From fdc4ffd25fc3b0bc9a2587b65dab8fd5777207cc Mon Sep 17 00:00:00 2001
From: ykcheungab <ykcheungab@connect.ust.hk>
Date: Fri, 17 Apr 2026 09:58:30 +0800
Subject: [PATCH] 0.3.1 stable release

---
 Readme.md                         |  10 ++-
 build_nuitka.py                   |  15 +++-
 config/ddjx01.json                |   2 +-
 doc/get_started.docx              | Bin 21170 -> 14952 bytes
 doc/get_started.md                |   2 +-
 example1_basic_pipeline.py        |  82 ++++++++++----------
 example2_two_sensor_pipeline.py   | 125 ++++++++++++++++--------------
 example3_plot_data.py             | 125 +++++++++++++++++-------------
 src/orisys/configs/ddjx01.json    |  17 ++--
 src/orisys/configs/prototype.json |  39 ----------
 src/orisys/sensor.py              |   2 +-
 11 files changed, 208 insertions(+), 211 deletions(-)
 delete mode 100644 src/orisys/configs/prototype.json

diff --git a/Readme.md b/Readme.md
index b731d13..665bc80 100644
--- a/Readme.md
+++ b/Readme.md
@@ -73,7 +73,7 @@ conda install conda-forge::pyvkfft
 
 * **vid_src** (`int | str`, 必填): 传感器 ID、相机编号或视频路径。默认为 `0`。  
 * **config_name** (`str`，可选): 传感器配置`json`文件路径，用于加载对应的参数配置，默认为 `./config/ddjx01`。  
-* **cal_path** (`str`，可选): 复眼拼接校准文件路径，默认为 `./config/cal.npy`。有多个传感器同时运行时建议传感器类创建时建议填写，避免校准文件覆写。在首次运行时程序会在找不到文件时自动创建。  
+* * **cal_path** (`str`，可选，已弃用): 该参数已弃用，当前版本中不再使用，传入后也会被忽略。为兼容旧代码暂时保留。: 复眼拼接校准文件路径，默认为 `./config/cal.npy`。有多个传感器同时运行时建议传感器类创建时建议填写，避免校准文件覆写。在首次运行时程序会在找不到文件时自动创建。  
 * **backend** (`str`，可选): 光流场解耦计算后端，默认为 `auto`，可选`cupy`,`opencl`,`cpu`。  
 * **verbose** (`bool`，可选): 是否输出初始化及运行过程中的详细日志信息，默认为 `False`。  
 
@@ -99,12 +99,12 @@ sensor = Sensor("/data/data.mp4")
 
 采集并预处理一帧图像并存到`Sensor` 类别中。
 
+
 ### 返回
-* `np.ndarray`：处理后的BGR格式图像(`uint8`，尺寸为json文件中`resolution_stitch`对应分辨率)。
-* `None`：当输入源读取失败（例如视频结束、摄像头断开）时返回。
+* `bool`：是否成功读取图像。
 
 ### 状态更新
-* 成功时更新`sensor`类里面的图像。
+* 成功时更新`sensor`类里面的`sensor.img`。处理后的BGR格式图像(`uint8`，尺寸为json文件中`resolution_stitch`对应分辨率)。
 ### 示例
 
 ```python
@@ -123,6 +123,8 @@ sensor.get_img()
 ### 输入参数
 * **check_motion**(`bool`，可选): 为 `True` 时先根据光流判断是否有运动，有运动才做 HHD 分解；为 `False` 时每帧都做 HHD。默认 `True`。
 * **threshold**(`float`，可选): **check_motion** 功能的阈值，默认为`6`。
+* **tail_frames** (`int`，可选):  光流变化低于阈值后继续解算多少帧，确保传感器回复，，默认为 `10`.
+
 ### 返回
 
 * `bool`：是否接触到物体，如果**check_motion**为`False`时返回`None`。
diff --git a/build_nuitka.py b/build_nuitka.py
index 51adb6d..e58c054 100644
--- a/build_nuitka.py
+++ b/build_nuitka.py
@@ -39,6 +39,16 @@ def find_compiled_extension(output_dir: Path) -> Path:
     return sorted(candidates, key=lambda p: len(p.name))[0]
 
 
+def move_wheel_to_sdk(wheel_path: Path, version: str) -> Path:
+    sdk_version_dir = Path("SDK") / version
+    sdk_version_dir.mkdir(parents=True, exist_ok=True)
+    dest_wheel = sdk_version_dir / wheel_path.name
+    if dest_wheel.exists():
+        dest_wheel.unlink()
+    shutil.move(str(wheel_path), str(dest_wheel))
+    return dest_wheel
+
+
 def build():
     install_nuitka()
 
@@ -143,9 +153,10 @@ setup(
     print("\n=== Building Wheel ===")
     subprocess.check_call([sys.executable, "setup.py", "bdist_wheel"], cwd=dist_dir)
 
-    wheels = list((dist_dir / "dist").glob("*.whl"))
+    wheels = sorted((dist_dir / "dist").glob("*.whl"))
     if wheels:
-        print(f"\n[SUCCESS] Wheel created: {wheels[0]}")
+        final_wheel = move_wheel_to_sdk(wheels[0], version)
+        print(f"\n[SUCCESS] Wheel created and moved to: {final_wheel}")
     else:
         print("\n[ERROR] Wheel not found.")
