn push_frame(&mut self, frame: CsiFrame) -> Result<(), SignalError>;
/// Attempts to process the buffered frames.
async fn try_process(&mut self) -> Result<Option<ProcessedSignal>, SignalError>;
/// Forces processing of whatever frames are buffered.
async fn force_process(&mut self) -> Result<ProcessedSignal, SignalError>;
/// Returns the number of frames currently buffered.
fn buffered_frame_count(&self) -> usize;
/// Clears the frame buffer.
async fn clear_buffer(&mut self);
/// Resets the processor to its initial state.
async fn reset(&mut self);
}
/// Async version of [`NeuralInference`].
#[cfg(feature = "async")]
#[async_trait]
pub trait AsyncNeuralInference: Send + Sync {
/// Returns the current configuration.
fn config(&self) -> &InferenceConfig;
/// Returns `true` if the model is loaded and ready.
fn is_ready(&self) -> bool;
/// Returns the model version string.
fn model_version(&self) -> &str;
/// Loads the model from the configured path.
async fn load_model(&mut self) -> Result<(), InferenceError>;
/// Unloads the current model to free resources.
async fn unload_model(&mut self);
/// Runs inference on a single processed signal.
async fn infer(&self, signal: &ProcessedSignal) -> Result<PoseEstimate, InferenceError>;
/// Runs inference on a batch of processed signals.
async fn infer_batch(
&self,
signals: &[ProcessedSignal],
) -> Result<Vec<PoseEstimate>, InferenceError>;
/// Warms up the model by running a dummy inference.
async fn warmup(&mut self) -> Result<(), InferenceError>;
/// Returns performance statistics.
fn stats(&self) -> InferenceStats;
}
/// Async version of [`DataStore`].
#[cfg(feature = "async")]
#[async_trait]
pub trait AsyncDataStore: Send + Sync {
/// Returns `true` if the store is connected and ready.
fn is_connected(&self) -> bool;
/// Stores a CSI frame.
async fn store_csi_frame(&self, frame: &CsiFrame) -> Result<(), StorageError>;
/// Retrieves a CSI frame by ID.
async fn get_csi_frame(&self, id: &FrameId) -> Result<CsiFrame, StorageError>;
/// Retrieves CSI frames matching the query options.
async fn query_csi_frames(&self, options: &QueryOptions) -> Result<Vec<CsiFrame>, StorageError>;
/// Stores a pose estimate.
async fn store_pose_estimate(&self, estimate: &PoseEstimate) -> Result<(), StorageError>;
/// Retrieves a pose estimate by ID.
async fn get_pose_estimate(&self, id: &FrameId) -> Result<PoseEstimate, StorageError>;
/// Retrieves pose estimates matching the query options.
async fn query_pose_estimates(
&self,
options: &QueryOptions,
) -> Result<Vec<PoseEstimate>, StorageError>;
/// Retrieves the N most recent pose estimates.
async fn get_recent_estimates(&self, count: usize) -> Result<Vec<PoseEstimate>, StorageError>;
/// Deletes CSI frames older than the given timestamp.
async fn delete_csi_frames_before(&self, timestamp: &Timestamp) -> Result<u64, StorageError>;
/// Deletes pose estimates older than the given timestamp.
async fn delete_pose_estimates_before(
&self,
timestamp: &Timestamp,
) -> Result<u64, StorageError>;
/// Returns storage statistics.
fn stats(&self) -> StorageStats;
}
// =============================================================================
// Extension Traits
// =============================================================================
/// Extension trait for pipeline composition.
pub trait Pipeline: Send + Sync {
/// The input type for this pipeline stage.
type Input;
/// The output type for this pipeline stage.
type Output;
/// The error type for this pipeline stage.
type Error;
/// Processes input and produces output.
///
/// # Errors
///
/// Returns an error if processing fails.
fn process(&self, input: Self::Input) -> Result<Self::Output, Self::Error>;
}
/// Trait for types that can validate themselves.
pub trait Validate {
/// Validates the instance.
