ref: Update some refactoring files for testing

scripts/run_landmarks.py

@@ -1,11 +1,14 @@
-"""Facial Landmark Detection Demo Script"""
+# 106-point facial landmark detection
+# Usage: python run_landmarks.py --image path/to/image.jpg
+#        python run_landmarks.py --webcam
 
-import os
-import cv2
 import argparse
+import os
 from pathlib import Path
 
-from uniface import RetinaFace, SCRFD, Landmark106
+import cv2
+
+from uniface import SCRFD, Landmark106, RetinaFace
 
 
 def process_image(detector, landmarker, image_path: str, save_dir: str = "outputs"):
@@ -14,39 +17,25 @@ def process_image(detector, landmarker, image_path: str, save_dir: str = "output
         print(f"Error: Failed to load image from '{image_path}'")
         return
 
-    print(f"Processing: {image_path}")
-
-    # Detect faces
     faces = detector.detect(image)
-    print(f"  Detected {len(faces)} face(s)")
+    print(f"Detected {len(faces)} face(s)")
 
     if not faces:
-        print("  No faces detected")
         return
 
-    # Process each face
     for i, face in enumerate(faces):
-        # Draw bounding box
-        bbox = face['bbox']
+        bbox = face["bbox"]
         x1, y1, x2, y2 = map(int, bbox)
         cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
 
-        # Get and draw 106 landmarks
         landmarks = landmarker.get_landmarks(image, bbox)
-        print(f"  Face {i+1}: Extracted {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)
 
-        # Add face count
-        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)
 
-    # Add total count
-    cv2.putText(image, f"Faces: {len(faces)}", (10, 30),
-               cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
-
-    # Save result
     os.makedirs(save_dir, exist_ok=True)
     output_path = os.path.join(save_dir, f"{Path(image_path).stem}_landmarks.jpg")
     cv2.imwrite(output_path, image)
@@ -54,91 +43,54 @@ def process_image(detector, landmarker, image_path: str, save_dir: str = "output
 
 
 def run_webcam(detector, landmarker):
-    cap = cv2.VideoCapture(0)
-
+    cap = cv2.VideoCapture(0)  # 0 = default webcam
     if not cap.isOpened():
         print("Cannot open webcam")
         return
 
-    print("Webcam opened")
-    print("Press 'q' to quit\n")
+    print("Press 'q' to quit")
 
-    frame_count = 0
+    while True:
+        ret, frame = cap.read()
+        frame = cv2.flip(frame, 1)  # mirror for natural interaction
+        if not ret:
+            break
 
-    try:
-        while True:
-            ret, frame = cap.read()
-            if not ret:
-                break
+        faces = detector.detect(frame)
 
-            frame_count += 1
+        for face in faces:
+            bbox = face["bbox"]
+            x1, y1, x2, y2 = map(int, bbox)
+            cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
 
-            # Detect faces
-            faces = detector.detect(frame)
+            landmarks = landmarker.get_landmarks(frame, bbox)  # 106 points
+            for x, y in landmarks.astype(int):
+                cv2.circle(frame, (x, y), 1, (0, 255, 0), -1)
 
-            # Process each face
-            for face in faces:
-                # Draw bounding box
-                bbox = face['bbox']
-                x1, y1, x2, y2 = map(int, bbox)
-                cv2.rectangle(frame, (x1, y1), (x2, y2), (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)
 
-                # Get and draw 106 landmarks
-                landmarks = landmarker.get_landmarks(frame, bbox)
-                for x, y in landmarks.astype(int):
-                    cv2.circle(frame, (x, y), 1, (0, 255, 0), -1)
+        if cv2.waitKey(1) & 0xFF == ord("q"):
+            break
 
-            # Add info
-            cv2.putText(frame, f"Faces: {len(faces)}", (10, 30),
-                       cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
-            cv2.putText(frame, "Press 'q' to quit", (10, frame.shape[0] - 10),
-                       cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
-
-            cv2.imshow("106-Point Landmarks", frame)
-
-            if cv2.waitKey(1) & 0xFF == ord('q'):
-                break
-
-    except KeyboardInterrupt:
-        print("\nInterrupted")
-    finally:
-        cap.release()
-        cv2.destroyAllWindows()
-        print(f"\nProcessed {frame_count} frames")
+    cap.release()
+    cv2.destroyAllWindows()
 
 
 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 instead of image")
-    parser.add_argument("--detector", type=str, default="retinaface",
-                       choices=['retinaface', 'scrfd'], help="Face detector to use")
-    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")
-
+    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()
 
-    # Validate input
     if not args.image and not args.webcam:
         parser.error("Either --image or --webcam must be specified")
 
-    if args.verbose:
-        from uniface import enable_logging
-        enable_logging()
-
-    # Initialize models
-    print(f"Initializing detector: {args.detector}")
-    if args.detector == 'retinaface':
-        detector = RetinaFace()
-    else:
-        detector = SCRFD()
-
-    print("Initializing landmark detector...")
+    detector = RetinaFace() if args.detector == "retinaface" else SCRFD()
     landmarker = Landmark106()
-    print("Models initialized\n")
 
-    # Process
     if args.webcam:
         run_webcam(detector, landmarker)
     else: