online-emotion-detection 0.1.1

Streaming, frame-by-frame facial emotion recognition (HSEmotion) with a unified torch/torchscript/onnx/trt runtime and export-once caching for edge devices.

online-emotion-detection

Streaming, frame-by-frame facial emotion recognition for real-time pipelines: face crops in, structured emotions out. Runs under torch / torchscript / onnx / tensorrt with export-once caching, on CPU, CUDA, Apple Silicon (MPS), and Jetson.

from online_emotion import EmotionRecognizer
emo = EmotionRecognizer("hsemotion", device="auto")
res = emo.predict_on_boxes(frame, boxes)    # res.emotions[i].label / .probs

Models today: HSEmotion (Savchenko EfficientNet). More emotion families plug in via the registry — coming later.

---

Install

pip install "online-emotion-detection[torch]"

That's all you need for most setups — [torch] is the default runtime and pulls the HSEmotion model weights; it works on CPU, CUDA, and Mac (MPS). Other backends (onnx, tensorrt, serving) are optional extras you can add anytime — see Install options. (Prefer uv? See Misc.)

---

Use it (Python)

The natural unit is a face crop (or a batch of crops — batching is the speed win).

from online_emotion import EmotionRecognizer

emo = EmotionRecognizer(
    "hsemotion",        # model family (the only one today)
    device="auto",      # "auto" (CUDA > MPS > CPU) | "cpu" | "cuda" | "mps"
    runtime="auto",     # "auto" | "torch" | "torchscript" | "onnx" | "trt"
    batch_max=16,       # max crops per batched forward pass
)

# (a) a full frame + face boxes (crops on-device — no host round-trip):
res = emo.predict_on_boxes(frame, boxes)     # boxes: (N,4) xyxy from a face detector

# (b) face crops directly (one crop, or a list — batched automatically):
res = emo(face_crop)
res = emo([crop_a, crop_b, crop_c])

for e in res.emotions:
    print(e.label, e.probs.max(), e.valence, e.arousal)   # valence/arousal only for *_va_mtl weights

Inputs — what to pass:

• frame / face_crop / each crop: a NumPy array in BGR, shape (H, W, 3), dtype uint8 (OpenCV's native format), or a torch.Tensor of shape (3, H, W).
• boxes (for predict_on_boxes): an array of shape (N, 4) in xyxy pixel coordinates (e.g. FaceFrameResult.boxes from online-face-detection).

Output — EmotionFrameResult(emotions=[EmotionResult(label, label_index, probs (C,), valence?, arousal?)], classes, frame_index), aligned to the input crop/box order. emo.stats.as_dict() gives rolling fps / latency.

Chain it after a face detector (each model independent):

from online_face import FaceDetector
from online_emotion import EmotionRecognizer

with FaceDetector("retinaface") as face, EmotionRecognizer("hsemotion", batch_max=16) as emo:
    for frame_ref, fr in face.run_source("video.mp4"):
        er = emo.predict_on_boxes(frame_ref.image, fr.boxes)

Or from the terminal

# whole-frame emotion on a video file, with a window + FPS (press q/ESC to quit)
online-emotion --source video.mp4 --device auto --runtime auto --batch-max 16 --display

# webcam (index 0) as a live stream
online-emotion --source 0 --device auto --runtime auto --stream --display

# discover weights, or see every flag
online-emotion --list-weights
online-emotion --help

online-emotion == python -m online_emotion.cli.run. All flags: --source (file path | webcam index | rtsp/http url) · --device {auto,cpu,cuda,mps} · --runtime {auto,torch,torchscript,onnx,trt} · --batch-max N · --stream · --display · --save-video PATH · --max-frames N · --list-weights. The CLI runs whole-frame emotion (no face boxes); in a real pipeline, feed boxes via predict_on_boxes (above).

---

Models & weights

model is the family (hsemotion — the only one today); weights is the actual weight (a key, auto-downloaded by the hsemotion package, or an artifact path). weights=None → default.

weights key	classes	valence/arousal	notes
enet_b0_8_best_vgaf (default)	8	–	EfficientNet-B0, fast, edge-friendly
enet_b0_8_best_afew	8	–	EfficientNet-B0
enet_b2_8	8	–	EfficientNet-B2, higher accuracy
enet_b0_8_va_mtl	8	✅	also regresses valence & arousal

The 8 AffectNet classes: Anger, Contempt, Disgust, Fear, Happiness, Neutral, Sadness, Surprise.

---

Runtimes & the export cache

runtime="auto" picks the best backend per device: Jetson/CUDA → tensorrt (else onnx-CUDA), macOS → torch (MPS), CPU → onnx/torch. The first non-torch run builds the artifact and caches it under ~/.cache/online_inference/; later runs load it. Batching many crops into one call is the main speed win — batch_max caps the batch.

On macOS the ONNX backend uses CoreML, which recompiles when the batch size (number of faces) changes — slow for a variable face count. On Mac, prefer torch (the default) or torchscript for emotion; ONNX/TensorRT are the edge (CUDA/Jetson) path.

---

Install options

[torch] is all most people need. Add extras for other backends. Extras are additive — if you already installed [torch], running pip install "online-emotion-detection[serve]" later just adds those packages (it won't reinstall torch). Install several at once: pip install "online-emotion-detection[torch,onnx,serve]".

