improve logging system with verbose flag

- silent by default (only warnings/errors)
- add --verbose flag to all scripts
- add enable_logging() function for library users
- cleaner output for end users

scripts/run_detection.py

@@ -26,7 +26,7 @@ def run_inference(detector, image_path: str, vis_threshold: float = 0.6, save_di
 
     # 1. Get the list of face dictionaries from the detector
     faces = detector.detect(image)
-    
+
     if faces:
         # 2. Unpack the data into separate lists
         bboxes = [face['bbox'] for face in faces]
@@ -56,9 +56,14 @@ def main():
     parser.add_argument("--threshold", type=float, default=0.6, help="Visualization confidence threshold")
     parser.add_argument("--iterations", type=int, default=1, help="Number of inference runs for benchmarking")
     parser.add_argument("--save_dir", type=str, default="outputs", help="Directory to save output images")
+    parser.add_argument("--verbose", action="store_true", help="Enable verbose logging")
 
     args = parser.parse_args()
 
+    if args.verbose:
+        from uniface import enable_logging
+        enable_logging()
+
     print(f"Initializing detector: {args.method}")
     detector = create_detector(method=args.method)
 

scripts/run_face_search.py

@@ -1,11 +1,12 @@
-import cv2
 import argparse
+
+import cv2
 import numpy as np
 
 # Use the new high-level factory functions
 from uniface.detection import create_detector
-from uniface.recognition import create_recognizer
 from uniface.face_utils import compute_similarity
+from uniface.recognition import create_recognizer
 
 
 def extract_reference_embedding(detector, recognizer, image_path: str) -> np.ndarray:
@@ -19,8 +20,8 @@ def extract_reference_embedding(detector, recognizer, image_path: str) -> np.nda
         raise RuntimeError("No faces found in reference image.")
 
     # Get landmarks from the first detected face dictionary
-    landmarks = np.array(faces[0]['landmarks'])
-    
+    landmarks = np.array(faces[0]["landmarks"])
+
     # Use normalized embedding for more reliable similarity comparison
     embedding = recognizer.get_normalized_embedding(image, landmarks)
     return embedding
@@ -43,17 +44,17 @@ def run_video(detector, recognizer, ref_embedding: np.ndarray, threshold: float
         # Loop through each detected face
         for face in faces:
             # Extract bbox and landmarks from the dictionary
-            bbox = face['bbox']
-            landmarks = np.array(face['landmarks'])
-            
+            bbox = face["bbox"]
+            landmarks = np.array(face["landmarks"])
+
             x1, y1, x2, y2 = map(int, bbox)
-            
+
             # Get the normalized embedding for the current face
             embedding = recognizer.get_normalized_embedding(frame, landmarks)
-            
+
             # Compare with the reference embedding
             sim = compute_similarity(ref_embedding, embedding)
-            
+
             # Draw results
             label = f"Match ({sim:.2f})" if sim > threshold else f"Unknown ({sim:.2f})"
             color = (0, 255, 0) if sim > threshold else (0, 0, 255)
@@ -61,7 +62,7 @@ def run_video(detector, recognizer, ref_embedding: np.ndarray, threshold: float
             cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)
 
         cv2.imshow("Face Recognition", frame)
-        if cv2.waitKey(1) & 0xFF == ord('q'):
+        if cv2.waitKey(1) & 0xFF == ord("q"):
             break
 
     cap.release()
@@ -72,30 +73,32 @@ def main():
     parser = argparse.ArgumentParser(description="Face recognition using a reference image.")
     parser.add_argument("--image", type=str, required=True, help="Path to the reference face image.")
     parser.add_argument(
-        "--detector",
-        type=str,
-        default="scrfd",
-        choices=['retinaface', 'scrfd'],
-        help="Face detection method."
+        "--detector", type=str, default="scrfd", choices=["retinaface", "scrfd"], help="Face detection method."
     )
     parser.add_argument(
         "--recognizer",
         type=str,
         default="arcface",
-        choices=['arcface', 'mobileface', 'sphereface'],
-        help="Face recognition method."
+        choices=["arcface", "mobileface", "sphereface"],
+        help="Face recognition method.",
     )
+    parser.add_argument("--verbose", action="store_true", help="Enable verbose logging")
     args = parser.parse_args()
 
+    if args.verbose:
+        from uniface import enable_logging
+
+        enable_logging()
+
     print("Initializing models...")
     detector = create_detector(method=args.detector)
     recognizer = create_recognizer(method=args.recognizer)
-    
+
     print("Extracting reference embedding...")
     ref_embedding = extract_reference_embedding(detector, recognizer, args.image)
-    
+
     run_video(detector, recognizer, ref_embedding)
 
 
 if __name__ == "__main__":
-    main()
+    main()

scripts/run_recognition.py

@@ -60,9 +60,14 @@ def main():
         choices=['arcface', 'mobileface', 'sphereface'],
         help="Face recognition method to use."
     )
+    parser.add_argument("--verbose", action="store_true", help="Enable verbose logging")
 
     args = parser.parse_args()
 
+    if args.verbose:
+        from uniface import enable_logging
+        enable_logging()
+
     print(f"Initializing detector: {args.detector}")
     detector = create_detector(method=args.detector)