# edgefirst-image
[](https://crates.io/crates/edgefirst-image)
[](https://docs.rs/edgefirst-image)
[](LICENSE)
**High-performance image processing for edge AI inference pipelines.**
This crate provides hardware-accelerated image loading, format conversion, resizing, rotation, and cropping operations optimized for ML preprocessing workflows.
## Role in edgefirst-hal
`edgefirst-image` sits at the centre of the EdgeFirst HAL workspace, owning
the GPU/G2D/CPU dispatch and segmentation-mask rendering. Its dependency
neighbours:
- Depends on [`edgefirst-tensor`](https://github.com/EdgeFirstAI/hal/blob/main/crates/tensor/) for `TensorDyn`, `BufferIdentity`, and the `PboOps` trait it implements for the GL backend.
- Depends unconditionally on [`edgefirst-decoder`](https://github.com/EdgeFirstAI/hal/blob/main/crates/decoder/) for `DetectBox`, `Segmentation`, and the proto-mask data feeding `draw_proto_masks` (there is no opt-out feature flag).
- Optionally depends on [`edgefirst-tracker`](https://github.com/EdgeFirstAI/hal/blob/main/crates/tracker/) (feature `tracker`) for `draw_masks_tracked`.
- Re-exported from [`edgefirst-hal`](https://github.com/EdgeFirstAI/hal/blob/main/crates/hal/) as `edgefirst_hal::image`.
- Bridged to C via [`edgefirst-hal-capi`](https://github.com/EdgeFirstAI/hal/blob/main/crates/capi/) (cbindgen-generated C ABI).
- Bridged to Python via [`crates/python`](https://github.com/EdgeFirstAI/hal/blob/main/crates/python/) (PyO3 binding over the Rust umbrella crate; does not go through the C ABI).
## Features
- **Multiple backends** — Automatic selection: OpenGL (GPU) → G2D (NXP i.MX) → CPU (fallback)
- **Format conversion** - RGBA, RGB, NV12, NV16, YUYV, GREY, planar formats
- **Geometric transforms** - Resize, rotate (90° increments), flip, crop
- **Zero-copy integration** - Works with `edgefirst-tensor` DMA/SHM buffers
- **JPEG/PNG support** - Load and save with EXIF orientation handling
## Quick Start
```rust
use edgefirst_image::{save_jpeg, ImageProcessor, ImageProcessorTrait, Rotation, Flip, Crop};
use edgefirst_codec::{peek_info, ImageDecoder, ImageLoad};
use edgefirst_tensor::{CpuAccess, PixelFormat, DType, Tensor, TensorDyn, TensorMemory};
// Decode an image into its native format. The codec reports the source's
// native pixel format (JPEG -> NV12/GREY, PNG -> RGB/RGBA/GREY) and sizes,
// then configures the destination tensor during the decode.
let bytes = std::fs::read("input.jpg")?;
let info = peek_info(&bytes)?;
let mut decoder = ImageDecoder::new();
let mut src = Tensor::<u8>::image(info.width, info.height, info.format, Some(TensorMemory::Mem),
CpuAccess::ReadWrite)?;
src.load_image(&mut decoder, &bytes)?;
let src = TensorDyn::from(src);
// Create processor (auto-selects best backend)
let mut processor = ImageProcessor::new()?;
// Create destination with desired size and format (the convert below
// handles NV12 -> RGBA color conversion, resize, and letterboxing)
let mut dst =
processor.create_image(640, 640, PixelFormat::Rgba, DType::U8, None, CpuAccess::ReadWrite)?;
// Convert with resize, rotation, letterboxing
processor.convert(
&src,
&mut dst,
Rotation::None,
Flip::None,
Crop::letterbox(), // Preserve aspect ratio
)?;
// Save result
save_jpeg(&dst, "output.jpg", 90)?;
```
## Backends
| G2D | Linux (i.MX8) | 2D GPU | Fastest for NXP platforms |
| OpenGL | Linux | GPU | EGL/GBM headless rendering |
| CPU | All | SIMD | Portable fallback |
## Supported Formats
| `PixelFormat::Rgba` | 32-bit RGBA | 4 |
| `PixelFormat::Rgb` | 24-bit RGB | 3 |
| `PixelFormat::Nv12` | YUV 4:2:0 semi-planar | 1.5 |
| `PixelFormat::Nv16` | YUV 4:2:2 semi-planar | 2 |
| `PixelFormat::Yuyv` | YUV 4:2:2 packed | 2 |
| `PixelFormat::Grey` | 8-bit grayscale | 1 |
| `PixelFormat::PlanarRgb` | Planar RGB | 3 |
| `PixelFormat::Vyuy` | YUV 4:2:2 packed (VYUY order) | 2 |
| `PixelFormat::Bgra` | 32-bit BGRA | 4 |
| `PixelFormat::PlanarRgba` | Planar RGBA | 4 |
Note: Int8 variants (e.g. packed RGB int8, planar RGB int8) use `DType::I8` with the corresponding `PixelFormat` rather than separate format constants.
