diff --git a/config/compound_eye.png b/config/compound_eye.png
index ef3b17b..3f6510b 100644
Binary files a/config/compound_eye.png and b/config/compound_eye.png differ
diff --git a/example1_basic_pipeline.py b/example1_basic_pipeline.py
index d7b2616..21d291a 100644
--- a/example1_basic_pipeline.py
+++ b/example1_basic_pipeline.py
@@ -14,15 +14,16 @@ os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0"
 import cv2
 import orisys
 import numpy as np
+import time
 def main():
     
     # 步骤1：创建传感器对象 (必要步骤)，config_name为配置文件名（内置配置文件名）或路径（自定义配置文件路径）
     # 方式1: 从视频文件读取
-    sensor = orisys.Sensor("s1.mp4", config_name="prototype", cal_path= "./config/cal.npy", isstitch=True, cuda=True, verbose=True)
+    # sensor = orisys.Sensor("s1.mp4", config_name="prototype", cal_path= "./config/cal.npy", isstitch=True, cuda=True, verbose=True)
 
     
     # 方式2: 从摄像头读取（取消注释使用） 
-    # sensor = orisys.Sensor(0, config_name="prototype", cal_path= "./config/cal.npy", isstitch=True, cuda=True, verbose=True)
+    sensor = orisys.Sensor(0, config_name="./config/lens_test.json", cal_path= "./config/cal.npy", isstitch=True, cuda=True, verbose=True)
 
     print("\n按 'q' 退出程序，按 'r' 重置光流追踪器")
 
@@ -32,14 +33,14 @@ def main():
         # 步骤 2.1: 获取并拼接图像 (必要步骤)
         sensor.get_img() 
         print("img_size_raw:", sensor.img_size_raw)
-        if sensor.detect_motion():
+        if sensor.detect_motion(threshold=2):
             print("motion detected")
-            break
-        # 步骤 2.2: 计算形变场 (必要步骤)
-        sensor.compute_deformation()
-        # 步骤 2.3: 计算触碰点 
-        sensor.compute_contact()
-        
+            # 步骤 2.2: 计算形变场 (必要步骤)
+            sensor.compute_deformation()
+            # 步骤 2.3: 计算触碰点 
+            sensor.compute_contact()
+
+    
         #步骤 2.4: 读取信息
         fps, flow, vnormal, img, contour, centroid, depth_map = sensor.read_info(
             sensor.info.FPS,
@@ -79,7 +80,7 @@ def main():
 
         #步骤 3.4: 打印资料
         print(f"fps: {fps}")
-        
+            
         #步骤 3.5: 键盘控制
         key = cv2.waitKey(1) & 0xFF
         if key == ord("q"):
@@ -88,8 +89,8 @@ def main():
         if key == ord("r"):
             # 重置光流追踪器（当追踪漂移时使用）
             sensor.reset()
-            print("光流追踪器已重置")
-    
+            print("传感器已重置")
+        
     # =========================================================================
     # 4. 清理资源
     # =========================================================================
diff --git a/src/orisys/sensor.py b/src/orisys/sensor.py
index 2d406fb..7c55f48 100644
--- a/src/orisys/sensor.py
+++ b/src/orisys/sensor.py
@@ -86,6 +86,13 @@ class Sensor:
         self.cal_path = cal_path
         self.mirror = mirror
 
+        
+        # Load configuration
+        self.__config_cam = None
+        self.__config_stitcher = None
+        self.__config_contact = None
+        self._load_config()
+
         # State variables
         self.stitch_valid = False
         self.stitch_calibration = []
@@ -93,28 +100,29 @@ class Sensor:
         self.compute_contact_valid = False
         
         # Data Variables (public)
+        shape = (self.__config_cam["resolution_stitch"][1], self.__config_cam["resolution_stitch"][0])
         self.img = None
         self.img_size_raw = None
         self.img_filtered = None
-        self.prev_img = None
-        self.flow = None
-        self.vnormal = None
-        self.vshear = None
+        self.initial_img = None
+        self.flow = np.zeros(shape + (2,), dtype=np.float32)
+        self.vnormal = np.zeros_like(self.flow)
+        self.vshear = np.zeros_like(self.flow)
         self.n_boxes = None
         self.fnormal = self.fshearx = self.fsheary = self.ftor = 0.0
 
         # Contact Data Variables (public)
-        self.centroid = None
+        self.centroid = (0, 0)
         self.centroid_found = False
-        self.contour = None
-        self.depth_map = None
+        self.contour = np.empty(0)
+        self.depth_map = np.zeros_like(self.img)
         
         # Timing
         self.last_log_time = time.perf_counter()
         self._fps = 0
         
         # Async pipeline state
-        self._async_enabled = False
+        # self._async_enabled = False
         self._capture_thread = None
         self._flow_thread = None
         self._compute_thread = None
@@ -123,11 +131,6 @@ class Sensor:
         self._result_queue = None
         self._stop_event = None
 
-        # Load configuration
-        self.__config_cam = None
-        self.__config_stitcher = None
-        self.__config_contact = None
-        self._load_config()
         
 
         # Initialize components
@@ -150,7 +153,7 @@ class Sensor:
     # Public API - Core Methods (Most Frequently Used)
     # =========================================================================
     
-    def get_img(self, log_timing=False):
+    def get_img(self):
         """
         Capture and preprocess a frame.
         
