MechMind结构光相机采图SDK python调用

测试效果

Mech-Mind结构光相机

Mech Mind（梅卡曼德）的结构光相机，特别是Mech-Eye系列，是工业级的高精度3D相机，广泛应用于工业自动化、机器人导航、质量检测等多个领域。以下是对Mech Mind结构光相机的详细解析：

一、产品概述

Mech Mind的结构光相机，如Mech-Eye PRO，采用了高速结构光技术，能够在保持高精度、高速度的同时，提供优异的抗环境光性能。这些相机通常包含丰富的视觉算法模块，可应用于多个典型实际场景，如制造业工件上下料、高精度定位、装配、螺丝拧紧及学术研究等。

二、工作原理

Mech Mind的结构光相机主要利用了结构光投影的原理。它们将特定图案（如激光产生的结构光）投射到被拍摄物体上，并通过摄像头捕捉到物体的轮廓和形状。这种技术通过分析光线在物体上的反射和折射，能够精确地计算出物体的位置和形状。

三、产品特点

高精度：Mech Mind的结构光相机能够在短时间内获取高精度的三维模型，对于不同的物体，只需一次拍摄即可获得准确的形状信息。例如，Mech-Eye PRO的Z向单点重复精度可达到0.05mm（在1.0m处）。
高速度：相机具备快速的数据采集和处理能力，如Mech-Eye PRO的典型采集时间为0.3~0.6秒。
大视野和大景深：部分型号如Mech-Eye Deep 3D相机，具备大视野和大景深的特点，可适用于多种常见垛型。
抗环境光性能强：Mech Mind的结构光相机在较强环境光干扰下（如>20000lx）仍能保持优异的成像效果。
部署灵活：相机适配了国内外大部分主流品牌的机器人，可以实现对已适配机器人的完全运动控制。
开放易用：相机提供了友好的用户接口和开放的API，方便用户进行二次开发和集成。
稳定可靠：Mech Mind的结构光相机具备高稳定性和可靠性，如Mech-Eye PRO的平均无故障运行时间(MTBF)≥40000小时。

四、应用领域

Mech Mind的结构光相机被广泛应用于汽车、航空、模具制造、工业自动化等领域。在汽车领域，它们能够快速精确地获取车身表面的形状信息；在航空领域，它们能够获取飞机的三维形状信息，为飞机的设计和制造提供准确的数据支持。

五、总结

Mech Mind的结构光相机以其高精度、高速度、大视野、大景深、强抗环境光性能以及稳定可靠的特点，在工业自动化和机器人导航等领域发挥着重要作用。随着技术的不断进步和应用场景的不断拓展，Mech Mind的结构光相机有望在更多领域展现其独特的价值。

搭建Python开发环境

创建虚拟环境

下载opencv-python包

下载梅卡曼德相机采图包

pip install MechEyeAPI
pip install python-opencv

步骤解析

连接相机

def ConnectCamera(self):camera_infos = Camera.discover_cameras()if len(camera_infos) != 1:print("相机连接出现异常，检查网线")returnerror_status = self.camera.connect(camera_infos[0])if not error_status.is_ok():show_error(error_status)return

断开相机

def DisConnectCamera(self):self.camera.disconnect()print("Disconnected from the camera successfully.")

采集2d图和3d图

def connect_and_capture(self):# Obtain the 2D image resolution and the depth map resolution of the camera.resolution = CameraResolutions()show_error(self.camera.get_camera_resolutions(resolution))print_camera_resolution(resolution)time1 = time.time()# Obtain the 2D image.frame2d = Frame2D()show_error(self.camera.capture_2d(frame2d))row, col = 222, 222color_map = frame2d.get_color_image()print("The size of the 2D image is {} (width) * {} (height).".format(color_map.width(), color_map.height()))rgb = color_map[row * color_map.width() + col]print("The RGB values of the pixel at ({},{}) is R:{},G:{},B{}\n".format(row, col, rgb.b, rgb.g, rgb.r))Image2d = color_map.data()time2 = time.time()print('grab 2d image : '+str((time2-time1)*1000)+'ms')# if not confirm_capture_3d():#     return# Obtain the depth map.frame3d = Frame3D()show_error(self.camera.capture_3d(frame3d))depth_map = frame3d.get_depth_map()print("The size of the depth map is {} (width) * {} (height).".format(depth_map.width(), depth_map.height()))depth = depth_map[row * depth_map.width() + col]print("The depth value of the pixel at ({},{}) is depth :{}mm\n".format(row, col, depth.z))Image3d = depth_map.data()time3 = time.time()print('grab depth image : '+str((time3-time2)*1000)+'ms')return Image2d,Image3d# Obtain the point cloud.# point_cloud = frame3d.get_untextured_point_cloud()# print("The size of the point cloud is {} (width) * {} (height).".format(#     point_cloud.width(), point_cloud.height()))# point_xyz = point_cloud[row * depth_map.width() + col]# print("The coordinates of the point corresponding to the pixel at ({},{}) is X: {}mm , Y: {}mm, Z: {}mm\n".#       format(row, col, point_xyz.x, point_xyz.y, point_xyz.z))

