remove depth and fix normal -> vnormal

example1_basic.py

@@ -58,14 +58,12 @@ def main():
         #   其他：
         #     - FPS:       当前帧率
         #     - TIMESTAMP: 时间戳字符串
-        #     - DEPTH:     深度图（归一化 0-1）
+        fps, fn, fx, fy, flow = sensor.read_info(
-        fps, fn, fx, fy, flow, depth = sensor.read_info(
             sensor.info.FPS,
             sensor.info.FNORMAL,
             sensor.info.FSHEARX,
             sensor.info.FSHEARY,
-            sensor.info.VRAW,
+            sensor.info.VRAW
-            sensor.info.DEPTH
         )
         
         # 打印力的数值
@@ -90,12 +88,6 @@ def main():
         )
         cv2.imshow("形变场 Deformation", arrows)
 
-        # 绘制深度图（热力图）
-        depth_img = cv2.applyColorMap(
-            (depth * 255.0).astype('uint8'),
-            cv2.COLORMAP_HOT
-        )
-        cv2.imshow("深度图 Depth", depth_img)
 
         # =====================================================================
         # 4. 键盘控制

example2_config.py

@@ -19,17 +19,12 @@ def main():
         # 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)
-        # 深度图: DEPTH
+        fps, fn, fx, fy, flow = sensor1.read_info(sensor1.info.FPS, sensor1.info.FNORMAL, sensor1.info.FSHEARX, sensor1.info.FSHEARY, sensor1.info.VRAW)
-        fps, fn, fx, fy, flow, depth = sensor1.read_info(sensor1.info.FPS, sensor1.info.FNORMAL, sensor1.info.FSHEARX, sensor1.info.FSHEARY, sensor1.info.VRAW, sensor1.info.DEPTH)
 
         # 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)
 
-        depth_img = cv2.applyColorMap((depth*1* 255.0).astype('uint8'), cv2.COLORMAP_HOT)
-
-        cv2.imshow('depth', depth_img)
-
         key = cv2.waitKey(1)
         if key & 0xFF == ord("q"):
             print("\n 停止程序")

example3_manual_box.py

@@ -25,17 +25,12 @@ def main():
         # 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)
-        # 深度图: DEPTH
+        fps, fn, fx, fy, flow = sensor1.read_info(sensor1.info.FPS, sensor1.info.FNORMAL, sensor1.info.FSHEARX, sensor1.info.FSHEARY, sensor1.info.VRAW)
-        fps, fn, fx, fy, flow, depth = sensor1.read_info(sensor1.info.FPS, sensor1.info.FNORMAL, sensor1.info.FSHEARX, sensor1.info.FSHEARY, sensor1.info.VRAW, sensor1.info.DEPTH)
         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)
 
-        depth_img = cv2.applyColorMap((depth*1* 255.0).astype('uint8'), cv2.COLORMAP_HOT)
-
-        cv2.imshow('depth', depth_img)
-
         key = cv2.waitKey(1)
         if key & 0xFF == ord("q"):
             print("\n 停止程序")

example4_multiple_sensor.py

@@ -44,7 +44,7 @@ def main():
             'cal_path': './config/cal.npy'
         },
         {
-            'vid_src': './s1_copy.mp4',
+            'vid_src': './s1.mp4',
             'isstitch': True,
             'cuda': True,
             'cal_path': './config/cal.npy'
@@ -84,13 +84,6 @@ def main():
                     )
                     cv2.imshow(f"传感器 {sid} - 形变", arrows)
                 
-                # 显示深度图
-                if data['depth'] is not None:
-                    depth_img = cv2.applyColorMap(
-                        (data['depth'] * 255).astype('uint8'),
-                        cv2.COLORMAP_HOT
-                    )
-                    cv2.imshow(f"传感器 {sid} - 深度", depth_img)
             
             # 检查退出键
             if cv2.waitKey(1) & 0xFF == ord('q'):

example5_async.py

@@ -80,13 +80,6 @@ def main():
                 )
                 cv2.imshow("异步模式 - 形变", arrows)
             
-            # 显示深度图
-            if data['depth'] is not None:
-                depth_img = cv2.applyColorMap(
-                    (data['depth'] * 255).astype('uint8'),
-                    cv2.COLORMAP_HOT
-                )
-                cv2.imshow("异步模式 - 深度", depth_img)
             
