fix: resolve face detection unpacking error and insightface parameter issue

- Fix FaceLandmarkerResult unpacking error by returning tuple (result, None)
  when no face is detected in detect() and detect_for_video() methods
- Fix insightface FaceAnalysis.get() invalid parameter by setting det_thresh
  via det_model attribute instead of passing it as argument
- Add mesh3d None check to properly trigger insightface fallback detection

express_adaption/media_pipe/face_landmark.py

@@ -3207,7 +3207,7 @@ class FaceLandmarker(base_vision_task_api.BaseVisionTaskApi):
     })
 
     if output_packets[_NORM_LANDMARKS_STREAM_NAME].is_empty():
-      return FaceLandmarkerResult([], [], [])
+      return FaceLandmarkerResult([], [], []), None
 
     return _build_landmarker_result2(output_packets)
 
@@ -3252,7 +3252,7 @@ class FaceLandmarker(base_vision_task_api.BaseVisionTaskApi):
     })
 
     if output_packets[_NORM_LANDMARKS_STREAM_NAME].is_empty():
-      return FaceLandmarkerResult([], [], [])
+      return FaceLandmarkerResult([], [], []), None
 
     return _build_landmarker_result2(output_packets)
 

express_adaption/media_pipe/mp_utils.py

@@ -81,8 +81,14 @@ class LMKExtractor():
             #     return None
             try:
                 detection_result, mesh3d = self.detector.detect(image)
+                # Check if detection failed (mesh3d is None means no face detected)
+                if mesh3d is None:
+                    raise Exception("No face detected by mediapipe")
             except:
-                faces = self.handler.get(frame, det_thresh=det_thresh)
+                # Set detection threshold for insightface
+                if hasattr(self.handler, 'det_model') and self.handler.det_model is not None:
+                    self.handler.det_model.det_thresh = det_thresh
+                faces = self.handler.get(frame)
                 if len(faces) < 1:
                     return None
                 h_orig, w_orig = frame.shape[:2]
@@ -105,6 +111,9 @@ class LMKExtractor():
                 try:
                     do_insightface = True
                     detection_result, mesh3d = self.detector.detect(mp_image)
+                    # Check if detection failed (mesh3d is None means no face detected)
+                    if mesh3d is None:
+                        raise Exception("No face detected in ROI by mediapipe")
                     # print('face detection success')
                     for landmark in detection_result.face_landmarks[0]:
                         landmark.x = (landmark.x * roi_w + cx1) / w_orig