@@ -162,59 +165,36 @@ class Sensor:
             print("Failed to get_img from video source.")
             return None
 
-        if log_timing:
-            timings = {}
-            t_start = time.perf_counter()
         
         # Convert to grayscale
-        if log_timing: t0 = time.perf_counter()
         gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-        # gray = frame
-        if log_timing: timings['cvt_color'] = (time.perf_counter() - t0) * 1000
 
         # Extract and stitch tiles
-        if log_timing: t0 = time.perf_counter()
         self.stitcher.get_stitch_unit(gray)
         self._extract_tiles(gray)
-        if log_timing: timings['get_tiles'] = (time.perf_counter() - t0) * 1000
 
-        if log_timing: t0 = time.perf_counter()
         img = np.ascontiguousarray(self.stitcher.stitch_rt_optimized())
-        if log_timing: timings['stitch'] = (time.perf_counter() - t0) * 1000
 
         self.img_size_raw = img.shape
         # Resize
         img = cv2.resize(img, tuple(self._config_resolution_stitch), interpolation=cv2.INTER_CUBIC)
-        
-        # # Temporal filtering (noise reduction)
-        # alpha = 0.7
-        # if self.img_filtered is None:
-        #     self.img_filtered = img.astype(np.float32)
-        # else:
-        #     self.img_filtered = alpha * img + (1 - alpha) * self.img_filtered
-        # Update previous image
+    
         if self.mirror:
             img = cv2.flip(img, 1)
 
-        if self.img is not None:
-            self.prev_img = self.img.copy()
-
         self.img = img.astype(np.uint8)
 
-        if self.prev_img is None:
-            self.prev_img = img.copy()
-
-        if log_timing:
-            timings['total'] = (time.perf_counter() - t_start) * 1000
-            self._print_timings("get_img", timings)
 
+        # FPS calculation
+        self._fps = 1.0 / (time.perf_counter() - self.last_log_time)
+        self.last_log_time = time.perf_counter()
             
         return self.img.copy()
 
-    def detect_motion(self, threshold=10):
-        if self.prev_img is None:
+    def detect_motion(self, threshold=4):
+        if self.initial_img is None:
             return False
-        diff = cv2.absdiff(self.img, self.prev_img)
+        diff = cv2.absdiff(self.img, self.initial_img)
         return diff.mean() > threshold
 
     def compute_deformation(self):
@@ -229,9 +209,6 @@ class Sensor:
         
         self.__compute_flow()
         self.__compute_hhd()
-        # FPS calculation
-        self._fps = 1.0 / (time.perf_counter() - self.last_log_time)
-        self.last_log_time = time.perf_counter()
 
         return True
 
@@ -322,11 +299,13 @@ class Sensor:
 
         if any(arg in [self.info.VRAW, self.info.VNORMAL, self.info.VSHEAR, self.info.FNORMAL, self.info.FSHEARX, self.info.FSHEARY] for arg in args):
             if not self.compute_deformation_valid:
-                raise Exception("Warning: Compute deformation first")
+                print("Warning: Compute deformation first")
+                # raise Exception("Warning: Compute deformation first")
 
         if any(arg in [self.info.CENTROID, self.info.CONTOUR, self.info.DEPTH] for arg in args):
             if not self.compute_contact_valid:
-                raise Exception("Warning: Compute contact first")
+                print("Warning: Compute contact first")
+                # raise Exception("Warning: Compute contact first")
 
         mapping = {
             # Return copies of arrays to prevent external modification of internal state
@@ -357,11 +336,12 @@ class Sensor:
     def reset(self):
         """Reset optical flow tracker."""
         self.__tracker.reset()
+        self.initial_img = self.img.copy()
 
     def disconnect(self):
         """Release resources and stop async threads."""
-        if self._async_enabled:
-            self.disable_async()
+        # if self._async_enabled:
+        #     self.disable_async()
         if self.cap.isOpened():
             self.cap.release()
             if self.verbose:
@@ -414,11 +394,12 @@ class Sensor:
     def _load_config(self):
         """Load sensor configuration from JSON file."""
         self._config = configs.load_config(self.config_name)
-        cam = self._config["camera"]
-        self._config_resolution_raw = cam["resolution_raw"]
-        self._config_fps = cam["fps"]
-        self._config_units = cam["units"]
-        self._config_resolution_stitch = cam["resolution_stitch"]
+        self.__config_cam = self._config["camera"]
+        self._config_resolution_raw = self.__config_cam["resolution_raw"]
+        self._config_fps = self.__config_cam["fps"]
+        self._config_units = self.__config_cam["units"]
+        self._config_resolution_stitch = self.__config_cam["resolution_stitch"]
+
         self._config_dis_preset = self._config["tracker"]["dis_preset"]
 
         self.__config_stitcher = self._config["stitcher"]
@@ -471,7 +452,13 @@ class Sensor:
         else:
             self._find_stitch_boxes(frame)
             self._extract_tiles(frame)
-            self.stitch_cal()
+            img = self.stitch_cal()
+            img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+            self.initial_img = cv2.resize(img, tuple(self._config_resolution_stitch), interpolation=cv2.INTER_CUBIC)
+            if self.mirror:
+                self.initial_img = cv2.flip(self.initial_img, 1)
+            self.initial_img = self.initial_img.astype(np.uint8)
+
 
     def _find_stitch_boxes(self, img_src, show=False):
         """Detect vision tile boxes with camera stabilization."""