add private variable and copy when return by readinfo

config/prototype.json

@@ -1,31 +0,0 @@
-{
-    "name": "prototype",
-    "version": "1.0",
-    "camera": {
-      "resolution_raw": [1920, 1080],
-      "fps": 60,
-      "units": 15,
-      "units_used": 9, 
-      "resolution_stitch": [400, 400]
-    },
-    "stitcher": {
-      "min_area_frac": 0.018,
-      "max_area_frac": 0.035,
-      "max_aspect_ratio": 20,
-      "index": [1,2,3,6,7,8,11,12,13],
-      "pos_default": [[260, 56], [577, 62], [897, 69], [1214, 75], [1530, 81], [256, 391], [570, 397], [889, 403], [1208, 409], [1523, 416], [248, 724], [563, 730], [882, 738], [1201, 743], [1516, 751]],
-      "margin": 10,
-      "nominal_ov_pos": 100
-    },
-    "contact": {
-      "min_contour_area": 3000,
-      "flow_threshold":1,
-      "div_threshold":0.02,
-      "expansion_threshold": 0.01,
-      "downsample_factor": 1,
-      "kernel_size": [5,5],
-      "denoise_sigma": 15
-    }
-}
-
-

example1_basic.py

@@ -28,7 +28,7 @@ def main():
     #   verbose:  是否打印详细初始化日志
     
     # 方式1: 从视频文件读取
-    sensor = orisys.Sensor("./s1.mp4", isstitch=True, cuda=False, verbose=True,config_name="prototype")
+    sensor = orisys.Sensor("./s1.mp4", isstitch=True, cuda=True, verbose=True,config_name="prototype")
     
     # 方式2: 从摄像头读取（取消注释使用）
     # sensor = orisys.Sensor(0, isstitch=True, cuda=True, verbose=True)

example_contact.py

@@ -7,10 +7,10 @@ import time
 def main():
     
     # 方式1: 从视频文件读取
-    sensor = orisys.Sensor("./s1.mp4", isstitch=True, cuda=True, verbose=True,config_name="./config/prototype.json")
+    # sensor = orisys.Sensor("./s1.mp4", isstitch=True, cuda=True, verbose=True,config_name="prototype")
     
     # 方式2: 从摄像头读取（取消注释使用）
-    # sensor = orisys.Sensor(0, isstitch=True, cuda=False, verbose=True,config_name="./config/prototype.json")
+    sensor = orisys.Sensor(0, isstitch=True, cuda=True, verbose=True,config_name="prototype")
 
     print("\n按 'q' 退出程序，按 'r' 重置光流追踪器")
 
@@ -20,15 +20,23 @@ def main():
         
         # 步骤 2.2: 计算形变场
         sensor.compute_deformation()
+        # 计算触碰点
+        # sensor.compute_contact(min_magnitude=0.1,expansion_threshold=0.01,denoise_sigma=15)
+        sensor.compute_contact()
         
-        fps, flow, vnormal = sensor.read_info(
+        #计算后读取信息
+        fps, flow, vnormal, img, contour, centroid, depth_map = sensor.read_info(
             sensor.info.FPS,
             sensor.info.VRAW,
-            sensor.info.VNORMAL
+            sensor.info.VNORMAL,
+            sensor.info.IMG,
+            sensor.info.CONTOUR,
+            sensor.info.CENTROID,
+            sensor.info.DEPTH
         )
      
         arrows = orisys.util.draw_arrows(
-            sensor.img.copy(),  # 使用 copy() 避免修改原图
+            img,  # 使用 copy() 避免修改原图
             vnormal,
             threshold=5,
             grid_spacing=10,
@@ -36,17 +44,10 @@ def main():
         )
         cv2.imshow("形变场 Deformation", arrows)
         
-        # 计算触碰点
-        # sensor.compute_contact(min_magnitude=0.1,expansion_threshold=0.01,denoise_sigma=15)
-        sensor.compute_contact()
-        
         # 在图像上触碰点和区域
-        image_with_foe = orisys.util.draw_contact(sensor.img.copy(), sensor.contour, sensor.centroid)
+        image_with_foe = orisys.util.draw_contact(img, contour, centroid)
         cv2.imshow("Contact Centroid", image_with_foe)
         
