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