chore: Code style formatting changes

pyproject.toml

@@ -19,7 +19,7 @@ dependencies = [
 requires-python = ">=3.10"
 
 [project.optional-dependencies]
-dev = ["pytest>=7.0.0"]
+dev = ["pytest>=7.0.0", "ruff>=0.4.0"]
 gpu = ["onnxruntime-gpu>=1.16.0"]
 
 [project.urls]
@@ -35,3 +35,13 @@ packages = { find = {} }
 
 [tool.setuptools.package-data]
 "uniface" = ["*.txt", "*.md"]
+
+[tool.ruff]
+line-length = 120
+target-version = "py310"
+
+[tool.ruff.lint]
+select = ["E", "F", "I", "W"]
+
+[tool.ruff.lint.isort]
+known-first-party = ["uniface"]

scripts/batch_process.py

@@ -33,7 +33,15 @@ def process_image(detector, image_path: Path, output_path: Path, threshold: floa
     landmarks = [f["landmarks"] for f in faces]
     draw_detections(image, bboxes, scores, landmarks, vis_threshold=threshold)
 
-    cv2.putText(image, f"Faces: {len(faces)}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+    cv2.putText(
+        image,
+        f"Faces: {len(faces)}",
+        (10, 30),
+        cv2.FONT_HERSHEY_SIMPLEX,
+        1,
+        (0, 255, 0),
+        2,
+    )
     cv2.imwrite(str(output_path), image)
 
     return len(faces)

scripts/run_age_gender.py

@@ -21,7 +21,13 @@ def draw_age_gender_label(image, bbox, gender: str, age: int):
     cv2.putText(image, text, (x1 + 5, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 0), 2)
 
 
-def process_image(detector, age_gender, image_path: str, save_dir: str = "outputs", threshold: float = 0.6):
+def process_image(
+    detector,
+    age_gender,
+    image_path: str,
+    save_dir: str = "outputs",
+    threshold: float = 0.6,
+):
     image = cv2.imread(image_path)
     if image is None:
         print(f"Error: Failed to load image from '{image_path}'")
@@ -75,7 +81,15 @@ def run_webcam(detector, age_gender, threshold: float = 0.6):
             gender, age = age_gender.predict(frame, face["bbox"])  # predict per face
             draw_age_gender_label(frame, face["bbox"], gender, age)
 
-        cv2.putText(frame, f"Faces: {len(faces)}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+        cv2.putText(
+            frame,
+            f"Faces: {len(faces)}",
+            (10, 30),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            1,
+            (0, 255, 0),
+            2,
+        )
         cv2.imshow("Age & Gender Detection", frame)
 
         if cv2.waitKey(1) & 0xFF == ord("q"):

scripts/run_detection.py

@@ -53,7 +53,15 @@ def run_webcam(detector, threshold: float = 0.6):
         landmarks = [f["landmarks"] for f in faces]
         draw_detections(frame, bboxes, scores, landmarks, vis_threshold=threshold)
 
-        cv2.putText(frame, f"Faces: {len(faces)}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+        cv2.putText(
+            frame,
+            f"Faces: {len(faces)}",
+            (10, 30),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            1,
+            (0, 255, 0),
+            2,
+        )
         cv2.imshow("Face Detection", frame)
 
         if cv2.waitKey(1) & 0xFF == ord("q"):

scripts/run_face_search.py

@@ -76,7 +76,12 @@ def main():
     parser.add_argument("--image", type=str, required=True, help="Reference face image")
     parser.add_argument("--threshold", type=float, default=0.4, help="Match threshold")
     parser.add_argument("--detector", type=str, default="scrfd", choices=["retinaface", "scrfd"])
-    parser.add_argument("--recognizer", type=str, default="arcface", choices=["arcface", "mobileface", "sphereface"])
+    parser.add_argument(
+        "--recognizer",
+        type=str,
+        default="arcface",
+        choices=["arcface", "mobileface", "sphereface"],
+    )
     args = parser.parse_args()
 
     detector = RetinaFace() if args.detector == "retinaface" else SCRFD()

scripts/run_landmarks.py

@@ -34,7 +34,15 @@ def process_image(detector, landmarker, image_path: str, save_dir: str = "output
         for x, y in landmarks.astype(int):
             cv2.circle(image, (x, y), 1, (0, 255, 0), -1)
 