 
diff --git a/config/ddjx01.json b/config/ddjx01.json
index 988ab1e..21b5e96 100644
--- a/config/ddjx01.json
+++ b/config/ddjx01.json
@@ -11,7 +11,7 @@
   },
   "stitcher": {
     "min_area_frac": 0.01,
-    "max_area_frac": 0.04,
+    "max_area_frac": 0.05,
     "max_aspect_ratio": 50,
     "index": [1,2,3,6,7,8,11,12,13],
     "margin": 10,
diff --git a/doc/get_started.docx b/doc/get_started.docx
index 54261174b8075f7c4f0a7d49099cdc5a0503038d..8fad3c33ad5fa988f62e7b212bf50b5a640b10c2 100644
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diff --git a/doc/get_started.md b/doc/get_started.md
index 5ef11c0..053ce13 100644
--- a/doc/get_started.md
+++ b/doc/get_started.md
@@ -1,6 +1,6 @@
 叠动科技软件使用手册  
 版本： 0.3.1  
-更新日期： 2026年3月6日
+更新日期： 2026年4月16日
 
 # 简介
 本文件介绍如何上手和运行叠动视触觉传感器配套的开发套件。本套件是配合叠动视触觉传感器进行图像处理，计算形变、力以及接触信息。它可以从摄像头或视频文件中读取图像，处理后打印和展示处理后信息。  
diff --git a/example1_basic_pipeline.py b/example1_basic_pipeline.py
index d8763c6..8faaa95 100644
--- a/example1_basic_pipeline.py
+++ b/example1_basic_pipeline.py
@@ -1,13 +1,13 @@
 """
-示例 1: 基础用法 - 单传感器同步处理
-
-本示例演示 orisys 的基本使用流程：
-1. 初始化传感器（视频文件或摄像头）
-2. 循环获取图像并计算形变和触碰点
-3. 读取各类传感器数据
-4. 可视化显示
+示例 1：单传感器基础流程
 
+本示例演示 Orisys SDK 的标准处理流程：
+1. 初始化传感器（摄像头或视频文件）
+2. 获取图像并计算形变
+3. 按需计算接触区域
+4. 读取并可视化结果
 
+SDK version: 0.3.1
 """
 import os
 os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0" 
@@ -18,39 +18,38 @@ import argparse
 
 def main():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--video","-v", type=str, default="0", help="Camera ID / Video file path")
-    parser.add_argument("--config","-c", type=str, default="./config/ddjx01.json", help="Configuration file path")
-    parser.add_argument("--cal","-cal", type=str, default="./config/ddjx01.npy", help="Calibration file path")
-    parser.add_argument("--verbose","-verbose", type=bool, default=True, help="Print detailed information")
+    parser.add_argument("--video","-v", type=str, default="0", help="视频源：摄像头编号或视频文件路径")
+    parser.add_argument("--config","-c", type=str, default="./config/ddjx01.json", help="配置文件名称或路径")
+    parser.add_argument("--verbose","-verbose", type=bool, default=True, help="是否输出详细日志")
     args = parser.parse_args()
 
-    # 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）
-    # cal_path为校准文件路径, 每一个设备需要创建/使用一个校准文件
+    # 步骤 1：创建传感器对象（必需）
+    # config_name 可以是内置配置名称，也可以是自定义配置文件路径
+    # cal_path 为标定文件路径；每台设备建议使用独立的标定文件
 
-    # 方式1: 从视频文件读取
+    # 输入既可以是摄像头编号，也可以是视频文件路径
     try:
         input = int(args.video)
         is_camera = True
-        sensor = orisys.Sensor(input, config_name=args.config, cal_path= args.cal, verbose=args.verbose)
+        sensor = orisys.Sensor(input, config_name=args.config, verbose=args.verbose)
     except:
         is_camera = False
-        sensor = orisys.Sensor(args.video, config_name=args.config, cal_path= args.cal, verbose=args.verbose)
+        sensor = orisys.Sensor(args.video, config_name=args.config, verbose=args.verbose)
 
-    print("\n按 'q' 退出程序，按 'r' 重置传感器追踪器")
+    print("\n按键说明：'q' 退出程序，'r' 重置追踪器。")
 
     while True:
-        # 步骤2：信息提取
-        # 步骤 2.1: 获取并拼接图像 (必要步骤)
+        # 步骤 2：数据处理
+        # 步骤 2.1：获取并拼接图像（必需）
         sensor.get_img() 
-        # 步骤 2.2: 计算形变场 (必要步骤)
+        # 步骤 2.2：计算形变场（必需）
         is_touched = sensor.compute_deformation(check_motion=True, threshold=6)
-        # 步骤 2.3: 计算触碰点 
+        # 步骤 2.3：检测到接触后再计算接触区域
         if is_touched:
             sensor.compute_contact()
 
     
-        #步骤 2.4: 读取信息
+        # 步骤 2.4：读取结果
         fps, flow, vnormal, img, contour, centroid, depth_map, fnormal, fshearx, fsheary = sensor.read_info(
             sensor.info.FPS,
             sensor.info.VRAW,
@@ -64,8 +63,8 @@ def main():
             sensor.info.FSHEARY
         )
         
-        #步骤 3: 可视化显示
-        # 步骤 3.1: 绘制形变场
+        # 步骤 3：可视化显示
+        # 步骤 3.1：显示形变矢量场
         arrows = orisys.util.draw_arrows(
             img,
             flow,
@@ -73,40 +72,43 @@ def main():
             grid_spacing=20,
             arrow_scale=1.0
         )
