点云学习笔记7——python pcl将点云数据转换成俯视图（鸟瞰图）

我会带着你远行 2022-09-10 00:15 28阅读 0赞

### 点云学习笔记7——python pcl将点云数据转换成俯视图（鸟瞰图） ###

*  环境安装
 *  点云数据
 *  代码
 *  效果图
 *  参考

# 环境安装 #

可以参考我另外一篇文章：

[点云学习笔记3——点云库(PCL)安装与测试教程][3_PCL]

# 点云数据 #

# 代码 #

import numpy as np
    import pcl
    
    #下面的代码将感兴趣的矩形设置为在原点的两侧，跨度为10米，并在其前面20米处。
    side_range=(-20, 20)     # left-most to right-most
    fwd_range=(-20, 20)       # back-most to forward-most
    
    print(side_range)
    print(fwd_range)
    
    pt = pcl.load('PointClouds/0120.pcd')
    
    shape = pt.to_array().transpose()
    # 现在shape是numpy.ndarray
    
    
    x = shape[0]
    y = shape[1]
    z = shape[2]
    
    # EXTRACT THE POINTS FOR EACH AXIS
    # x_points = x[:, 0]
    # y_points = y[:, 1]
    # z_points = z[:, 2]
    
    x_points =  shape[0]
    y_points =  shape[1]
    z_points =  shape[2]
    print(x_points)
    # FILTER - To return only indices of points within desired cube
    # Three filters for: Front-to-back, side-to-side, and height ranges
    # Note left side is positive y axis in LIDAR coordinates
    
    #创建一个过滤器，仅保留实际位于指定的矩形内的点。
    
    f_filt = np.logical_and((x_points > fwd_range[0]), (x_points < fwd_range[1]))
    s_filt = np.logical_and((y_points > -side_range[1]), (y_points < -side_range[0]))
    filter = np.logical_and(f_filt, s_filt)
    indices = np.argwhere(filter).flatten()
    
    # KEEPERS
    x_points = x_points[indices]
    y_points = y_points[indices]
    z_points = z_points[indices]
    
    
    #将点位置映射到像素位置
    res = 0.05
    # CONVERT TO PIXEL POSITION VALUES - Based on resolution
    x_img = (-y_points / res).astype(np.int32)  # x axis is -y in LIDAR
    y_img = (-x_points / res).astype(np.int32)  # y axis is -x in LIDAR
    
    #平移原点
    #x和y值仍有负数，还不能投影到图像上，因此还需要平移数据，使得（0,0）位置的数据最小。
    
    # SHIFT PIXELS TO HAVE MINIMUM BE (0,0)
    # floor and ceil used to prevent anything being rounded to below 0 after shift
    x_img -= int(np.floor(side_range[0] / res))
    y_img += int(np.ceil(fwd_range[1] / res))
    
    
    height_range = (-2, 0.5)  # bottom-most to upper-most
    
    # CLIP HEIGHT VALUES - to between min and max heights
    pixel_values = np.clip(a = z_points,
                               a_min=height_range[0],
                               a_max=height_range[1])
    
    
    def scale_to_255(a, min, max, dtype=np.uint8):
        """ Scales an array of values from specified min, max range to 0-255
            Optionally specify the data type of the output (default is uint8)
        """
        return (((a - min) / float(max - min)) * 255).astype(dtype)
    
    # RESCALE THE HEIGHT VALUES - to be between the range 0-255
    pixel_values  = scale_to_255(pixel_values, min=height_range[0], max=height_range[1])
    
    
    # INITIALIZE EMPTY ARRAY - of the dimensions we want
    x_max = 1+int((side_range[1] - side_range[0])/res)
    y_max = 1+int((fwd_range[1] - fwd_range[0])/res)
    im = np.zeros([y_max, x_max], dtype=np.uint8)
    
    # FILL PIXEL VALUES IN IMAGE ARRAY
    im[y_img, x_img] = pixel_values
    
    # CONVERT FROM NUMPY ARRAY TO A PIL IMAGE
    from PIL import Image
    im2 = Image.fromarray(im)
    im2.show()
    
    import matplotlib.pyplot as plt
    plt.imshow(im, cmap="spectral", vmin=0, vmax=255)
    plt.show()

# 效果图 #

![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]

![在这里插入图片描述][watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA57-f57695ZqE_size_19_color_FFFFFF_t_70_g_se_x_16]

# 参考 #

[https://blog.csdn.net/learning\_tortosie/article/details/88828388][https_blog.csdn.net_learning_tortosie_article_details_88828388]

[3_PCL]: https://blog.csdn.net/mao_hui_fei/article/details/119227067
[watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA57-f57695ZqE_size_20_color_FFFFFF_t_70_g_se_x_16]: /images/20220829/3f7bbdab40e945629b326fee4425d89a.png
[watermark_type_ZHJvaWRzYW5zZmFsbGJhY2s_shadow_50_text_Q1NETiBA57-f57695ZqE_size_19_color_FFFFFF_t_70_g_se_x_16]: /images/20220829/c4398570ea1c4940b4e7e6e5a98b55a2.png
[https_blog.csdn.net_learning_tortosie_article_details_88828388]: https://blog.csdn.net/learning_tortosie/article/details/88828388