detect-face-lib 0.1.1

detect-face-lib is a modular face detection + JSON augmentation toolkit that attaches face detections to det-v1(detections) or track-v1(tracked) payloads produced by earlier stages in the Vision Pipeline.

detect-face-lib

Python: ==3.10.*

detect-face-lib is a modular face detection + JSON augmentation toolkit that attaches face detections to det-v1 (detections) or track-v1 (tracked) payloads produced by earlier stages in the Vision Pipeline.

This is the Face Augmentation stage of the Vision Pipeline.

Detectors included:

• retinaface: retinaface-pytorch backend (named variant registry)

By default, detect-face-lib does not write any files. You opt-in to saving JSON, frames, or annotated video via flags.

---

Vision Pipeline

Original Video (.mp4)
        │
        ▼
  detect-lib
  (Detection Stage)
        │
        └── detections.json (det-v1)
                   │
                   ▼
                track-lib
           (Tracking + ReID)
                   │
                   └── tracked.json (track-v1)
                           │
                           ▼
                      detect-face-lib
                    (Face Augmentation)
                           │
                           └── faces.json (augmented; face-v1 meta)

Note: Each stage consumes the original video + the upstream JSON from the previous stage.

Stage 1 (Detection):

• PyPI: https://pypi.org/project/detect-lib/
• GitHub: https://github.com/Surya-Rayala/VideoPipeline-detection

Stage 2 (Tracking + ReID):

• PyPI: https://pypi.org/project/gallery-track-lib/
• GitHub: https://github.com/Surya-Rayala/VisionPipeline-gallery-track

---

Output: augmented det-v1 / track-v1 (returned + optionally saved)

detect-face-lib returns an augmented JSON payload in-memory that preserves the upstream schema (det-v1 or track-v1) and adds:

• face_augment: metadata about the face detector + association rules (versioned)
• detections[*].faces: for each frame, matched faces are attached under the matched person detection

What gets attached to a person

Each face entry is intentionally minimal:

• bbox: face box [x1,y1,x2,y2]
• score: face confidence
• landmarks: 5-point landmarks [[x,y] x5] when provided by the backend

---

Minimal schema example

This example assumes the upstream JSON is track-v1; the same structure applies for det-v1 (it will simply lack tracker fields).

{
  "schema_version": "track-v1",
  "parent_schema_version": "det-v1",
  "video": {
    "path": "in.mp4",
    "fps": 30.0,
    "frame_count": 120,
    "width": 1920,
    "height": 1080
  },
  "tracker": {
    "name": "gallery_hybrid"
  },
  "face_augment": {
    "version": "face-v1",
    "parent_schema_version": "track-v1",
    "detector": {
      "name": "retinaface",
      "variant": "resnet50_2020-07-20",
      "max_size": 1024,
      "conf_thresh": 0.5,
      "nms_thresh": 0.3,
      "device": "mps"
    },
    "association": {
      "associate_class_ids": [0],
      "gallery_filter": false,
      "iou_thresh": 0.0,
      "containment": true,
      "inclusive": true,
      "kpt_indices": null,
      "kpt_conf": 0.3,
      "flexible_kpt": null
    }
  },
  "frames": [
    {
      "frame_index": 0,
      "detections": [
        {
          "bbox": [100.0, 50.0, 320.0, 240.0],
          "score": 0.91,
          "class_id": 0,
          "class_name": "person",
          "track_id": "3",
          "gallery_id": "person_A",
          "faces": [
            {
              "bbox": [140.0, 70.0, 210.0, 150.0],
              "score": 0.98,
              "landmarks": [[160.0, 95.0], [195.0, 95.0], [178.0, 110.0], [165.0, 130.0], [192.0, 130.0]]
            }
          ]
        }
      ]
    }
  ]
}

Returned vs saved

• Returned (always): the augmented payload is available as FaceResult.payload (Python) and is always produced in-memory.
• Saved (opt-in): nothing is written unless you enable artifacts:
  • --json writes <run>/faces.json
  • --frames writes annotated frames under <run>/frames/
  • --save-video writes an annotated video under <run>/...