Extra	Adds	Install when you want to…
[torch]	torch, torchvision, hsemotion, timm	default runtime + model weights
[onnx]	onnxruntime, onnx, onnxsim	run or export the ONNX backend
[trt]	tensorrt (also pulls [onnx])	build/run TensorRT engines on NVIDIA. Needs a CUDA build of torch with torch + tensorrt + CUDA on the same version (e.g. cu12). Jetson: use JetPack's TensorRT (below)
[serve]	fastapi, uvicorn, python-multipart	host the model as an HTTP service (below)
[client]	requests	call a remote service (torch-free, below)

Which do I actually need?

• pip install online-emotion-detection (no [...]) → core only (numpy/opencv); no runtime, can't run inference. Use this only when torch is provided another way (e.g. Jetson/JetPack wheels).
• [torch] → the foundation; required to run the model locally (CPU/CUDA/MPS) and it pulls the model weights. Start here.
• [onnx] / [trt] → add a backend on top of torch (they don't replace it). [trt] also pulls [onnx] and needs a matching CUDA build of torch (see the table). Install together: pip install "online-emotion-detection[torch,trt]".
• [serve] → runs the model in-process, so it needs torch too: pip install "online-emotion-detection[torch,serve]".
• [client] → the only torch-free one — it just calls a remote service, so pip install "online-emotion-detection[client]" alone is enough.

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(Optional) Serve it as an HTTP service

Besides the in-process use above, the model can run as its own HTTP service (local or cloud) and be called by URL. Needs the [serve] extra (adds only fastapi/uvicorn on top of [torch]).

pip install "online-emotion-detection[serve]"
online-emotion-serve --model hsemotion --device auto --runtime auto --host 127.0.0.1 --port 8002

Server flags (all optional; defaults shown): --model hsemotion · --weights KEY|PATH (default: family default) · --device {auto,cpu,cuda,mps} · --runtime {auto,torch,torchscript,onnx,trt} · --precision {auto,fp32,fp16,int8} · --batch-max 32 · --input-size N · --host 127.0.0.1 · --port 8002.

Route	What it does
GET /meta	self-describing: inputs frame: image + boxes: ndarray; output emotions; plus the class list
GET /healthz	readiness + resolved runtime/device
POST /predict	multipart: a frame image part + a boxes .npy part → JSON {outputs, stats}

curl http://127.0.0.1:8002/meta

Call it from another process with the torch-free [client] proxy (mirrors predict_on_boxes):

pip install "online-emotion-detection[client]"

from online_emotion.client import EmotionClient

emo = EmotionClient(
    "http://127.0.0.1:8002",   # the service URL (local or cloud)
    encode="png",              # how frames go over the wire: "png" (lossless) | "jpeg" (smaller)
    timeout=30,                # request timeout, seconds
)
res = emo.predict_on_boxes(frame, boxes)   # res.emotions[i].label / .score
emo.meta()                                 # the service's /meta;  emo.healthz() -> readiness

Compose with a hosted face service into a pipeline by URL — see ../testing-pipeline for a ready-to-run example.

---

Misc

Install with uv

Same as pip, with uv:

uv add "online-emotion-detection[torch]"            # into a uv project
uv pip install "online-emotion-detection[torch]"    # into the active venv

Jetson (JetPack)

On Jetson the whole GPU stack (CUDA / cuDNN / TensorRT) is part of JetPack, and torch/onnxruntime must be NVIDIA's Jetson wheels — the PyPI [torch]/[onnx] wheels are x86_64 and won't use the GPU.

1. Pick a JetPack version.

Board	JetPack	Stack
Orin (AGX/NX/Nano)	6.x	CUDA 12.6 · TensorRT 10.3 · PyTorch 2.6 wheel
Xavier / older	5.1.x	torch ~2.1

Both are above this package's torch>=2.1 floor.

2. Install these into the JetPack env first — from NVIDIA's PyTorch for Jetson guide, or the jetson-ai-lab wheel index matched to your JetPack (e.g. --index-url https://pypi.jetson-ai-lab.io/jp6/cu126 for JetPack 6.x):

• torch, torchvision — the Jetson GPU wheels (not from PyPI)
• onnxruntime-gpu — only if you'll use the ONNX backend
• hsemotion, timm — the HSEmotion model source (plain pip, not Jetson-specific)
• opencv-python, numpy — usually already present in JetPack; install if missing
• TensorRT — already installed by JetPack (nothing to do)

3. Then install this package with NO runtime extra, so it uses the system ones:

pip install online-emotion-detection      # no [torch] / [onnx]

It adapts to whatever JetPack provides and keys each cached TensorRT engine to the exact board.

Conflicting model requirements? One Jetson has a single system torch/TRT. If two models need incompatible torch/CUDA, run each as its own HTTP service (e.g. an nvcr.io/nvidia/l4t-pytorch container) and compose them by URL with the [client] proxy.

Pre-build & cache an artifact

Optional — otherwise built on first use. Choose the runtime you'll deploy with for the target device:

online-emotion-export --model hsemotion --weights enet_b0_8_best_vgaf --runtime trt --device auto

Flags: --model · --weights KEY|PATH · --runtime {torchscript,onnx,trt} · --device {auto,cpu,cuda,mps} · --precision {auto,fp32,fp16,int8} · --input-size N.

License

MIT © Surya Chand Rayala