update example and coomputation dependency error handling

example1_basic.py

@@ -1,112 +0,0 @@
-"""
-示例 1: 基础用法 - 单传感器同步处理
-
-本示例演示 OrisysSensor 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
-3. 读取各类传感器数据
-4. 可视化显示
-
-适用场景：单传感器、简单应用、学习入门
-"""
-
-import os
-os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0"  # Windows 下禁用硬件加速，避免兼容性问题
-
-import cv2
-import orisys
-
-
-def main():
-    # =========================================================================
-    # 1. 初始化传感器
-    # =========================================================================
-    # 参数说明：
-    #   vid_src:  视频源，可以是文件路径（如 "./s1.mp4"）或摄像头索引（如 0）
-    #   isstitch: 是否启用复眼拼接模式（多单元传感器需要开启）
-    #   cuda:     是否使用 CUDA GPU 加速（需要 CuPy 和 CUDA 支持）
-    #   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=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)
-
-    print("\n按 'q' 退出程序，按 'r' 重置光流追踪器")
-
-    # =========================================================================
-    # 2. 主循环：获取图像 → 计算形变 → 读取数据 → 显示
-    # =========================================================================
-    while True:
-        # 步骤 2.1: 获取并拼接图像
-        sensor.get_img()
-        
-        # 步骤 2.2: 计算形变场
-        sensor.compute_deformation()
-        
-        # 步骤 2.3: 读取传感器数据
-        # read_info() 支持以下数据类型：
-        #   向量场数据：
-        #     - VRAW:    原始光流场 (H, W, 2) 的 numpy 数组
-        #     - VNORMAL: 法向分量场
-        #     - VSHEAR:  切向分量场
-        #   标量数据：
-        #     - FNORMAL: 法向力总和（正压力）
-        #     - FSHEARX: X方向切向力
-        #     - FSHEARY: Y方向切向力
-        #   其他：
-        #     - FPS:       当前帧率
-        #     - TIMESTAMP: 时间戳字符串
-        fps, fn, fx, fy, flow = sensor.read_info(
-            sensor.info.FPS,
-            sensor.info.FNORMAL,
-            sensor.info.FSHEARX,
-            sensor.info.FSHEARY,
-            sensor.info.VRAW
-        )
-        
-        # 打印力的数值
-        print(f"FPS: {fps:5.1f}, 法向力: {fn:8.2f}, 切向力X: {fx:8.2f}, 切向力Y: {fy:8.2f}")
-        
-        # =====================================================================
-        # 3. 可视化显示
-        # =====================================================================
-        # 绘制光流箭头图
-        # 参数说明：
-        #   img:          背景图像
-        #   flow:         光流场
-        #   threshold:    显示阈值（小于此值的箭头不显示）
-        #   grid_spacing: 箭头网格间距（像素）
-        #   arrow_scale:  箭头缩放比例
-        arrows = orisys.util.draw_arrows(
-            sensor.img.copy(),  # 使用 copy() 避免修改原图
-            flow,
-            threshold=5,
-            grid_spacing=40,
-            arrow_scale=1.0
-        )
-        cv2.imshow("形变场 Deformation", arrows)
-
-
-        # =====================================================================
-        # 4. 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("光流追踪器已重置")
-    
-    # =========================================================================
-    # 5. 清理资源
-    # =========================================================================
-    sensor.disconnect()  # 释放视频源和相关资源
-    cv2.destroyAllWindows()
-
-
-if __name__ == '__main__':
-    main()

example1_basic_pipeline.py

@@ -1,30 +1,42 @@
+"""
+示例 1: 基础用法 - 单传感器同步处理
+
+本示例演示 OrisysSensor 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
+3. 读取各类传感器数据
+
+适用场景：单传感器、简单应用、学习入门
+"""
 import os
-os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0"  # Windows 下禁用硬件加速，避免兼容性问题
+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", 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,config_name="prototype")
+    sensor = orisys.Sensor(0, cuda=True, verbose=True, config_name="prototype")
 
     print("\n按 'q' 退出程序，按 'r' 重置光流追踪器")
 
+
     while True:
-        # 步骤 2.1: 获取并拼接图像
+        # 步骤2：信息提取
+        # 步骤 2.1: 获取并拼接图像 (必要步骤)
         sensor.get_img() 
         
-        # 步骤 2.2: 计算形变场
+        # 步骤 2.2: 计算形变场 (必要步骤)
         sensor.compute_deformation()
-        # 计算触碰点
+        # 步骤 2.3: 计算触碰点 
-        # sensor.compute_contact(min_magnitude=0.1,expansion_threshold=0.01,denoise_sigma=15)
         sensor.compute_contact()
         