             # 键盘控制
             key = cv2.waitKey(1) & 0xFF

example6_multi_async.py

@@ -98,7 +98,7 @@ def main_manual():
     """
     configs = [
         {'vid_src': './s1.mp4', 'isstitch': True, 'cuda': True, 'cal_path': './config/cal.npy'},
-        {'vid_src': './s1_copy.mp4', 'isstitch': True, 'cuda': True, 'cal_path': './config/cal.npy'},
+        {'vid_src': './s1.mp4', 'isstitch': True, 'cuda': True, 'cal_path': './config/cal.npy'},
     ]
     
     # 创建 MultiSensor 实例

example_foe.py

@@ -25,11 +25,10 @@ def main():
         # 步骤 2.2: 计算形变场
         sensor.compute_deformation()
         
-        fps, flow,vnormal, depth = sensor.read_info(
+        fps, flow,vnormal = sensor.read_info(
             sensor.info.FPS,
             sensor.info.VRAW,
-            sensor.info.VNORMAL,
+            sensor.info.VNORMAL
-            sensor.info.DEPTH
         )
 
         # depth = get_depth_improved(vnormal, fps)
@@ -37,8 +36,6 @@ def main():
         # print(f"FPS: {fps:5.1f}, 深度: {np.min(depth):8.2f}, {np.mean(depth):8.2f}, {np.max(depth):8.2f}")
 
      
-        
-     
         arrows = orisys.util.draw_arrows(
             sensor.img.copy(),  # 使用 copy() 避免修改原图
             vnormal,
@@ -60,17 +57,11 @@ 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=10)
         # 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)
         
-        # 计算多个扩张焦点
-        # foe_result = orisys.util.compute_multiple_foe(vnormal, min_contour_area=3000,min_magnitude=1,expansion_threshold=0.001,downsample_factor=10)
-
-        # # 在图像上绘制多个焦点和区域
-        # image_with_foe = orisys.util.draw_multiple_foe(sensor.img.copy(), foe_result)
-        # cv2.imshow("Multiple FOE", image_with_foe)
 
         # =====================================================================
         # 4. 键盘控制

src/orisys/MultiSensor.py

@@ -75,7 +75,7 @@ def _sensor_worker(sensor_id, config, data_queue, stop_event, use_async=False):
                     'sensor_id': sensor_id,
                     'img': sensor.img.copy() if sensor.img is not None else None,
                     'flow': sensor.flow.copy() if sensor.flow is not None else None,
-                    'normal': sensor.normal.copy() if sensor.normal is not None else None,
+                    'normal': sensor.vnormal.copy() if sensor.vnormal is not None else None,
                     'shear': sensor.shear.copy() if sensor.shear is not None else None,
                     'depth': sensor.depth.copy() if sensor.depth is not None else None,
                     'fps': sensor.fps,

src/orisys/get_depth.py

@@ -1,7 +1,8 @@
 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=1.5):
+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):
     """
     计算光流场的扩张焦点(Focus of Expansion)。
     
@@ -87,7 +88,7 @@ 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=1.5, kernel_size=(5,5)):
+                         downsample_factor=10, min_contour_area=3000, denoise_sigma=10, kernel_size=(5,5)):
     """
     计算光流场的多个扩张焦点(Foci of Expansion)。
     
@@ -142,3 +143,31 @@ 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

test_depth_v2.py

@@ -42,13 +42,19 @@ def main():
         cv2.imshow("形变场 Deformation", arrows)
 
         mag = np.sqrt(vnormal[:, :, 0]**2 + vnormal[:, :, 1]**2)
+        mag = gaussian_filter(mag, sigma=12)
+
+        mag_div = np.gradient(mag, axis=1) + np.gradient(mag, axis=0)
+        mag_div = np.where(mag_div>0, mag_div, 0)
+        
         mag_img = cv2.applyColorMap(
-            (mag/20 * 255.0).astype('uint8'),
+            (mag_div/0.2 * 255.0).astype('uint8'),
             cv2.COLORMAP_JET
         )
         cv2.imshow("magnitude map", mag_img)
 
-        print(np.median(mag), np.min(mag), np.max(mag))
+        # print(np.median(mag), np.min(mag), np.max(mag))
+        print(np.median(mag_div), np.min(mag_div), np.max(mag_div))
 
         # =====================================================================
         # 4. 键盘控制

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)