-        cv2.putText(image, f"Face {i + 1}", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
+        cv2.putText(
+            image,
+            f"Face {i + 1}",
+            (x1, y1 - 10),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            0.5,
+            (0, 255, 0),
+            2,
+        )
 
     os.makedirs(save_dir, exist_ok=True)
     output_path = os.path.join(save_dir, f"{Path(image_path).stem}_landmarks.jpg")
@@ -67,7 +75,15 @@ def run_webcam(detector, landmarker):
             for x, y in landmarks.astype(int):
                 cv2.circle(frame, (x, y), 1, (0, 255, 0), -1)
 
-        cv2.putText(frame, f"Faces: {len(faces)}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+        cv2.putText(
+            frame,
+            f"Faces: {len(faces)}",
+            (10, 30),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            1,
+            (0, 255, 0),
+            2,
+        )
         cv2.imshow("106-Point Landmarks", frame)
 
         if cv2.waitKey(1) & 0xFF == ord("q"):

scripts/run_recognition.py

@@ -79,7 +79,12 @@ def main():
     parser.add_argument("--image2", type=str, help="Second image for comparison")
     parser.add_argument("--threshold", type=float, default=0.35, help="Similarity threshold")
     parser.add_argument("--detector", type=str, default="retinaface", choices=["retinaface", "scrfd"])
-    parser.add_argument("--recognizer", type=str, default="arcface", choices=["arcface", "mobileface", "sphereface"])
+    parser.add_argument(
+        "--recognizer",
+        type=str,
+        default="arcface",
+        choices=["arcface", "mobileface", "sphereface"],
+    )
     args = parser.parse_args()
 
     detector = RetinaFace() if args.detector == "retinaface" else SCRFD()

scripts/run_video_detection.py

@@ -11,7 +11,13 @@ from uniface import SCRFD, RetinaFace
 from uniface.visualization import draw_detections
 
 
-def process_video(detector, input_path: str, output_path: str, threshold: float = 0.6, show_preview: bool = False):
+def process_video(
+    detector,
+    input_path: str,
+    output_path: str,
+    threshold: float = 0.6,
+    show_preview: bool = False,
+):
     cap = cv2.VideoCapture(input_path)
     if not cap.isOpened():
         print(f"Error: Cannot open video file '{input_path}'")
@@ -51,7 +57,15 @@ def process_video(detector, input_path: str, output_path: str, threshold: float
         landmarks = [f["landmarks"] for f in faces]
         draw_detections(frame, bboxes, scores, landmarks, vis_threshold=threshold)
 
-        cv2.putText(frame, f"Faces: {len(faces)}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
+        cv2.putText(
+            frame,
+            f"Faces: {len(faces)}",
+            (10, 30),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            1,
+            (0, 255, 0),
+            2,
+        )
         out.write(frame)
 
         if show_preview:

tests/test_age_gender.py

@@ -31,7 +31,7 @@ def test_prediction_output_format(age_gender_model, mock_image, mock_bbox):
 
 def test_gender_values(age_gender_model, mock_image, mock_bbox):
     gender, age = age_gender_model.predict(mock_image, mock_bbox)
-    assert gender in ['Male', 'Female'], f"Gender should be 'Male' or 'Female', got '{gender}'"
+    assert gender in ["Male", "Female"], f"Gender should be 'Male' or 'Female', got '{gender}'"
 
 
 def test_age_range(age_gender_model, mock_image, mock_bbox):
@@ -48,7 +48,7 @@ def test_different_bbox_sizes(age_gender_model, mock_image):
 
     for bbox in test_bboxes:
         gender, age = age_gender_model.predict(mock_image, bbox)
-        assert gender in ['Male', 'Female'], f"Failed for bbox {bbox}"
+        assert gender in ["Male", "Female"], f"Failed for bbox {bbox}"
         assert 0 <= age <= 120, f"Age out of range for bbox {bbox}"
 
