great-e-nerf / indoor_motor /get_trajectory_in_blender.py
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import bpy
import csv
from math import pi
# 设置导出文件的路径
export_file = "/home/falcary/workstation/blender_env_indoors_dataset/outputs/Capsule/output_in_blender.csv"
# 假设动画以24帧每秒的速度播放
# 每帧之间的时间间隔(秒)
frame_duration = 1 / 10
# 获取当前激活的相机对象
camera = bpy.context.scene.camera
# 如果存在相机且相机类型是'CAMERA'
if camera is not None and camera.type == 'CAMERA':
# 获取当前场景的起始和结束帧
start_frame = bpy.context.scene.frame_start
end_frame = bpy.context.scene.frame_end
# 准备存储相机变换数据的列表
camera_transforms = []
# 遍历指定的帧范围内的每一帧
for frame in range(start_frame, end_frame + 1):
# 设置当前帧
bpy.context.scene.frame_set(frame)
# 更新场景以获取最新数据
bpy.context.view_layer.update()
bpy.context.evaluated_depsgraph_get().update()
# 获取相机的世界坐标位置
loc = camera.matrix_world.to_translation()
# 获取相机的世界旋转(欧拉角)
rot = camera.matrix_world.to_euler('XYZ')
# 计算时间戳,假设帧率为24fps,将帧转换为微秒
timestamp = (frame - start_frame) * frame_duration * 1e7
scale = 1.0
# 添加位置和旋转数据到列表
camera_transforms.append({
'timestamp': int(timestamp), # 时间戳为整数微秒
'x': loc.x * scale,
'y': loc.y * scale,
'z': loc.z * scale,
# 将旋转角度转换为弧度,确保在[0, 2*pi)范围内
'rx': (rot.x + 2 * pi) % (2 * pi),
'ry': (rot.y + 2 * pi) % (2 * pi),
'rz': (rot.z + 2 * pi) % (2 * pi),
})
# 导出到CSV文件
with open(export_file, 'w', newline='') as csvfile:
fieldnames = ['timestamp', 'x', 'y', 'z', 'rx', 'ry', 'rz']
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
# 写入表头
csvfile.write('# ' + ', '.join(fieldnames) + '\n')
# 写入变换数据
for transform in camera_transforms:
writer.writerow(transform)
print(f"Camera transforms exported to {export_file}")
else:
print("No camera selected or active object is not a camera.")