update foe to contact for easy understanding

src/orisys/get_depth.py

@@ -41,6 +41,7 @@ def compute_foe(flow_field, min_magnitude=1.0, expansion_threshold=1, downsample
         # 降噪：在计算散度之后对散度图进行平滑
         divergence = cv2.GaussianBlur(divergence, (0, 0), denoise_sigma)
     
+    divergence = np.where(divergence>0.02, divergence, 0)
     # 创建掩码
     valid_mask = magnitude > min_magnitude
     expansion_mask = (valid_mask & (divergence > expansion_threshold))
@@ -130,31 +131,3 @@ def compute_multiple_foe(flow_field, min_magnitude=1.0, expansion_threshold=0.00
                     result['expansion_regions'].append(contour)
     
     return result
-
-def smooth_magnitude(mag, method='moving_average', window_size=5, downsample_factor=4):
-    """
-    平滑幅度图以减少梯度计算时的伪影。
-    
-    Args:
-        mag: 幅度图，形状为 (H, W) 的 numpy 数组
-        method: 平滑方法，'moving_average' 或 'windowed_interpolation'
-        window_size: 移动平均的窗口大小（默认5）
-        downsample_factor: 窗口插值的下采样因子（默认4）
-        
-    Returns:
-        np.ndarray: 平滑后的幅度图
-    """
-    if method == 'moving_average':
-        # 使用移动平均滤波器（box filter）
-        kernel = np.ones((window_size, window_size), dtype=np.float32) / (window_size * window_size)
-        mag_smooth = cv2.filter2D(mag, -1, kernel)
-    elif method == 'windowed_interpolation':
-        # 使用窗口插值：下采样然后插值回原尺寸
-        h, w = mag.shape
-        mag_downsampled = mag[::downsample_factor, ::downsample_factor]
-        mag_smooth = cv2.resize(mag_downsampled, (w, h), interpolation=cv2.INTER_CUBIC)
-    else:
-        # 默认使用高斯滤波
-        mag_smooth = gaussian_filter(mag, sigma=1.5)
-    
-    return mag_smooth

src/orisys/sensor.py

@@ -52,7 +52,7 @@ class Sensor:
     # =========================================================================
     
     def __init__(self, vid_src, isstitch=False, verbose=True, cuda=True, 
-                 config_name="prototype", cal_path=None, override=False, box=None):
+                 config_name="prototype", cal_path=None, override=False, box=None,mirror=True):
         """
         Initialize Sensor with video source and configuration.
         
@@ -83,6 +83,7 @@ class Sensor:
         self.verbose = verbose
         self.config_name = config_name
         self.cal_path = cal_path
+        self.mirror = mirror
 
         # State variables
         self.stitch_valid = False
@@ -183,6 +184,8 @@ class Sensor:
         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)
             
         return self.img
 
@@ -241,13 +244,16 @@ class Sensor:
 
         return self.flow
 
-    def get_foe(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 get_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:
             return 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)
         else:
             return compute_foe(self.vnormal, min_magnitude=min_magnitude, expansion_threshold=expansion_threshold, downsample_factor=downsample_factor, kernel_size=kernel_size, denoise_sigma=denoise_sigma)
 
+    def get_depth(self, denoise_sigma=15):
+        return getFilteredDensity(self.vnormal, use_cuda=self.cuda)
+        self.depth = getFilteredDensity(self.vnormal, use_cuda=self.cuda)   
     def update(self):
         """
         Convenience method: get_img + compute_deformation in one call.