When no artifacts are enabled, no output directory/run folder is created.

---

Install with pip (PyPI)

Use this if you want to install and use the tool without cloning the repo. Requires Python >= 3.10.

Install

pip install detect-face-lib

Note: the PyPI package name is detect-face-lib, but the Python module/import name remains detect_face.

CUDA note (optional)

If you want GPU acceleration on NVIDIA CUDA, install a CUDA-matching build of torch and torchvision.

If you installed CPU-only wheels by accident, uninstall and reinstall the correct CUDA wheels (use the official PyTorch selector for your CUDA version).

pip uninstall -y torch torchvision
# then install the CUDA-matching wheels for your system
# (see: https://pytorch.org/get-started/locally/)

---

CLI usage (pip)

Global help:

python -m detect_face.cli.detect_faces -h

List detectors:

python -m detect_face.cli.detect_faces --list-detectors

List variants for a detector:

python -m detect_face.cli.detect_faces --detector retinaface --list-variants

---

Face augmentation CLI: detect_face.cli.detect_faces

Quick start (track-v1 input)

python -m detect_face.cli.detect_faces \
  --json-in tracked.json \
  --video in.mp4

Quick start (det-v1 input)

python -m detect_face.cli.detect_faces \
  --json-in detections.json \
  --video in.mp4

Save artifacts (opt-in)

python -m detect_face.cli.detect_faces \
  --json-in tracked.json \
  --video in.mp4 \
  --json \
  --frames \
  --save-video annotated.mp4 \
  --out-dir out --run-name demo

---

CLI arguments

Required (for running augmentation)

• --json-in <path>: Path to the det-v1 or track-v1 JSON to augment.
• --video <path>: Path to the original source video used to generate the JSON. Frame order must align.

Discovery

• --list-detectors: Print available face detector backends and exit.
• --list-variants: Print supported named variants for --detector and exit.

Detector selection

• --detector <name>: Face backend to use (default: retinaface).
• --variant <name>: Backend model variant (named variant). For retinaface, this is typically resnet50_2020-07-20.
• --max-size <int>: Cap longer side for detector input.
  • Lower → faster, may miss small faces.
  • Higher → slower, better for small faces.
• --device <auto|cpu|mps|cuda|cuda:0|0|1...>: Compute device.
  • auto resolves to cuda:0 if available, else mps, else cpu.

Detector thresholds

• --conf-thresh <float>: Minimum face confidence.
  • Increase → fewer false positives (fewer faces).
  • Decrease → more faces (more false positives).
• --nms-thresh <float>: Non-maximum suppression threshold inside the face detector.
  • Lower → more aggressive suppression.

Association rules

• --associate-classes <ids>: Class IDs eligible for face association (e.g., 0 for person). If omitted, the tool tries to auto-detect class_name=='person'; if not found, all classes are eligible.
• --iou-thresh <float>: IoU threshold for candidate person-face pairing.
  • 0.0 considers any overlap.
  • 0.1–0.3 is stricter and can reduce wrong assignments.
• --containment / --no-containment: Require (or not) that the face box lies fully inside the person box.
  • Containment on → safer (fewer bad matches).
  • Containment off → more permissive (may increase mismatches in crowded frames).
• --gallery-filter: Track-v1 only: only assign faces where track_id == gallery_id (when both exist).

Keypoint constraints (optional)

• --kpt-indices <ids>: Body keypoint indices to require inside the face box.
  • Only used if the input JSON contains keypoints. If keypoints are missing, a warning is emitted and the rule is skipped.
• --kpt-conf <float>: Minimum confidence for keypoints used by --kpt-indices.
• --flexible-kpt <N>: If set, requires at least N keypoints inside the face box when >=N survive the confidence threshold; if fewer survive, requires all surviving keypoints inside.

