random pictures get and save pose.py

import airsim
import os
import numpy as np
import pandas as pd

# 连接到AirSim模拟器
client = airsim.MultirotorClient()
client.confirmConnection()

# 获取图像路径
folder_path = "E:/FunctionMethod/airsim_images/desert_datasets/circular_10k9"

# 保存位姿信息的空DataFrame
poses_df = pd.DataFrame(columns=['index', 'x', 'y', 'z', 'yaw', 'pitch', 'roll'])

# 设置随机采样的范围和数量
num_samples = 2000  # 需要采样的数量
x_min, x_max, y_min, y_max, z_min, z_max = -2, 2, -2, 2, -2, 0  # 位置范围
yaw_min, yaw_max, pitch_min, pitch_max, roll_min, roll_max = -90, 90, -45, 45, -45, 45  # 姿态范围

# 相机列表
camera_list = ["0", "1", "2", "3", "4"]

# 随机采样并保存图像和位姿信息
poses_list = []
for i in range(num_samples):
    # 随机生成目标位置，并设置姿态朝向
    x = np.random.uniform(x_min, x_max)
    y = np.random.uniform(y_min, y_max)
    z = np.random.uniform(z_min, z_max)
    yaw = np.random.uniform(yaw_min, yaw_max)
    pitch = np.random.uniform(pitch_min, pitch_max)
    roll = np.random.uniform(roll_min, roll_max)
    pose = airsim.Pose(airsim.Vector3r(x, y, z), airsim.to_quaternion(pitch, roll, yaw))
    poses_list.append({'index': i, 'x': x, 'y': y, 'z': z, 'yaw': yaw, 'pitch': pitch, 'roll': roll})

    # 移动到目标位置
    client.simSetVehiclePose(pose, True)

    # # 获取相机图像
    # responses = client.simGetImages([airsim.ImageRequest("1", airsim.ImageType.Scene, False, False)])
    # img_raw = responses[0]

    # 遍历相机列表，获取每个相机的图像
    for j, camera_name in enumerate(camera_list):
        # 获取相机图像
        responses = client.simGetImages([airsim.ImageRequest(camera_name, airsim.ImageType.Scene, False, False)])
        img_raw = responses[0]

        # 将字节流转换为PIL的Image对象
        img1d = np.frombuffer(img_raw.image_data_uint8, dtype=np.uint8)
        img_rgb = img1d.reshape(img_raw.height, img_raw.width, 3)

        # 保存PNG格式的图像
        img_filename = "pose_{0}_x_{1:.2f}_y_{2:.2f}_z_{3:.2f}_yaw_{4:.2f}_pitch_{5:.2f}_roll_{6:.2f}_camera_{4}.png".format(i, x, y, z, yaw, pitch, roll, j)
        img_filepath = os.path.join(folder_path, img_filename)
        airsim.write_png(os.path.normpath(img_filepath), img_rgb)

print("全部图像和位姿信息均已保存到文件夹：", folder_path)

# 将位姿信息保存到csv文件中
poses_df = pd.DataFrame(poses_list)
poses_df.to_csv(os.path.join(folder_path, 'poses.csv'), index=False)