feat: Enhace emotion inference speed on ARM and add FaceAnalyzer, Face classes for ease of use. (#25)

* feat: Update linting and type annotations, return types in detect

* feat: add face analyzer and face classes

* chore: Update the format and clean up some docstrings

* docs: Update usage documentation

* feat: Change AgeGender model output to 0, 1 instead of string (Female, Male)

* test: Update testing code

* feat: Add Apple silicon backend for torchscript inference

* feat: Add face analyzer example and add run emotion for testing

scripts/run_landmarks.py

@@ -11,32 +11,32 @@ import cv2
 from uniface import SCRFD, Landmark106, RetinaFace
 
 
-def process_image(detector, landmarker, image_path: str, save_dir: str = "outputs"):
+def process_image(detector, landmarker, image_path: str, save_dir: str = 'outputs'):
     image = cv2.imread(image_path)
     if image is None:
         print(f"Error: Failed to load image from '{image_path}'")
         return
 
     faces = detector.detect(image)
-    print(f"Detected {len(faces)} face(s)")
+    print(f'Detected {len(faces)} face(s)')
 
     if not faces:
         return
 
     for i, face in enumerate(faces):
-        bbox = face["bbox"]
+        bbox = face['bbox']
         x1, y1, x2, y2 = map(int, bbox)
         cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
 
         landmarks = landmarker.get_landmarks(image, bbox)
-        print(f"  Face {i + 1}: {len(landmarks)} landmarks")
+        print(f'  Face {i + 1}: {len(landmarks)} landmarks')
 
         for x, y in landmarks.astype(int):
             cv2.circle(image, (x, y), 1, (0, 255, 0), -1)
 
         cv2.putText(
             image,
-            f"Face {i + 1}",
+            f'Face {i + 1}',
             (x1, y1 - 10),
             cv2.FONT_HERSHEY_SIMPLEX,
             0.5,
@@ -45,15 +45,15 @@ def process_image(detector, landmarker, image_path: str, save_dir: str = "output
         )
 
     os.makedirs(save_dir, exist_ok=True)
-    output_path = os.path.join(save_dir, f"{Path(image_path).stem}_landmarks.jpg")
+    output_path = os.path.join(save_dir, f'{Path(image_path).stem}_landmarks.jpg')
     cv2.imwrite(output_path, image)
-    print(f"Output saved: {output_path}")
+    print(f'Output saved: {output_path}')
 
 
 def run_webcam(detector, landmarker):
     cap = cv2.VideoCapture(0)  # 0 = default webcam
     if not cap.isOpened():
-        print("Cannot open webcam")
+        print('Cannot open webcam')
         return
 
     print("Press 'q' to quit")
@@ -67,7 +67,7 @@ def run_webcam(detector, landmarker):
         faces = detector.detect(frame)
 
         for face in faces:
-            bbox = face["bbox"]
+            bbox = face['bbox']
             x1, y1, x2, y2 = map(int, bbox)
             cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
 
@@ -77,16 +77,16 @@ def run_webcam(detector, landmarker):
 
         cv2.putText(
             frame,
-            f"Faces: {len(faces)}",
+            f'Faces: {len(faces)}',
             (10, 30),
             cv2.FONT_HERSHEY_SIMPLEX,
             1,
             (0, 255, 0),
             2,
         )
-        cv2.imshow("106-Point Landmarks", frame)
+        cv2.imshow('106-Point Landmarks', frame)
 
-        if cv2.waitKey(1) & 0xFF == ord("q"):
+        if cv2.waitKey(1) & 0xFF == ord('q'):
             break
 
     cap.release()
@@ -94,17 +94,17 @@ def run_webcam(detector, landmarker):
 
 
 def main():
-    parser = argparse.ArgumentParser(description="Run facial landmark detection")
-    parser.add_argument("--image", type=str, help="Path to input image")
-    parser.add_argument("--webcam", action="store_true", help="Use webcam")
-    parser.add_argument("--detector", type=str, default="retinaface", choices=["retinaface", "scrfd"])
-    parser.add_argument("--save_dir", type=str, default="outputs")
+    parser = argparse.ArgumentParser(description='Run facial landmark detection')
+    parser.add_argument('--image', type=str, help='Path to input image')
+    parser.add_argument('--webcam', action='store_true', help='Use webcam')
+    parser.add_argument('--detector', type=str, default='retinaface', choices=['retinaface', 'scrfd'])
+    parser.add_argument('--save_dir', type=str, default='outputs')
     args = parser.parse_args()
 
     if not args.image and not args.webcam:
-        parser.error("Either --image or --webcam must be specified")
+        parser.error('Either --image or --webcam must be specified')
 
-    detector = RetinaFace() if args.detector == "retinaface" else SCRFD()
+    detector = RetinaFace() if args.detector == 'retinaface' else SCRFD()
     landmarker = Landmark106()
 
     if args.webcam:
@@ -113,5 +113,5 @@ def main():
         process_image(detector, landmarker, args.image, args.save_dir)
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     main()