-        cv2.imshow("Deformation", arrows)
+        cv2.imshow("形变矢量场", arrows)
         
-        # 步骤 3.2: 在图像上触碰点和区域
+        # 步骤 3.2：显示接触区域和质心
         image_with_foe = orisys.util.draw_contact(img, contour, centroid)
-        cv2.imshow("Contact Centroid", image_with_foe)
+        cv2.imshow("接触区域与质心", image_with_foe)
 
-        # 步骤 3.3: 绘制深度图
-        # 归一化散度值到0-255范围（使用绝对值，然后归一化）
+        # 步骤 3.3：显示深度图
+        # 将深度值归一化到 0-255 范围
         div_abs = depth_map
         if div_abs.max() > div_abs.min():
             div_normalized = ((div_abs - div_abs.min()) / (div_abs.max() - div_abs.min()) * 255).astype(np.uint8)
         else:
             div_normalized = np.zeros_like(div_abs, dtype=np.uint8)
         
-        # 应用颜色映射（使用JET：蓝色=低散度，红色=高散度）
-        cv2.imshow("Depth Map", cv2.applyColorMap(div_normalized, cv2.COLORMAP_JET))
+        # 应用伪彩色映射
+        cv2.imshow("深度图", cv2.applyColorMap(div_normalized, cv2.COLORMAP_JET))
 
-        #步骤 3.4: 打印资料
-        print(f"fps: {fps}, fnormal: {fnormal}, fshearx: {fshearx}, fsheary: {fsheary}, depth_shape: {depth_map.shape}, sensor.resolution_raw: {sensor.img_size_raw}")
+        # 步骤 3.4：打印关键结果
+        print(
+            f"FPS={fps:.2f}, 法向力={fnormal:.4f}, 切向力X={fshearx:.4f}, "
+            f"切向力Y={fsheary:.4f}, 深度图尺寸={depth_map.shape}, 原始分辨率={sensor.img_size_raw}"
+        )
             
-        #步骤 3.5: 键盘控制
+        # 步骤 3.5：键盘控制
         key = cv2.waitKey(1) & 0xFF
         if key == ord("q"):
-            print("\n退出程序...")
+            print("\n正在退出示例程序...")
             break
         if key == ord("r"):
-            # 重置光流追踪器（当追踪漂移时使用）
+            # 当光流追踪出现漂移时，可手动重置追踪器
             sensor.reset()
-            print("传感器已重置")
+            print("追踪器已重置。")
         
     # =========================================================================
-    # 4. 清理资源
+    # 4. 释放资源
     # =========================================================================
-    sensor.disconnect()  # 释放视频源和相关资源
+    sensor.disconnect()  # 释放视频源及相关资源
 
 
 if __name__ == '__main__':
diff --git a/example2_two_sensor_pipeline.py b/example2_two_sensor_pipeline.py
index 60e5fd4..0bf7c5a 100644
--- a/example2_two_sensor_pipeline.py
+++ b/example2_two_sensor_pipeline.py
@@ -1,22 +1,24 @@
 """
-示例 4: 多传感器并行处理（多进程模式 - 不使用 MultiSensor）
+示例 2：双传感器并行处理（多进程）
 
 本示例演示如何使用 multiprocessing 同时运行两个传感器：
-- 每个传感器运行在独立的进程中
+- 每个传感器运行在独立进程中
 - 每个进程拥有独立的 CUDA 上下文
-- 使用 multiprocessing.Process 和 Queue 进行进程间通信
+- 通过 Process 和 Queue 在主进程中汇总结果并显示
 
 架构说明：
-    进程 1 (传感器 0):  采集 → 光流 → HHD → 接触检测 → 队列
-    进程 2 (传感器 1):  采集 → 光流 → HHD → 接触检测 → 队列
+    进程 1（传感器 0）：采集 → 光流 → HHD → 接触检测 → 队列
+    进程 2（传感器 1）：采集 → 光流 → HHD → 接触检测 → 队列
                                               ↓
-    主进程:            显示 ← ───────────────┘
+    主进程：              显示 ← ───────────────┘
 
-适用场景：多指触觉传感器、多视角采集、需要 GPU 加速的多路处理
+适用场景：多指触觉传感器、多视角采集，以及需要 GPU 加速的多路处理。
 
 注意事项：
 - Windows 下必须在 if __name__ == '__main__' 中调用 mp.freeze_support()
-- 每个传感器进程会占用独立的 GPU 显存
+- 每个传感器进程都会占用独立的 GPU 显存
+
+SDK version: 0.3.1
 """
 import os
 os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0" 
@@ -26,20 +28,20 @@ import numpy as np
 import multiprocessing as mp
 from multiprocessing import Queue, Process
 import time
+import argparse
 
 
-def sensor_worker(sensor_id, vid_src, config_name, cal_path, result_queue, stop_event, target_fps=None):
+def sensor_worker(sensor_id, vid_src, config_name, result_queue, stop_event, target_fps=None):
     """
-    传感器工作进程函数
+    传感器工作进程。
 
     Args:
         sensor_id: 传感器编号
         vid_src: 视频源（文件路径或摄像头索引）
         config_name: 配置名称
-        cal_path: 标定路径
         result_queue: 结果队列，用于向主进程发送数据
         stop_event: 停止事件，用于通知工作进程退出
-        target_fps: 目标帧率；若设置则用定时器限速，使多传感器 FPS 接近一致
+        target_fps: 目标帧率；设置后将对处理循环限速，使多路传感器 FPS 更接近
     """
     try:
         # 在每个进程中创建独立的传感器实例
@@ -48,27 +50,26 @@ def sensor_worker(sensor_id, vid_src, config_name, cal_path, result_queue, stop_
             isstitch=True,
             cuda=True,
             verbose=False,  # 减少子进程输出