-        # 计算触碰区域
-        depth_map = sensor.depth_map
-        
         # 归一化散度值到0-255范围（使用绝对值，然后归一化）
         div_abs = depth_map
         if div_abs.max() > div_abs.min():

src/orisys/Stitcher.py

@@ -6,7 +6,7 @@ import cv2
 
 class Stitcher:
 
-    def __init__(self,isstitch=False, n=[3,3], config_stitcher=None,verbose=False):
+    def __init__(self,isstitch=False, config_stitcher=None,verbose=False):
 
         # Settings
         self.isstitch = isstitch
@@ -22,8 +22,8 @@ class Stitcher:
         self.boxes = self.units = None
         self.index = config_stitcher["index"]
         self.stitch_calibration = self.tile_list = []
-        self.ncol = n[0]
+        self.ncol = config_stitcher["n"][0]
-        self.nrow = n[1]
+        self.nrow = config_stitcher["n"][1]
         self.nominal_ov_pos=config_stitcher["nominal_ov_pos"] 
         self.margin=config_stitcher["margin"]
         self.bound=self.nominal_ov_pos - self.margin

src/orisys/configs/__init__.py

@@ -47,10 +47,10 @@ def load_config(config_name_or_path):
         available = [f.stem for f in CONFIG_DIR.glob("*.json")]
         available_str = ", ".join(available) if available else "none"
         raise FileNotFoundError(
-            f"Config file not found: {config_name_or_path}\n"
+            f"Input config file not found with name or path: {config_name_or_path}\n"
-            f"Tried as file path: {config_path} (not found)\n"
+            f"  Tried as file path: {config_path} (not found)\n"
-            f"Tried as package config: {package_config_path} (not found)\n"
+            f"  Tried as package config: {package_config_path} (not found)\n"
-            f"Available package configs: {available_str}"
+            f"  Available package configs: {available_str}"
         )
     
     with open(package_config_path, 'r', encoding='utf-8') as f:

src/orisys/configs/prototype.json

@@ -15,7 +15,8 @@
     "index": [1,2,3,6,7,8,11,12,13],
     "pos_default": [[260, 56], [577, 62], [897, 69], [1214, 75], [1530, 81], [256, 391], [570, 397], [889, 403], [1208, 409], [1523, 416], [248, 724], [563, 730], [882, 738], [1201, 743], [1516, 751]],
     "margin": 10,
-    "nominal_ov_pos": 100
+    "nominal_ov_pos": 100,
+    "n": [3,3]
   },
   "contact": {
     "min_contour_area": 3000,

src/orisys/sensor.py

@@ -18,7 +18,6 @@ from queue import Queue, Empty
 from .Stitcher import Stitcher
 from .tracker import Tracker
 from .nHHD import nHHD
-# from .getDensity import getFilteredDensity
 from . import configs 
 from .get_depth import Contact3D
 
@@ -48,6 +47,7 @@ class Sensor:
         CENTROID = auto()
         CONTOUR = auto()
         DEPTH = auto()
+        IMG = auto()
 
     # =========================================================================
     # Initialization
@@ -94,25 +94,24 @@ class Sensor:
         self.stitch_calibration = []
         
         # Data Variables
-        self.img = None
+        self.__img = None
-        self.img_size_raw = None
+        self.__img_size_raw = None
-        self.img_filtered = None
+        self.__img_filtered = None
-        self.flow = None
+        self.__flow = None
-        self.vnormal = None
+        self.__vnormal = None
-        self.vshear = None
+        self.__vshear = None
-        # self.depth = None
+        self.__n_boxes = None
-        self.n_boxes = None
+        self.__fnormal = self.__fshearx = self.__fsheary = self.__ftor = 0.0
-        self.fnormal = self.fshearx = self.fsheary = self.ftor = 0.0
 
         # Contact Data Variables
-        self.centroid = None
+        self.__centroid = None
-        self.centroid_found = False
+        self.__centroid_found = False
-        self.contour = None
+        self.__contour = None
-        self.depth_map = None
+        self.__depth_map = None
         
         # Timing
         self.last_log_time = time.perf_counter()
-        self.fps = 0
+        self._fps = 0
         
         # Async pipeline state
         self._async_enabled = False
@@ -125,17 +124,17 @@ class Sensor:
         self._stop_event = None
 
