Merge remote-tracking branch 'origin/master'

Trace/README.md

@@ -1,3 +1,7 @@
+# com_pose.txt  彩色图对齐深度图
+# com_pose2.txt 深度图对齐彩色图
+
+
 # 路径处理
 
 使用model的Position类和Expection的code定义

Trace/com_pose2.txt

@@ -0,0 +1,4 @@
+9.6751729364544639e-02 -9.9459449602428807e-01 3.7694712403242861e-02 4.1708226127179734e+02
+-9.9527317207468335e-01 -9.6998182736478769e-02 -4.7608291523444628e-03 1.9086796578832980e+03
+8.3914130733227475e-03 -3.7055917530319504e-02 -9.9927796091108601e-01 2.7052748714031904e+03
+0. 0. 0. 1

Trace/handeye_calibration.py

@@ -56,7 +56,7 @@ def R_matrix(x,y,z,u,v,w):
 # 图像识别结果：xyz和法向量
 def getPosition(x,y,z,a,b,c,rotation,points):
     target = np.asarray([x, y, z,1])
-    camera2robot = np.loadtxt('./Trace/com_pose.txt', delimiter=' ') #相对目录且分隔符采用os.sep
+    camera2robot = np.loadtxt('./Trace/com_pose2.txt', delimiter=' ') #相对目录且分隔符采用os.sep
     # robot2base = rotation
     # camera2base = robot2base @ camera2robot
     target_position = np.dot(camera2robot, target)

Vision/camera_coordinate_dete.py

@@ -75,7 +75,7 @@ class Detection:
             self.model = yolov8_segment_openvino(model_path, device, conf_thres=0.3, iou_thres=0.3)
 
 
-    def get_position(self, Point_isVision=False, Box_isPoint=True, First_Depth =True, Iter_Max_Pixel = 30, save_img_point=0, Height_reduce = 60, width_reduce = 100):
+    def get_position(self, Point_isVision=False, Box_isPoint=True, First_Depth =True, Iter_Max_Pixel = 30, save_img_point=0, Height_reduce = 60, width_reduce = 100, Xmin =330, Xmax = 1050, Ymin =290 ,Ymax = 690 ):
         """
             检测料袋相关信息
             :param Point_isVision:  点云可视化
@@ -85,6 +85,11 @@ class Detection:
             :param save_img_point:  0不保存 ; 1保存原图  ;2保存处理后的图 ; 3保存点云和原图；4 保存点云和处理后的图; 5 异常数据保存（点云NAN）
             :param Height_reduce:   检测框的高内缩像素
             :param width_reduce:    检测框的宽内缩像素
+            :param Xmin:    限定料袋中心点的范围
+            :param Xmax:    限定料袋中心点的范围
+            :param Ymin:    限定料袋中心点的范围
+            :param Ymax:    限定料袋中心点的范围
+
             :return ret: bool  相机是否正常工作
             :return img: ndarray   返回img
             :return xyz:  list 目标中心点云值形如[x,y,z]
@@ -270,6 +275,8 @@ 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 = remove_nan_mean_value(pm, y_rotation_center, x_rotation_center, iter_max=Iter_Max_Pixel)
+                        if x_rotation_center<Xmin or x_rotation_center>Xmax or y_rotation_center<Ymin or y_rotation_center>Ymax:
+                            continue
                         cv2.circle(img, (x_rotation_center, y_rotation_center), 4, (255, 255, 255), 5)  # 标出中心点
                         if np.isnan(point_x):     # 点云值为无效值
                             continue

Vision/find_img_point.py

@@ -8,18 +8,20 @@
 import cv2
 import numpy as np
 
-img = cv2.imread('./model/data/0925_01_28.png')
+img = cv2.imread('./model/data/2024_12_16_18_32_40.png')
-img2 = img[:600, 380:]
+# img2 = img[:600, 380:]
-cv2.imshow('img2',img2)
+img = cv2.resize(img,(640,480))
-cv2.imshow('img',img)
+# cv2.imshow('img3',img3)
-cv2.waitKey(0)
+# cv2.imshow('img2',img2)
+# cv2.imshow('img',img)
+# cv2.waitKey(0)
 
 def on_EVENT_LBUTTONDOWN(event, x, y, flags, param):
     if event == cv2.EVENT_LBUTTONDOWN:
         xy = "%d,%d" % (x, y)
-        cv2.circle(img, (x, y), 1, (255, 0, 0), thickness = -1)
+        cv2.circle(img, (x, y), 1, (0, 0, 255), thickness = -1)
         cv2.putText(img, xy, (x, y), cv2.FONT_HERSHEY_PLAIN,
-                    1.0, (0,0,0), thickness = 1)
+                    1.0, (0,0,255), thickness = 1)
         cv2.imshow("image", img)
 cv2.namedWindow("image")
 cv2.setMouseCallback("image", on_EVENT_LBUTTONDOWN)