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")
return
error_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, 222
color_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 time
from mecheye.shared import *
from mecheye.area_scan_3d_camera import *
from mecheye.area_scan_3d_camera_utils import *
import cv2
class 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, 222
color_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,d3
def GrabImages(self):
d2, d3 = self.connect_and_capture()
return d2, d3
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")
return
error_status = self.camera.connect(camera_infos[0])
if not error_status.is_ok():
show_error(error_status)
return
def DisConnectCamera(self):
self.camera.disconnect()
print("Disconnected from the camera successfully.")
if __name__ == '__main__':
#pip install MechEyeAPI
print('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()