-        #计算后读取信息
+        #步骤 2.4: 读取信息
         fps, flow, vnormal, img, contour, centroid, depth_map = sensor.read_info(
             sensor.info.FPS,
             sensor.info.VRAW,
@@ -35,19 +47,22 @@ def main():
             sensor.info.DEPTH
         )
         
+        #步骤 3: 可视化显示
+        # 步骤 3.1: 绘制形变场
         arrows = orisys.util.draw_arrows(
-            img,  # 使用 copy() 避免修改原图
+            img,
-            vnormal,
+            flow,
             threshold=5,
             grid_spacing=10,
             arrow_scale=1.0
         )
         cv2.imshow("形变场 Deformation", arrows)
         
-        # 在图像上触碰点和区域
+        # 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():
@@ -58,12 +73,10 @@ def main():
         # 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}")
         
-
+        #步骤 3.5: 键盘控制
-        # =====================================================================
-        # 4. 键盘控制
-        # =====================================================================
         key = cv2.waitKey(1) & 0xFF
         if key == ord("q"):
             print("\n退出程序...")
@@ -74,10 +87,9 @@ def main():
             print("光流追踪器已重置")
     
     # =========================================================================
-    # 5. 清理资源
+    # 4. 清理资源
     # =========================================================================
     sensor.disconnect()  # 释放视频源和相关资源
-    cv2.destroyAllWindows()
 
 
 if __name__ == '__main__':

example3_manual_box.py

@@ -1,45 +0,0 @@
-import os
-os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0"
-import cv2
-import orisys
-
-def main():
-    global start_time
-    
-    box = []
-    dx = 90
-    dy = -95
-    for i in range(15):
-        box.append((410+dx, 900+dy, 620+dx, 1110+dy))
-    # 打开视频，手动设置box 的坐标
-    sensor1 = orisys.Sensor("./s1.mp4",isstitch=False,cuda = True,verbose = True, override=True, box=box)
-    # sensor1 = orisys.Sensor("./uv_2.mp4",isstitch=True,cuda = True,verbose = True, override=True, box=box)
-    # 打开摄像头
-    # sensor1 = Sensor(0,isstitch=True,cuda = True,verbose = True)
-
-
-    while True:
-        sensor1.get_img() # 提取并拼接复眼图像
-        sensor1.compute_deformation() # 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)
-        fps, fn, fx, fy, flow = sensor1.read_info(sensor1.info.FPS, sensor1.info.FNORMAL, sensor1.info.FSHEARX, sensor1.info.FSHEARY, sensor1.info.VRAW)
-        print(fps,fn,fx,fy)
-        # 显示向量场
-        arrows = orisys.util.draw_arrows(sensor1.img, flow, threshold=5, grid_spacing=40, arrow_scale=1.0)
-        cv2.imshow("Deformation", arrows)
-
-        key = cv2.waitKey(1)
-        if key & 0xFF == ord("q"):
-            print("\n 停止程序")
-            break
-        if key & 0xFF == ord("r"):
-            sensor1.reset()
-    
-    sensor1.disconnect() # 断开传感器连接
-    
-
-if __name__ == '__main__':
-    main()

src/orisys/Stitcher.py

@@ -28,8 +28,8 @@ class Stitcher:
         self.margin=config_stitcher["margin"]
         self.bound=self.nominal_ov_pos - self.margin
 
-        # for sensor 1
+        if self.verbose:
-        self.pos_default = config_stitcher["pos_default"]
+            print(f"Stitcher initialized with config: {self.config_stitcher}")
  
     def stitch_rt_optimized(self, change_sv=False, s_mean=28., v_mean=180.):
         tile_list = self.tile_list

src/orisys/configs/prototype.json

@@ -11,19 +11,24 @@
   "stitcher": {
     "min_area_frac": 0.018,
     "max_area_frac": 0.035,
-    "max_aspect_ratio": 20,
+    "max_aspect_ratio": 100,
     "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,
     "n": [3,3]
   },
+  "tracker": {
+    "adaptive": true,
+    "lower_threshold": 12.0,
+    "upper_threshold": 18.0,
+    "loop_closure_interval": 1,
+    "dis_preset": "medium"
+  },
   "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
   }

