python 从深度相机realsense生成pcl点云

蔚落 2022-10-07 11:49 394阅读 0赞

简单说下步骤：

一、通过realsense取得深度信息和彩色信息

二、获取坐标和色彩信息

三、通过pcl可视化点云

一、通过realsense取得深度信息和彩色信息

ubuntu下intel realsense的软件可以打开realsen的界面，里面可以得到彩色图像和深度图像，我们通过realsense的python接口获取彩色信息和深度信息。

1.基础的获取彩色和深度信息，realsense中的视频流转换为python的numpy格式，通过opencv输出

import pyrealsense2 as rs
    import numpy as np
    import cv2
    import pcl
     
    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)
        #深度图像向彩色对齐
        align_to_color=rs.align(rs.stream.color)
     
        try:
            while True:
                # Wait for a coherent pair of frames: depth and color
                frames = pipeline.wait_for_frames()
                
                frames = align_to_color.process(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()

2.获取内参和保存图片

分别用opencv和scipy.misc保存图片，略微会有一些差异，同时我们也获取了相机参数。

import pyrealsense2 as rs
    import numpy as np
    import cv2
    import scipy.misc
    import pcl
     
     
    def get_image():
        # 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)
     
        #获取图像，realsense刚启动的时候图像会有一些失真，我们保存第100帧图片。
        for i in range(100):
            data = pipeline.wait_for_frames()
            depth = data.get_depth_frame()
            color = data.get_color_frame()
     
        #获取内参
        dprofile = depth.get_profile()
        cprofile = color.get_profile()
     
        cvsprofile = rs.video_stream_profile(cprofile)
        dvsprofile = rs.video_stream_profile(dprofile)
     
        color_intrin=cvsprofile.get_intrinsics()
        print(color_intrin)
        depth_intrin=dvsprofile.get_intrinsics()
        print(color_intrin)
        extrin = dprofile.get_extrinsics_to(cprofile)
        print(extrin)
     
        depth_image = np.asanyarray(depth.get_data())
        color_image = np.asanyarray(color.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)
     
        cv2.imwrite('color.png', color_image)
        cv2.imwrite('depth.png', depth_image)
        cv2.imwrite('depth_colorMAP.png', depth_colormap)
        scipy.misc.imsave('outfile1.png', depth_image)
        scipy.misc.imsave('outfile2.png', color_image)

二、获取坐标和色彩信息

1. 通过realsense摄像头，获取了顶点坐标和色彩信息。具体并不是很了解，pc.mac\_to() 和 points=pc.calculate()是把色彩和深度结合了？ 再通过points获取顶点坐标。我们将顶点坐标和彩色相机得到的像素存入txt文件，。

def my_depth_to_cloud():
        pc = rs.pointcloud()
        points = rs.points()
     
        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)
        pipe_profile = pipeline.start(config)
     
        for i in range(100):
            data = pipeline.wait_for_frames()
     
            depth = data.get_depth_frame()
            color = data.get_color_frame()
     
        frames = pipeline.wait_for_frames()
        depth = frames.get_depth_frame()
        color = frames.get_color_frame()
     
        colorful = np.asanyarray(color.get_data())
        colorful=colorful.reshape(-1,3)
     
        pc.map_to(color)
        points = pc.calculate(depth)
     
        #获取顶点坐标
        vtx = np.asanyarray(points.get_vertices())
        #获取纹理坐标
        #tex = np.asanyarray(points.get_texture_coordinates())
     
     
        with open('could.txt','w') as f:
            for i in range(len(vtx)):
                f.write(str(np.float(vtx[i][0])*1000)+' '+str(np.float(vtx[i][1])*1000)+' '+str(np.float(vtx[i][2])*1000)+' '+str(np.float(colorful[i][0]))+' '+str(np.float(colorful[i][1]))+' '+str(np.float(colorful[i][2]))+'\n')

