Signed-off-by: bluepulsar <cheungken3@yahoo.com.hk>

example_foe.py

@@ -57,7 +57,7 @@ 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,denoise_sigma=10)
+        foe_result = sensor.get_foe(multiple_foe=False, min_magnitude=1, expansion_threshold=0.01,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)

src/orisys/get_depth.py

@@ -1,8 +1,7 @@
 import numpy as np
 import cv2
-from scipy.ndimage import gaussian_filter
 
-def compute_foe(flow_field, min_magnitude=1.0, expansion_threshold=1, downsample_factor=10, return_all_contours=False, kernel_size=(5,5), denoise_sigma=10):
+def compute_foe(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)。
     
@@ -25,34 +24,22 @@ def compute_foe(flow_field, min_magnitude=1.0, expansion_threshold=1, downsample
         flow_ds = flow_field[::downsample_factor, ::downsample_factor, :]
         u_ds, v_ds = flow_ds[:, :, 0], flow_ds[:, :, 1]
         
-        # 降噪：在计算散度之前对光流场进行平滑
-        if denoise_sigma > 0:
-            u_ds = cv2.GaussianBlur(u_ds, (0, 0), denoise_sigma)
-            v_ds = cv2.GaussianBlur(v_ds, (0, 0), denoise_sigma)
-        
         magnitude_ds = np.sqrt(u_ds**2 + v_ds**2)
+        magnitude_ds = cv2.GaussianBlur(magnitude_ds, (0, 0), denoise_sigma)
         divergence_ds = np.gradient(u_ds, axis=1) + np.gradient(v_ds, axis=0)
-        
-        # 降噪：在计算散度之后对散度图进行平滑
-        if denoise_sigma > 0:
-            divergence_ds = cv2.GaussianBlur(divergence_ds, (0, 0), denoise_sigma)
+        divergence_ds = cv2.GaussianBlur(divergence_ds, (0, 0), denoise_sigma)
         
         magnitude = cv2.resize(magnitude_ds, (W, H), interpolation=cv2.INTER_CUBIC)
         divergence = cv2.resize(divergence_ds, (W, H), interpolation=cv2.INTER_CUBIC)
     else:
         u, v = flow_field[:, :, 0], flow_field[:, :, 1]
         
-        # 降噪：在计算散度之前对光流场进行平滑
-        if denoise_sigma > 0:
-            u = cv2.GaussianBlur(u, (0, 0), denoise_sigma)
-            v = cv2.GaussianBlur(v, (0, 0), denoise_sigma)
-        
         magnitude = np.sqrt(u**2 + v**2)
+        magnitude = cv2.GaussianBlur(magnitude, (0, 0), denoise_sigma)
         divergence = np.gradient(u, axis=1) + np.gradient(v, axis=0)
         
         # 降噪：在计算散度之后对散度图进行平滑
-        if denoise_sigma > 0:
-            divergence = cv2.GaussianBlur(divergence, (0, 0), denoise_sigma)
+        divergence = cv2.GaussianBlur(divergence, (0, 0), denoise_sigma)
     
     # 创建掩码
     valid_mask = magnitude > min_magnitude
@@ -88,7 +75,7 @@ def compute_foe(flow_field, min_magnitude=1.0, expansion_threshold=1, downsample
     return result
 
 def compute_multiple_foe(flow_field, min_magnitude=1.0, expansion_threshold=0.001, 
-                         downsample_factor=10, min_contour_area=3000, denoise_sigma=10, kernel_size=(5,5)):
+                         downsample_factor=1, min_contour_area=3000, denoise_sigma=15, kernel_size=(5,5)):
     """
     计算光流场的多个扩张焦点(Foci of Expansion)。
     

src/orisys/sensor.py

@@ -241,7 +241,7 @@ class Sensor:
 
         return self.flow
 
-    def get_foe(self, multiple_foe=False, min_contour_area=3000, min_magnitude=1.0, expansion_threshold=1, downsample_factor=10, kernel_size=(5,5), denoise_sigma=1.5):
+    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):
         
         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)