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run_model_server.py | 10 months ago |
单目深度估计,是指以单目RGB图像作为输入,根据图像中的结构信息、角点信息、相对位置信息等等对输入中的每个像素的深度值进行估计,输出稠密深度图。本模型来自于From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation论文。模型主要探索如果充分利用多尺度的信息来提升稠密深度图的效果,例如在主架构中加入ASPP模块,在不同尺度的分支之间添加充足的skip-connection来将多尺度信息融合,而在各个分支内设计Local Planar Guidance模块来利用局部信息。
引用:
@article{lee2019big,
title={From big to small: Multi-scale local planar guidance for monocular depth estimation},
author={Lee, Jin Han and Han, Myung-Kyu and Ko, Dong Wook and Suh, Il Hong},
journal={arXiv preprint arXiv:1907.10326},
year={2019}
}
@misc{ErenBalatkan/Bts-PyTorch,
title={https://github.com/ErenBalatkan/Bts-PyTorch}
}
本模型基于 ServiceBoot微服务引擎 进行服务化封装,参见: 《CubeAI模型开发指南》
$ sh pip-install-reqs.sh
$ serviceboot start
或
$ python3 run_model_server.py
一键式本地容器化部署和运行,参见: 《CubeAI模型独立部署指南》 或 CubeAI Docker Builder
本模型服务可一键发布至 CubeAI智立方平台 进行共享和部署,参见: 《CubeAI模型发布指南》
本模型提供了4个API接口:
API接口1:
API端点: /api/data
HTTP方法: POST
HTTP请求体:
{
"action": "predict",
"args": {
"img": <压缩图像的base64编码字符串(或其Data URL表示)>
}
}
HTTP响应体:
{
"status": "ok"|"err",
"value": [<results>, <base64编码压缩图像URL>]
}
API接口2:
API端点: /api/data
HTTP方法: POST
HTTP请求体:
{
"action": "predict_video",
"args": {
"url": <云端视频流媒体URL, 例如: rtmp://localhost/live/ch1>
}
}
HTTP响应体:
{
"status": "ok"|"err",
"value": <(流媒体当前帧图像)AI处理结果的base64编码压缩图像URL>
}
API接口3:
API端点: /api/stream/predict
HTTP方法: POST
HTTP请求体: <二进制编码的压缩图像字节流>
HTTP响应体: 同API接口1
API接口4:
API端点: /api/file/predict
HTTP方法: POST
HTTP请求体: <用于HTTP文件上传的XHR格式请求体>
HTTP响应体: 同API接口1
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Python Shell Dockerfile Text other
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