         # Load configuration
-        self.config_cam = None
+        self.__config_cam = None
-        self.config_stitcher = None
+        self.__config_stitcher = None
-        self.config_contact = None
+        self.__config_contact = None
         self._load_config()
         
 
         # Initialize components
-        self._stitcher = Stitcher(isstitch=isstitch, config_stitcher=self.config_stitcher, verbose=self.verbose)
+        self.__stitcher = Stitcher(isstitch=isstitch, config_stitcher=self.__config_stitcher, verbose=self.verbose)
-        self.tracker = Tracker(adaptive=True, cuda=cuda, verbose=self.verbose)
+        self.__tracker = Tracker(adaptive=True, cuda=cuda, verbose=self.verbose)
-        self.contact3d = Contact3D(config_contact=self.config_contact, verbose=self.verbose)
+        self.__contact3d = Contact3D(config_contact=self.__config_contact, verbose=self.verbose)
-        self.hhd = None
+        self.__hhd = None
         
         # Setup video capture
         self._open_video_source(override, box)
@@ -174,35 +173,35 @@ class Sensor:
 
         # Extract and stitch tiles
         if log_timing: t0 = time.perf_counter()
-        self._stitcher.get_stitch_unit(gray)
+        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())
+        img = np.ascontiguousarray(self.__stitcher.stitch_rt_optimized())
         if log_timing: timings['stitch'] = (time.perf_counter() - t0) * 1000
 
-        self.img_size_raw = img.shape
+        self.__img_size_raw = img.shape
         # Resize
-        img = cv2.resize(img, tuple(self.config_resolution_stitch), interpolation=cv2.INTER_CUBIC)
+        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:
+        if self.__img_filtered is None:
-            self.img_filtered = img.astype(np.float32)
+            self.__img_filtered = img.astype(np.float32)
         else:
-            self.img_filtered = alpha * img + (1 - alpha) * self.img_filtered
+            self.__img_filtered = alpha * img + (1 - alpha) * self.__img_filtered
         
-        self.img = self.img_filtered.astype(np.uint8)
+        self.__img = self.__img_filtered.astype(np.uint8)
-        self.imgupdate = True
+        self.__imgupdate = True
 
         if log_timing:
             timings['total'] = (time.perf_counter() - t_start) * 1000
             self._print_timings("get_img", timings)
         if self.mirror:
-            self.img = cv2.flip(self.img, 1)
+            self.__img = cv2.flip(self.__img, 1)
             
-        return self.img
+        return True
 
     def compute_deformation(self, log_timing=False):
         """
@@ -217,27 +216,27 @@ class Sensor:
         
         # Optical flow
         if log_timing: t0 = time.perf_counter()
-        flow = self.tracker.track(self.img)
+        flow = self.__tracker.track(self.__img)
         if hasattr(flow, 'download'):
             flow = flow.download()
         if log_timing: timings['optical_flow'] = (time.perf_counter() - t0) * 1000
         
         # Initialize HHD solver
-        if self.hhd is None:
+        if self.__hhd is None:
-            self.hhd = nHHD(grid=flow.shape[:2], spacings=(18, 18))
+            self.__hhd = nHHD(grid=flow.shape[:2], spacings=(18, 18))
 
         # Force computation
         if log_timing: t0 = time.perf_counter()
         if self.cuda and self.CUPY_AVAILABLE:
-            _, forces = self.hhd.get_force_gpu_v2(flow)
+            _, forces = self.__hhd.get_force_gpu_v2(flow)
         else:
-            _, forces = self.hhd.get_force(flow)
+            _, forces = self.__hhd.get_force(flow)
-        self.fnormal, self.fshearx, self.fsheary, self.ftor = forces
+        self.__fnormal, self.__fshearx, self.__fsheary, self.__ftor = forces
         if log_timing: timings['hhd_decompose'] = (time.perf_counter() - t0) * 1000
         
         # Get components
-        self.vnormal = self.hhd.get_normal()
+        self.__vnormal = self.__hhd.get_normal()
-        self.vshear = self.hhd.get_shear()
+        self.__vshear = self.__hhd.get_shear()
         
         # # Depth computation
         # if log_timing: t0 = time.perf_counter()
@@ -245,40 +244,37 @@ class Sensor:
         # if log_timing: timings['get_density'] = (time.perf_counter() - t0) * 1000
         
         # Update state
-        self.flow = flow
+        self.__flow = flow
         self.imgupdate = False
         self.vupdate = True
         
