国内室内设计,优化是什么意思?,jsp网站制作详细教程,霞山网站建设公司机型M350RTK#xff0c;其飞行记录文件为加密的#xff0c;我的完善代码如下
gitgithub.com:huashu996/DJFlightRecordParsing2TXT.git
一、下载安装官方的DJIFlightRecord
git clone gitgithub.com:dji-sdk/FlightRecordParsingLib.git飞行记录文件在打开【我的电脑】其飞行记录文件为加密的我的完善代码如下
gitgithub.com:huashu996/DJFlightRecordParsing2TXT.git
一、下载安装官方的DJIFlightRecord
git clone gitgithub.com:dji-sdk/FlightRecordParsingLib.git飞行记录文件在打开【我的电脑】进入遥控器内存 文件路径此电脑 pm430 内部共享存储空间 DJI com.dji.industry.pilot FlightRecord
二、注册成为大疆开发者获取SDK 密钥
网址如下DJI Developer 注册完之后新建APP获得密钥 登录 DJI 开发者平台点击创建应用App Type 选择 Open API自行填写“App 名称”“分类”和“描述”点击 创建。 通过个人邮箱激活 App在开发者网平台个人点击查看对应 App 信息其中 App Key 即为下文所需的 SDK 密钥参数。 三、编译运行
编译
cd FlightRecordParsingLib-master/dji-flightrecord-kit/build/Ubuntu/FlightRecordStandardizationCpp
sh generate.sh
运行
cd ~/FlightRecordParsingLib-master/dji-flightrecord-kit/build/Ubuntu/FRSample
export SDK_KEYyour_sdk_key_value
./FRSample /home/cxl/FlightRecordParsingLib-master/DJrecord/DJIFlightRecord_2023-07-18_[16-14-57].txt 此时会在终端打印出一系列无人机的状态信息但还是不能够使用
于是我更改了main.cc文件使它能够保存数据到txt文件 四、获取单个数据
上一步生成的txt文件由于参数众多是巨大的这里我写了一个py文件用于提取重要的信息例如提取经纬度和高度
import json# Read JSON fragment from txt file
with open(output.txt, r) as file:json_data json.load(file)# Extract all aircraftLocation and altitude data
location_altitude_data []
for frame_state in json_data[info][frameTimeStates]:aircraft_location frame_state[flightControllerState][aircraftLocation]altitude frame_state[flightControllerState][altitude]location_altitude_data.append({latitude: aircraft_location[latitude], longitude: aircraft_location[longitude], altitude: altitude})# Write values to a new txt file
with open(xyz_output.txt, w) as f:for data in location_altitude_data:f.write(flatitude: {data[latitude]}, longitude: {data[longitude]}, altitude: {data[altitude]}\n)print(Values extracted and written to output.txt file.)
就能够生成只有这几个参数的txt文件 五、生成轨迹
将经纬度和高度生成xyz坐标和画图暂定以前20个点的均值作为投影坐标系的原点
from pyproj import Proj
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d as mplot3d
def read_coordinates_from_txt(file_path):coordinates []with open(file_path, r) as file:for line in file:parts line.strip().split(,)latitude float(parts[0].split(:)[1].strip())longitude float(parts[1].split(:)[1].strip())altitude float(parts[2].split(:)[1].strip())coordinates.append((latitude, longitude, altitude))return coordinatesdef calculate_avg_coordinates(coordinates):num_points len(coordinates)avg_latitude sum(coord[0] for coord in coordinates) / num_pointsavg_longitude sum(coord[1] for coord in coordinates) / num_pointsreturn avg_latitude, avg_longitudedef project_coordinates_to_xyz(coordinates, avg_latitude, avg_longitude, origin_x, origin_y):# Define the projection coordinate system (UTM zone 10, WGS84 ellipsoid)p Proj(projutm, zone20, ellpsWGS84, preserve_unitsFalse)# Project all points in the coordinates list to xy coordinate systemprojected_coordinates [p(longitude, latitude) for latitude, longitude, _ in coordinates]# Calculate the relative xyz values for each point with respect to the originrelative_xyz_coordinates []for (x, y), (_, _, altitude) in zip(projected_coordinates, coordinates):relative_x x - origin_xrelative_y y - origin_yrelative_xyz_coordinates.append((relative_x, relative_y, altitude))return relative_xyz_coordinatesdef three_plot_coordinates(coordinates):# Separate the x, y, z coordinates for plottingx_values, y_values, z_values zip(*coordinates)# Create a new figure for the 3D plotfig plt.figure()ax fig.add_subplot(111, projection3d)# Plot points as a 3D scatter plotax.scatter(x_values, y_values, z_values, cblue, labelPoints)# Connect points with linesfor i in range(1, len(x_values)):ax.plot([x_values[i - 1], x_values[i]], [y_values[i - 1], y_values[i]], [z_values[i - 1], z_values[i]], k-, linewidth0.5)# Add labels and titleax.set_xlabel(X (meters))ax.set_ylabel(Y (meters))ax.set_zlabel(Z (meters))ax.set_title(Projected Coordinates - 3D)# Display the 3D plot in a separate windowplt.show()def two_plot_coordinates(coordinates):# Separate the x, y, z coordinates for plottingx_values, y_values, z_values zip(*coordinates)# Create a new figure for the 2D plotfig plt.figure()# Plot pointsplt.scatter(x_values, y_values, cblue, labelPoints)# Connect points with linesfor i in range(1, len(x_values)):plt.plot([x_values[i - 1], x_values[i]], [y_values[i - 1], y_values[i]], k-, linewidth0.5)# Add labels and titleplt.xlabel(X (meters))plt.ylabel(Y (meters))plt.title(Projected Coordinates - 2D)plt.legend()# Display the 2D plot in a separate windowplt.show()if __name__ __main__:input_file_path xyz_output.txt # Replace with the actual input file pathcoordinates read_coordinates_from_txt(input_file_path)# Use the first 10 points to define the projection coordinate systemnum_points_for_avg 20avg_coordinates coordinates[:num_points_for_avg]avg_latitude, avg_longitude calculate_avg_coordinates(avg_coordinates)# Project the average latitude and longitude to xy coordinate systemp Proj(projutm, zone20, ellpsWGS84, preserve_unitsFalse)origin_x, origin_y p(avg_longitude, avg_latitude)print(fAverage Latitude: {avg_latitude}, Average Longitude: {avg_longitude})print(fProjected Coordinates (x, y): {origin_x}, {origin_y})# Project all points in the coordinates list to xy coordinate systemfirst_coordinates [(0, 0, 0)] * min(len(coordinates), num_points_for_avg)projected_coordinates2 project_coordinates_to_xyz(coordinates[num_points_for_avg:], avg_latitude, avg_longitude, origin_x, origin_y)projected_coordinates first_coordinatesprojected_coordinates2# Save projected coordinates to a new text fileoutput_file_path projected_coordinates.txtwith open(output_file_path, w) as output_file:for x, y, z in projected_coordinates:output_file.write(fx: {x}, y: {y}, z: {z}\n)three_plot_coordinates(projected_coordinates)two_plot_coordinates(projected_coordinates)生成xyz的txt文档 上述只以坐标为例子想获取其他数据改变参数即可。