-            config_name=config_name,
-            cal_path=cal_path
+            config_name=config_name
         )
 
-        print(f"传感器 {sensor_id} 已启动" + (f" (target_fps={target_fps})" if target_fps else ""))
+        print(f"传感器 {sensor_id} 已启动" + (f"（目标帧率：{target_fps} FPS）" if target_fps else ""))
 
         frame_interval = 1.0 / target_fps if target_fps and target_fps > 0 else 0.0
 
         while not stop_event.is_set():
             t_start = time.perf_counter()
             try:
-                # 步骤1: 获取图像
+                # 步骤 1：获取图像
                 sensor.get_img()
 
-                # 步骤2: 计算形变场
+                # 步骤 2：计算形变场
                 sensor.compute_deformation()
 
-                # 步骤3: 计算触碰点
+                # 步骤 3：计算接触区域（如需要可取消注释）
                 # sensor.compute_contact()
 
-                # 步骤4: 读取信息
+                # 步骤 4：读取结果
                 fps, flow, vnormal, img, contour, centroid, depth_map = sensor.read_info(
                     sensor.info.FPS,
                     sensor.info.VRAW,
@@ -79,7 +80,7 @@ def sensor_worker(sensor_id, vid_src, config_name, cal_path, result_queue, stop_
                     sensor.info.DEPTH
                 )
 
-                # 步骤5: 准备数据字典（确保所有数据都是可序列化的）
+                # 步骤 5：整理结果字典（确保数据可序列化）
                 data = {
                     'sensor_id': sensor_id,
                     'fps': fps,
@@ -92,100 +93,104 @@ def sensor_worker(sensor_id, vid_src, config_name, cal_path, result_queue, stop_
                     'timestamp': time.time()
                 }
 
-                # 步骤6: 发送数据到主进程（非阻塞）
+                # 步骤 6：将结果发送到主进程（非阻塞）
                 try:
                     result_queue.put_nowait(data)
                 except Exception:
-                    # 如果队列已满，跳过这一帧
+                    # 如果队列已满，则丢弃当前帧，避免阻塞工作进程
                     pass
 
             except Exception as e:
-                print(f"传感器 {sensor_id} 处理错误: {e}")
+                print(f"传感器 {sensor_id} 处理失败：{e}")
                 time.sleep(0.1)
 
-            # 步骤7: 按 target_fps 限速，使多传感器 FPS 接近
+            # 步骤 7：按 target_fps 限速，使多路传感器 FPS 更接近
             if frame_interval > 0:
                 elapsed = time.perf_counter() - t_start
                 sleep_time = frame_interval - elapsed
                 if sleep_time > 0.001:  # 至少 1ms 才 sleep，避免无意义调度
                     time.sleep(sleep_time)
         
-        # 清理资源
+        # 释放资源
         sensor.disconnect()
-        print(f"传感器 {sensor_id} 已停止")
+        print(f"传感器 {sensor_id} 已停止。")
         
     except Exception as e:
-        print(f"传感器 {sensor_id} 初始化失败: {e}")
+        print(f"传感器 {sensor_id} 初始化失败：{e}")
         result_queue.put({'sensor_id': sensor_id, 'error': str(e)})
 
 
 def main():
     # =========================================================================
-    # 1. 配置多个传感器
+    # 1. 配置多路传感器
     # =========================================================================
-    # 目标帧率：设为正数时，两路传感器会按此帧率限速，使 FPS 接近一致；设为 None 则不限速
+    # 目标帧率：设为正数时，两路传感器会按该帧率限速；设为 None 则不限速
+    
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--video1","-v1", type=str, default="0", help="视频文件路径")
+    parser.add_argument("--video2","-v2", type=str, default="0", help="视频文件路径")
+    args = parser.parse_args()
+    
     target_fps = 30
 
     num_sensors = 2
     sensor_configs = [
         {
             'sensor_id': 0,
-            'vid_src': 2,
-            'config_name': './config/prototype_of3_720p.json',
-            'cal_path': './config/ddjx01.npy'
+            'vid_src': args.video1,
+            'config_name': './config/ddjx01.json'
         },  
         {
             'sensor_id': 1,
-            'vid_src': 4,  # 可以改为不同的视频源或摄像头索引
-            'config_name': './config/prototype_of3_720p.json',
-            'cal_path': './config/ddjx01.npy'
+            'vid_src': args.video2,  # 可替换为其他视频源或摄像头索引
+            'config_name': './config/ddjx01.json'
         },
     ]
 
     # =========================================================================
