chore: Change import order and style changes by Ruff

uniface/__init__.py

@@ -22,6 +22,7 @@ from uniface.model_store import verify_model_weights
 from uniface.visualization import draw_detections
 
 from .attribute import AgeGender
+
 try:
     from .attribute import Emotion
 except ImportError:

uniface/attribute/__init__.py

@@ -2,7 +2,8 @@
 # Author: Yakhyokhuja Valikhujaev
 # GitHub: https://github.com/yakhyo
 
-from typing import Dict, Any, List, Union
+from typing import Any, Dict, List, Union
+
 import numpy as np
 
 from uniface.attribute.age_gender import AgeGender

uniface/attribute/base.py

@@ -4,6 +4,7 @@
 
 from abc import ABC, abstractmethod
 from typing import Any
+
 import numpy as np
 
 

uniface/attribute/emotion.py

@@ -2,15 +2,16 @@
 # Author: Yakhyokhuja Valikhujaev
 # GitHub: https://github.com/yakhyo
 
+from typing import List, Tuple, Union
+
 import cv2
-import torch
 import numpy as np
-from typing import Tuple, Union, List
+import torch
 
 from uniface.attribute.base import Attribute
-from uniface.log import Logger
 from uniface.constants import DDAMFNWeights
 from uniface.face_utils import face_alignment
+from uniface.log import Logger
 from uniface.model_store import verify_model_weights
 
 __all__ = ["Emotion"]
@@ -77,7 +78,7 @@ class Emotion(Attribute):
             torch.Tensor: The preprocessed image tensor ready for inference.
         """
         landmark = np.asarray(landmark)
-        
+
         aligned_image, _ = face_alignment(image, landmark)
 
         # Convert BGR to RGB, resize, normalize, and convert to a CHW tensor
@@ -151,7 +152,7 @@ if __name__ == "__main__":
 
             # Predict attributes using the landmark
             emotion, confidence = emotion_predictor.predict(frame, landmark)
-            
+
             # Prepare text and draw on the frame
             label = f"{emotion} ({confidence:.2f})"
             cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), 2)
@@ -167,4 +168,4 @@ if __name__ == "__main__":
     # Release resources
     cap.release()
     cv2.destroyAllWindows()
-    print("Inference stopped.")
+    print("Inference stopped.")

uniface/common.py

@@ -2,12 +2,12 @@
 # Author: Yakhyokhuja Valikhujaev
 # GitHub: https://github.com/yakhyo
 
-import cv2
-import math
 import itertools
-import numpy as np
+import math
+from typing import List, Tuple
 
-from typing import Tuple, List
+import cv2
+import numpy as np
 
 
 def resize_image(frame, target_shape: Tuple[int, int] = (640, 640)) -> Tuple[np.ndarray, float]:

uniface/constants.py

@@ -5,6 +5,7 @@
 from enum import Enum
 from typing import Dict
 
+
 # fmt: off
 class SphereFaceWeights(str, Enum):
     """

uniface/detection/__init__.py

@@ -3,12 +3,13 @@
 # GitHub: https://github.com/yakhyo
 
 
-import numpy as np
-from typing import Tuple, Dict, Any, List
+from typing import Any, Dict, List
+
+import numpy as np
 
-from .scrfd import SCRFD
 from .base import BaseDetector
 from .retinaface import RetinaFace
+from .scrfd import SCRFD
 
 # Global cache for detector instances
 _detector_cache: Dict[str, BaseDetector] = {}
@@ -38,7 +39,7 @@ def detect_faces(image: np.ndarray, method: str = 'retinaface', **kwargs) -> Lis
         ...     print(f"BBox: {face['bbox']}")
     """
     method_name = method.lower()
-    
+
     sorted_kwargs = sorted(kwargs.items())
     cache_key = f"{method_name}_{str(sorted_kwargs)}"
 

uniface/detection/base.py

@@ -6,9 +6,10 @@
 Base classes for face detection.
 """
 
-import numpy as np
 from abc import ABC, abstractmethod
-from typing import Tuple, Dict, Any
+from typing import Any, Dict, Tuple
+
+import numpy as np
 
 
 class BaseDetector(ABC):

uniface/detection/retinaface.py

@@ -2,23 +2,17 @@
 # Author: Yakhyokhuja Valikhujaev
 # GitHub: https://github.com/yakhyo
 
+from typing import Any, Dict, List, Literal, Tuple
+
 import numpy as np
 
-from typing import Tuple, List, Literal, Dict, Any
-
+from uniface.constants import RetinaFaceWeights
 from uniface.log import Logger
 from uniface.model_store import verify_model_weights
-from uniface.constants import RetinaFaceWeights
 from uniface.onnx_utils import create_onnx_session
 
 from .base import BaseDetector
-from .utils import (
-    non_max_supression,
-    resize_image,
-    decode_boxes,
-    generate_anchors,
-    decode_landmarks
-)
+from .utils import decode_boxes, decode_landmarks, generate_anchors, non_max_supression, resize_image
 
 
 class RetinaFace(BaseDetector):

uniface/detection/utils.py

@@ -2,12 +2,12 @@
 # Author: Yakhyokhuja Valikhujaev
 # GitHub: https://github.com/yakhyo
 
-import cv2
-import math
 import itertools
-import numpy as np
+import math
+from typing import List, Tuple
 
-from typing import Tuple, List
+import cv2
+import numpy as np
 
 
 def resize_image(frame, target_shape: Tuple[int, int] = (640, 640)) -> Tuple[np.ndarray, float]:

uniface/face_utils.py

@@ -2,11 +2,11 @@
 # Author: Yakhyokhuja Valikhujaev
 # GitHub: https://github.com/yakhyo
 
