diff --git a/examples/pose_landmarker/raspberry_pi/detect.py b/examples/pose_landmarker/raspberry_pi/detect.py
index 131f715d9..a9f554835 100644
--- a/examples/pose_landmarker/raspberry_pi/detect.py
+++ b/examples/pose_landmarker/raspberry_pi/detect.py
@@ -21,6 +21,7 @@
 import mediapipe as mp
 import numpy as np
 
+from picamera2 import Picamera2
 from mediapipe.tasks import python
 from mediapipe.tasks.python import vision
 from mediapipe.framework.formats import landmark_pb2
@@ -37,32 +38,18 @@
 
 def run(model: str, num_poses: int,
         min_pose_detection_confidence: float,
-        min_pose_presence_confidence: float, min_tracking_confidence: float,
+        min_pose_presence_confidence: float, 
+	min_tracking_confidence: float,
         output_segmentation_masks: bool,
-        camera_id: int, width: int, height: int) -> None:
-    """Continuously run inference on images acquired from the camera.
-
-  Args:
-      model: Name of the pose landmarker model bundle.
-      num_poses: Max number of poses that can be detected by the landmarker.
-      min_pose_detection_confidence: The minimum confidence score for pose
-        detection to be considered successful.
-      min_pose_presence_confidence: The minimum confidence score of pose
-        presence score in the pose landmark detection.
-      min_tracking_confidence: The minimum confidence score for the pose
-        tracking to be considered successful.
-      output_segmentation_masks: Choose whether to visualize the segmentation
-        mask or not.
-      camera_id: The camera id to be passed to OpenCV.
-      width: The width of the frame captured from the camera.
-      height: The height of the frame captured from the camera.
-  """
-
-    # Start capturing video input from the camera
-    cap = cv2.VideoCapture(camera_id)
-    cap.set(cv2.CAP_PROP_FRAME_WIDTH, width)
-    cap.set(cv2.CAP_PROP_FRAME_HEIGHT, height)
-
+	width: int, height: int) -> None:
+   
+    picam2 = Picamera2()
+    picam2.preview_configuration.main.size = (width, height)
+    picam2.preview_configuration.main.format = "RGB888"
+    picam2.preview_configuration.align()
+    picam2.configure("preview")
+    picam2.start()   
+ 
     # Visualization parameters
     row_size = 50  # pixels
     left_margin = 24  # pixels
@@ -99,17 +86,14 @@ def save_result(result: vision.PoseLandmarkerResult,
     detector = vision.PoseLandmarker.create_from_options(options)
 
     # Continuously capture images from the camera and run inference
-    while cap.isOpened():
-        success, image = cap.read()
-        if not success:
-            sys.exit(
-                'ERROR: Unable to read from webcam. Please verify your webcam settings.'
-            )
-
-        image = cv2.flip(image, 1)
+    while True:
+        frame = picam2.capture_array()
+	
+	
+        frame = cv2.flip(frame, 1)
 
         # Convert the image from BGR to RGB as required by the TFLite model.
-        rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        rgb_image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
         mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=rgb_image)
 
         # Run pose landmarker using the model.
@@ -118,7 +102,7 @@ def save_result(result: vision.PoseLandmarkerResult,
         # Show the FPS
         fps_text = 'FPS = {:.1f}'.format(FPS)
         text_location = (left_margin, row_size)
-        current_frame = image
+        current_frame = frame
         cv2.putText(current_frame, fps_text, text_location,
                     cv2.FONT_HERSHEY_DUPLEX,
                     font_size, text_color, font_thickness, cv2.LINE_AA)
@@ -142,7 +126,7 @@ def save_result(result: vision.PoseLandmarkerResult,
         if (output_segmentation_masks and DETECTION_RESULT):
             if DETECTION_RESULT.segmentation_masks is not None:
                 segmentation_mask = DETECTION_RESULT.segmentation_masks[0].numpy_view()
-                mask_image = np.zeros(image.shape, dtype=np.uint8)
+                mask_image = np.zeros(frame.shape, dtype=np.uint8)
                 mask_image[:] = mask_color
                 condition = np.stack((segmentation_mask,) * 3, axis=-1) > 0.1
                 visualized_mask = np.where(condition, mask_image, current_frame)
@@ -157,7 +141,6 @@ def save_result(result: vision.PoseLandmarkerResult,
             break
 
     detector.close()
-    cap.release()
     cv2.destroyAllWindows()
 
 
@@ -198,12 +181,6 @@ def main():
              'mask.',
         required=False,
         action='store_true')
-    # Finding the camera ID can be very reliant on platform-dependent methods.
-    # One common approach is to use the fact that camera IDs are usually indexed sequentially by the OS, starting from 0.
-    # Here, we use OpenCV and create a VideoCapture object for each potential ID with 'cap = cv2.VideoCapture(i)'.
-    # If 'cap' is None or not 'cap.isOpened()', it indicates the camera ID is not available.
-    parser.add_argument(
-        '--cameraId', help='Id of camera.', required=False, default=0)
     parser.add_argument(
         '--frameWidth',
         help='Width of frame to capture from camera.',
@@ -218,8 +195,7 @@ def main():
 
     run(args.model, int(args.numPoses), args.minPoseDetectionConfidence,
         args.minPosePresenceConfidence, args.minTrackingConfidence,
-        args.outputSegmentationMasks,
-        int(args.cameraId), args.frameWidth, args.frameHeight)
+        args.outputSegmentationMasks, args.frameWidth, args.frameHeight)
 
 
 if __name__ == '__main__':