edgefirst-hal 0.18.0

Hardware Abstraction Layer for edge AI with zero-copy tensors, image processing, and YOLO decoding

edgefirst-hal

Hardware-accelerated image processing, zero-copy tensors, and YOLO decoding for edge AI inference pipelines. Built in Rust with Python bindings via PyO3.

Installation

pip install edgefirst-hal

Pre-built wheels are available for Linux (x86_64, aarch64), macOS, and Windows. No Rust toolchain required.

Python 3.11+ wheels use the improved stable ABI for zero-copy buffer protocol support. Python 3.8–3.10 wheels use a compatible fallback. Pip selects the best wheel automatically.

Quick Start

import edgefirst_hal as ef

# Load a source image
src = ef.Tensor.load("photo.jpg", ef.PixelFormat.Rgb)

# Create an image processor (auto-selects best backend: GPU > G2D > CPU)
processor = ef.ImageProcessor()

# Allocate a GPU-optimal output buffer — always use create_image() for
# destinations passed to convert(), so the processor can select the best
# memory type (DMA-buf, PBO, or system memory) for zero-copy GPU paths.
dst = processor.create_image(640, 640, ef.PixelFormat.Rgb)

# Convert with letterbox resize (preserves aspect ratio)
processor.convert(src, dst)

# Access pixel data as a numpy array
import numpy as np
pixels = np.frombuffer(dst.map(), dtype=np.uint8).reshape(dst.shape())

Key Features

• Zero-copy tensors — DMA-BUF, POSIX shared memory, and PBO-backed buffers with automatic fallback to system memory
• Hardware-accelerated image processing — OpenGL, NXP G2D, and optimized CPU backends with automatic selection
• Letterbox resize — aspect-ratio-preserving resize with configurable padding color, rotation, and flip
• Int8 output — create_image(..., dtype="int8") for direct signed int8 tensor output with GPU-accelerated XOR bias
• YOLO decoding — YOLOv5, YOLOv8, YOLO11, and YOLO26 detection and instance segmentation (including end-to-end models)
• Object tracking — ByteTrack multi-object tracker with Kalman filtering
• Fully typed — ships with .pyi stubs for IDE autocompletion and type checking with mypy / pyright

Image Processing

import edgefirst_hal as ef

processor = ef.ImageProcessor()
src = ef.Tensor.load("frame.jpg", ef.PixelFormat.Rgb)

# Letterbox resize to model input size
dst = processor.create_image(640, 640, ef.PixelFormat.Rgb)
processor.convert(src, dst)

# With rotation and horizontal flip
processor.convert(src, dst, rotation=ef.Rotation.Rotate90, flip=ef.Flip.Horizontal)

# Crop source region
processor.convert(src, dst, src_crop=ef.Rect(100, 100, 400, 400))

# Int8 output for quantized models
dst_i8 = processor.create_image(640, 640, ef.PixelFormat.Rgb, dtype="int8")
processor.convert(src, dst_i8)

Zero-Copy External Buffer (Linux)

When integrating with an NPU delegate that owns DMA-BUF buffers, render directly into the delegate's buffer to eliminate a memcpy:

import edgefirst_hal as ef

processor = ef.ImageProcessor()
src = ef.Tensor.load("frame.jpg", ef.PixelFormat.Rgb)

# Render directly into the delegate's DMA-BUF — zero copies
dst = processor.import_image(fd=vx_fd, width=640, height=640, format=ef.PixelFormat.Rgb)
processor.convert(src, dst)

# Reverse: HAL allocates, consumer imports the fd
hal_dst = processor.create_image(640, 640, ef.PixelFormat.Rgb)
fd = hal_dst.dmabuf_clone()  # Raises if not DMA-backed
delegate.register(fd)

You can also attach format metadata to any raw tensor created via from_fd():

t = ef.Tensor.from_fd(some_fd, [480, 640, 3])
t.set_format(ef.PixelFormat.Rgb)
processor.convert(src, t)

Performance tip: When rotating through a pool of DMA-BUFs (e.g. 2-3 from an NPU delegate), create the Tensor wrappers once at init and reuse them across frames. This avoids EGL image cache misses (~100-300us each on Vivante GPUs).

YOLO Decoding

import edgefirst_hal as ef

# Configure decoder from model metadata
decoder = ef.Decoder(
    {"detection": {"shape": [1, 84, 8400], "dtype": "float32"}},
    score_threshold=0.5,
    iou_threshold=0.45,
)

# Decode model outputs → (boxes, scores, class_ids)
boxes, scores, classes = decoder.decode([output_tensor])

Object Tracking

ByteTrack is a multi-object tracker based on ByteTrack with Kalman filtering. It assigns consistent track IDs across frames.

import edgefirst_hal as ef

tracker = ef.ByteTrack(
    high_conf=0.7,         # High-confidence detection threshold
    iou=0.25,              # IoU threshold for association
    update=0.25,           # Update/low-confidence threshold
    lifespan_ns=500_000_000,  # Track lifespan without detection (nanoseconds)
)

# Decode and track in one call (returns boxes, scores, classes, masks, track_infos)
boxes, scores, classes, masks, tracks = decoder.decode_tracked(
    tracker, timestamp_ns, [output_tensor]
)
# masks is empty for detection-only models

# Or query currently active tracks
active = tracker.get_active_tracks()

Segmentation Mask Rendering

draw_decoded_masks()

Draw pre-decoded masks onto a destination image:

processor.draw_decoded_masks(
    dst,
    bbox,           # numpy array [N, 4]
    scores,         # numpy array [N]
    classes,        # numpy array [N]
    seg=[],         # list of segmentation arrays (optional)
    background=None,  # optional background tensor to blit before drawing
    opacity=1.0,    # mask alpha scale (0.0 – 1.0)
)

draw_masks()

Decode model outputs and draw segmentation masks in a single call. Masks never leave Rust, eliminating the Python round-trip overhead of decode() + draw_decoded_masks().

Without a tracker, returns (boxes, scores, classes). With a tracker, returns (boxes, scores, classes, track_infos).

import edgefirst_hal as ef

processor = ef.ImageProcessor()
tracker = ef.ByteTrack()

# Without tracking
boxes, scores, classes = processor.draw_masks(decoder, outputs, dst)

# With overlay parameters
boxes, scores, classes = processor.draw_masks(
    decoder, outputs, dst,
    background=bg_tensor,  # blit bg_tensor into dst before masks
    opacity=0.7,           # semi-transparent masks
)

# With tracking (requires tracker= and timestamp=)
import time
ts = time.monotonic_ns()
boxes, scores, classes, tracks = processor.draw_masks(
    decoder, outputs, dst,
    tracker=tracker,
    timestamp=ts,
)

Platform Support

Platform	GPU Acceleration	Memory Types
Linux (NXP i.MX8/i.MX95)	OpenGL + G2D	DMA-buf, SHM, PBO, Mem
Linux (x86_64, other ARM)	OpenGL	SHM, PBO, Mem
macOS / Windows	CPU only	Mem

Hardware acceleration is used automatically when available. All platforms fall back to CPU.

Part of the EdgeFirst Ecosystem

edgefirst-hal is the runtime inference library in the EdgeFirst platform for deploying AI at the edge.

• EdgeFirst Studio — label, train, and deploy models for edge devices
• Rust crates — use the same library directly from Rust or C
• GitHub — source code, architecture docs, benchmarks, and contribution guide

License

Apache-2.0 — see LICENSE.