///
/// # Errors
///
/// Returns an error describing validation failures.
fn validate(&self) -> CoreResult<()>;
}
/// Trait for types that can be reset to a default state.
pub trait Resettable {
/// Resets the instance to its initial state.
fn reset(&mut self);
}
/// Trait for types that track health status.
pub trait HealthCheck {
/// Health status of the component.
type Status;
/// Performs a health check and returns the current status.
fn health_check(&self) -> Self::Status;
/// Returns `true` if the component is healthy.
fn is_healthy(&self) -> bool;
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_signal_processor_config_default() {
let config = SignalProcessorConfig::default();
assert_eq!(config.buffer_size, 64);
assert!(config.apply_noise_filter);
assert!(config.sample_rate_hz > 0.0);
}
#[test]
fn test_inference_config_default() {
let config = InferenceConfig::default();
assert_eq!(config.device, InferenceDevice::Cpu);
assert!(config.confidence_threshold > 0.0);
assert!(config.max_batch_size > 0);
}
#[test]
fn test_query_options_default() {
let options = QueryOptions::default();
assert!(options.limit.is_none());
assert!(options.offset.is_none());
assert_eq!(options.sort_order, SortOrder::Ascending);
}
#[test]
fn test_inference_device_variants() {
let cpu = InferenceDevice::Cpu;
let cuda = InferenceDevice::Cuda { device_id: 0 };
let tensorrt = InferenceDevice::TensorRt { device_id: 1 };
assert_eq!(cpu, InferenceDevice::Cpu);
assert!(matches!(cuda, InferenceDevice::Cuda { device_id: 0 }));
assert!(matches!(tensorrt, InferenceDevice::TensorRt { device_id: 1 }));
}
}

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//! Common utility functions for the WiFi-DensePose system.
//!
//! This module provides helper functions used throughout the crate.
use ndarray::{Array1, Array2};
use num_complex::Complex64;
/// Computes the magnitude (absolute value) of complex numbers.
#[must_use]
pub fn complex_magnitude(data: &Array2<Complex64>) -> Array2<f64> {
data.mapv(num_complex::Complex::norm)
}
/// Computes the phase (argument) of complex numbers in radians.
#[must_use]
pub fn complex_phase(data: &Array2<Complex64>) -> Array2<f64> {
data.mapv(num_complex::Complex::arg)
}
/// Unwraps phase values to remove discontinuities.
///
/// Phase unwrapping corrects for the 2*pi jumps that occur when phase
/// values wrap around from pi to -pi.
#[must_use]
pub fn unwrap_phase(phase: &Array1<f64>) -> Array1<f64> {
let mut unwrapped = phase.clone();
let pi = std::f64::consts::PI;
let two_pi = 2.0 * pi;
for i in 1..unwrapped.len() {
let diff = unwrapped[i] - unwrapped[i - 1];
if diff > pi {
for j in i..unwrapped.len() {
unwrapped[j] -= two_pi;
}
} else if diff < -pi {
for j in i..unwrapped.len() {
unwrapped[j] += two_pi;
}
}
}
unwrapped
}
/// Normalizes values to the range [0, 1].
#[must_use]
pub fn normalize_min_max(data: &Array1<f64>) -> Array1<f64> {
let min = data.iter().copied().fold(f64::INFINITY, f64::min);
let max = data.iter().copied().fold(f64::NEG_INFINITY, f64::max);
if (max - min).abs() < f64::EPSILON {
return Array1::zeros(data.len());
}
data.mapv(|x| (x - min) / (max - min))
}
/// Normalizes values using z-score normalization.
#[must_use]
pub fn normalize_zscore(data: &Array1<f64>) -> Array1<f64> {
let mean = data.mean().unwrap_or(0.0);
let std = data.std(0.0);
if std.abs() < f64::EPSILON {
return Array1::zeros(data.len());
}
data.mapv(|x| (x - mean) / std)
}
/// Calculates the Signal-to-Noise Ratio in dB.
#[must_use]
#[allow(clippy::cast_precision_loss)]
pub fn calculate_snr_db(signal: &Array1<f64>, noise: &Array1<f64>) -> f64 {
let signal_power: f64 = signal.iter().map(|x| x * x).sum::<f64>() / signal.len() as f64;
let noise_power: f64 = noise.iter().map(|x| x * x).sum::<f64>() / noise.len() as f64;
if noise_power.abs() < f64::EPSILON {
return f64::INFINITY;
}
10.0 * (signal_power / noise_power).log10()
}
/// Applies a moving average filter.
///
/// # Panics
///
/// Panics if the data array is not contiguous in memory.
#[must_use]
#[allow(clippy::cast_precision_loss)]
pub fn moving_average(data: &Array1<f64>, window_size: usize) -> Array1<f64> {
if window_size == 0 || window_size > data.len() {
return data.clone();
}
let mut result = Array1::zeros(data.len());
let half_window = window_size / 2;
// Safe unwrap: ndarray Array1 is always contiguous
let slice = data.as_slice().expect("Array1 should be contiguous");
for i in 0..data.len() {
let start = i.saturating_sub(half_window);
let end = (i + half_window + 1).min(data.len());
let window = &slice[start..end];
result[i] = window.iter().sum::<f64>() / window.len() as f64;
}
result
}
/// Clamps a value to a range.
