remove depth and fix normal -> vnormal

test_foe_depth.py

@@ -204,11 +204,11 @@ def estimate_depth(foe=None, flow=None, init_depth=None, min_radial_flow=0.01, T
     
     # 处理FOE附近区域：使用平滑后的值
     if np.any(mask_foe_region):
-        depth_smoothed = gaussian_filter(depth, sigma=2.0)
+        depth_smoothed = gaussian_filter(depth, sigma=15)
         depth[mask_foe_region] = depth_smoothed[mask_foe_region]
     
     # 应用高斯滤波平滑整个深度图，减少噪声和伪影
-    depth = gaussian_filter(depth, sigma=1.5)
+    depth = gaussian_filter(depth, sigma=15)
     
     # 缩放深度（如果提供了初始深度，只使用掩码内的值）
     if init_depth is not None:
@@ -248,7 +248,7 @@ def estimate_depth(foe=None, flow=None, init_depth=None, min_radial_flow=0.01, T
         depth = np.clip(depth, p1, p99)
     
     # 最终平滑：对FOE附近区域再次应用平滑，确保伪影被消除
-    depth_final_smooth = gaussian_filter(depth, sigma=1.0)
+    depth_final_smooth = gaussian_filter(depth, sigma=15)
     
     # 重新计算FOE附近掩码
     rx = xx - fx
@@ -321,7 +321,7 @@ def main():
 
         # 计算扩张焦点
         # foe_result = orisys.util.compute_foe(vnormal,min_magnitude=1,expansion_threshold=0.01,downsample_factor=1)
-        foe_result = sensor.get_foe(multiple_foe=False, min_magnitude=1, expansion_threshold=0.01, downsample_factor=1)
+        foe_result = sensor.get_foe(multiple_foe=False, min_magnitude=1, expansion_threshold=0.01, downsample_factor=1,denoise_sigma=15)
         # 在图像上绘制焦点和区域
         image_with_foe = orisys.util.draw_foe(sensor.img.copy(), foe_result)
         cv2.imshow("Focus of Expansion", image_with_foe)