     # 2. 创建进程间通信对象
     # =========================================================================
-    # 增大队列大小以缓冲更多帧，避免因主进程处理延迟导致的丢帧
+    # 适当增大队列容量，以缓冲更多帧并减轻主进程偶发延迟带来的丢帧
     result_queues = [Queue(maxsize=30) for _ in sensor_configs]
     stop_events = [mp.Event() for _ in sensor_configs]
 
     # =========================================================================
-    # 3. 启动传感器工作进程
+    # 3. 启动工作进程
     # =========================================================================
     processes = []
     for i, config in enumerate(sensor_configs):
         p = Process(
             target=sensor_worker,
-            args=(i, config['vid_src'], config['config_name'], config['cal_path'],
+            args=(i, config['vid_src'], config['config_name'],
                   result_queues[i], stop_events[i], target_fps)
         )
         p.start()
         processes.append(p)
     
-    print(f"\n已启动 {len(processes)} 个传感器进程")
-    print("按 'q' 退出程序")
+    print(f"\n已启动 {len(processes)} 个传感器进程。")
+    print("按键说明：'q' 退出程序。")
     
-    # 用于存储每个传感器的最新数据
+    # 保存每个传感器的最新一帧数据
     latest_data = [None] * len(sensor_configs)
     
-    # 用于控制打印频率，减少I/O开销
+    # 控制打印频率，减少 I/O 开销
     print_counter = 0
-    print_interval = 30  # 每30帧打印一次，进一步减少I/O
+    print_interval = 30  # 每 30 帧打印一次
     
-    # 用于控制按键检查频率
+    # 控制按键检查频率
     key_check_counter = 0
-    key_check_interval = 5  # 每5帧检查一次按键
+    key_check_interval = 5  # 每 5 帧检查一次按键
     
     try:
         # =====================================================================
-        # 4. 主循环：从队列获取数据并显示
+        # 4. 主循环：从队列获取结果并显示
         # =====================================================================
         while True:
-            # 从所有队列中快速获取最新数据（高效清空队列，只保留最新帧）
+            # 从所有队列中快速获取最新数据，只保留最后一帧
             for i, queue in enumerate(result_queues):
-                # 快速清空队列，只保留最后一帧
+                # 清空队列，仅保留最后一帧
                 data = None
-                # 一次性获取所有可用数据，只保留最后一个
+                # 读取所有可用数据，只保留最新结果
                 while True:
                     try:
                         data = queue.get_nowait()
@@ -195,14 +200,14 @@ def main():
                 if data is not None:
                     latest_data[i] = data
             
-            # 显示所有传感器的数据
+            # 显示所有传感器的最新结果
             for data in latest_data:
                 if data is None or 'error' in data:
                     continue
                 
                 sid = data['sensor_id']
                 
-                # 显示形变场
+                # 显示形变矢量场
                 if data['img'] is not None and data['flow'] is not None:
                     arrows = orisys.util.draw_arrows(
                         data['img'],
@@ -211,7 +216,7 @@ def main():
                         grid_spacing=10,
                         arrow_scale=1.0
                     )
-                    cv2.imshow(f"Deformation {sid}", arrows)
+                    cv2.imshow(f"传感器 {sid} - 形变矢量场", arrows)
                 
                 # # 显示触碰点和区域
                 # if data['img'] is not None:
@@ -233,33 +238,33 @@ def main():
                 #     cv2.imshow(f"Depth Map {sid}", 
                 #              cv2.applyColorMap(div_normalized, cv2.COLORMAP_JET))
             
-            # 降低打印频率以减少I/O开销（在主循环级别控制）
+            # 降低打印频率，减少主循环中的 I/O 开销
             print_counter += 1
             if print_counter >= print_interval:
                 for data in latest_data:
                     if data is not None and 'error' not in data:
                         sid = data['sensor_id']
-                        print(f"传感器 {sid}: FPS={data['fps']:5.1f}")
+                        print(f"传感器 {sid}：FPS={data['fps']:5.1f}")
                 print_counter = 0
             
-            # 降低按键检查频率以减少开销
+            # 降低按键检查频率，减少额外开销
             key_check_counter += 1
             if key_check_counter >= key_check_interval:
                 key = cv2.waitKey(1) & 0xFF
                 if key == ord('q'):
-                    print("\n退出程序...")
