gaze-estimation-lib 0.1.6

Gaze augmentation stage: attach gaze (yaw/pitch + gaze_vec) using face boxes in upstream JSON.

gaze-estimation-lib

Minimum Python: >=3.10

gaze-estimation-lib is a modular gaze estimation + JSON augmentation toolkit that attaches gaze predictions to detections containing face boxes.

This is the Gaze Augmentation stage of the Vision Pipeline.

Estimators included:

• l2cs: L2CS-Net backend (face-box driven; no internal detector)

By default, gaze-estimation-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 (face-v1 meta)
                                   │
                                   ▼
                              gaze-estimation-lib
                         (Gaze Augmentation)
                                   │
                                   └── gaze.json (augmented; gaze-v1 meta)

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

Stage 3 (Face Augmentation):

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

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

---

What gaze-estimation-lib expects

gaze-estimation-lib does not run a face detector.

Input JSON must contain:

• frames[*].detections[*]
• Inside detections: faces with valid bbox

The parent schema may be:

• face-v1
• det-v1
• track-v1
• or unknown

As long as detections and face boxes exist, normalization will adapt.

---

Output: augmented JSON (returned + optionally saved)

gaze-estimation-lib returns an augmented JSON payload in-memory that preserves the upstream schema and adds:

• gaze_augment: metadata about the estimator + association rules (versioned)
• detections[*].gaze: minimal gaze payload

What gets attached to a detection

Each gaze entry is intentionally minimal:

• yaw (radians)
• pitch (radians)
• gaze_vec: [x,y,z] unit vector
• face_ind: which face entry was used
• origin: [x,y] pixel location (if available)
• origin_source: "kpt" or "box"

No redundant or derivable data is stored.

---

Minimal schema example

{
  "schema_version": "track-v1",
  "gaze_augment": {
    "version": "gaze-v1",
    "parent_schema_version": "track-v1",
    "estimator": {
      "name": "l2cs",
      "variant": "resnet50",
      "weights": "weights.pkl",
      "device": "auto"
    }
  },
  "frames": [
    {
      "frame_index": 0,
      "detections": [
        {
          "bbox": [100.0, 50.0, 320.0, 240.0],
          "faces": [
            {
              "bbox": [140.0, 70.0, 210.0, 150.0],
              "score": 0.98
            }
          ],
          "gaze": {
            "yaw": -0.12,
            "pitch": 0.08,
            "gaze_vec": [0.11, -0.08, -0.99],
            "face_ind": 0
          }
        }
      ]
    }
  ]
}

---

Returned vs saved

• Returned (always): payload available in memory via GazeResult.payload
• Saved (opt-in):
  • --json → <run>/gaze.json
  • --frames → <run>/frames/
  • --save-video → <run>/annotated.mp4

If no artifact flags are enabled, nothing is written.

---

Install with pip (future PyPI)

Requires Python >= 3.10.

pip install gaze-estimation-lib

# Install the L2CS backend (required to run gaze estimation)
pip install "l2cs @ git+https://github.com/edavalosanaya/L2CS-Net.git@main"

Module import name remains:

import gaze

Installing the L2CS backend (pip)

PyPI packages cannot declare Git/VCS dependencies. The default l2cs backend must be installed separately:

pip install "l2cs @ git+https://github.com/edavalosanaya/L2CS-Net.git@main"

If you already installed gaze-estimation-lib, you can run the command above at any time to add the backend.

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/)

---

L2CS Weights

Pretrained weights: https://drive.google.com/drive/folders/17p6ORr-JQJcw-eYtG2WGNiuS_qVKwdWd?usp=sharing

Currently supported variant:

• resnet50

If using custom weights, ensure they match the correct L2CS variant.

---

CLI Usage (pip or installed package)

Global help:

python -m gaze.cli.estimate_gaze -h

List estimators:

python -m gaze.cli.estimate_gaze --list-estimators

List variants:

python -m gaze.cli.estimate_gaze --estimator l2cs --list-variants

---

Quick Start

python -m gaze.cli.estimate_gaze \
  --json-in faces.json \
  --video in.mp4 \
  --weights weights.pkl

---

Save artifacts (opt-in)

python -m gaze.cli.estimate_gaze \
  --json-in faces.json \
  --video in.mp4 \
  --weights weights.pkl \
  --json \
  --frames \
  --save-video annotated.mp4 \
  --out-dir out --run-name demo

---

CLI arguments

Required (for running augmentation)

• --json-in <path>: Path to the upstream JSON to augment.
  • Accepts face-v1, det-v1, track-v1, or unknown schemas as long as the JSON contains frames[*].detections[*].
• --video <path>: Path to the original source video used to generate the upstream JSON. Frame order must align.
• --weights <path>: Path to L2CS weights (.pkl).

Discovery

• --list-estimators: Print available gaze estimator backends and exit.
• --list-variants: Print supported variants for --estimator and exit.

Estimator selection

• --estimator <name>: Gaze estimator backend to use (default: l2cs).
• --variant <name>: Backend variant (named variant registry).
  • For l2cs, this selects the backbone. The pretrained weights linked above currently support only resnet50.

