Merge remote-tracking branch 'origin/master'

Vision/camera_coordinate_dete.py

@@ -11,8 +11,8 @@
 import numpy as np
 import cv2
 import open3d as o3d
-from .tool.CameraRVC import camera
+from Vision.tool.CameraRVC import camera
-from .yolo.yolov8_pt_seg import yolov8_segment
+from Vision.yolo.yolov8_pt_seg import yolov8_segment
 
 
 class Detection:
@@ -39,6 +39,9 @@ class Detection:
                 if flag == 1:
                     xyz = []
                     nx_ny_nz = []
+                    RegionalArea = []
+                    Depth_Z = []
+                    uv = []
                     for i, item in enumerate(det_cpu):
 
                         # 画box
@@ -89,8 +92,7 @@ class Detection:
                         # result = np.zeros_like(color_image)
                         select_point = []
                         for i in range(pixel_point2.shape[0]):
-                            # k = pixel_point2[i]
+                            select_point.append(pm[pixel_point2[i][0][1]+box_y1, pixel_point2[i][0][0]]+box_x1)
-                            select_point.append(pm[pixel_point2[i][0][1], pixel_point2[i][0][0]])
                         select_point = np.array(select_point)
                         pm_seg = select_point.reshape(-1, 3)
                         pm_seg = pm_seg[~np.isnan(pm_seg).all(axis=-1), :]      # 剔除 nan
@@ -102,9 +104,9 @@ class Detection:
                         pcd = o3d.geometry.PointCloud()
                         pcd.points = o3d.utility.Vector3dVector(pm_seg)
                         # pcd = o3d.io.read_point_cloud("./Data/seg_point.xyz")
-                        plane_model, inliers = pcd.segment_plane(distance_threshold=0.1,
+                        plane_model, inliers = pcd.segment_plane(distance_threshold=0.01,
-                                                                 ransac_n=3,
+                                                                 ransac_n=5,
-                                                                 num_iterations=100)
+                                                                 num_iterations=1000)
                         [a, b, c, d] = plane_model
                         print(f"Plane equation: {a:.2f}x + {b:.2f}y + {c:.2f}z + {d:.2f} = 0")
 
@@ -130,25 +132,23 @@ class Detection:
 
                         x_rotation_center = int((box[0][0][0] + box[1][0][0] + box[2][0][0] + box[3][0][0]) / 4)
                         y_rotation_center = int((box[0][0][1] + box[1][0][1] + box[2][0][1] + box[3][0][1]) / 4)
-                        point_x, point_y, point_z =pm[x_rotation_center, y_rotation_center]
+                        point_x, point_y, point_z = pm[y_rotation_center, x_rotation_center]
                         cv2.circle(img, (x_rotation_center, y_rotation_center), 4, (255, 255, 255), 5)  # 标出中心点
-                        x1, y1, z1, = pm[x_rotation_center+1, y_rotation_center+1]
+                        if np.isnan(point_x):     # 点云值为无效值
-                        print('x1 y1 z1 :', x1, y1, z1)
-                        #print('像素坐标（x, y）：', x_rotation_center,y_rotation_center)
-                        print('x y z :', point_x, point_y, point_z)
-                        # point_x=point_x*1000
-                        # point_y=point_y*1000
-                        # point_z=point_z*1000
-                        print('nx ny nz :', a, b, c)
-                        # getPosition(x, y, z, a, b, c)
-                        if(point_x == np.nan):
                             continue
                         else:
-                            xyz.append([x1*1000, y1*1000, z1*1000])
+                            xyz.append([point_x*1000, point_y*1000, point_z*1000])
+                            Depth_Z.append(point_z*1000)
+
                             nx_ny_nz.append([a, b, c])
+                            RegionalArea.append(cv2.contourArea(max_contour))
+                            uv.append([x_rotation_center,y_rotation_center])
                         cv2.polylines(img, [box], True, (0, 255, 0), 2)
 
-                    return 1, img, xyz, nx_ny_nz
+                    min_value = min(Depth_Z)            # 求深度最大值
+                    min_idx = Depth_Z.index(min_value)  # 求最大值对应索引
+                    cv2.circle(img, (uv[min_idx][0], uv[min_idx][1]), 20, (255, 0, 0), 5)  # 标出中心点
+                    return 1, img, xyz[min_idx], nx_ny_nz[min_idx]
                 else:
                     return 1, img, None, None
 

Vision/camera_coordinate_dete_test.py

@@ -0,0 +1,23 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+'''
+# @Time    : 2024/9/5 14:57
+# @Author  : hjw
+# @File    : camera_coordinate_dete_test.py.py
+'''
+from camera_coordinate_dete import Detection
+import cv2
+model_path = './pt_model/last-0903.pt'
+device = 'cpu'
+detection = Detection(model_path, device)
+
+while True:
+    ret, img, xyz, nx_ny_nz = detection.get_position()
+    if ret==1:
+        print('xyz点云坐标：', xyz)
+        print('nx_ny_nz法向量：', nx_ny_nz)
+        img = cv2.resize(img,(720, 540))
+        cv2.imshow('img', img)
+        cv2.waitKey(1)
+
+

requirements

@@ -1,7 +1,7 @@
 colorthief=0.2.1
 darkdetect=0.8.0
 numpy=2.0.1
-pillow=10.4.
+pillow=10.4
 pip=24.0
 PyQt5=5.15.11
 PyQt5-Frameless-Window=0.3.9
@@ -16,3 +16,9 @@ setuptools=69.5.1
 shiboken6=6.7.2
 wheel=0.43.0
 python=3.9.19
+open3d=0.18.0
+opencv-python=4.7.0
+PyRVC=1.10.0
+torch=1.13.1
+torchvision=0.14.1
+ultralytics=8.2.86