闲鱼 159元冲全新Intel R200实感相机

https://github.com/IntelRealSense/librealsense/tree/v1.12.1

https://github.com/IntelRealSense/librealsense

https://blog.csdn.net/jy1023408440/article/details/105732098?utm_medium=distribute.wap_relevant.none-task-blog-2%7Edefault%7EBlogCommendFromBaidu%7Edefault-9.wap_blog_relevant_pic&depth_1-utm_source=distribute.wap_relevant.none-task-blog-2%7Edefault%7EBlogCommendFromBaidu%7Edefault-9.wap_blog_relevant_pic

https://github.com/IntelRealSense/librealsense/releases/tag/v2.34.0

 pip install pyrealsense2

https://www.intel.cn/content/www/cn/zh/support/products/92256/emerging-technologies/intel-realsense-technology/intel-realsense-cameras/intel-realsense-camera-r200.html

https://www.intelrealsense.com/

github.com/IntelRealSense/librealsense/blob/master/doc/sample-data.md

% 使管道对象管理流式处理
管道 = 实感.管道();
% 定义点云对象
pcl_obj = 实感.点云();
% 使用默认设置在任意摄像机上开始流式传输
管道.开始();
% 获取帧。我们抛弃了第一对夫妇
摄像机结算时间的百分比
对于 i = 1:5
帧 = 管道.wait_for_帧();
结束
% 选择深度帧
深度 = 帧.get_深度_帧();
% 获得不带颜色的点云点
pnts = pcl_obj.计算(深度);
顶点 = pnts.get_vertice();
% 可选:填充 MATLAB 点云对象
pCloud = 点云(顶点);
%显示点云
pcshow(pCloud);
管道.停止();

MATLAB:
% Make Pipeline object to manage streaming
pipe = realsense.pipeline();
% define point cloud object
pcl_obj = realsense.pointcloud();
% define colorizer to give point cloud color
colorizer = realsense.colorizer();
% Start streaming on an arbitrary camera with default settings
profile = pipe.start();
% Get frames. We discard the first couple to allow
% the camera time to settle
for i = 1:5
frames = pipe.wait_for_frames();
end
% Stop streaming
pipe.stop();
% Select depth frame
depth = frames.get_depth_frame();
% get point cloud points without color
points = pcl_obj.calculate(depth);
% get texture mapping
color = frames.get_color_frame();
% map point cloud to color
pcl_obj.map_to(color);
% get vertices (nx3)
vertices = points.get_vertices();
% get texture coordinates (nx2)
tex_coords = points.get_texture_coordinates();

import pyrealsense2 as rs
import numpy as np
import cv2

if __name__ == "__main__":
    # Configure depth and color streams
    pipeline = rs.pipeline()
    config = rs.config()
    config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
    config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)
    # Start streaming
    pipeline.start(config)
    try:
        while True:
            # Wait for a coherent pair of frames: depth and color
            frames = pipeline.wait_for_frames()
            depth_frame = frames.get_depth_frame()
            color_frame = frames.get_color_frame()
            if not depth_frame or not color_frame:
                continue
            # Convert images to numpy arrays

            depth_image = np.asanyarray(depth_frame.get_data())

            color_image = np.asanyarray(color_frame.get_data())

            # Apply colormap on depth image (image must be converted to 8-bit per pixel first)
            depth_colormap = cv2.applyColorMap(cv2.convertScaleAbs(depth_image, alpha=0.03), cv2.COLORMAP_JET)
            # Stack both images horizontally
            images = np.hstack((color_image, depth_colormap))
            # Show images
            cv2.namedWindow('RealSense', cv2.WINDOW_AUTOSIZE)
            cv2.imshow('RealSense', images)
            key = cv2.waitKey(1)
            # Press esc or 'q' to close the image window
            if key & 0xFF == ord('q') or key == 27:
                cv2.destroyAllWindows()
                break
    finally:
        # Stop streaming
        pipeline.stop()