 
@@ -58,7 +58,7 @@ def test_different_image_sizes(age_gender_model, mock_bbox):
     for size in test_sizes:
         mock_image = np.random.randint(0, 255, size, dtype=np.uint8)
         gender, age = age_gender_model.predict(mock_image, mock_bbox)
-        assert gender in ['Male', 'Female'], f"Failed for image size {size}"
+        assert gender in ["Male", "Female"], f"Failed for image size {size}"
         assert 0 <= age <= 120, f"Age out of range for image size {size}"
 
 
@@ -73,14 +73,14 @@ def test_consistency(age_gender_model, mock_image, mock_bbox):
 def test_bbox_list_format(age_gender_model, mock_image):
     bbox_list = [100, 100, 300, 300]
     gender, age = age_gender_model.predict(mock_image, bbox_list)
-    assert gender in ['Male', 'Female'], "Should work with bbox as list"
+    assert gender in ["Male", "Female"], "Should work with bbox as list"
     assert 0 <= age <= 120, "Age should be in valid range"
 
 
 def test_bbox_array_format(age_gender_model, mock_image):
     bbox_array = np.array([100, 100, 300, 300])
     gender, age = age_gender_model.predict(mock_image, bbox_array)
-    assert gender in ['Male', 'Female'], "Should work with bbox as numpy array"
+    assert gender in ["Male", "Female"], "Should work with bbox as numpy array"
     assert 0 <= age <= 120, "Age should be in valid range"
 
 
@@ -98,7 +98,7 @@ def test_multiple_predictions(age_gender_model, mock_image):
 
     assert len(results) == 3, "Should have 3 predictions"
     for gender, age in results:
-        assert gender in ['Male', 'Female']
+        assert gender in ["Male", "Female"]
         assert 0 <= age <= 120
 
 

tests/test_factory.py

@@ -16,7 +16,7 @@ def test_create_detector_retinaface():
     """
     Test creating a RetinaFace detector using factory function.
     """
-    detector = create_detector('retinaface')
+    detector = create_detector("retinaface")
     assert detector is not None, "Failed to create RetinaFace detector"
 
 
@@ -24,7 +24,7 @@ def test_create_detector_scrfd():
     """
     Test creating a SCRFD detector using factory function.
     """
-    detector = create_detector('scrfd')
+    detector = create_detector("scrfd")
     assert detector is not None, "Failed to create SCRFD detector"
 
 
@@ -33,10 +33,10 @@ def test_create_detector_with_config():
     Test creating detector with custom configuration.
     """
     detector = create_detector(
-        'retinaface',
+        "retinaface",
         model_name=RetinaFaceWeights.MNET_V2,
         conf_thresh=0.8,
-        nms_thresh=0.3
+        nms_thresh=0.3,
     )
     assert detector is not None, "Failed to create detector with custom config"
 
@@ -46,18 +46,14 @@ def test_create_detector_invalid_method():
     Test that invalid detector method raises an error.
     """
     with pytest.raises((ValueError, KeyError)):
-        create_detector('invalid_method')
+        create_detector("invalid_method")
 
 
 def test_create_detector_scrfd_with_model():
     """
     Test creating SCRFD detector with specific model.
     """
-    detector = create_detector(
-        'scrfd',
-        model_name=SCRFDWeights.SCRFD_10G_KPS,
-        conf_thresh=0.5
-    )
+    detector = create_detector("scrfd", model_name=SCRFDWeights.SCRFD_10G_KPS, conf_thresh=0.5)
     assert detector is not None, "Failed to create SCRFD with specific model"
 
 
@@ -66,7 +62,7 @@ def test_create_recognizer_arcface():
     """
     Test creating an ArcFace recognizer using factory function.
     """
-    recognizer = create_recognizer('arcface')
+    recognizer = create_recognizer("arcface")
     assert recognizer is not None, "Failed to create ArcFace recognizer"
 