## Feature Flags
- `opengl` (default) - Enable OpenGL backend on Linux
- `decoder` (default) - Enable detection box rendering
- `tracker` (optional) - Enable multi-object tracking support in `draw_masks_tracked()`. Requires `features = ["tracker"]` in your dependency declaration.
## Environment Variables
- `EDGEFIRST_DISABLE_G2D` - Disable G2D backend
- `EDGEFIRST_DISABLE_GL` - Disable OpenGL backend
- `EDGEFIRST_DISABLE_CPU` - Disable CPU backend
- `EDGEFIRST_FORCE_BACKEND` — Force a single backend: `cpu`, `g2d`, or `opengl`. Disables fallback chain.
- `EDGEFIRST_FORCE_TRANSFER` — Force GPU transfer method: `pbo` or `dmabuf`
- `EDGEFIRST_TENSOR_FORCE_MEM` — Set to `1` to force heap memory (disables DMA/SHM)
- `EDGEFIRST_OPENGL_RENDERSURFACE` — Set to `1` to use renderbuffer-backed EGLImages for DMA destinations. Required on i.MX 95 / Mali-G310 with Neutron NPU DMA-BUF destinations. Defaults to `0` (texture path).
- `EDGEFIRST_PROTO_COMPUTE` — Set to `1` to enable the experimental GLES 3.1 compute shader path for proto repack. Requires GLES 3.1 hardware support.
## Segmentation Mask Rendering
Three rendering pipelines for YOLO instance segmentation masks:
### MaskOverlay
`MaskOverlay` controls how segmentation masks are composited onto the destination image:
```rust,ignore
use edgefirst_image::MaskOverlay;
// Default: no background replacement, full opacity
let overlay = MaskOverlay::default();
// With a background image and 50% transparent masks
let overlay = MaskOverlay { background: Some(&bg_tensor), opacity: 0.5 };
```
Fields:
- `background: Option<&TensorDyn>` — Optional tensor to blit into `dst` before drawing masks. Must match `dst`'s shape. `None` keeps the existing `dst` content.
- `opacity: f32` — Scales mask alpha in the range `0.0` (invisible) to `1.0` (fully opaque, default).
### draw_masks()
Convenience method that decodes model outputs, runs NMS, and draws segmentation masks in a single call:
```rust,ignore
let boxes = processor.draw_masks(&decoder, &outputs, &mut frame, MaskOverlay::default())?;
```
### draw_masks_tracked()
Like `draw_masks()` but integrates a `Tracker` for maintaining object identities across frames. The tracker runs after NMS but before mask extraction. Requires the `tracker` feature flag.
```rust,ignore
#[cfg(feature = "tracker")]
let (boxes, tracks) = processor.draw_masks_tracked(
&decoder,
&mut tracker,
timestamp_ns,
&outputs,
&mut frame,
MaskOverlay::default(),
)?;
```
Returns `(Vec<DetectBox>, Vec<TrackInfo>)`.
### Fused GPU Proto Path (`draw_proto_masks`)
Computes `sigmoid(coefficients @ protos)` per-pixel in a fragment shader — no intermediate mask materialization. Preferred for real-time overlay.
```rust,ignore
let (detections, proto_data) = decoder.decode_quantized_proto(&outputs)?;
processor.draw_proto_masks(&mut frame, &detections, &proto_data)?;
```
### Hybrid CPU+GPU Path
CPU materializes binary masks (`materialize_segmentations()`), then OpenGL overlays them. Auto-selected when both CPU and GL backends are available.
### Shader Variants
| int8-nearest | R8I quantized | Nearest neighbor |
| int8-bilinear | R8I quantized | Manual 4-tap bilinear |
| f32 | R32F float | Hardware GL_LINEAR |
| f16 | R16F half | Hardware GL_LINEAR |
### Int8 Interpolation Mode
Control quantized proto interpolation quality:
```rust,ignore
processor.set_int8_interpolation_mode(Int8InterpolationMode::Bilinear);
```
See [BENCHMARKS.md](https://github.com/EdgeFirstAI/hal/blob/main/BENCHMARKS.md) for per-platform performance numbers.
## Zero-Copy Model Input
Use `create_image()` to allocate the destination tensor with the processor's
optimal memory backend (DMA-buf, PBO, or system memory). This enables
zero-copy GPU paths that direct `Tensor::new()` allocation cannot achieve:
```rust,ignore
let mut dst =
processor.create_image(640, 640, PixelFormat::Rgb, DType::U8, None, CpuAccess::ReadWrite)?;
processor.convert(&src, &mut dst, Rotation::None, Flip::None, Crop::letterbox())?;
```
If you need to write into a pre-allocated buffer with a specific memory type
(e.g. an NPU-bound tensor), you can still use direct allocation:
```rust,ignore
let mut model_input = Tensor::<u8>::new(&[640, 640, 3], None, None)?;
model_input.set_format(PixelFormat::Rgb)?;
let mut dst = TensorDyn::from(model_input);
processor.convert(&src, &mut dst, Rotation::None, Flip::None, Crop::letterbox())?;
```
## Tiled Preprocessing (SAHI)
For small-object detection in high-resolution frames (e.g. 4K), run the model at
its native tile resolution over an overlapping grid of native-resolution crops —
**SAHI** (Slicing Aided Hyper Inference). `ImageProcessor` renders every tile into
a single tall packed batch tensor with **one** GL import and **one** flush, so the
N tiles cost roughly one GPU sync rather than N.
