# Gaze Estimation

Gaze estimation predicts where a person is looking (pitch and yaw angles).

<figure markdown="span">
  ![Gaze Estimation](https://raw.githubusercontent.com/yakhyo/uniface/main/assets/demos/gaze.jpg){ width="100%" }
  <figcaption>Gaze direction arrows with pitch/yaw angle labels</figcaption>
</figure>

---

## Available Models

| Model | Backbone | Size | MAE* |
|-------|----------|------|------|
| ResNet18 | ResNet18 | 43 MB | 12.84° |
| **ResNet34** :material-check-circle: | ResNet34 | 82 MB | 11.33° |
| ResNet50 | ResNet50 | 91 MB | 11.34° |
| MobileNetV2 | MobileNetV2 | 9.6 MB | 13.07° |
| MobileOne-S0 | MobileOne | 4.8 MB | 12.58° |

*MAE = Mean Absolute Error on Gaze360 test set (lower is better)

---

## Basic Usage

```python
import cv2
import numpy as np
from uniface.detection import RetinaFace
from uniface.gaze import MobileGaze

detector = RetinaFace()
gaze_estimator = MobileGaze()

image = cv2.imread("photo.jpg")
faces = detector.detect(image)

for face in faces:
    # Crop face
    x1, y1, x2, y2 = map(int, face.bbox)
    face_crop = image[y1:y2, x1:x2]

    if face_crop.size > 0:
        # Estimate gaze
        result = gaze_estimator.estimate(face_crop)

        # Convert to degrees
        pitch_deg = np.degrees(result.pitch)
        yaw_deg = np.degrees(result.yaw)

        print(f"Pitch: {pitch_deg:.1f}°, Yaw: {yaw_deg:.1f}°")
```

---

## Model Variants

```python
from uniface.gaze import MobileGaze
from uniface.constants import GazeWeights

# Default (ResNet34, recommended)
gaze = MobileGaze()

# Lightweight for mobile/edge
gaze = MobileGaze(model_name=GazeWeights.MOBILEONE_S0)

# Higher accuracy
gaze = MobileGaze(model_name=GazeWeights.RESNET50)
```

---

## Output Format

```python
result = gaze_estimator.estimate(face_crop)

# GazeResult dataclass
result.pitch  # Vertical angle in radians
result.yaw    # Horizontal angle in radians
```

### Angle Convention

```
          pitch = +90° (looking up)
               │
               │
yaw = -90° ────┼──── yaw = +90°
(looking left) │     (looking right)
               │
          pitch = -90° (looking down)
```

- **Pitch**: Vertical gaze angle
  - Positive = looking up
  - Negative = looking down

- **Yaw**: Horizontal gaze angle
  - Positive = looking right
  - Negative = looking left

---

## Visualization

```python
from uniface.draw import draw_gaze

# Detect faces
faces = detector.detect(image)

for face in faces:
    x1, y1, x2, y2 = map(int, face.bbox)
    face_crop = image[y1:y2, x1:x2]

    if face_crop.size > 0:
        result = gaze_estimator.estimate(face_crop)

        # Draw gaze arrow on image
        draw_gaze(image, face.bbox, result.pitch, result.yaw)

cv2.imwrite("gaze_output.jpg", image)
```

### Custom Visualization

```python
import cv2
import numpy as np

def draw_gaze_custom(image, bbox, pitch, yaw, length=100, color=(0, 255, 0)):
    """Draw custom gaze arrow."""
    x1, y1, x2, y2 = map(int, bbox)

    # Face center
    cx = (x1 + x2) // 2
    cy = (y1 + y2) // 2

    # Calculate endpoint
    dx = -length * np.sin(yaw) * np.cos(pitch)
    dy = -length * np.sin(pitch)

    # Draw arrow
    end_x = int(cx + dx)
    end_y = int(cy + dy)

    cv2.arrowedLine(image, (cx, cy), (end_x, end_y), color, 2, tipLength=0.3)

    return image
```

---

## Real-Time Gaze Tracking

```python
import cv2
import numpy as np
from uniface.detection import RetinaFace
from uniface.gaze import MobileGaze
from uniface.draw import draw_gaze

detector = RetinaFace()
gaze_estimator = MobileGaze()

cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    if not ret:
        break

    faces = detector.detect(frame)

    for face in faces:
        x1, y1, x2, y2 = map(int, face.bbox)
        face_crop = frame[y1:y2, x1:x2]

        if face_crop.size > 0:
            result = gaze_estimator.estimate(face_crop)

            # Draw bounding box
            cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)

            # Draw gaze
            draw_gaze(frame, face.bbox, result.pitch, result.yaw)

            # Display angles
            pitch_deg = np.degrees(result.pitch)
            yaw_deg = np.degrees(result.yaw)
            label = f"P:{pitch_deg:.0f} Y:{yaw_deg:.0f}"
            cv2.putText(frame, label, (x1, y1 - 10),
                       cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

    cv2.imshow("Gaze Estimation", frame)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()
```

---

## Use Cases

### Attention Detection

```python
def is_looking_at_camera(result, threshold=15):
    """Check if person is looking at camera."""
    pitch_deg = abs(np.degrees(result.pitch))
    yaw_deg = abs(np.degrees(result.yaw))

    return pitch_deg < threshold and yaw_deg < threshold

# Usage
result = gaze_estimator.estimate(face_crop)
if is_looking_at_camera(result):
    print("Looking at camera")
else:
    print("Looking away")
```

### Gaze Direction Classification

```python
def classify_gaze_direction(result, threshold=20):
    """Classify gaze into directions."""
    pitch_deg = np.degrees(result.pitch)
    yaw_deg = np.degrees(result.yaw)

    directions = []

    if pitch_deg > threshold:
        directions.append("up")
    elif pitch_deg < -threshold:
        directions.append("down")

    if yaw_deg > threshold:
        directions.append("right")
    elif yaw_deg < -threshold:
        directions.append("left")

    if not directions:
        return "center"

    return " ".join(directions)

# Usage
result = gaze_estimator.estimate(face_crop)
direction = classify_gaze_direction(result)
print(f"Looking: {direction}")
```

---

## Factory Function

```python
from uniface.gaze import create_gaze_estimator

gaze = create_gaze_estimator()  # Returns MobileGaze
```

---

## Next Steps

- [Head Pose Estimation](headpose.md) - 3D head orientation
- [Anti-Spoofing](spoofing.md) - Face liveness detection
- [Privacy](privacy.md) - Face anonymization
- [Video Recipe](../recipes/video-webcam.md) - Real-time processing