         # FPS calculation
-        self.fps = 1.0 / (time.perf_counter() - self.last_log_time)
+        self._fps = 1.0 / (time.perf_counter() - self.last_log_time)
         self.last_log_time = time.perf_counter()
         
         if log_timing:
             timings['total'] = (time.perf_counter() - t_start) * 1000
             self._print_timings("compute_deformation", timings)
 
-        return self.flow
+        return True
 
     def compute_contact(self, multiple_foe=False, min_contour_area=3000, min_magnitude=1.0, expansion_threshold=0.01, downsample_factor=1, kernel_size=(5,5), denoise_sigma=15):
         
         if multiple_foe:
-            # result = compute_multiple_foe(self.vnormal, min_contour_area=min_contour_area, min_magnitude=min_magnitude, expansion_threshold=expansion_threshold, downsample_factor=downsample_factor, kernel_size=kernel_size, denoise_sigma=denoise_sigma)
+            result = self.__contact3d.compute_multiple_foe(self.__vnormal)
-            result = self.contact3d.compute_multiple_foe(self.vnormal)
         else:
             if self.cuda: 
-                # result = compute_foe_gpu(self.vnormal, min_magnitude=min_magnitude, expansion_threshold=expansion_threshold, downsample_factor=downsample_factor, kernel_size=kernel_size, denoise_sigma=denoise_sigma)
+                result = self.__contact3d.compute_foe_gpu(self.__vnormal)
-                result = self.contact3d.compute_foe_gpu(self.vnormal)
             else:
-                # result = compute_foe(self.vnormal, min_magnitude=min_magnitude, expansion_threshold=expansion_threshold, downsample_factor=downsample_factor, kernel_size=kernel_size, denoise_sigma=denoise_sigma)
+                result = self.__contact3d.compute_foe(self.__vnormal)
-                result = self.contact3d.compute_foe(self.vnormal)
 
-        self.centroid = result['centroid']
+        self.__centroid = result['centroid']
-        self.centroid_found = result['centroid_found']
+        self.__centroid_found = result['centroid_found']
         if 'contour' in result:
-            self.contour = result['contour']
+            self.__contour = result['contour']
-        self.depth_map = result['divergence_map']
+        self.__depth_map = result['divergence_map']
 
-        return result
+        return True
     
 
     # def get_depth(self, denoise_sigma=15):
@@ -295,10 +291,10 @@ class Sensor:
         self.get_img()
         self.compute_deformation()
         return {
-            'img': self.img, 'flow': self.flow,
+            'img': self.__img, 'flow': self.__flow,
-            'normal': self.vnormal, 'shear': self.vshear, 'depth': self.depth,
+            'normal': self.__vnormal, 'shear': self.__vshear, 'depth': self.__depth_map,
-            'fps': self.fps, 'fnormal': self.fnormal,
+            'fps': self._fps, 'fnormal': self.__fnormal,
-            'fshearx': self.fshearx, 'fsheary': self.fsheary, 'ftor': self.ftor,
+            'fshearx': self.__fshearx, 'fsheary': self.__fsheary, 'ftor': self.__ftor,
         }
 
     def read_info(self, *args) -> Tuple:
@@ -315,17 +311,27 @@ class Sensor:
             print("Warning: Call compute_deformation() first.")
         
