UPDATE Vision 修正box输出

Vision/camera_coordinate_dete.py

@@ -18,7 +18,6 @@ from Vision.tool.utils import find_position
 from Vision.tool.utils import class_names
 from Vision.tool.utils import get_disk_space
 from Vision.tool.utils import remove_nan
-from Vision.tool.utils import compute_pca_direction
 import time
 import os
 
@@ -77,7 +76,6 @@ class Detection:
                     uv = []
                     seg_point = []
                     box_list = []
-                    pca_list = []
                     if Point_isVision==True:
                         pm2 = pm.copy()
                         pm2 = pm2.reshape(-1, 3)
@@ -153,9 +151,6 @@ class Detection:
                             continue
 
                         # cv2.imshow('result', piont_result)
-
-                        pca_list.append(compute_pca_direction(pm_seg))
-
                         '''
                         拟合平面，计算法向量
                         '''
@@ -231,20 +226,20 @@ class Detection:
                         if save_img_piont == True:
                             save_img = cv2.resize(img, (720, 540))
                             cv2.imwrite(save_img_name, save_img)
-                        return 1, img, xyz[_idx], nx_ny_nz[_idx], box_list[_idx], pca_list[_idx]
+                        return 1, img, xyz[_idx], nx_ny_nz[_idx], box_list[_idx]
                 else:
                     if save_img_piont == True:
                         save_img = cv2.resize(img,(720, 540))
                         cv2.imwrite(save_img_name, save_img)
-                    return 1, img, None, None, None, None
+                    return 1, img, None, None, None
 
             else:
                 print("RVC X Camera capture failed!")
-                return 0, None, None, None, None, None
+                return 0, None, None, None, None
 
         else:
             print("RVC X Camera is not opened!")
-            return 0, None, None, None, None, None
+            return 0, None, None, None, None
 
     def release(self):
         self.camera_rvc.release()

Vision/camera_coordinate_dete_img.py

@@ -19,8 +19,6 @@ from Vision.tool.utils import find_position
 from Vision.tool.utils import class_names
 from Vision.tool.utils import get_disk_space
 from Vision.tool.utils import remove_nan
-from Vision.tool.utils import compute_pca_direction
-from Vision.tool.utils import pca
 
 import os
 
@@ -86,8 +84,6 @@ class Detection:
                 uv = []
                 seg_point = []
                 box_list = []
-                pca_list = []
-                pca_nx_ny_nz = []
 
                 if Point_isVision == True:
                     pm2 = pm.copy()
@@ -163,8 +159,6 @@ class Detection:
                         print("分割点云数量较少，无法拟合平面")
                         continue
                     # cv2.imshow('result', piont_result)
-                    pca_list.append(compute_pca_direction(pm_seg))
-                    pca_nx_ny_nz.append(pca(pm_seg))
                     '''
                     拟合平面，计算法向量
                     '''
@@ -240,17 +234,17 @@ class Detection:
                         save_img = cv2.resize(img,(720, 540))
                         cv2.imwrite(save_img_name, save_img)
 
-                    return 1, img, xyz[_idx], nx_ny_nz[_idx], box_list[_idx], pca_list[_idx], pca_nx_ny_nz[_idx]
+                    return 1, img, xyz[_idx], nx_ny_nz[_idx], box_list[_idx]
             else:
                 if save_img_piont == True:
                     save_img = cv2.resize(img, (720, 540))
                     cv2.imwrite(save_img_name, save_img)
 
-                return 1, img, None, None, None, None, None
+                return 1, img, None, None, None
 
         else:
             print("RVC X Camera capture failed!")
-            return 0, None, None, None, None, None, None
+            return 0, None, None, None, None
 
     def release(self):
         self.model.clear()

Vision/camera_coordinate_dete_test.py

@@ -14,7 +14,7 @@ detection = Detection()
 
 while True:
     t1 = time.time()
-    ret, img, xyz, nx_ny_nz = detection.get_position()
+    ret, img, xyz, nx_ny_nz, box = detection.get_position()
     if ret==1:
         print('xyz点云坐标：', xyz)
         print('nx_ny_nz法向量：', nx_ny_nz)

