add script and data for debug speed

benchmark.py

@@ -0,0 +1,113 @@
+
+import os
+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）
+    # 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
+
+    # 方式1: 从视频文件读取
+    sensor = orisys.Sensor(0, config_name="./config/prototype_of3.json", cal_path= "./config/dd01a_0.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="./config/prototype_of3.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")
+
+
+    while True:
+        # 步骤2：信息提取
+        # 步骤 2.1: 获取并拼接图像 (必要步骤)
+        start_time = time.perf_counter()
+        sensor.get_img() 
+        end_time = time.perf_counter()
+        get_img_time = end_time - start_time
+        # if sensor.detect_motion(threshold=2):
+        #     print("motion detected")
+        # 步骤 2.2: 计算形变场 (必要步骤)
+        start_time = time.perf_counter()
+        sensor.compute_deformation()
+        end_time = time.perf_counter()
+        compute_deformation_time = end_time - start_time
+        # 步骤 2.3: 计算触碰点 
+        start_time = time.perf_counter()
+        sensor.compute_contact()
+        end_time = time.perf_counter()
+        compute_contact_time = end_time - start_time
+
+    
+        #步骤 2.4: 读取信息
+        start_time = time.perf_counter()
+        fps, flow, vnormal, img, contour, centroid, depth_map = sensor.read_info(
+            sensor.info.FPS,
+            sensor.info.VRAW,
+            sensor.info.VNORMAL,
+            sensor.info.IMG,
+            sensor.info.CONTOUR,
+            sensor.info.CENTROID,
+            sensor.info.DEPTH
+        )
+        end_time = time.perf_counter()
+        read_info_time = end_time - start_time
+        #步骤 3: 可视化显示
+        # 步骤 3.1: 绘制形变场
+        start_time = time.perf_counter()
+        # arrows = orisys.util.draw_arrows(
+        #     img,
+        #     flow,
+        #     threshold=4,
+        #     grid_spacing=40,
+        #     arrow_scale=1.0
+        # )
+        # cv2.imshow("Deformation", arrows)
+        
+        # # 步骤 3.2: 在图像上触碰点和区域
+        # image_with_foe = orisys.util.draw_contact(img, contour, centroid)
+        # cv2.imshow("Contact Centroid", 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）
+        # 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))
+
+        #步骤 3.4: 打印资料
+        # print(f"fps: {fps}")
+        end_time = time.perf_counter()
+        display_time = end_time - start_time
+        
+        # Get detailed timing from sensor (capture vs processing)
+        capture_time = sensor._last_capture_time
+        processing_time = sensor._last_processing_time
+        
+        total_time = get_img_time + compute_deformation_time + compute_contact_time + read_info_time + display_time
+        print(f"get_img: {get_img_time*1000:.2f}ms (capture: {capture_time*1000:.2f}ms, processing: {processing_time*1000:.2f}ms), deformation: {compute_deformation_time*1000:.2f}ms, contact: {compute_contact_time*1000:.2f}ms, read: {read_info_time*1000:.2f}ms, display: {display_time*1000:.2f}ms, total: {total_time*1000:.2f}ms")
+            
+        #步骤 3.5: 键盘控制
+        key = cv2.waitKey(1) & 0xFF
+        if key == ord("q"):
+            print("\n退出程序...")
+            break
+        if key == ord("r"):
+            # 重置光流追踪器（当追踪漂移时使用）
+            sensor.reset()
+            print("传感器已重置")
+        
+    # =========================================================================
+    # 4. 清理资源
+    # =========================================================================
+    sensor.disconnect()  # 释放视频源和相关资源
+
+
+if __name__ == '__main__':
+    main()

src/orisys/sensor.py

@@ -121,6 +121,8 @@ class Sensor:
         # Timing
         self.last_log_time = time.perf_counter()
         self._fps = 0
+        self._last_capture_time = 0.0
+        self._last_processing_time = 0.0
         
         # Async pipeline state
         # self._async_enabled = False
@@ -161,10 +163,20 @@ class Sensor:
         Returns:
             np.ndarray: Processed grayscale image
         """
+        # Simple direct read - buffer size of 1 should minimize frame accumulation
+        # Timing measurement helps identify if capture or processing is the bottleneck
+        t_capture_start = time.perf_counter()
         ret, frame = self.cap.read()
+        t_capture_end = time.perf_counter()
+        self._last_capture_time = t_capture_end - t_capture_start
+        
         if not ret:
             print("Failed to get_img from video source.")
             return None
+        
+        # Start processing timing
+        t_processing_start = time.perf_counter()
+        
         self.frame = frame.copy()
 
         # Extract and stitch tiles
@@ -181,7 +193,9 @@ class Sensor:
             img = cv2.flip(img, 1)
 
         self.img = img.astype(np.uint8)
-
+        
+        t_processing_end = time.perf_counter()
+        self._last_processing_time = t_processing_end - t_processing_start
 
         # FPS calculation
         self._fps = 1.0 / (time.perf_counter() - self.last_log_time)
@@ -414,9 +428,13 @@ class Sensor:
 
         self.cap = cv2.VideoCapture(self.vid_src)        
         # Set buffer size to 1 to reduce latency and avoid frame accumulation
-        self.cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)
+        # Note: Not all backends support this, but it helps when available
+        try:
+            self.cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)
+        except:
+            pass  # Some backends don't support this property
         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_HEIGHT, self._config_resolution_raw[1])