完整测试代码

# With this sample, you can connect to a camera and obtain the 2D image, depth map, and point cloud data.
import timefrom mecheye.shared import *
from mecheye.area_scan_3d_camera import *
from mecheye.area_scan_3d_camera_utils import *
import cv2class ConnectAndCaptureImages(object):def __init__(self):self.camera = Camera()def connect_and_capture(self):# Obtain the 2D image resolution and the depth map resolution of the camera.resolution = CameraResolutions()show_error(self.camera.get_camera_resolutions(resolution))print_camera_resolution(resolution)time1 = time.time()# Obtain the 2D image.frame2d = Frame2D()show_error(self.camera.capture_2d(frame2d))row, col = 222, 222color_map = frame2d.get_color_image()print("The size of the 2D image is {} (width) * {} (height).".format(color_map.width(), color_map.height()))rgb = color_map[row * color_map.width() + col]print("The RGB values of the pixel at ({},{}) is R:{},G:{},B{}\n".format(row, col, rgb.b, rgb.g, rgb.r))Image2d = color_map.data()time2 = time.time()print('grab 2d image : '+str((time2-time1)*1000)+'ms')# if not confirm_capture_3d():#     return# Obtain the depth map.frame3d = Frame3D()show_error(self.camera.capture_3d(frame3d))depth_map = frame3d.get_depth_map()print("The size of the depth map is {} (width) * {} (height).".format(depth_map.width(), depth_map.height()))depth = depth_map[row * depth_map.width() + col]print("The depth value of the pixel at ({},{}) is depth :{}mm\n".format(row, col, depth.z))Image3d = depth_map.data()time3 = time.time()print('grab depth image : '+str((time3-time2)*1000)+'ms')return Image2d,Image3d# Obtain the point cloud.# point_cloud = frame3d.get_untextured_point_cloud()# print("The size of the point cloud is {} (width) * {} (height).".format(#     point_cloud.width(), point_cloud.height()))# point_xyz = point_cloud[row * depth_map.width() + col]# print("The coordinates of the point corresponding to the pixel at ({},{}) is X: {}mm , Y: {}mm, Z: {}mm\n".#       format(row, col, point_xyz.x, point_xyz.y, point_xyz.z))def main(self):# List all available cameras and connect to a camera by the displayed index.if find_and_connect(self.camera):d2,d3 = self.connect_and_capture()self.camera.disconnect()print("Disconnected from the camera successfully.")return d2,d3def GrabImages(self):d2, d3 = self.connect_and_capture()return d2, d3def ConnectCamera(self):camera_infos = Camera.discover_cameras()if len(camera_infos) != 1:print("相机连接出现异常，检查网线")returnerror_status = self.camera.connect(camera_infos[0])if not error_status.is_ok():show_error(error_status)returndef DisConnectCamera(self):self.camera.disconnect()print("Disconnected from the camera successfully.")if __name__ == '__main__':#pip install MechEyeAPIprint('初始化相机对象')MechMindGraber = ConnectAndCaptureImages()# d2,d3 = a.main()print('连接相机')MechMindGraber.ConnectCamera()for i in range(60):print(str(i)+'\r\n')print('采集亮度图和深度图')d2,d3  = MechMindGraber.GrabImages()cv2.imshow('1',d2)cv2.waitKey()cv2.imshow('1', d3)cv2.waitKey()print('断开连接')MechMindGraber.DisConnectCamera()