+                    print("\n正在退出示例程序...")
                     break
                 key_check_counter = 0
     
     finally:
         # =====================================================================
-        # 5. 清理资源
+        # 5. 释放资源
         # =====================================================================
         # 通知所有工作进程停止
         for event in stop_events:
             event.set()
         
-        # 等待所有进程结束
+        # 等待所有工作进程结束
         for p in processes:
             p.join(timeout=2)
             if p.is_alive():
@@ -267,10 +272,10 @@ def main():
                 p.join()
         
         cv2.destroyAllWindows()
-        print("所有传感器进程已停止")
+        print("所有传感器进程已停止。")
 
 
 if __name__ == '__main__':
-    # Windows 下多进程必需的初始化
+    # Windows 下运行多进程示例所需的初始化
     mp.freeze_support()
     main()
diff --git a/example3_plot_data.py b/example3_plot_data.py
index 1463545..ca28bb5 100644
--- a/example3_plot_data.py
+++ b/example3_plot_data.py
@@ -1,3 +1,15 @@
+"""
+示例 3：实时曲线与幅值图显示
+
+本示例演示如何在读取传感器结果的同时：
+1. 显示形变矢量场
+2. 显示流场幅值热力图
+3. 使用 PyQtGraph 实时绘制法向力与切向力曲线
+4. 按键控制录像开始与结束
+
+SDK version: 0.3.1
+"""
+
 import os
 os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0"
 import cv2
@@ -5,10 +17,14 @@ import orisys
 import numpy as np
 import time
 import argparse
-import pyqtgraph as pg
+try:
+    import pyqtgraph as pg
+except:
+    print("PyQtGraph is not installed. Please install it using 'pip install pyqt5 pyqtgraph'.")
+    exit()
 
 def create_plot():
-    p1 = win.addPlot(title="法相力")
+    p1 = win.addPlot(title="法向力")
     p1.setLabel('bottom', '时间', units='s')
     win.nextRow()
     p2 = win.addPlot(title="切向力 X")
@@ -33,53 +49,53 @@ def update(plot_af, plot_tfx, plot_tfy, data, p1, p2, p3):
 # 深度图
 def draw_magnitude_map(vfield, threshold=3.0, colormap=cv2.COLORMAP_JET) -> np.ndarray:
         """
-        Draw color map of pointwise magnitude of velocity field.
+        绘制流场幅值伪彩色图。
         
         Args:
-            vfield: Velocity field to visualize (2D vector field)
-            threshold: Minimum magnitude to display (below threshold shows as zero/blue)
-            colormap: OpenCV colormap to use (default: COLORMAP_JET)
+            vfield: 需要可视化的二维矢量场
+            threshold: 最小显示阈值；低于阈值的区域显示为低值颜色
+            colormap: OpenCV 颜色映射类型（默认 COLORMAP_JET）
         
         Returns:
-            np.ndarray: 400x400 BGR image with colored magnitude map
+            np.ndarray: 400x400 的 BGR 伪彩图像
         """
         flow_field = vfield
             
-        # Compute magnitude at each point
+        # 计算每个像素点的幅值
         magnitude = np.sqrt(flow_field[:, :, 0]**2 + flow_field[:, :, 1]**2)
         
-        # Smooth by downsampling and interpolating back
+        # 先降采样再插值回原尺寸，使显示更平滑
         downsample_factor = 10
         h, w = magnitude.shape
         magnitude_downsampled = magnitude[::downsample_factor, ::downsample_factor]
         magnitude_smoothed = cv2.resize(magnitude_downsampled, (w, h), interpolation=cv2.INTER_CUBIC)
         
-        # Apply threshold - set below-threshold values to zero
+        # 应用阈值，将低于阈值的区域置零
         magnitude_thresholded = magnitude_smoothed.copy()
         magnitude_thresholded[magnitude_smoothed < threshold] = 0
         
-        # Normalize to 0-255 range
+        # 归一化到 0-255
         mag_min, mag_max = magnitude_thresholded.min(), magnitude_thresholded.max()
         if mag_max > mag_min:
             magnitude_norm = ((magnitude_thresholded - mag_min) / (mag_max - mag_min) * 255).astype(np.uint8)
         else:
             magnitude_norm = np.zeros_like(magnitude_thresholded, dtype=np.uint8)
         
-        # Apply colormap
+        # 应用伪彩色映射
         magnitude_colored = cv2.applyColorMap(magnitude_norm, colormap)
         
-        # Resize to output size
+        # 缩放到固定输出尺寸
         out_img = cv2.resize(magnitude_colored, (400, 400), interpolation=cv2.INTER_CUBIC)
         
         return out_img
 
 # 实时图表
-npoints = 100 # 图表数据长度
+npoints = 100 # 曲线缓冲区长度
 pg.setConfigOption('background', 'w')
 pg.setConfigOption('foreground', 'k')
-app = pg.mkQApp("Plotting Example")
-win = pg.GraphicsLayoutWidget(show=True, title="Basic plotting examples")
-win.setWindowTitle('pyqtgraph example: Plotting')
+app = pg.mkQApp("Orisys Plot Example")
+win = pg.GraphicsLayoutWidget(show=True, title="Orisys 实时力曲线")
+win.setWindowTitle('Orisys 实时力曲线')
 p1, p2, p3, plot_af, plot_tfx, plot_tfy = create_plot()
 data = np.zeros([npoints,2,3])
 start_time = None  # Will be set when sensor starts