 
@@ -74,7 +70,7 @@ def test_create_recognizer_mobileface():
     """
     Test creating a MobileFace recognizer using factory function.
     """
-    recognizer = create_recognizer('mobileface')
+    recognizer = create_recognizer("mobileface")
     assert recognizer is not None, "Failed to create MobileFace recognizer"
 
 
@@ -82,7 +78,7 @@ def test_create_recognizer_sphereface():
     """
     Test creating a SphereFace recognizer using factory function.
     """
-    recognizer = create_recognizer('sphereface')
+    recognizer = create_recognizer("sphereface")
     assert recognizer is not None, "Failed to create SphereFace recognizer"
 
 
@@ -91,7 +87,7 @@ def test_create_recognizer_invalid_method():
     Test that invalid recognizer method raises an error.
     """
     with pytest.raises((ValueError, KeyError)):
-        create_recognizer('invalid_method')
+        create_recognizer("invalid_method")
 
 
 # create_landmarker tests
@@ -99,7 +95,7 @@ def test_create_landmarker():
     """
     Test creating a Landmark106 detector using factory function.
     """
-    landmarker = create_landmarker('2d106det')
+    landmarker = create_landmarker("2d106det")
     assert landmarker is not None, "Failed to create Landmark106 detector"
 
 
@@ -116,7 +112,7 @@ def test_create_landmarker_invalid_method():
     Test that invalid landmarker method raises an error.
     """
     with pytest.raises((ValueError, KeyError)):
-        create_landmarker('invalid_method')
+        create_landmarker("invalid_method")
 
 
 # detect_faces tests
@@ -125,7 +121,7 @@ def test_detect_faces_retinaface():
     Test high-level detect_faces function with RetinaFace.
     """
     mock_image = np.random.randint(0, 255, (640, 640, 3), dtype=np.uint8)
-    faces = detect_faces(mock_image, method='retinaface')
+    faces = detect_faces(mock_image, method="retinaface")
 
     assert isinstance(faces, list), "detect_faces should return a list"
 
@@ -135,7 +131,7 @@ def test_detect_faces_scrfd():
     Test high-level detect_faces function with SCRFD.
     """
     mock_image = np.random.randint(0, 255, (640, 640, 3), dtype=np.uint8)
-    faces = detect_faces(mock_image, method='scrfd')
+    faces = detect_faces(mock_image, method="scrfd")
 
     assert isinstance(faces, list), "detect_faces should return a list"
 
@@ -145,13 +141,13 @@ def test_detect_faces_with_threshold():
     Test detect_faces with custom confidence threshold.
     """
     mock_image = np.random.randint(0, 255, (640, 640, 3), dtype=np.uint8)
-    faces = detect_faces(mock_image, method='retinaface', conf_thresh=0.8)
+    faces = detect_faces(mock_image, method="retinaface", conf_thresh=0.8)
 
     assert isinstance(faces, list), "detect_faces should return a list"
 
     # All detections should respect threshold
     for face in faces:
-        assert face['confidence'] >= 0.8, "All detections should meet confidence threshold"
+        assert face["confidence"] >= 0.8, "All detections should meet confidence threshold"
 
 
 def test_detect_faces_default_method():
@@ -169,7 +165,7 @@ def test_detect_faces_empty_image():
     Test detect_faces on a blank image.
     """
     empty_image = np.zeros((640, 640, 3), dtype=np.uint8)
-    faces = detect_faces(empty_image, method='retinaface')
+    faces = detect_faces(empty_image, method="retinaface")
 
     assert isinstance(faces, list), "Should return a list even for empty image"
     assert len(faces) == 0, "Should detect no faces in blank image"
@@ -193,8 +189,8 @@ def test_list_available_detectors_contents():
     detectors = list_available_detectors()
 