Face crop behavior

• --expand-face <float>: Expand each face box by this fraction before cropping.
  • Example: --expand-face 0.25 expands width/height by +25%.
  • Increase → includes more context (forehead/hair/ears); can improve stability but may include background.
  • Decrease → tighter crop; can be sharper but may clip parts of the face.
  • Practical range: 0.0–0.35 (start around 0.2–0.3).

Association / filtering

• --associate-classes <ids...>: Only attach gaze to detections whose class_id is in this list.
  • Example: --associate-classes 0 (often person).
  • If omitted, gaze-lib tries to infer class_name == "person"; if not found, all classes are eligible.
• --face-index <int>: Which face entry to use per detection.
  • If set, always uses that index when present.
  • If omitted, uses the highest-score face in faces.

Gaze origin behavior (optional)

• --kpt-origin <ids...>: Keypoint indices (from detections[*].keypoints) used to compute a gaze origin.
  • Origin is computed as the mean of the selected keypoints that pass confidence.
  • Example: --kpt-origin 0 1.
• --kpt-conf <float>: Minimum keypoint confidence for origin computation (default: 0.3).
  • Increase → fewer keypoints qualify (more robust, but more detections may fall back/skip).
  • Decrease → more keypoints qualify (more coverage, but noisier origins).
• --fallback: If set, when keypoint-origin is unavailable, fall back to the face box center (preferred) or detection box center.
  • If not set and --kpt-origin is provided, detections without a valid keypoint-origin are skipped.

If you pass --kpt-origin but the JSON contains no keypoints, gaze-estimation-lib emits a warning and continues.

Artifact saving (all opt-in)

• --json: Write augmented JSON to <run>/gaze.json.
• --frames: Save annotated frames under <run>/frames/.
• --save-video [name.mp4]: Save an annotated video under <run>/.
• --out-dir <dir>: Output root used only when saving artifacts (default: out).
• --run-name <name>: Optional subfolder under --out-dir.
• --fourcc <fourcc>: FourCC codec for saved video (default: mp4v).
• --display: Show a live annotated preview (ESC to stop). Does not write files unless saving flags are set.

UX

• --no-progress: Disable progress bar.

---

Python usage (import)

You can use gaze-estimation-lib as a library after installing it.

Quick sanity check

python -c "import gaze; print(gaze.available_estimators()); print(gaze.available_variants('l2cs'))"

Python API reference (keywords)

gaze.estimate_gaze_video(...)

Required

• json_in: Path to the upstream JSON.
• video: Path to the original source video.
• weights: Path to L2CS weights (.pkl).

Estimator

• estimator: Backend name (default: "l2cs").
• variant: Named variant for the backend.
  • For L2CS pretrained weights linked above, use "resnet50".
• device: "auto", "cpu", "mps", "cuda", "cuda:0", or an index string like "0".
• expand_face: Expand face crop by fraction (0.0–0.35, start 0.2–0.3).

Association / selection

• associate_class_ids: List of class_id values eligible for gaze attachment.
  • If None, the tool tries to infer class_name == "person"; if not found, all classes are eligible.
• face_index: If set, use that face index per detection; otherwise choose the highest-score face.

Origin (optional)

• kpt_origin: List of keypoint indices used to compute gaze origin.
• kpt_conf: Keypoint confidence threshold.
• fallback: If True, fall back to box center when keypoint-origin is unavailable.

Artifacts (all off by default)

• save_json_flag: Write <run>/gaze.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 GazeResult with payload (augmented JSON), paths (only populated when saving), and stats.

Run gaze augmentation from a Python file

Create run_gaze.py:

from gaze import estimate_gaze_video

res = estimate_gaze_video(
    json_in="faces.json",
    video="in.mp4",
    estimator="l2cs",
    variant="resnet50",
    weights="weights.pkl",
    device="auto",

    # Optional filtering
    associate_class_ids=[0],

    # Optional crop tuning
    expand_face=0.25,

    # Optional origin behavior
    kpt_origin=[0, 1],
    kpt_conf=0.3,
    fallback=True,

    # Opt-in artifacts
    save_json_flag=True,
    save_video="annotated.mp4",
    out_dir="out",
    run_name="demo",
)

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

Run:

python run_gaze.py

Using uv (recommended for development)

Install uv: https://docs.astral.sh/uv/

Clone the repo:

git clone https://github.com/Surya-Rayala/VisionPipeline-gaze.git
cd VisionPipeline-gaze

Sync environment:

uv sync

Installing the L2CS backend (uv)

Add the backend to your local uv environment from Git:

uv add --dev "l2cs @ git+https://github.com/edavalosanaya/L2CS-Net.git@main"
uv sync

Note: this updates your local project environment; it is intended for development/use in this repo.

Run CLI:

uv run python -m gaze.cli.estimate_gaze -h

Run augmentation:

uv run python -m gaze.cli.estimate_gaze \
  --json-in faces.json \
  --video in.mp4 \
  --weights weights.pkl

---

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

---

License

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