feat: Add usage examples for face detection and face alignment

README.md

@@ -12,7 +12,7 @@
 
 </div>
 
-**uniface** is a lightweight face detection library designed for high-performance face localization and landmark detection. The library supports ONNX models and provides utilities for bounding box visualization and landmark plotting. To train RetinaFace model, see https://github.com/yakhyo/retinaface-pytorch.
+**uniface** is a lightweight face detection library designed for high-performance face localization, landmark detection and face alignment. The library supports ONNX models and provides utilities for bounding box visualization and landmark plotting. To train RetinaFace model, see https://github.com/yakhyo/retinaface-pytorch.
 
 ---
 
@@ -47,6 +47,19 @@ pip install .
 
 ## Quick Start
 
+To get started with face detection using **UniFace**, check out the [example notebook](examples/face_detection.ipynb).
+It demonstrates how to initialize the model, run inference, and visualize the results.
+
+---
+
+## Examples
+
+Explore the following example notebooks to learn how to use **UniFace** effectively:
+
+- [Face Detection](examples/face_detection.ipynb): Demonstrates how to perform face detection, draw bounding boxes, and landmarks on an image.
+- [Face Alignment](examples/face_alignment.ipynb): Shows how to align faces using detected landmarks.
+- [Age and Gender Detection](examples/age_gender.ipynb): Example for detecting age and gender from faces. (underdevelopment)
+
 ### Initialize the Model
 
 ```python

examples/face_detection.ipynb

@@ -5,7 +5,7 @@
    "metadata": {},
    "source": [
     "## Example Usage of UniFace Library\n",
-    "This guide demonstrates how to use the **UniFace** library for face detection and visualization in a Jupyter Notebook. Follow the steps below to set up and execute the example.\n",
+    "This guide demonstrates how to use the **UniFace** library for face detection and visualization in this Jupyter Notebook. Follow the steps below to set up and execute the example.\n",
     "\n",
     "## 1. Install UniFace\n",
     "Install the **UniFace** library using `pip`. The `-q` flag suppresses logs for a clean output."

uniface/log.py

@@ -4,4 +4,4 @@ logging.basicConfig(
     level=logging.INFO,
     format="%(asctime)s - %(levelname)s - %(message)s"
 )
-Logger = logging.getLogger("retinaface")
+Logger = logging.getLogger("uniface")

uniface/visualization.py

@@ -28,8 +28,6 @@ def draw_detections(image, detections, vis_threshold=0.6):
     landmarks = landmarks[filtered]
     scores = scores[filtered]
 
-    print(f"#faces: {len(scores)}")
-
     # Draw bounding boxes, scores, and landmarks
     for box, score, landmark in zip(boxes, scores, landmarks):
         cv2.rectangle(image, box[:2], box[2:], (0, 0, 255), 2)