@@ -91,17 +107,17 @@ for i in range(3):
 def main():
     global start_time
     parser = argparse.ArgumentParser()
-    parser.add_argument("--video","-v", type=str, default="0", help="Camera ID / Video file path")
-    parser.add_argument("--config","-c", type=str, default="./config/ddjx01.json", help="Configuration file path")
-    parser.add_argument("--cal","-cal", type=str, default="./config/ddjx01.npy", help="Calibration file path")
-    parser.add_argument("--verbose","-verbose", type=bool, default=True, help="Print detailed information")
+    parser.add_argument("--video","-v", type=str, default="0", help="视频源：摄像头编号或视频文件路径")
+    parser.add_argument("--config","-c", type=str, default="./config/ddjx01.json", help="配置文件名称或路径")
+    parser.add_argument("--cal","-cal", type=str, default="./config/ddjx01.npy", help="标定文件路径")
+    parser.add_argument("--verbose","-verbose", type=bool, default=True, help="是否输出详细日志")
     args = parser.parse_args()
 
-    # 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）
-    # cal_path为校准文件路径, 每一个设备需要创建/使用一个校准文件
+    # 步骤 1：创建传感器对象（必需）
+    # config_name 可以是内置配置名称，也可以是自定义配置文件路径
+    # cal_path 为标定文件路径；每台设备建议使用独立的标定文件
 
-    # 方式1: 从视频文件读取
+    # 输入既可以是摄像头编号，也可以是视频文件路径
     try:
         input = int(args.video)
         is_camera = True
@@ -109,23 +125,23 @@ def main():
     except:
         is_camera = False
         sensor = orisys.Sensor(args.video, config_name=args.config, cal_path= args.cal, verbose=args.verbose)
-    # 打开视频
-    # sensor1 = Sensor("./s1.mp4",isstitch=True,cuda = True,verbose = True)
-   # 方式1: 从视频文件读取
+    # 下面保留了历史示例写法，便于参考
     # sensor1 = orisys.Sensor("./s1.mp4", config_name="./config/ddjx01.json", cal_path= "./config/ddjx01.npy", cuda=False, verbose=True)
 
     start_time = time.time()
     is_save = False
     frame_count = 0
     writer = None
+    save_path = None
+    print("\n按键说明：'q' 退出程序，'s' 开始录像，'e' 结束录像。")
     while True:
-        sensor.get_img() # 提取并拼接复眼图像
+        sensor.get_img() # 获取并拼接复眼图像
         
         if is_save and writer is not None and sensor.frame is not None:
             frame_count += 1
             save_img = sensor.frame
             
-            # Convert grayscale (1 channel) to BGR (3 channels) if needed
+            # 若为灰度图，则先转换为 BGR 再写入视频
             if len(save_img.shape) == 2 or (len(save_img.shape) == 3 and save_img.shape[2] == 1):
                 if len(save_img.shape) == 3:
                     save_img = save_img.squeeze(2)
@@ -133,44 +149,43 @@ def main():
             
             writer.write(save_img)
 
-        sensor.compute_deformation(check_motion=False) # 计算形变和分解
-        # 提取信息：
-        # 向量场: VRAW, VNORMAL, VSHEAR: 原始，法向，切向 
-        # 帧率，时间戳: FPS, TIMESTAMP
-        # 变形量总和: FNORMAL , FSHEARX, FSHEARY : 法向，切向（X)，切向（Y)
+        sensor.compute_deformation(check_motion=False) # 计算形变与分解结果
+        # 读取信息：
+        # 向量场：VRAW、VNORMAL、VSHEAR -> 原始 / 法向 / 切向
+        # 标量：FNORMAL、FSHEARX、FSHEARY -> 法向力 / X 向切向力 / Y 向切向力
         fps, fn, fx, fy, flow, vnormal, vshear = sensor.read_info(sensor.info.FPS, sensor.info.FNORMAL, sensor.info.FSHEARX, sensor.info.FSHEARY, sensor.info.VRAW, sensor.info.VNORMAL,sensor.info.VSHEAR)
-        print(fps,fn,fx,fy)
-        # 显示向量场
+        print(f"FPS={fps:.2f}, 法向力={fn:.4f}, 切向力X={fx:.4f}, 切向力Y={fy:.4f}")
+        # 显示形变矢量场
         arrows  = orisys.util.draw_arrows(sensor.img, flow, threshold=5, grid_spacing=10, arrow_scale=1.0) 
-        cv2.imshow("Deformation", arrows)
-        # 显示深度图
+        cv2.imshow("形变矢量场", arrows)
+        # 显示流场幅值图
         mag = draw_magnitude_map(flow)
-        cv2.imshow("Magnitude", mag)
+        cv2.imshow("流场幅值图", mag)
 
-        # 显示图表 - 更新时间轴
+        # 更新实时曲线
         current_time = time.time() - start_time
         for i in range(3):
-            data[:-1, 0, i] = data[1:, 0, i]  # shift time data left
-            data[:-1, 1, i] = data[1:, 1, i]  # shift force data left
-        data[-1, 0, :] = current_time  # set current time for all plots
+            data[:-1, 0, i] = data[1:, 0, i]  # 时间轴左移
+            data[:-1, 1, i] = data[1:, 1, i]  # 力数据左移
+        data[-1, 0, :] = current_time  # 写入当前时间
         data[-1, 1, 0] = fn
         data[-1, 1, 1] = fx
         data[-1, 1, 2] = fy
-        update(plot_af, plot_tfx, plot_tfy, data, p1, p2, p3) # Update plot
+        update(plot_af, plot_tfx, plot_tfy, data, p1, p2, p3) # 更新曲线
 
         key = cv2.waitKey(1)
         if key & 0xFF == ord("q"):
-            print("\n 停止程序")
+            print("\n正在退出示例程序...")