三、通过pcl可视化点云

https://github.com/strawlab/python-pcl/blob/master/examples/example.py

1.在pcl中，要显示三维加色彩的点云坐标，每个点云包含了 x，y，z，rgb四个参数，特别的，rbg这个参数是由三维彩色坐标转换过来的。刚才得到的could.txt中，x,y,z,r,g,b 转换为x,y,z,rgb，只改颜色数据np.int(data\[i\]\[3\])<<16|np.int(data\[i\]\[4\])<<8|np.int(data\[i\]\[5\])。保存为cloud\_rbg.txt。

with open('could.txt','r') as f:
            lines = f.readlines()
            num=0
            for line in lines:
                l=line.strip().split()
                data.append([np.float(l[0]),np.float(l[1]),np.float(l[2]),np.float(l[3]),np.float(l[4]),np.float(l[5])])
                #data.append([np.float(l[0]), np.float(l[1]), np.float(l[2])])
                num = num+1
     
     
        with open('cloud_rgb.txt', 'w') as f:
            for i in range(len(data)):
                f.write(str(np.float(data[i][0])) + ' ' + str(np.float(data[i][1])) + ' ' + str(np.float(data[i][2]))+ ' '  + str(np.int(data[i][3])<<16|np.int(data[i][4])<<8|np.int(data[i][5]))+'\n')

2. 显示

def visual():
        cloud = pcl.PointCloud_PointXYZRGB()
        points = np.zeros((307200,4),dtype=np.float32)
     
        n=0
        with open('cloud_rgb.txt','r') as f:
            lines = f.readlines()
            for line in lines:
                l=line.strip().split()
                #保存xyz时候扩大了1000倍，发现并没有用
                points[n][0] = np.float(l[0])/1000
                points[n][1] = np.float(l[1])/1000
                points[n][2] = np.float(l[2])/1000
                points[n][3] = np.int(l[3])
                n=n+1
     
        print(points[0:100]) #看看数据是怎么样的
        cloud.from_array(points) #从array构建点云的方式
     
        visual = pcl.pcl_visualization.CloudViewing()
        visual.ShowColorCloud(cloud)
     
        v = True
        while v:
            v = not (visual.WasStopped())

3.想生成pcd，再读取pcd，但是下面的程序在可视化的时候报错

def txt2pcd():
        import time
        filename='cloud_rgb.txt'
        print("the input file name is:%r." % filename)
     
        start = time.time()
        print("open the file...")
        file = open(filename, "r+")
        count = 0
     
        # 统计源文件的点数
        for line in file:
            count = count + 1
        print("size is %d" % count)
        file.close()
     
        # output = open("out.pcd","w+")
        f_prefix = filename.split('.')[0]
        output_filename = '{prefix}.pcd'.format(prefix=f_prefix)
        output = open(output_filename, "w+")
     
        list = ['# .PCD v0.7 - Point Cloud Data file format\n', 'VERSION 0.7\n', 'FIELDS x y z rgb\n', 'SIZE 4 4 4 4\n',
                'TYPE F F F U\n', 'COUNT 1 1 1 1\n']
     
        output.writelines(list)
        output.write('WIDTH ')  # 注意后边有空格
        output.write(str(count))
        output.write('\nHEIGHT ')
        output.write(str(1))  # 强制类型转换，文件的输入只能是str格式
        output.write('\nPOINTS ')
        output.write(str(count))
        output.write('\nDATA ascii\n')
        file1 = open(filename, "r")
        all = file1.read()
        output.write(all)
        output.close()
        file1.close()
        end = time.time()
        print("run time is: ", end - start)
     
     
        import pcl.pcl_visualization
        cloud = pcl.load_XYZRGB('cloud_rgb.pcd')
        visual = pcl.pcl_visualization.CloudViewing()
        visual.ShowColorCloud(cloud, 'cloud')
     
        flag = True
        while flag:
            flag != visual.WasStopped()
    TypeError: expected bytes, str found

原图，深度图，云图(云图一片黑，鼠标滚轮翻一下) 如下：

opencv保存的颜色图：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21vc2h1aWxhbmd0aW5n_size_16_color_FFFFFF_t_70][]

scipy保存的颜色图

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21vc2h1aWxhbmd0aW5n_size_16_color_FFFFFF_t_70 1][]

深度图

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21vc2h1aWxhbmd0aW5n_size_16_color_FFFFFF_t_70 2][]

深度图可视化（这个是每有对齐的深度图align）：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21vc2h1aWxhbmd0aW5n_size_16_color_FFFFFF_t_70 3][]

云图（深度和色彩没有对齐的图）：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21vc2h1aWxhbmd0aW5n_size_16_color_FFFFFF_t_70 4][]  
  
原文链接：https://blog.csdn.net/moshuilangting/article/details/89383371

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