         mapping = {
-            self.info.VRAW: lambda: self.flow if self.flow is not None else np.array([]),
+            # Return copies of arrays to prevent external modification of internal state
-            self.info.VNORMAL: lambda: self.vnormal if self.vnormal is not None else np.array([]),
+            self.info.VRAW: lambda: self.__flow.copy() if self.__flow is not None else np.array([]),
-            self.info.VSHEAR: lambda: self.vshear if self.vshear is not None else np.array([]),
+            self.info.VNORMAL: lambda: self.__vnormal.copy() if self.__vnormal is not None else np.array([]),
-            self.info.FNORMAL: lambda: self.fnormal,
+            self.info.VSHEAR: lambda: self.__vshear.copy() if self.__vshear is not None else np.array([]),
-            self.info.FSHEARX: lambda: self.fshearx,
+            # Scalar values are safe to return directly
-            self.info.FSHEARY: lambda: self.fsheary,
+            self.info.FNORMAL: lambda: self.__fnormal,
-            self.info.FPS: lambda: self.fps,
+            self.info.FSHEARX: lambda: self.__fshearx,
+            self.info.FSHEARY: lambda: self.__fsheary,
+            self.info.FPS: lambda: self._fps,
             self.info.TIMESTAMP: lambda: time.strftime('%H:%M:%S'),
-            self.info.DEPTH: lambda: self.depth_map if self.depth_map is not None else np.array([]),
+            # Return copies of arrays to prevent external modification
-            self.info.CENTROID: lambda: self.centroid if self.centroid is not None else np.array([]),
+            self.info.DEPTH: lambda: self.__depth_map.copy() if self.__depth_map is not None else np.array([]),
-            self.info.CONTOUR: lambda: self.contour if self.contour is not None else np.array([]),
+            # Centroid: can be tuple, list, or array - return a copy to prevent modification
+            # Return None if centroid is not found or empty
+            self.info.CENTROID: lambda: (
+                np.array(self.__centroid).copy() 
+                if self.__centroid is not None and len(self.__centroid) > 0 
+                else None
+            ),
+            self.info.CONTOUR: lambda: self.__contour.copy() if self.__contour is not None else np.array([]),
+            self.info.IMG: lambda: self.__img.copy() if self.__img is not None else np.array([]),
         }
         
         return tuple(mapping.get(arg, lambda: None)() for arg in args if isinstance(arg, self.info))
@@ -365,8 +371,8 @@ class Sensor:
         self._async_enabled = True
         
         self._capture_thread = Thread(target=self._capture_worker, daemon=True)
-        self._flow_thread = Thread(target=self._flow_worker, daemon=True)
+        self._flow_thread = Thread(target=self.__flow_worker, daemon=True)
-        self._compute_thread = Thread(target=self._hhd_worker, daemon=True)
+        self._compute_thread = Thread(target=self.__hhd_worker, daemon=True)
         self._contact_thread = Thread(target=self._contact_worker, daemon=True)
 
         self._capture_thread.start()
@@ -419,7 +425,7 @@ class Sensor:
     
     def export_config(self, path="./config/exported_config.json"):
         """Return current configuration dict."""
-        configs.export_config(self.config, path)
+        configs.export_config(self._config, path)
         if self.verbose:
             print(f"Config exported to {path}")
         return True
@@ -437,11 +443,11 @@ class Sensor:
         if os.path.exists(self.cal_path):
             print("Loading calibration from", self.cal_path)
             self.stitch_calibration = np.load(self.cal_path, allow_pickle=True)
-            self._stitcher.stitch_calibration = self.stitch_calibration
+            self.__stitcher.stitch_calibration = self.stitch_calibration
-            return self._stitcher.stitch_rt_optimized()
+            return self.__stitcher.stitch_rt_optimized()
         else:
             print("Creating new calibration...")
-            img, self.stitch_calibration = self._stitcher.stitch_cal()
+            img, self.stitch_calibration = self.__stitcher.stitch_cal()
             os.makedirs("./config", exist_ok=True)
             np.save(self.cal_path, self.stitch_calibration)
             print("Calibration saved to", self.cal_path)
@@ -453,30 +459,29 @@ class Sensor:
     
     def _load_config(self):
         """Load sensor configuration from JSON file."""
-        self.config = configs.load_config(self.config_name)
+        self._config = configs.load_config(self.config_name)
-        cam = self.config["camera"]
+        cam = self._config["camera"]
-        self.config_resolution_raw = cam["resolution_raw"]
+        self._config_resolution_raw = cam["resolution_raw"]
-        self.config_fps = cam["fps"]
+        self._config_fps = cam["fps"]
-        self.config_units = cam["units"]
+        self._config_units = cam["units"]
-        self.config_resolution_stitch = cam["resolution_stitch"]
+        self._config_resolution_stitch = cam["resolution_stitch"]
-        # self.config_index = cam["index"]
-        # self.config_pos_default = cam["pos_default"]
 