             break
         if key == ord("s"):
             save_path = f"./data/pulse_test.mp4"
-            # Ensure directory exists
+            # 确保输出目录存在
             os.makedirs(os.path.dirname(save_path), exist_ok=True)
-            # Get frame dimensions from the actual frame
+            # 从当前帧获取视频尺寸
             save_img = sensor.frame
 
             if save_img is not None:
-                # Convert grayscale (1 channel) to BGR (3 channels) if needed for dimension check
+                # 如为灰度图，先转换为 BGR 以获取视频尺寸
                 temp_img = save_img.copy()
                 if len(temp_img.shape) == 2 or (len(temp_img.shape) == 3 and temp_img.shape[2] == 1):
                     if len(temp_img.shape) == 3:
@@ -181,24 +196,24 @@ def main():
                 writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), 30, (frame_width, frame_height))
                 if writer.isOpened():
                     is_save = True
-                    print(f"Start writing to {save_path}")
+                    print(f"开始录像：{save_path}")
                 else:
-                    print(f"Failed to open video writer for {save_path}")
+                    print(f"无法创建录像文件：{save_path}")
                     writer = None
             else:
-                print("No frame available to determine video dimensions")
+                print("当前没有可用帧，无法确定录像尺寸。")
         if key == ord("e"):
             is_save = False
             if writer is not None:
                 writer.release()
                 writer = None
             if save_path is not None:
-                print(f"Stop writing to {save_path}, total frames: {frame_count}")
+                print(f"录像结束：{save_path}，总帧数：{frame_count}")
             else:
-                print(f"Stop writing, total frames: {frame_count}")
+                print(f"录像结束，总帧数：{frame_count}")
             frame_count = 0
     
-    sensor.disconnect() # 断开传感器连接
+    sensor.disconnect() # 断开传感器连接并释放资源
     
 
 
diff --git a/src/orisys/configs/ddjx01.json b/src/orisys/configs/ddjx01.json
index dc1e174..ac8eba6 100644
--- a/src/orisys/configs/ddjx01.json
+++ b/src/orisys/configs/ddjx01.json
@@ -1,6 +1,6 @@
 {
   "name": "prototype",
-  "version": "1.0",
+  "version": "0.3.1",
   "camera": {
     "resolution_raw": [1920, 1080],
     "fps": 30,
@@ -11,14 +11,14 @@
   },
   "stitcher": {
     "min_area_frac": 0.01,
-    "max_area_frac": 0.04,
-    "max_aspect_ratio": 100,
+    "max_area_frac": 0.05,
+    "max_aspect_ratio": 50,
     "index": [1,2,3,6,7,8,11,12,13],
     "margin": 10,
     "nominal_ov_pos": 100,
     "n": [3,3],
     "row_tol": 0.6,
-    "box_size": [220, 220]
+    "box_size": [250, 250]
   },
   "tracker": {
     "adaptive": true,
@@ -28,15 +28,16 @@
     "algorithm": "dis",
     "dis_preset": "medium",
     "dis_mode": 3,
-    "algorithm_list": ["raft", "dis"]
+    "algorithm_list": ["raft", "dis"],
+    "hhd_downsample": 3
   },
   "contact": {
     "min_contour_area": 3000,
-    "flow_threshold":1,
-    "div_threshold":0.02,
+    "flow_threshold":5,
+    "div_threshold":0.05,
     "expansion_threshold": 0.01,
     "kernel_size": [5,5],
-    "denoise_sigma": 15
+    "denoise_sigma": 12
   }
 }
 
diff --git a/src/orisys/configs/prototype.json b/src/orisys/configs/prototype.json
deleted file mode 100644
index b4aecb7..0000000
--- a/src/orisys/configs/prototype.json
+++ /dev/null
@@ -1,39 +0,0 @@
-{
-  "name": "prototype",
-  "version": "1.0",
-  "camera": {
-    "resolution_raw": [1920, 1080],
-    "fps": 30,
-    "units": 15,
-    "units_used": 9, 
-    "resolution_stitch": [400, 400]
-  },
-  "stitcher": {
-    "min_area_frac": 0.01,
-    "max_area_frac": 0.04,
-    "max_aspect_ratio": 100,
-    "index": [1,2,3,6,7,8,11,12,13],
-    "margin": 10,
-    "nominal_ov_pos": 100,
-    "n": [3,3],
-    "row_tol": 0.6
-  },
-  "tracker": {
-    "adaptive": true,
-    "lower_threshold": 12.0,
-    "upper_threshold": 18.0,
-    "loop_closure_interval": 1,
-    "dis_preset": "medium",
-    "finest_scale": 3
-  },
-  "contact": {
-    "min_contour_area": 3000,
-    "flow_threshold":1,
-    "div_threshold":0.02,
-    "expansion_threshold": 0.01,
-    "kernel_size": [5,5],
-    "denoise_sigma": 15
-  }
-}
-
-
diff --git a/src/orisys/sensor.py b/src/orisys/sensor.py
index 04fc6d6..577d223 100644
--- a/src/orisys/sensor.py
+++ b/src/orisys/sensor.py
@@ -282,7 +282,7 @@ class Sensor:
                     self.compute_hhd()
                     return True
                 return False
-
+        else:
             return None
         # return True