     # Should include at least these detectors
-    assert 'retinaface' in detectors, "Should include 'retinaface'"
-    assert 'scrfd' in detectors, "Should include 'scrfd'"
+    assert "retinaface" in detectors, "Should include 'retinaface'"
+    assert "scrfd" in detectors, "Should include 'scrfd'"
 
 
 # Integration tests
@@ -202,7 +198,7 @@ def test_detector_inference_from_factory():
     """
     Test that detector created from factory can perform inference.
     """
-    detector = create_detector('retinaface')
+    detector = create_detector("retinaface")
     mock_image = np.random.randint(0, 255, (640, 640, 3), dtype=np.uint8)
 
     faces = detector.detect(mock_image)
@@ -213,7 +209,7 @@ def test_recognizer_inference_from_factory():
     """
     Test that recognizer created from factory can perform inference.
     """
-    recognizer = create_recognizer('arcface')
+    recognizer = create_recognizer("arcface")
     mock_image = np.random.randint(0, 255, (112, 112, 3), dtype=np.uint8)
 
     embedding = recognizer.get_embedding(mock_image)
@@ -225,7 +221,7 @@ def test_landmarker_inference_from_factory():
     """
     Test that landmarker created from factory can perform inference.
     """
-    landmarker = create_landmarker('2d106det')
+    landmarker = create_landmarker("2d106det")
     mock_image = np.random.randint(0, 255, (640, 640, 3), dtype=np.uint8)
     mock_bbox = [100, 100, 300, 300]
 
@@ -238,8 +234,8 @@ def test_multiple_detector_creation():
     """
     Test that multiple detectors can be created independently.
     """
-    detector1 = create_detector('retinaface')
-    detector2 = create_detector('scrfd')
+    detector1 = create_detector("retinaface")
+    detector2 = create_detector("scrfd")
 
     assert detector1 is not None
     assert detector2 is not None
@@ -250,8 +246,8 @@ def test_detector_with_different_configs():
     """
     Test creating multiple detectors with different configurations.
     """
-    detector_high_thresh = create_detector('retinaface', conf_thresh=0.9)
-    detector_low_thresh = create_detector('retinaface', conf_thresh=0.3)
+    detector_high_thresh = create_detector("retinaface", conf_thresh=0.9)
+    detector_low_thresh = create_detector("retinaface", conf_thresh=0.3)
 
     mock_image = np.random.randint(0, 255, (640, 640, 3), dtype=np.uint8)
 
@@ -269,9 +265,9 @@ def test_factory_returns_correct_types():
     """
     from uniface import RetinaFace, ArcFace, Landmark106
 
-    detector = create_detector('retinaface')
-    recognizer = create_recognizer('arcface')
-    landmarker = create_landmarker('2d106det')
+    detector = create_detector("retinaface")
+    recognizer = create_recognizer("arcface")
+    landmarker = create_landmarker("2d106det")
 
     assert isinstance(detector, RetinaFace), "Should return RetinaFace instance"
     assert isinstance(recognizer, ArcFace), "Should return ArcFace instance"

tests/test_recognition.py

@@ -41,13 +41,16 @@ def mock_landmarks():
     """
     Create mock 5-point facial landmarks.
     """
-    return np.array([
-        [38.2946, 51.6963],
-        [73.5318, 51.5014],
-        [56.0252, 71.7366],
-        [41.5493, 92.3655],
-        [70.7299, 92.2041]
-    ], dtype=np.float32)
+    return np.array(
+        [
+            [38.2946, 51.6963],
+            [73.5318, 51.5014],
+            [56.0252, 71.7366],
+            [41.5493, 92.3655],
+            [70.7299, 92.2041],
+        ],
+        dtype=np.float32,
+    )
 
 
 # ArcFace Tests
@@ -173,8 +176,7 @@ def test_different_models_different_embeddings(arcface_model, mobileface_model,
 
     # Embeddings should be different (with high probability for random input)
     # We check that they're not identical
-    assert not np.allclose(arcface_emb, mobileface_emb), \
-        "Different models should produce different embeddings"
+    assert not np.allclose(arcface_emb, mobileface_emb), "Different models should produce different embeddings"
 
 
 def test_embedding_similarity_computation(arcface_model, mock_aligned_face):
@@ -191,6 +193,7 @@ def test_embedding_similarity_computation(arcface_model, mock_aligned_face):
 