Artifact saving (all opt-in)

• --json: Write augmented JSON to <run>/faces.json.
• --frames: Save annotated frames under <run>/frames/ (can be large).
• --save-video <name.mp4>: Save an annotated video under <run>/<name.mp4>.
• --out-dir <dir>: Output root used only when saving artifacts (default: out).
• --run-name <name>: Run folder name under --out-dir. If omitted, artifacts go directly under --out-dir.
• --fourcc <fourcc>: FourCC codec for saved video (default: mp4v).
• --display: Show a live annotated preview (press ESC to stop). Does not write files unless saving flags are set.

UX

• --no-progress: Disable progress bar.

---

Python usage (import)

You can use detect-face-lib as a library after installing it with pip.

Quick sanity check

python -c "import detect_face; print(detect_face.available_detectors())"

Python API reference (keywords)

detect_face.detect_faces_video(...)

Required

• json_in: Path to det-v1 or track-v1 JSON.
• video: Path to the source video.

Detector

• detector: Backend name (e.g., "retinaface").
• variant: Variant name for the backend.
• max_size: Cap longer side.
• device: "auto", "cpu", "mps", "cuda", "cuda:0", or an index like "0".
• conf_thresh, nms_thresh: Detector thresholds.

Association

• associate_class_ids: List of class_ids eligible for association.
• iou_thresh, containment: Pairing rules.
• gallery_filter: Track-v1 only.

Keypoints (optional)

• kpt_indices, kpt_conf, flexible_kpt: Keypoint-based validation (used only when keypoints exist).

Artifacts (all off by default)

• save_json_flag: Write <run>/faces.json.
• save_frames: Write <run>/frames/*.jpg.
• save_video: Filename for annotated video under the run folder.
• out_dir, run_name, fourcc, display, no_progress.

Returns a FaceResult with payload (augmented JSON), paths (only populated when saving), and stats.

Run face augmentation from a Python file

Create run_faces.py:

from detect_face import detect_faces_video

res = detect_faces_video(
    json_in="tracked.json",
    video="in.mp4",
    detector="retinaface",
    variant="resnet50_2020-07-20",
    device="auto",
)

print(res.stats)
print("face_augment" in res.payload)
print(res.paths)  # populated only if you enable saving artifacts

Run:

python run_faces.py

---

Install from GitHub (uv)

Use this if you are developing locally or want reproducible project environments.

Install uv: https://docs.astral.sh/uv/getting-started/installation/#standalone-installer

Verify:

uv --version

Install dependencies

git clone https://github.com/Surya-Rayala/VisionPipeline-detect-face.git
cd VisionPipeline-detect-face
uv sync

CUDA note (optional)

For best performance on NVIDIA GPUs, make sure torch and torchvision are installed with a build that matches your CUDA toolkit / driver stack.

If you added CPU-only builds earlier, remove them and add the correct CUDA wheels, then re-sync.

uv remove torch torchvision
# then add the CUDA-matching wheels for your system
# (see: https://pytorch.org/get-started/locally/)
uv add <compatible torch torchvision>
uv sync

---

CLI usage (uv)

Global help:

uv run python -m detect_face.cli.detect_faces -h

List detectors / variants:

uv run python -m detect_face.cli.detect_faces --list-detectors
uv run python -m detect_face.cli.detect_faces --detector retinaface --list-variants

Basic command (track-v1 input):

uv run python -m detect_face.cli.detect_faces \
  --json-in tracked.json \
  --video in.mp4

Basic command (det-v1 input):

uv run python -m detect_face.cli.detect_faces \
  --json-in detections.json \
  --video in.mp4

Save artifacts (opt-in):

uv run python -m detect_face.cli.detect_faces \
  --json-in tracked.json \
  --video in.mp4 \
  --json --frames --save-video annotated.mp4 \
  --out-dir out --run-name demo

---

License

This project is licensed under the MIT License. See LICENSE.