+from typing import Tuple, Union
+
 import cv2
 import numpy as np
 from skimage.transform import SimilarityTransform
-from typing import Tuple, Union
-
 
 __all__ = ["face_alignment", "compute_similarity", "bbox_center_alignment", "transform_points_2d"]
 

uniface/landmark/__init__.py

@@ -2,8 +2,8 @@
 # Author: Yakhyokhuja Valikhujaev
 # GitHub: https://github.com/yakhyo
 
-from .models import Landmark106
 from .base import BaseLandmarker
+from .models import Landmark106
 
 
 def create_landmarker(method: str = '2d106det', **kwargs) -> BaseLandmarker:

uniface/landmark/base.py

@@ -3,6 +3,7 @@
 # GitHub: https://github.com/yakhyo
 
 from abc import ABC, abstractmethod
+
 import numpy as np
 
 

uniface/landmark/models.py

@@ -2,15 +2,17 @@
 # Author: Yakhyokhuja Valikhujaev
 # GitHub: https://github.com/yakhyo
 
-import cv2
-import numpy as np
 from typing import Tuple
 
-from uniface.log import Logger
+import cv2
+import numpy as np
+
 from uniface.constants import LandmarkWeights
-from uniface.model_store import verify_model_weights
 from uniface.face_utils import bbox_center_alignment, transform_points_2d
+from uniface.log import Logger
+from uniface.model_store import verify_model_weights
 from uniface.onnx_utils import create_onnx_session
+
 from .base import BaseLandmarker
 
 __all__ = ['Landmark']

uniface/model_store.py

@@ -2,14 +2,14 @@
 # Author: Yakhyokhuja Valikhujaev
 # GitHub: https://github.com/yakhyo
 
-import os
 import hashlib
+import os
+
 import requests
 from tqdm import tqdm
 
-from uniface.log import Logger
 import uniface.constants as const
-
+from uniface.log import Logger
 
 __all__ = ['verify_model_weights']
 

uniface/recognition/__init__.py

@@ -2,12 +2,12 @@
 # Author: Yakhyokhuja Valikhujaev
 # GitHub: https://github.com/yakhyo
 
-from typing import Dict
-from .models import ArcFace, MobileFace, SphereFace
-from .base import BaseRecognizer
-from uniface.constants import ArcFaceWeights, MobileFaceWeights, SphereFaceWeights
 
-def create_recognizer(method: str = 'arcface', **kwargs) -> BaseRecognizer:
+from .base import BaseRecognizer
+from .models import ArcFace, MobileFace, SphereFace
+
+
+def create_recognizer(method: str = "arcface", **kwargs) -> BaseRecognizer:
     """
     Factory function to create face recognizers.
 
@@ -44,20 +44,21 @@ def create_recognizer(method: str = 'arcface', **kwargs) -> BaseRecognizer:
     """
     method = method.lower()
 
-    if method == 'arcface':
+    if method == "arcface":
         return ArcFace(**kwargs)
-    elif method == 'mobileface':
+    elif method == "mobileface":
         return MobileFace(**kwargs)
-    elif method == 'sphereface':
+    elif method == "sphereface":
         return SphereFace(**kwargs)
     else:
-        available = ['arcface', 'mobileface', 'sphereface']
+        available = ["arcface", "mobileface", "sphereface"]
         raise ValueError(f"Unsupported method: '{method}'. Available: {available}")
 
+
 __all__ = [
     "create_recognizer",
     "ArcFace",
     "MobileFace",
     "SphereFace",
     "BaseRecognizer",
-]
+]

uniface/recognition/base.py

@@ -3,13 +3,14 @@
 # GitHub: https://github.com/yakhyo
 
 from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from typing import List, Tuple, Union
+
 import cv2
 import numpy as np
-from dataclasses import dataclass
-from typing import Tuple, Union, List
 
-from uniface.log import Logger
 from uniface.face_utils import face_alignment
+from uniface.log import Logger
 from uniface.onnx_utils import create_onnx_session
 
 

uniface/recognition/models.py

@@ -6,6 +6,7 @@ from typing import Optional
 
 from uniface.constants import ArcFaceWeights, MobileFaceWeights, SphereFaceWeights
 from uniface.model_store import verify_model_weights
+
 from .base import BaseRecognizer, PreprocessConfig
 
 __all__ = ["ArcFace", "MobileFace", "SphereFace"]

uniface/visualization.py

@@ -2,9 +2,10 @@
 # Author: Yakhyokhuja Valikhujaev
 # GitHub: https://github.com/yakhyo
 
+from typing import List, Union
+
 import cv2
 import numpy as np
-from typing import List, Union
 
 
 def draw_detections(
@@ -12,7 +13,7 @@ def draw_detections(
     bboxes: Union[np.ndarray, List[List[float]]],
     scores: Union[np.ndarray, List[float]],
     landmarks: Union[np.ndarray, List[List[List[float]]]],
-    vis_threshold: float = 0.6
+    vis_threshold: float = 0.6,
 ):
     """
     Draws bounding boxes, scores, and landmarks from separate lists onto an image.
@@ -42,8 +43,9 @@ def draw_detections(
         cv2.rectangle(image, tuple(bbox[:2]), tuple(bbox[2:]), (0, 0, 255), thickness)
 
         # Draw score
-        cv2.putText(image, f"{score:.2f}", (bbox[0], bbox[1] - 10),
-                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), thickness)
+        cv2.putText(
+            image, f"{score:.2f}", (bbox[0], bbox[1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), thickness
+        )
 
         # Draw landmarks
         for j, point in enumerate(landmark_set):