Vision/get_position_test.py

@@ -13,13 +13,14 @@ import cv2
 detection = Detection()
 
 while True:
-    ret, img, xyz, nx_ny_nz = detection.get_position()
+    ret, img, xyz, nx_ny_nz, box = detection.get_position()
     if ret==1:
         print('xyz点云坐标：', xyz)
         print('nx_ny_nz法向量：', nx_ny_nz)
+        print('矩形框四顶点：', box)
         img = cv2.resize(img,(720, 540))
         transformation_matrix = R_matrix(521.534, 0.705, 850.03, 0.0, 90.0, 0.0)   # (x, y, z, u, v, w)
-        target_position, noraml_base = getPosition(xyz[0], xyz[1], xyz[2], nx_ny_nz[0], nx_ny_nz[1], nx_ny_nz[2], transformation_matri,box)
+        target_position, noraml_base = getPosition(xyz[0], xyz[1], xyz[2], nx_ny_nz[0], nx_ny_nz[1], nx_ny_nz[2], transformation_matrix, box)
         print("target_position:", target_position)
         print("noraml_base", noraml_base)
         cv2.imshow('img', img)

Vision/tool/utils.py

@@ -12,37 +12,6 @@ import numpy as np
 import cv2
 import psutil
 from psutil._common import bytes2human
-from sklearn.decomposition import PCA
-
-def pca(points):
-    # 计算PCA
-    mean = np.mean(points, axis=0)
-    cov = np.cov(points, rowvar=False)
-    eigenvalues, eigenvectors = np.linalg.eigh(cov)
-
-    # 对特征值进行排序并找到对应的特征向量
-    sorted_indices = np.argsort(eigenvalues)[::-1]
-    sorted_eigenvalues = eigenvalues[sorted_indices]
-    sorted_eigenvectors = eigenvectors[:, sorted_indices]
-
-    # # 打印PCA结果
-    # print("PCA Mean:", mean)
-    # print("PCA Eigenvalues:", sorted_eigenvalues)
-    # print("PCA Eigenvectors:\n", sorted_eigenvectors)
-
-    # 通常，最小特征值对应的特征向量是平面的法线
-    normal_vector = sorted_eigenvectors[:, 0] if sorted_eigenvalues[0] < sorted_eigenvalues[
-        1] else sorted_eigenvectors[:, 1]
-    # print("法向量：", normal_vector)
-    return normal_vector
-
-
-def compute_pca_direction(points):
-    # points 是物体点云的坐标
-    pca = PCA(n_components=3)
-    pca.fit(points)
-    primary_direction = pca.components_[0]  # 第一主成分对应的方向向量
-    return primary_direction
 
 def remove_nan(pm, y, x):
     piont_x, piont_y, piont_z = pm[y, x]

get_position_test.py

@@ -14,20 +14,15 @@ import cv2
 detection = Detection()
 
 while True:
-    ret, img, xyz, nx_ny_nz = detection.get_position()
+    ret, img, xyz, nx_ny_nz, box = detection.get_position()
     if ret==1:
         print('xyz点云坐标：', xyz)
         print('nx_ny_nz法向量：', nx_ny_nz)
+        print('矩形框四顶点：', box)
         img = cv2.resize(img,(720, 540))
         transformation_matrix = R_matrix(521.534, 0.705, 850.03, 0.0, 90.0, 0.0)   # (x, y, z, u, v, w)
-        target_position, noraml_base = getPosition(xyz[0], xyz[1], xyz[2], nx_ny_nz[0], nx_ny_nz[1], nx_ny_nz[2], transformation_matrix)
+        target_position, noraml_base = getPosition(xyz[0], xyz[1], xyz[2], nx_ny_nz[0], nx_ny_nz[1], nx_ny_nz[2], transformation_matrix, box)
         print("target_position:", target_position)
         print("noraml_base", noraml_base)
         cv2.imshow('img', img)
         cv2.waitKey(0)
-# sysstr = platform.system()
-# if sysstr == "Windows":
-#     space_free = get_disk_space(path=os.getcwd())
-# else:
-#     space_free = get_disk_space(path = "D:")
-# print('剩余空间: ', space_free)