在開始之前，了解電腦視覺中的傳統座標係是很重要的。其次是Open3D和Microsoft Kinect感測器。在電腦視覺中，影像以獨立的2D座標系表示，其中x軸從左向右指向，y軸是上下指向。對於相機，3D座標係原點位於相機的焦點處，x軸指向右，y軸指向下，z軸指向前。
電腦視覺座標系
我們先匯入所需的Python函式庫：
import numpy as np import open3d as o3d
為了更好地理解，讓我們從PLY 檔案中匯入點雲，使用Open3D 建立預設的3D 座標系並顯示它們：
# Read point cloud:
pcd = o3d.io.read_point_cloud("data/depth_2_pcd.ply")
# Create a 3D coordinate system:
origin = o3d.geometry.TriangleMesh.create_coordinate_frame(size=0.5)
# geometries to draw:
geometries = [pcd, origin]
# Visualize:
o3d.visualization.draw_geometries(geometries)
# Get max and min points of each axis x, y and z: x_max = max(pcd.points, key=lambda x: x[0]) y_max = max(pcd.points, key=lambda x: x[1]) z_max = max(pcd.points, key=lambda x: x[2]) x_min = min(pcd.points, key=lambda x: x[0]) y_min = min(pcd.points, key=lambda x: x[1]) z_min = min(pcd.points, key=lambda x: x[2])
# Colors: RED = [1., 0., 0.] GREEN = [0., 1., 0.] BLUE = [0., 0., 1.] YELLOW = [1., 1., 0.] MAGENTA = [1., 0., 1.] CYAN = [0., 1., 1.] positions = [x_max, y_max, z_max, x_min, y_min, z_min] colors = [RED, GREEN, BLUE, MAGENTA, YELLOW, CYAN] for i in range(len(positions)): # Create a sphere mesh: sphere = o3d.geometry.TriangleMesh.create_sphere(radius=0.05) # move to the point position: sphere.translate(np.asarray(positions[i])) # add color: sphere.paint_uniform_color(np.asarray(colors[i])) # compute normals for vertices or faces: sphere.compute_vertex_normals() # add to geometry list to display later: geometries.append(sphere) # Display: o3d.visualization.draw_geometries(geometries)
# Define a threshold: THRESHOLD = 0.075 # Get the max value along the y-axis: y_max = max(pcd.points, key=lambda x: x[1])[1] # Get the original points color to be updated: pcd_colors = np.asarray(pcd.colors) # Number of points: n_points = pcd_colors.shape[0] # update color: for i in range(n_points): # if the current point is aground point: if pcd.points[i][1] >= y_max - THRESHOLD: pcd_colors[i] = GREEN# color it green pcd.colors = o3d.utility.Vector3dVector(pcd_colors) # Display: o3d.visualization.draw_geometries([pcd, origin])
左：3D 可视化的屏幕截图 右：深度图像的结果
让我们从之前的深度图像创建一个有组织的点云。我们首先导入相机参数。我们还导入深度图像并将其转换为3通道灰度图像，以便我们可以将地面像素设置为绿色：
import imageio.v3 as iio
import numpy as np
import matplotlib.pyplot as plt
# Camera parameters:
FX_DEPTH = 5.8262448167737955e+02
FY_DEPTH = 5.8269103270988637e+02
CX_DEPTH = 3.1304475870804731e+02
CY_DEPTH = 2.3844389626620386e+02
# Read depth image:
depth_image = iio.imread('../data/depth_2.png')
# Compute the grayscale image:
depth_grayscale = np.array(256 * depth_image / 0x0fff, dtype=np.uint8)
# Convert a grayscale image to a 3-channel image:
depth_grayscale = np.stack((depth_grayscale,) * 3, axis=-1)要计算一个有组织的点云，我们使用与上一篇教程相同的方法（Python：基于 RGB-D 图像的点云计算）。我们没有将深度图像扁平化，而是将jj和ii重塑为与深度图像相同的形状，如下所示:
# get depth image resolution: height, width = depth_image.shape # compute indices and reshape it to have the same shape as the depth image: jj = np.tile(range(width), height).reshape((height, width)) ii = np.repeat(range(height), width).reshape((height, width)) # Compute constants: xx = (jj - CX_DEPTH) / FX_DEPTH yy = (ii - CY_DEPTH) / FY_DEPTH # compute organised point cloud: organized_pcd = np.dstack((xx * depth_image, yy * depth_image, depth_image))
如果你打印出创建的点云的形状，你可以看到它是一个有3个通道的矩阵(480,640,3)。如果你觉得这个代码很难理解，请回到之前的教程（Python：基于 RGB-D 图像的点云计算）。
类似地，我们像上面那样检测地面，但不是更新点的颜色并显示点云，而是更新灰度图像的像素并显示它:
# Ground_detection: THRESHOLD = 0.075 * 1000# Define a threshold y_max = max(organized_pcd.reshape((height * width, 3)), key=lambda x: x[1])[ 1]# Get the max value along the y-axis # Set the ground pixels to green: for i in range(height): for j in range(width): if organized_pcd[i][j][1] >= y_max - THRESHOLD: depth_grayscale[i][j] = [0, 255, 0]# Update the depth image # Display depth_grayscale: plt.imshow(depth_grayscale) plt.show()
在本教程中，为了熟悉点云，我们引入了默认坐标系统，并实现了一个简单的地面检测算法。事实上，地面检测在某些应用(如导航)中是一项重要的任务，文献中已经提出了几种算法。实现算法简单;它认为最低点是地面。然而，它的限制是，深度相机必须与地面平行，这是大多数现实应用的情况不是这样的。
以上是利用 Python 實現點雲的地面檢測的詳細內容。更多資訊請關注PHP中文網其他相關文章！