#[must_use]
pub fn clamp<T: PartialOrd>(value: T, min: T, max: T) -> T {
if value < min {
min
} else if value > max {
max
} else {
value
}
}
/// Linearly interpolates between two values.
#[must_use]
pub fn lerp(a: f64, b: f64, t: f64) -> f64 {
(b - a).mul_add(t, a)
}
/// Converts degrees to radians.
#[must_use]
pub fn deg_to_rad(degrees: f64) -> f64 {
degrees.to_radians()
}
/// Converts radians to degrees.
#[must_use]
pub fn rad_to_deg(radians: f64) -> f64 {
radians.to_degrees()
}
/// Calculates the Euclidean distance between two points.
#[must_use]
pub fn euclidean_distance(p1: (f64, f64), p2: (f64, f64)) -> f64 {
let dx = p2.0 - p1.0;
let dy = p2.1 - p1.1;
dx.hypot(dy)
}
/// Calculates the Euclidean distance in 3D.
#[must_use]
pub fn euclidean_distance_3d(p1: (f64, f64, f64), p2: (f64, f64, f64)) -> f64 {
let dx = p2.0 - p1.0;
let dy = p2.1 - p1.1;
let dz = p2.2 - p1.2;
(dx.mul_add(dx, dy.mul_add(dy, dz * dz))).sqrt()
}
#[cfg(test)]
mod tests {
use super::*;
use ndarray::array;
#[test]
fn test_normalize_min_max() {
let data = array![0.0, 5.0, 10.0];
let normalized = normalize_min_max(&data);
assert!((normalized[0] - 0.0).abs() < 1e-10);
assert!((normalized[1] - 0.5).abs() < 1e-10);
assert!((normalized[2] - 1.0).abs() < 1e-10);
}
#[test]
fn test_normalize_zscore() {
let data = array![1.0, 2.0, 3.0, 4.0, 5.0];
let normalized = normalize_zscore(&data);
// Mean should be approximately 0
assert!(normalized.mean().unwrap().abs() < 1e-10);
}
#[test]
fn test_moving_average() {
let data = array![1.0, 2.0, 3.0, 4.0, 5.0];
let smoothed = moving_average(&data, 3);
// Middle value should be average of 2, 3, 4
assert!((smoothed[2] - 3.0).abs() < 1e-10);
}
#[test]
fn test_clamp() {
assert_eq!(clamp(5, 0, 10), 5);
assert_eq!(clamp(-5, 0, 10), 0);
assert_eq!(clamp(15, 0, 10), 10);
}
#[test]
fn test_lerp() {
assert!((lerp(0.0, 10.0, 0.5) - 5.0).abs() < 1e-10);
assert!((lerp(0.0, 10.0, 0.0) - 0.0).abs() < 1e-10);
assert!((lerp(0.0, 10.0, 1.0) - 10.0).abs() < 1e-10);
}
#[test]
fn test_deg_rad_conversion() {
let degrees = 180.0;
let radians = deg_to_rad(degrees);
assert!((radians - std::f64::consts::PI).abs() < 1e-10);
let back = rad_to_deg(radians);
assert!((back - degrees).abs() < 1e-10);
}
#[test]
fn test_euclidean_distance() {
let dist = euclidean_distance((0.0, 0.0), (3.0, 4.0));
assert!((dist - 5.0).abs() < 1e-10);
}
#[test]
fn test_unwrap_phase() {
let pi = std::f64::consts::PI;
// Simulate a phase wrap
let phase = array![0.0, pi / 2.0, pi, -pi + 0.1, -pi / 2.0];
let unwrapped = unwrap_phase(&phase);
// After unwrapping, the phase should be monotonically increasing
for i in 1..unwrapped.len() {
// Allow some tolerance for the discontinuity correction
assert!(
unwrapped[i] >= unwrapped[i - 1] - 0.5,
"Phase should be mostly increasing after unwrapping"
);
}
}
#[test]
fn test_snr_calculation() {
let signal = array![1.0, 1.0, 1.0, 1.0];
let noise = array![0.1, 0.1, 0.1, 0.1];
let snr = calculate_snr_db(&signal, &noise);
// SNR should be 20 dB (10 * log10(1/0.01) = 10 * log10(100) = 20)
assert!((snr - 20.0).abs() < 1e-10);
}
}