点云学习笔记8——python pcl将点云数据转换成正视图

小咪咪 2022-09-10 02:23 335阅读 0赞

### 点云学习笔记8——python pcl将点云数据转换成正投影 ###

*  原始点云图效果：
 *  转换代码
 *  参考

# 原始点云图效果： #

![在这里插入图片描述][watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA57-f57695ZqE_size_20_color_FFFFFF_t_70_g_se_x_16]  
原始bin文件：

# 转换代码 #

import numpy as np
    import pcl
    from mpl_toolkits.mplot3d import Axes3D
    import matplotlib.pyplot as plt
    
    
    def lidar_to_2d_front_view(points,
                               v_res,
                               h_res,
                               v_fov,
                               val="depth",
                               cmap="jet",
                               saveto=None,
                               y_fudge=0.0
                               ):
        """ Takes points in 3D space from LIDAR data and projects them to a 2D
            "front view" image, and saves that image.
    
        Args:
            points: (np array)
                The numpy array containing the lidar points.
                The shape should be Nx4
                - Where N is the number of points, and
                - each point is specified by 4 values (x, y, z, reflectance)
            v_res: (float)
                vertical resolution of the lidar sensor used.
            h_res: (float)
                horizontal resolution of the lidar sensor used.
            v_fov: (tuple of two floats)
                (minimum_negative_angle, max_positive_angle)
            val: (str)
                What value to use to encode the points that get plotted.
                One of { "depth", "height", "reflectance"}
            cmap: (str)
                Color map to use to color code the `val` values.
                NOTE: Must be a value accepted by matplotlib's scatter function
                Examples: "jet", "gray"
            saveto: (str or None)
                If a string is provided, it saves the image as this filename.
                If None, then it just shows the image.
            y_fudge: (float)
                A hacky fudge factor to use if the theoretical calculations of
                vertical range do not match the actual data.
    
                For a Velodyne HDL 64E, set this value to 5.
        """
    
        # DUMMY PROOFING
        assert len(v_fov) ==2, "v_fov must be list/tuple of length 2"
        assert v_fov[0] <= 0, "first element in v_fov must be 0 or negative"
        assert val in { "depth", "height", "reflectance"}, \
            'val must be one of {"depth", "height", "reflectance"}'
    
    
        x_lidar = points[:, 0]
        y_lidar = points[:, 1]
        z_lidar = points[:, 2]
        r_lidar = points[:, 3] # Reflectance
        # Distance relative to origin when looked from top
        d_lidar = np.sqrt(x_lidar ** 2 + y_lidar ** 2)
        # Absolute distance relative to origin
        # d_lidar = np.sqrt(x_lidar ** 2 + y_lidar ** 2, z_lidar ** 2)
    
        v_fov_total = -v_fov[0] + v_fov[1]
    
        # Convert to Radians
        v_res_rad = v_res * (np.pi/180)
        h_res_rad = h_res * (np.pi/180)
    
        # PROJECT INTO IMAGE COORDINATES
        x_img = np.arctan2(-y_lidar, x_lidar)/ h_res_rad
        y_img = np.arctan2(z_lidar, d_lidar)/ v_res_rad
    
        # SHIFT COORDINATES TO MAKE 0,0 THE MINIMUM
        x_min = -360.0 / h_res / 2  # Theoretical min x value based on sensor specs
        x_img -= x_min              # Shift
        x_max = 360.0 / h_res       # Theoretical max x value after shifting
    
        y_min = v_fov[0] / v_res    # theoretical min y value based on sensor specs
        y_img -= y_min              # Shift
        y_max = v_fov_total / v_res # Theoretical max x value after shifting
    
        y_max += y_fudge            # Fudge factor if the calculations based on
                                    # spec sheet do not match the range of
                                    # angles collected by in the data.
    
