啟動realsense相機

• • 平臺及系統

平臺及系統

jetson nano
Ubuntu18.04
參考教程：https://github.com/IntelRealSense/librealsense/tree/master/wrappers/python#examples
嘗試過pip安裝但是失敗了，讀友可以試試看
需要之前下載過librealsense
主要步驟：

sudo apt-get update && sudo apt-get upgrade

sudo apt-get install python python-dev

或者

sudo apt-get install python3 python3-dev

進入librealsense目錄，之前應該創建過build目錄，直接進去就好

mkdir buildcd buildcmake ../ -DBUILD_PYTHON_BINDINGS:bool=true

make -j4sudo make install

注意上一步編譯的過程中，librealsense2.so和pyrealsense2.so兩個文件的安裝位置，不過應該是因為我的是python3的緣故，pyrealsense2.so沒有而是出現了pyrealsense2.cpython-35m-arm-linux-gnueabihf.so文件你可以把它改名成pyrealsense2.so
然后把這兩個文件拷貝到你的python文件工程夾下就可以在代碼里使用了

檢測是否能啟動realsense相機，我用的是sr300，然后成功啟動了
以下是實例代碼，參考自https://github.com/IntelRealSense/librealsense/blob/development/wrappers/python/examples/opencv_viewer_example.py
注意點：
需要把pyrealsense2.cpython-35m-arm-linux-gnueabihf.so文件改名成pyrealsense2.so不然會報錯
numpy事先安裝好就行
opencv 是jetson nano的系統自帶的，無需安裝

## License: Apache 2.0. See LICENSE file in root directory. ## Copyright(c) 2015-2017 Intel Corporation. All Rights Reserved.############################################### ## Open CV and Numpy integration ## ###############################################import pyrealsense2 as rs import numpy as np import cv2# Configure depth and color streams pipeline = rs.pipeline() config = rs.config()# Get device product line for setting a supporting resolution pipeline_wrapper = rs.pipeline_wrapper(pipeline) pipeline_profile = config.resolve(pipeline_wrapper) device = pipeline_profile.get_device() device_product_line = str(device.get_info(rs.camera_info.product_line))found_rgb = False for s in device.sensors:if s.get_info(rs.camera_info.name) == 'RGB Camera':found_rgb = Truebreak if not found_rgb:print("The demo requires Depth camera with Color sensor")exit(0)config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)if device_product_line == 'L500':config.enable_stream(rs.stream.color, 960, 540, rs.format.bgr8, 30) else: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 colorframes = 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 arraysdepth_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)depth_colormap_dim = depth_colormap.shapecolor_colormap_dim = color_image.shape# If depth and color resolutions are different, resize color image to match depth image for displayif depth_colormap_dim != color_colormap_dim:resized_color_image = cv2.resize(color_image, dsize=(depth_colormap_dim[1], depth_colormap_dim[0]), interpolation=cv2.INTER_AREA)images = np.hstack((resized_color_image, depth_colormap))else:images = np.hstack((color_image, depth_colormap))# Show imagescv2.namedWindow('RealSense', cv2.WINDOW_AUTOSIZE)cv2.imshow('RealSense', images)cv2.waitKey(1)finally:# Stop streamingpipeline.stop()

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