src/orisys/configs/sample_1.json

@@ -19,7 +19,7 @@
       "min_contour_area": 3000,
       "min_magnitude": 1.0,
       "expansion_threshold": 0.01,
-      "downsample_factor": 1,
+      "contact_ds_factor": 10,
       "kernel_size": [5,5],
       "denoise_sigma": 15
     }

src/orisys/get_depth.py

@@ -19,7 +19,7 @@ class Contact3D:
         self.config_contact = config_contact
         self.flow_threshold = config_contact["flow_threshold"]
         self.expansion_threshold = config_contact["expansion_threshold"]
-        self.downsample_factor = config_contact["downsample_factor"]
+        self.downsample_factor = 1
         self.kernel_size = config_contact["kernel_size"]
         self.denoise_sigma = config_contact["denoise_sigma"]
         self.min_contour_area = config_contact["min_contour_area"]
@@ -75,7 +75,6 @@ class Contact3D:
         return result
     
     def compute_foe(self, flow_field, return_all_contours=False):
-    # def compute_foe(self, flow_field, min_magnitude=1.0, expansion_threshold=1, downsample_factor=1, return_all_contours=False, kernel_size=(5,5), denoise_sigma=10):
         """
         计算光流场的扩张焦点(Focus of Expansion)。
         

src/orisys/sensor.py

@@ -53,16 +53,16 @@ class Sensor:
     # Initialization
     # =========================================================================
     
-    def __init__(self, vid_src, isstitch=False, verbose=True, cuda=True, 
+    def __init__(self, vid_src, cuda=True, isstitch=True, verbose=True, 
                  config_name="prototype", cal_path=None, override=False, box=None, mirror=True):
         """
         Initialize Sensor with video source and configuration.
         
         Args:
             vid_src: Video source (int for camera ID, str for file path)
+            cuda: Enable CUDA acceleration (default: True)
             isstitch: Enable stitching mode for multi-tile sensor (default: False)
             verbose: Print initialization logs (default: True)
-            cuda: Enable CUDA acceleration (default: True)
             config_name: Configuration name (default: "prototype")
             cal_path: Path to calibration file (default: None)
             override: Skip auto-detection, use provided box (default: False)
@@ -89,9 +89,9 @@ class Sensor:
 
         # State variables
         self.stitch_valid = False
-        self.imgupdate = False
-        self.vupdate = False
         self.stitch_calibration = []
+        self.compute_deformation_valid = False
+        self.compute_contact_valid = False
         
         # Data Variables
         self.__img = None
@@ -193,7 +193,6 @@ class Sensor:
             self.__img_filtered = alpha * img + (1 - alpha) * self.__img_filtered
         
         self.__img = self.__img_filtered.astype(np.uint8)
-        self.__imgupdate = True
 
         if log_timing:
             timings['total'] = (time.perf_counter() - t_start) * 1000
@@ -210,6 +209,8 @@ class Sensor:
         Returns:
             np.ndarray: Optical flow field
         """
+        if self.__img is None:
+            raise Exception("Warning: Get image first")
         if log_timing:
             timings = {}
             t_start = time.perf_counter()
@@ -245,9 +246,7 @@ class Sensor:
         
         # Update state
         self.__flow = flow
-        self.imgupdate = False
+        self.compute_deformation_valid = True
-        self.vupdate = True
-        
         # FPS calculation
         self._fps = 1.0 / (time.perf_counter() - self.last_log_time)
         self.last_log_time = time.perf_counter()
@@ -258,7 +257,12 @@ class Sensor:
 
         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):
+    def compute_contact(self):
+        
+        multiple_foe=False
+
+        if not self.compute_deformation_valid:
+            raise Exception("Warning: Compute deformation first")
 
         if multiple_foe:
             result = self.__contact3d.compute_multiple_foe(self.__vnormal)
@@ -274,6 +278,7 @@ class Sensor:
             self.__contour = result['contour']
         self.__depth_map = result['divergence_map']
 
+        self.compute_contact_valid = True
         return True
     
 
@@ -307,8 +312,17 @@ class Sensor:
         Returns:
             Tuple: Requested data in order
         """
-        if not self.vupdate:
+        if any(arg in [self.info.IMG] for arg in args):
-            print("Warning: Call compute_deformation() first.")
+            if self.__img is None:
+                raise Exception("Warning: Get image first")
+
+        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")
+
+        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")
 
         mapping = {
             # Return copies of arrays to prevent external modification of internal state

src/orisys/util.py

@@ -75,62 +75,6 @@ def draw_arrows(image, optical_flow,fps=True, threshold=5, grid_spacing=40, arro
             cv2.arrowedLine(image, (x_axis[i], y_axis[i]), (end_x[i], end_y[i]), color, thickness=thickness, tipLength=0.3)
     return image
 