```rust,ignore
use edgefirst_image::TilingConfig;
use edgefirst_tensor::{CpuAccess, DType, PixelFormat};
// 640x640 tiles, >=20% overlap (the minimum; actual overlap is redistributed
// evenly so every tile is full-size and the last tile lands flush at frame-tile).
let cfg = TilingConfig::new(640, 640).with_overlap(0.2);
// plan_tiles is pure geometry (no GPU): use its length to size the batch.
let placements = processor.plan_tiles(src_w, src_h, &cfg)?;
// One tall [tile_w, N*tile_h] destination (width × stacked height) —
// allocate once, reuse per frame.
let mut batch = processor.alloc_tile_batch(
placements.len(), &cfg,
PixelFormat::Rgb, DType::U8, None, CpuAccess::None,
)?;
// Render all tiles (deferred convert per tile + single flush). Returns the same
// placements mapping each tile band back to full-frame coordinates.
let placements = processor.tile_into(&src, &mut batch, &cfg)?;
```
Each tile selects its source crop by **sampling** the whole-frame tensor (texture
coordinates), never by viewing it — a viewed source would mint one EGLImage import
per tile and defeat the zero-copy property. The destination tiles are sibling views
of one parent buffer rendered as `glViewport`/`glScissor` bands.
For pipelined I/O (overlapping preprocessing, inference, and collection), use
`tile_one` to render into a caller-owned model-input slot (e.g. one slot of a ring)
instead of the batched `tile_into`:
```rust,ignore
let placements = processor.plan_tiles(src_w, src_h, &cfg)?; // pure, no GPU
let mut slot = processor.create_image(cfg.tile_w, cfg.tile_h,
PixelFormat::Rgb, DType::U8, None)?;
for p in &placements {
processor.tile_one(&src, &mut slot, p, &cfg)?; // deferred: caller owns sync
processor.flush()?; // flush per-slot or per-ring cadence
// ... submit slot for inference, collect output keyed by p.index ...
}
```
`plan_tiles` is pure geometry (no GPU work) so a profiler can size pools up front;
`tile_one` issues one deferred convert into a caller-owned slot so tiles overlap
with inference. The returned `TilePlacement` values are consumed by the decoder's
`lift_tile_boxes` / `TiledFrameAccumulator` to merge per-tile detections back into
full-frame results (see the `edgefirst-decoder` README).
## Zero-Copy External Buffer (Linux)
When integrating with an NPU delegate (e.g. VxDelegate) that owns its own
DMA-BUF buffers, use `import_image()` to render directly into the
delegate's buffer — eliminating the `memcpy` between HAL's buffer and the
delegate's buffer:
```rust,ignore
use edgefirst_tensor::PlaneDescriptor;
// UC1: Render into VxDelegate's DMA-BUF — zero copies
let pd = PlaneDescriptor::new(vx_fd.as_fd())?; // dups fd — caller keeps ownership
let mut dst = processor.import_image(pd, None, 640, 640, PixelFormat::Rgb, DType::U8)?;
processor.convert(&src, &mut dst, Rotation::None, Flip::None, Crop::letterbox())?;
// dst's backing memory IS vx_fd — no memcpy needed
```
For the reverse direction (HAL allocates, consumer imports):
```rust,ignore
let hal_dst =
processor.create_image(640, 640, PixelFormat::Rgb, DType::U8, None, CpuAccess::ReadWrite)?;
let fd = hal_dst.dmabuf_clone()?; // Error if not DMA-backed
vxdelegate.register_buffer(fd)?;
```
**Performance tip:** When rotating through a pool of DMA-BUFs (e.g. 2-3
from VxDelegate), create the `TensorDyn` wrappers once at init and reuse
them across frames. This avoids EGL image cache misses (~100-300us each).
## Multiplane NV12/NV16
For V4L2 multi-planar DMA-BUF buffers (separate Y and UV file descriptors):
```rust,ignore
let img = Tensor::from_planes(y_tensor, uv_tensor, PixelFormat::Nv12)?;
let src = TensorDyn::from(img);
processor.convert(&src, &mut dst, Rotation::None, Flip::None, Crop::default())?;
```
The OpenGL backend imports each plane's DMA-BUF fd separately for zero-copy GPU access.
## Documentation
- Architecture overview: [ARCHITECTURE.md](https://github.com/EdgeFirstAI/hal/blob/main/crates/image/ARCHITECTURE.md)
- Testing guide: [TESTING.md](https://github.com/EdgeFirstAI/hal/blob/main/crates/image/TESTING.md)
- Full API reference: [docs.rs/edgefirst-image](https://docs.rs/edgefirst-image)
- Project README: [../../README.md](https://github.com/EdgeFirstAI/hal/blob/main/README.md)
## License
Licensed under the Apache License, Version 2.0. See [LICENSE](https://github.com/EdgeFirstAI/hal/blob/main/LICENSE) for details.