        # WHAT DATA TO USE TO ENCODE THE VALUE FOR EACH PIXEL
        if val == "reflectance":
            pixel_values = r_lidar
        elif val == "height":
            pixel_values = z_lidar
        else:
            pixel_values = -d_lidar
    
        # PLOT THE IMAGE
        cmap = "jet"            # Color map to use
        dpi = 100               # Image resolution
        fig, ax = plt.subplots(figsize=(x_max/dpi, y_max/dpi), dpi=dpi)
        ax.scatter(x_img,y_img, s=1, c=pixel_values, linewidths=0, alpha=1, cmap=cmap)
        ax.set_axis_bgcolor((0, 0, 0)) # Set regions with no points to black
        ax.axis('scaled')              # { equal, scaled}
        ax.xaxis.set_visible(False)    # Do not draw axis tick marks
        ax.yaxis.set_visible(False)    # Do not draw axis tick marks
        plt.xlim([0, x_max])   # prevent drawing empty space outside of horizontal FOV
        plt.ylim([0, y_max])   # prevent drawing empty space outside of vertical FOV
    
        if saveto is not None:
            fig.savefig(saveto, dpi=dpi, bbox_inches='tight', pad_inches=0.0)
        else:
            fig.show()
    
    
    if __name__ == "__main__":
        pt = pcl.load('PointClouds/0010.pcd')
        shape = pt.to_array().transpose()
        x = shape[0]
        y = shape[1]
        z = shape[2]
    
        pointcloud = np.fromfile(str('um_000000.bin'), dtype=np.float32, count=-1).reshape([-1, 4])
    
    
        lidar=pointcloud
        #我自己16线参数，效果不好
        HRES = 0.2         # horizontal resolution (assuming 20Hz setting)
        VRES = 2          # vertical res
        VFOV = (-15.0, 15.0)  # 在垂直方向的角度范围是-15°~+15°
        Y_FUDGE = 5         # y fudge factor for velodyne HDL 64E
        
        #64线点云效果
        HRES = 0.35  # horizontal resolution (assuming 20Hz setting)
        VRES = 0.4  # vertical res
        VFOV = (-24.9, 2.0)  # Field of view (-ve, +ve) along vertical axis
        Y_FUDGE = 5  # y fudge factor for velodyne HDL 64E
    
        lidar_to_2d_front_view(lidar, v_res=VRES, h_res=HRES, v_fov=VFOV, val="depth",
                               saveto="lidar_depth.png", y_fudge=Y_FUDGE)
    
        lidar_to_2d_front_view(lidar, v_res=VRES, h_res=HRES, v_fov=VFOV, val="height",
                               saveto="lidar_height.png", y_fudge=Y_FUDGE)
    
        lidar_to_2d_front_view(lidar, v_res=VRES, h_res=HRES, v_fov=VFOV,
                               val="reflectance", saveto="lidar_reflectance.png",
                               y_fudge=Y_FUDGE)

可以看到有两个骑自行车的人  
![在这里插入图片描述][f611ae956ec84ccd9a31788f27e1ebfe.png]

![在这里插入图片描述][be19dab107ea4ed5b53448aa6358ac41.png]  
![在这里插入图片描述][d1f79c8531f545c7b89a1bfccfb64257.png]

# 参考 #

[http://ronny.rest/tutorials/module/pointclouds\_01/point\_cloud\_panoramic360/][http_ronny.rest_tutorials_module_pointclouds_01_point_cloud_panoramic360]

[https://blog.csdn.net/learning\_tortosie/article/details/88841127][https_blog.csdn.net_learning_tortosie_article_details_88841127]

[watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA57-f57695ZqE_size_20_color_FFFFFF_t_70_g_se_x_16]: /images/20220829/3609c4507b604d28a69304589e761fea.png
[f611ae956ec84ccd9a31788f27e1ebfe.png]: /images/20220829/15f10dffb8b9412bb6ce3521f0785442.png
[be19dab107ea4ed5b53448aa6358ac41.png]: /images/20220829/f80db0fcef8041e1948f0d5d055584e7.png
[d1f79c8531f545c7b89a1bfccfb64257.png]: /images/20220829/5c9f5dea93cd4667a64f98331d9472ac.png
[http_ronny.rest_tutorials_module_pointclouds_01_point_cloud_panoramic360]: http://ronny.rest/tutorials/module/pointclouds_01/point_cloud_panoramic360/
[https_blog.csdn.net_learning_tortosie_article_details_88841127]: https://blog.csdn.net/learning_tortosie/article/details/88841127