-        self.config_stitcher = self.config["stitcher"]
+        self.__config_stitcher = self._config["stitcher"]
-        self.config_contact = self.config["contact"]
+        self.__config_contact = self._config["contact"]
 
         if self.verbose:
             print(f"Config: {self.config_name}")
-            print(f"  Resolution: {self.config_resolution_raw} → {self.config_resolution_stitch}")
+            print(f"  Resolution: {self._config_resolution_raw} → {self._config_resolution_stitch}")
-            print(f"  FPS: {self.config_fps}, Units: {self.config_units}")
+            print(f"  FPS: {self._config_fps}, Units: {self._config_units}")
+        return True
 
     def _open_video_source(self, override, box):
         """Initialize video capture and calibration."""
         self.cap = cv2.VideoCapture(self.vid_src)
         self.cap.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter.fourcc('M', 'J', 'P', 'G'))
-        self.cap.set(cv2.CAP_PROP_FPS, self.config_fps)
+        self.cap.set(cv2.CAP_PROP_FPS, self._config_fps)
-        self.cap.set(cv2.CAP_PROP_FRAME_WIDTH, self.config_resolution_raw[0])
+        self.cap.set(cv2.CAP_PROP_FRAME_WIDTH, self._config_resolution_raw[0])
-        self.cap.set(cv2.CAP_PROP_FRAME_HEIGHT, self.config_resolution_raw[1])
+        self.cap.set(cv2.CAP_PROP_FRAME_HEIGHT, self._config_resolution_raw[1])
         
         if self.verbose:
             print("Connecting to video source...")
@@ -493,8 +498,8 @@ class Sensor:
 
         if override:
             if box is not None:
-                self._stitcher.isstitch = self.isstitch = False
+                self.__stitcher.isstitch = self.isstitch = False
-                self._stitcher.boxes = box
+                self.__stitcher.boxes = box
             else:
                 print("Override=True but no box provided.")
         else:
@@ -507,21 +512,21 @@ class Sensor:
         n_boxes, v_mean0, timeout = 0, 0, 5
         start_time = cv2.getTickCount()
         
-        while n_boxes < self.config_units:
+        while n_boxes < self._config_units:
             ret, frame = self.cap.read()
             v_mean = np.sum(cv2.mean(frame))
             
             if abs(v_mean - v_mean0) < 5:
-                n_boxes = self._stitcher.find_stitch_boxes(frame, show=show)
+                n_boxes = self.__stitcher.find_stitch_boxes(frame, show=show)
             
             if (cv2.getTickCount() - start_time) / cv2.getTickFrequency() > timeout:
                 print("Timeout finding vision units.")
-                self._stitcher.isstitch = self.isstitch = False
+                self.__stitcher.isstitch = self.isstitch = False
                 break
             v_mean0 = v_mean
         
-        self.n_boxes = n_boxes
+        self.__n_boxes = n_boxes
-        self.stitch_valid = self.isstitch and n_boxes == self.config_units
+        self.stitch_valid = self.isstitch and n_boxes == self._config_units
         
         status = "valid for stitching" if self.stitch_valid else "non-stitch mode"
         print(f"Sensor: {status}")
@@ -529,8 +534,8 @@ class Sensor:
 
     def _extract_tiles(self, img_src):
         """Extract tiles from source image."""
-        self._stitcher.get_stitch_unit(img_src)
+        self.__stitcher.get_stitch_unit(img_src)
-        self._stitcher.extract_tiles()
+        self.__stitcher.extract_tiles()
         return True
 
     # =========================================================================
@@ -554,15 +559,15 @@ class Sensor:
                     continue
                 
                 gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-                self._stitcher.get_stitch_unit(gray)
+                self.__stitcher.get_stitch_unit(gray)
                 self._extract_tiles(gray)
-                img = np.ascontiguousarray(self._stitcher.stitch_rt_optimized())
+                img = np.ascontiguousarray(self.__stitcher.stitch_rt_optimized())
-                img = cv2.resize(img, tuple(self.config_resolution_stitch), interpolation=cv2.INTER_CUBIC)
+                img = cv2.resize(img, tuple(self._config_resolution_stitch), interpolation=cv2.INTER_CUBIC)
                 
-                if self.img_filtered is None:
+                if self.__img_filtered is None:
-                    self.img_filtered = img.astype(np.float32)
+                    self.__img_filtered = img.astype(np.float32)
                 else:
-                    self.img_filtered = alpha * img + (1 - alpha) * self.img_filtered
+                    self.__img_filtered = alpha * img + (1 - alpha) * self.__img_filtered
                 
                 self._queue_put(self._capture_queue, self.img_filtered.astype(np.uint8))
             except Exception as e: