From bffc3841ab7eae6e9082bf5b48ceff08fc137503 Mon Sep 17 00:00:00 2001 From: "Ziwei.He" Date: Thu, 29 May 2025 16:47:49 +0800 Subject: [PATCH] =?UTF-8?q?=E8=A7=86=E8=A7=89=E6=A0=87=E5=AE=9A=E5=AE=8C?= =?UTF-8?q?=E6=88=90?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Massage/Massage.pyc | Bin .../__pycache__/dobot_api.cpython-38.pyc | Bin 136685 -> 136688 bytes .../__pycache__/dobot_nova5.cpython-39.pyc | Bin 23743 -> 23567 bytes .../__pycache__/remote_cam.cpython-39.pyc | Bin 0 -> 5518 bytes Massage/MassageControl/hardware/capture.py | 124 +++++++++ .../MassageControl/hardware/dobot_nova5.py | 26 +- Massage/MassageControl/hardware/remote_cam.py | 235 ++++++++++++++++++ .../tools/auto_visual_valibration.py | 106 ++++---- Massage/controller.service | 0 Massage/test_manual.py | 0 Massage/test_manual.pyc | Bin Massage/tools/__pycache__/log.cpython-38.pyc | Bin 0 -> 3223 bytes Restart_Speaker.py | 0 Restart_Speaker.pyc | Bin clear_pyc.sh | 0 example_startup.pyc | Bin py2json.py | 0 py2json.pyc | Bin restart_language.sh | 0 版本说明.txt | 0 20 files changed, 416 insertions(+), 75 deletions(-) mode change 100644 => 100755 Massage/Massage.pyc create mode 100644 Massage/MassageControl/hardware/__pycache__/remote_cam.cpython-39.pyc create mode 100644 Massage/MassageControl/hardware/capture.py mode change 100644 => 100755 Massage/controller.service mode change 100644 => 100755 Massage/test_manual.py mode change 100644 => 100755 Massage/test_manual.pyc create mode 100644 Massage/tools/__pycache__/log.cpython-38.pyc mode change 100644 => 100755 Restart_Speaker.py mode change 100644 => 100755 Restart_Speaker.pyc mode change 100644 => 100755 clear_pyc.sh mode change 100644 => 100755 example_startup.pyc mode change 100644 => 100755 py2json.py mode change 100644 => 100755 py2json.pyc mode change 100644 => 100755 restart_language.sh mode change 100644 => 100755 版本说明.txt diff --git a/Massage/Massage.pyc b/Massage/Massage.pyc old mode 100644 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zFC(d8>Lx^(IcX_{FO76dL+=c*4x9z1$TI>=ktY|#!`MlAzx18T(kSS-pr2bBL!ves zj)|GZ!*OOD)%d&NSU7UK6ppWx!VKnEhaS0JIg9dbVG5CIU3gnKK8F~FG%SsBvd@Pl zHi=~L*t^FrBDy8Qlt|f#N`p!xZl&63M8^q=VC^imhZELFuY^7*)$bdb*n{{y%t5YXGia9j9Z)T2{{DxgAwQP!p z<_P2Vg4JVLa{a>kwZ1?E6`efZyeZHEeje^#XiwQZaj!s4)?GP#=nj& z^2Z5$mB2#;9wx9CAd-(qawC#kk-QkmO*19dZ6QT&u(keZec!*(97FxifRBG4fRq`N z$5gl`bu(g6PQ~@Im*W~#iMI1kNq`*b5TLx7lb+GNQQSHaPP0bBNF?tz z3Pw(}(cD$LRI{vB;+|di@7Rg=9;}QUEq@T8HEB1TdZXq94ess|BAUYOs92iwryAle zRjSUR1}d?mUYq-T-NmVk?xS0tncPyH+`_Ca2Pd~YF}Y>7HF=AFA>dzuHoDBBykkn_ zgCb=4Pa_L7a2G;?=pAh(Ru_^OOj5}6t+buH2~a#UV|0((&G=&|M7jvP!fZJGF5;UA zkl^?sYF9j@bZ`}2>2IcbA^6oGM~dCeztpO|7$pEKF?*y|OEqyqQM^=L;H3aRjSx7C z;yRZW>0=ESOuF0|l#W@f#POTS4(21R40AJ6MX!QOA$~2P%Bb2$UG(sWPFWnf_!VM2 s*j1f}D5wkN?Ow+EF#COJFVZU6P?ET?C6z2u@ 0: # 排除无效深度 + x = (u - cx) * z / fx + y = (v - cy) * z / fy + points.append([x, y, z]) + colors.append(rgb_image_cropped[v, u] / 255.0) # 颜色归一化到[0,1] + + # 将点云和颜色转换为NumPy数组 + points = np.array(points) + colors = np.array(colors) + + # 创建Open3D点云对象 + point_cloud = o3d.geometry.PointCloud() + point_cloud.points = o3d.utility.Vector3dVector(points) + point_cloud.colors = o3d.utility.Vector3dVector(colors) + + # 显示点云 + o3d.visualization.draw_geometries([point_cloud]) + +if __name__ == "__main__": + import time + import os + # 初始化客户端并指定服务器地址 + cam = ToolCamera(host='127.0.0.1') + # cam.stop() + cam.start() + time.sleep(2) + # 获取最新的RGB和深度图像 + rgb_image, depth_image, camera_intrinsics = cam.get_latest_frame() + print(camera_intrinsics) + + max_depth = np.max(depth_image) + print(np.min(depth_image),np.max(depth_image)) + # print(depth_image[200, 320]) + # depth_image = (depth_image / max_depth * 65535).astype(np.uint16) + # print(np.min(depth_image),np.max(depth_image)) + # 对图像进行水平翻转 + # rgb_image = cv2.flip(rgb_image, 1) + # depth_image = cv2.flip(depth_image, 1) + + # 显示图像 + cam.display_images(rgb_image, depth_image) + cv2.imwrite("aucpuncture2point/configs/using_img/color.png",rgb_image) + cv2.imwrite("aucpuncture2point/configs/using_img/depth.png",depth_image) + # 获取当前时间并格式化为文件夹名 (格式: 年-月-日_时-分-秒) + current_time = time.strftime("%Y-%m-%d_%H-%M-%S") + base_dir = "/home/jsfb/jsfb_ws/collected_data/test_images" + # 创建一个根据时间命名的新文件夹 + folder_path = os.path.join(base_dir, current_time) + os.makedirs(folder_path, exist_ok=True) # 如果文件夹不存在则创建 + + cv2.imwrite(os.path.join(folder_path, "color.png"), rgb_image) + cv2.imwrite(os.path.join(folder_path, "depth.png"), depth_image) + + # 显示RGB点云 + # cam.display_rgb_point_cloud(rgb_image, depth_image, camera_intrinsics) diff --git a/Massage/MassageControl/tools/auto_visual_valibration.py b/Massage/MassageControl/tools/auto_visual_valibration.py index e5858b1..d6bb8b9 100644 --- a/Massage/MassageControl/tools/auto_visual_valibration.py +++ b/Massage/MassageControl/tools/auto_visual_valibration.py @@ -10,7 +10,7 @@ from scipy.spatial.transform import Rotation as R sys.path.append(str(Path(__file__).resolve().parent.parent)) sys.path.append('/home/jsfb/jsfb_ws/MassageRobot_Dobot/Massage/MassageControl') from hardware.remote_cam import ToolCamera -from hardware.aubo_C5 import AuboC5 +from hardware.dobot_nova5 import dobot_nova5 np.set_printoptions(precision=8, suppress=True) # 设置寻找亚像素角点的参数，采用的停止准则是最大循环次数30和最大误差容限0.001 @@ -41,16 +41,20 @@ class Calibration: self.size = None self.intrinsics_mtx = None self.disorder = None + time.sleep(2) rgb_image, depth_image, camera_intrinsics = self.cam.get_latest_frame() + self.save_directory = '/home/jsfb/jsfb_ws/global_config/captured_images' + if rgb_image is None : print(f'===============相机连接失败请检查并重试==============') else: - print(f'===============相机连接成功==============') self.size = rgb_image.shape[:2] + print(f'===============相机连接成功============== ',self.size) + self.intrinsics = camera_intrinsics - self.arm = AuboC5() - self.arm.init() + self.arm = dobot_nova5("192.168.5.1") + self.arm.start() self.obj_points = [] print(f'===============机械臂连接成功==============') @@ -62,24 +66,29 @@ class Calibration: def collect_data(self): #移动到初始位置 - pose = [(90) * (np.pi / 180), 4.23 * (np.pi / 180), 124.33 * (np.pi / 180), 61.56 * (np.pi / 180), -89.61 * (np.pi / 180), 0 * (np.pi / 180)] - code = self.arm.move_joint(pose,4,wait=True) + pose = [-45.0009079,55.5785141,-120.68821716,5.11103201,-90.00195312,-90.00085449] + code = self.arm.RunPoint_P_inJoint(pose) if code == -1: print("运动到拍照位置失败") #拍照 self.check_corners() #运动到下一个位置 # 目前只设计了44条轨迹 - for i in range (0,45): + for i in range (0,18): next_pose = self.get_next_pose_ugly(i) - self.arm.move_joint(next_pose, 4,wait=True) + code=self.arm.RunPoint_P_inJoint(next_pose) + time.sleep(0.5) if code == -1: print("运动到拍照位置失败") else: self.check_corners() - + time.sleep(0.5) + def check_corners(self): rgb, depth, intrinsics = self.cam.get_latest_frame() + # img_path = f'photo{i}' + # filename = os.path.join(self.save_directory, f'{img_path}/rgb_image.png') + # rgb=cv2.imread(filename) gray = cv2.cvtColor(rgb, cv2.COLOR_BGR2GRAY) ret, corners = cv2.findChessboardCorners(gray, self.board_size, None) if ret: @@ -115,14 +124,12 @@ class Calibration: self.robot_poses.append(robot_pose) def get_pose_homomat(self): - pose = self.arm.robot_rpc_client.getRobotInterface(self.arm.robot_name).getRobotState().getTcpPose() - x, y, z, rx, ry, rz = pose - r = R.from_euler('xyz', [rx, ry, rz]) - rotation_matrix = r.as_matrix() - translation_vector = np.array([x, y, z]).reshape(3, 1) + arm_position,arm_orientation = self.arm.get_arm_position() + rotation_matrix = R.from_quat(arm_orientation).as_matrix() + translation_vector = arm_position homo_mat = np.eye(4) # 创建 4x4 单位矩阵 homo_mat[:3, :3] = rotation_matrix - homo_mat[:3, 3] = translation_vector.flatten() + homo_mat[:3, 3] = translation_vector return homo_mat def camera_calib(self): @@ -132,6 +139,7 @@ class Calibration: if ret: print("内参矩阵:\n", mtx) # 内参数矩阵 print("畸变系数:\n", dist) # 畸变系数 distortion cofficients = (k_1,k_2,p_1,p_2,k_3) + print("重投影误差:\n",ret) print("++++++++++相机标定完成++++++++++++++") self.intrinsics_mtx = mtx self.disorder = dist @@ -185,52 +193,24 @@ class Calibration: return None def get_next_pose_ugly(self,index): poselist =[ - [1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.0744246875277093, -1.5639895427121187, 0.0], - [1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.1267845650875392, -1.5116296651522887, 0.0], - [1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.1791444426473692, -1.4592697875924587, 0.0], - [1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.0744246875277093, -1.5639895427121187, 0.0], - [1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.0220648099678793, -1.5116296651522887, 0.0], - [1.5707963267948966, 0.07382742735936015, 2.16996785900455, 0.9697049324080494, -1.4592697875924587, 0.0], - [1.5707963267948966, 0.07382742735936015, 2.16996785900455, 0.9173450548482196, -1.4069099100326288, 0.0], - [1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.0744246875277093, -1.5639895427121187, 0.0], - [1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.1267845650875392, -1.6163494202719486, 0.0], - [1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.1791444426473692, -1.6687092978317786, 0.0], - [1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.2315043202071991, -1.7210691753916085, 0.0], - [1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.0744246875277093, -1.5639895427121187, 0.0], - [1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.109331272567596, -1.4941763726323454, 0.0], - [1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.1442378576074825, -1.424363202552572, 0.0], - [1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.1791444426473692, -1.3545500324727988, 0.0], - [1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.2140510276872558, -1.2847368623930255, 0.0], - [1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.0744246875277093, -1.5639895427121187, 0.0], - [1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.109331272567596, -1.633802712791892, 0.0], - [1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.1442378576074825, -1.7036158828716652, 0.0], - [1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.0744246875277093, -1.5639895427121187, 0.0], - [1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.0395181024878226, -1.4941763726323454, 0.0], - [1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.004611517447936, -1.424363202552572, 0.0], - [1.5707963267948966, 0.07382742735936015, 2.16996785900455, 0.9697049324080493, -1.3545500324727988, 0.0], - [1.5707963267948966, 0.07382742735936015, 2.16996785900455, 0.9347983473681627, -1.2847368623930255, 0.0], - [1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.0744246875277093, -1.5639895427121187, 0.0], - [1.5707963267948966, 0.06510078109938851, 2.16996785900455, 1.0395181024878226, -1.4941763726323454, 0.0], - [1.5707963267948966, 0.05637413483941686, 2.16996785900455, 1.004611517447936, -1.424363202552572, 0.0], - [1.5707963267948966, 0.047647488579445216, 2.16996785900455, 0.9697049324080493, -1.3545500324727988, 0.0], - [1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.0744246875277093, -1.5639895427121187, 0.0], - [1.562069680534925, 0.07382742735936015, 2.16996785900455, 1.0395181024878226, -1.4941763726323454, 0.0], - [1.5533430342749535, 0.07382742735936015, 2.16996785900455, 1.004611517447936, -1.424363202552572, 0.0], - [1.544616388014982, 0.07382742735936015, 2.16996785900455, 0.9697049324080493, -1.3545500324727988, 0.0], - [1.5358897417550104, 0.07382742735936015, 2.16996785900455, 0.9347983473681627, -1.2847368623930255, 0.0], - [1.5271630954950388, 0.07382742735936015, 2.16996785900455, 0.8998917623282761, -1.2149236923132523, 0.0], - [1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.0744246875277093, -1.5639895427121187, 0.0], - [1.579522973054868, 0.07382742735936015, 2.16996785900455, 1.109331272567596, -1.5988961277520053, 0.0], - [1.5882496193148397, 0.07382742735936015, 2.16996785900455, 1.1442378576074825, -1.633802712791892, 0.0], - [1.5969762655748112, 0.07382742735936015, 2.16996785900455, 1.1791444426473692, -1.6687092978317786, 0.0], - [1.6057029118347828, 0.07382742735936015, 2.16996785900455, 1.2140510276872558, -1.7036158828716652, 0.0], - [1.6144295580947543, 0.07382742735936015, 2.16996785900455, 1.2489576127271425, -1.7385224679115518, 0.0], - [1.5707963267948966, 0.07382742735936015, 2.16996785900455, 1.0744246875277093, -1.5639895427121187, 0.0], - [1.579522973054868, 0.07382742735936015, 2.16996785900455, 1.1267845650875392, -1.581442835232062, 0.0], - [1.5882496193148397, 0.07382742735936015, 2.16996785900455, 1.1791444426473692, -1.5988961277520053, 0.0], - [1.5969762655748112, 0.07382742735936015, 2.16996785900455, 1.2315043202071991, -1.6163494202719486, 0.0], - [1.6057029118347828, 0.07382742735936015, 2.16996785900455, 1.283864197767029, -1.633802712791892, 0.0], - [1.6144295580947543, 0.07382742735936015, 2.16996785900455, 1.336224075326859, -1.6512560053118353, 0.0] + [-4.513801574707031250e+01,5.518265151977539062e+01,-1.237858963012695312e+02,9.085088729858398438e+00,-9.337700653076171875e+01,-9.000016784667968750e+01], + [-4.594435882568359375e+01,3.636156082153320312e+01,-1.064173660278320312e+02,-2.125140428543090820e+00,-9.337675476074218750e+01,-9.000016784667968750e+01], + [-4.595432662963867188e+01,1.059147930145263672e+01,-8.284246063232421875e+01,-1.484527587890625000e+01,-9.337491607666015625e+01,-9.000013732910156250e+01], + [-4.638331604003906250e+01,-6.824899196624755859e+00,-6.758448791503906250e+01,-2.286166954040527344e+01,-9.337348937988281250e+01,-9.000016784667968750e+01], + [-4.636801910400390625e+01,-1.807893753051757812e+01,-6.532709503173828125e+01,-2.261142921447753906e+01,-9.336659240722656250e+01,-9.000010681152343750e+01], + [-4.637482833862304688e+01,-4.540402412414550781e+00,-1.098018112182617188e+02,3.086268234252929688e+01,-9.305430603027343750e+01,-9.000022125244140625e+01], + [-4.640084075927734375e+01,8.068140029907226562e+00,-1.362033843994140625e+02,6.259086990356445312e+01,-9.334789276123046875e+01,-9.000019073486328125e+01], + [-4.640056610107421875e+01,8.061657905578613281e+00,-1.511797027587890625e+02,8.927394104003906250e+01,-9.331819915771484375e+01,-9.000019073486328125e+01], + [-4.638466262817382812e+01,3.287191867828369141e+00,-1.566032409667968750e+02,1.035896453857421875e+02,-9.330465698242187500e+01,-9.000022125244140625e+01], + [-3.178509473800659180e+00,6.977911472320556641e+00,-1.417592010498046875e+02,6.005132293701171875e+01,-1.280102996826171875e+02,-9.000010681152343750e+01], + [-1.424913024902343750e+01,-2.980865538120269775e-01,-1.193396759033203125e+02,3.752756500244140625e+01,-1.278784713745117188e+02,-8.999975585937500000e+01], + [-2.393187522888183594e+01,-2.973175048828125000e-01,-1.121088027954101562e+02,3.928178024291992188e+01,-1.183067703247070312e+02,-8.999983215332031250e+01], + [-3.699544525146484375e+01,-2.981689572334289551e-01,-1.078408889770507812e+02,3.178039932250976562e+01,-1.024006271362304688e+02,-8.999983215332031250e+01], + [-6.734904479980468750e+01,-4.368630886077880859e+00,-1.035898971557617188e+02,2.342595481872558594e+01,-7.085527801513671875e+01,-8.999980926513671875e+01], + [-8.632910919189453125e+01,-1.039798259735107422e+01,-1.038105545043945312e+02,2.330961036682128906e+01,-5.476583099365234375e+01,-8.999980926513671875e+01], + [-8.156823730468750000e+01,5.878097534179687500e+00,-1.439221954345703125e+02,6.846669006347656250e+01,-4.482905197143554688e+01,-8.999922943115234375e+01], + [-8.601764678955078125e+01,3.570293045043945312e+01,-1.308228912353515625e+02,2.318510818481445312e+01,-5.378013610839843750e+01,-8.999922943115234375e+01], + [-7.132132053375244141e+00,3.722338104248046875e+01,-1.065378265380859375e+02,-3.337371826171875000e+00,-1.185582199096679688e+02,-8.999931335449218750e+01] ] return poselist[index] @@ -241,7 +221,7 @@ if __name__ == '__main__': # 标定板的width 对应的角点的个数 6 # 标定板的height 对应的角点的个数 3 calibration_board_size = [6,3] - calibration_board_square_size = 0.027 #unit - meter + calibration_board_square_size = 0.03 #unit - meter systemPath = "/home/jsfb/Documents/" now = datetime.now() @@ -252,7 +232,9 @@ if __name__ == '__main__': calib = Calibration(calibration_board_size,calibration_board_square_size,systemPath) calib.collect_data() + print("=================数据采集完成===================") R, t,intrin = calib.calibrate() + print("旋转矩阵：") print(R) print("平移向量：") diff --git a/Massage/controller.service b/Massage/controller.service old mode 100644 new 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