-def draw_magnitude_map(image, flow_field, threshold=3.0, colormap=cv2.COLORMAP_JET,downsample_factor=10):
-    """
-    Draw color map of pointwise magnitude of velocity field.
-    
-    Args:
-        info_type: Type of flow field to visualize (VRAW, VNORMAL, VSHEAR, or None for total)
-        threshold: Minimum magnitude to display (below threshold shows as zero/blue)
-        colormap: OpenCV colormap to use (default: COLORMAP_JET)
-    
-    Returns:
-        np.ndarray: 400x400 BGR image with colored magnitude map
-    """
-    
-    # Compute magnitude at each point
-    magnitude = np.sqrt(flow_field[:, :, 0]**2 + flow_field[:, :, 1]**2)
-    
-    # Smooth by downsampling and interpolating back
-    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
-    magnitude_thresholded = magnitude_smoothed.copy()
-    magnitude_thresholded[magnitude_smoothed < threshold] = 0
-    
-    # Normalize to 0-255 range
-    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
-    magnitude_colored = cv2.applyColorMap(magnitude_norm, colormap)
-    
-    return magnitude_colored
-
-def draw_fps(image, fps):
-    """
-    Draw FPS counter on the top left corner of the image.
-    """
-    font_scale=0.5
-    thickness=2 
-    color=(0, 255, 0)
-    image = image.copy()
-    
-    # Draw FPS text
-    fps_text = f"FPS: {fps:.1f}"
-    position = (10, 30)  # Top left corner with margin
-    font = cv2.FONT_HERSHEY_SIMPLEX
-    
-    # Draw FPS text
-    cv2.putText(image, fps_text, position, font, font_scale, color, thickness, cv2.LINE_AA)
-    
-    return image
-
 
 def draw_contact(image, contour, centroid, point_color=(0, 255, 0), region_color=(0, 255, 255)):
     """
@@ -159,60 +103,3 @@ def draw_contact(image, contour, centroid, point_color=(0, 255, 0), region_color
         cv2.circle(image, (cx, cy), 10, point_color, 2)
     
     return image
-
-
-def draw_multiple_foe(image, foe_result, point_color=(0, 255, 0), 
-                       region_color=(0, 255, 255), label_colors=None):
-    """
-    在图像上绘制多个扩张焦点和扩张区域。
-    
-    Args:
-        image: 背景图像
-        foe_result: compute_multiple_foe 函数的返回结果
-        point_color: 默认焦点点的颜色 (BGR)
-        region_color: 扩张区域轮廓的颜色 (BGR)
-        label_colors: 可选的颜色列表，用于不同焦点的标签颜色
-        
-    Returns:
-        np.ndarray: 绘制了多个焦点和区域的图像
-    """
-    image = image.copy()
-    
-    # 默认标签颜色（彩虹色系）
-    if label_colors is None:
-        label_colors = [
-            (255, 0, 0),    # 蓝色
-            (0, 255, 0),    # 绿色
-            (0, 0, 255),    # 红色
-            (255, 255, 0),  # 青色
-            (255, 0, 255),  # 洋红色
-            (0, 255, 255),  # 黄色
-            (255, 128, 0),  # 橙色
-            (128, 0, 255)   # 紫色
-        ]
-    
-    # 绘制扩张区域轮廓
-    if 'expansion_regions' in foe_result and foe_result['expansion_regions']:
-        cv2.drawContours(image, foe_result['expansion_regions'], -1, region_color, 2)
-    
-    # 绘制焦点点并添加标签
-    if 'foe_points' in foe_result:
-        for i, (cx, cy) in enumerate(foe_result['foe_points']):
-            # 选择颜色（循环使用颜色列表）
-            color = label_colors[i % len(label_colors)]
-            
-            # 绘制焦点点
-            cv2.circle(image, (cx, cy), 8, color, -1)
-            cv2.circle(image, (cx, cy), 10, color, 2)
-            
-            # 添加标签
-            label = f"FOE{i+1}"
-            cv2.putText(image, label, (cx + 15, cy), 
-                       cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)
-    
-    # 显示检测到的焦点数量
-    foe_count = len(foe_result.get('foe_points', []))
-    cv2.putText(image, f"FOE Count: {foe_count}", (10, 60), 
-               cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
-    
-    return image