     # Compute cosine similarity
     from uniface import compute_similarity
+
     similarity = compute_similarity(emb1, emb2)
 
     # Similarity should be between -1 and 1
@@ -205,6 +208,7 @@ def test_same_face_high_similarity(arcface_model, mock_aligned_face):
     emb2 = arcface_model.get_embedding(mock_aligned_face)
 
     from uniface import compute_similarity
+
     similarity = compute_similarity(emb1, emb2)
 
     # Same image should have similarity close to 1.0

tests/test_utils.py

@@ -18,13 +18,16 @@ def mock_landmarks():
     Create mock 5-point facial landmarks.
     Standard positions for a face roughly centered at (112/2, 112/2).
     """
-    return np.array([
-        [38.2946, 51.6963],  # Left eye
-        [73.5318, 51.5014],  # Right eye
-        [56.0252, 71.7366],  # Nose
-        [41.5493, 92.3655],  # Left mouth corner
-        [70.7299, 92.2041]   # Right mouth corner
-    ], dtype=np.float32)
+    return np.array(
+        [
+            [38.2946, 51.6963],  # Left eye
+            [73.5318, 51.5014],  # Right eye
+            [56.0252, 71.7366],  # Nose
+            [41.5493, 92.3655],  # Left mouth corner
+            [70.7299, 92.2041],  # Right mouth corner
+        ],
+        dtype=np.float32,
+    )
 
 
 # compute_similarity tests
@@ -166,7 +169,7 @@ def test_face_alignment_landmarks_as_list(mock_image):
         [73.5318, 51.5014],
         [56.0252, 71.7366],
         [41.5493, 92.3655],
-        [70.7299, 92.2041]
+        [70.7299, 92.2041],
     ]
 
     # Convert list to numpy array before passing to face_alignment
@@ -201,9 +204,18 @@ def test_face_alignment_from_different_positions(mock_image):
     """
     # Landmarks at different positions
     positions = [
-        np.array([[100, 100], [150, 100], [125, 130], [110, 150], [140, 150]], dtype=np.float32),
-        np.array([[300, 200], [350, 200], [325, 230], [310, 250], [340, 250]], dtype=np.float32),
-        np.array([[500, 400], [550, 400], [525, 430], [510, 450], [540, 450]], dtype=np.float32),
+        np.array(
+            [[100, 100], [150, 100], [125, 130], [110, 150], [140, 150]],
+            dtype=np.float32,
+        ),
+        np.array(
+            [[300, 200], [350, 200], [325, 230], [310, 250], [340, 250]],
+            dtype=np.float32,
+        ),
+        np.array(
+            [[500, 400], [550, 400], [525, 430], [510, 450], [540, 450]],
+            dtype=np.float32,
+        ),
     ]
 
     for landmarks in positions:
@@ -216,13 +228,16 @@ def test_face_alignment_landmark_count(mock_image):
     Test that face_alignment works specifically with 5-point landmarks.
     """
     # Standard 5-point landmarks
-    landmarks_5pt = np.array([
-        [38.2946, 51.6963],
-        [73.5318, 51.5014],
-        [56.0252, 71.7366],
-        [41.5493, 92.3655],
-        [70.7299, 92.2041]
-    ], dtype=np.float32)
+    landmarks_5pt = np.array(
+        [
+            [38.2946, 51.6963],
+            [73.5318, 51.5014],
+            [56.0252, 71.7366],
+            [41.5493, 92.3655],
+            [70.7299, 92.2041],
+        ],
+        dtype=np.float32,
+    )
 
     aligned, _ = face_alignment(mock_image, landmarks_5pt, image_size=(112, 112))
     assert aligned.shape == (112, 112, 3), "Should work with 5-point landmarks"