developing depth estimation

test_depth.py

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+import os
+os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0"  # Windows 下禁用硬件加速，避免兼容性问题
+
+import cv2
+import orisys
+import numpy as np
+from scipy.ndimage import gaussian_filter
+
+
+def get_depth(div, fps, H=400, W=400, Z0=30, win_size=10):
+        # Precompute
+    dt = 1.0 / fps  # e.g., 1/30
+    Z_map = np.full((H, W), Z0, dtype=np.float32)  # initial depth map
+
+
+    for win_y in range(0, H, win_size):
+        for win_x in range(0, W, win_size):
+            y1, y2 = win_y, min(win_y + win_size, H)
+            x1, x2 = win_x, min(win_x + win_size, W)
+            
+            # Robust divergence estimate
+            D_win = np.median(div[y1:y2, x1:x2])
+            
+            # Avoid numerical issues
+            if np.abs(D_win) < 1e-3:
+                continue
+                
+            # Update depth in window
+            Z_old = Z_map[y1, x1]  # or use window mean
+            Z_new = Z_old * np.exp(-0.5 * D_win * dt)
+            
+            # Optionally fuse with dot-based depth if available
+            # Z_new = alpha * Z_new + (1 - alpha) * Z_from_dots
+            
+            Z_map[y1:y2, x1:x2] = Z_new
+    return Z_map
+
+def main():
+   
+    
+    # 方式1: 从视频文件读取
+    # sensor = orisys.Sensor("./s1.mp4", isstitch=True, cuda=True, verbose=True)
+    
+    # 方式2: 从摄像头读取（取消注释使用）
+    sensor = orisys.Sensor(0, isstitch=True, cuda=True, verbose=True)
+
+    print("\n按 'q' 退出程序，按 'r' 重置光流追踪器")
+
+    while True:
+        # 步骤 2.1: 获取并拼接图像
+        sensor.get_img()
+        
+        # 步骤 2.2: 计算形变场
+        sensor.compute_deformation()
+        
+        fps, flow,vnormal, depth = sensor.read_info(
+            sensor.info.FPS,
+            sensor.info.VRAW,
+            sensor.info.VNORMAL,
+            sensor.info.DEPTH
+        )
+
+        # depth = get_depth_improved(vnormal, fps)
+        
+        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,
+            threshold=5,
+            grid_spacing=10,
+            arrow_scale=1.0
+        )
+        cv2.imshow("形变场 Deformation", arrows)
+
+        # # 绘制深度图（热力图）
+        # # 深度值缩放：中心值 30，范围 ±10（即 20-40 映射到 0-255）
+        # depth_center = 30  # 中心深度值
+        # depth_range = 5  # 变化范围 ±10
+        # depth_normalized = (depth - (depth_center - depth_range)) / (2 * depth_range)  # 归一化到 0-1
+        # depth_normalized = np.clip(depth_normalized, 0, 1)  # 限制在 0-1 范围
+        depth_img = cv2.applyColorMap(
+            (depth * 255.0).astype('uint8'),
+            cv2.COLORMAP_HOT
+        )
+        cv2.imshow("深度图 Depth", depth_img)
+
+        mag_img = orisys.util.draw_magnitude_map(
+            sensor.img.copy(),
+            vnormal,
+            threshold=5,
+            colormap=cv2.COLORMAP_HOT
+        )
+        cv2.imshow("magnitude map", mag_img)
+
+        # =====================================================================
+        # 4. 键盘控制
+        # =====================================================================
+        key = cv2.waitKey(1) & 0xFF
+        if key == ord("q"):
+            print("\n退出程序...")
+            break
+        if key == ord("r"):
+            # 重置光流追踪器（当追踪漂移时使用）
+            sensor.reset()
+            print("光流追踪器已重置")
+    
+    # =========================================================================
+    # 5. 清理资源
+    # =========================================================================
+    sensor.disconnect()  # 释放视频源和相关资源
+    cv2.destroyAllWindows()
+
+
+if __name__ == '__main__':
+    main()