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FastDeploy/examples/vision
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ChaoII d70b6c99df [Model] update pptracking letterboxresize and add some comment (#438) 
* add override mark

* delete some

* recovery

* recovery

* add tracking

* add tracking py_bind and example

* add pptracking

* add pptracking

* iomanip head file

* add opencv_video lib

* add python libs package

Signed-off-by: ChaoII <849453582@qq.com>

* complete comments

Signed-off-by: ChaoII <849453582@qq.com>

* add jdeTracker_ member variable

Signed-off-by: ChaoII <849453582@qq.com>

* add 'FASTDEPLOY_DECL' macro

Signed-off-by: ChaoII <849453582@qq.com>

* remove kwargs params

Signed-off-by: ChaoII <849453582@qq.com>

* [Doc]update pptracking docs

* delete 'ENABLE_PADDLE_FRONTEND' switch

* add pptracking unit test

* update pptracking unit test

Signed-off-by: ChaoII <849453582@qq.com>

* modify test video file path and remove trt test

* update unit test model url

* remove 'FASTDEPLOY_DECL' macro

Signed-off-by: ChaoII <849453582@qq.com>

* fix build python packages about pptracking on win32

Signed-off-by: ChaoII <849453582@qq.com>

* update comment

Signed-off-by: ChaoII <849453582@qq.com>

* add pptracking model explain

Signed-off-by: ChaoII <849453582@qq.com>

Signed-off-by: ChaoII <849453582@qq.com>
Co-authored-by: Jason <jiangjiajun@baidu.com>
2022-10-28 20:46:00 +08:00
..
classification
[Doc] Update 0.3.0 to 0.4.0 in all docs (#443)
2022-10-27 14:33:05 +08:00
detection
[Doc] Update 0.3.0 to 0.4.0 in all docs (#443)
2022-10-27 14:33:05 +08:00
facedet
[Doc] Update 0.3.0 to 0.4.0 in all docs (#443)
2022-10-27 14:33:05 +08:00
faceid
[Doc] Update 0.3.0 to 0.4.0 in all docs (#443)
2022-10-27 14:33:05 +08:00
keypointdetection
[Doc] Update 0.3.0 to 0.4.0 in all docs (#443)
2022-10-27 14:33:05 +08:00
matting
[Doc] Update 0.3.0 to 0.4.0 in all docs (#443)
2022-10-27 14:33:05 +08:00
ocr
[Doc] Update 0.3.0 to 0.4.0 in all docs (#443)
2022-10-27 14:33:05 +08:00
segmentation/paddleseg
[Bug Fix ]Update ppseg visualize python api && demo (#454)
2022-10-28 20:38:05 +08:00
tracking/pptracking
[Model] update pptracking letterboxresize and add some comment (#438)
2022-10-28 20:46:00 +08:00
README.md
[Model] Add tinypose single && pipeline model (#177)
2022-10-21 09:28:23 +08:00

README.md

视觉模型部署

本目录下提供了各类视觉模型的部署，主要涵盖以下任务类型

任务类型 说明 预测结果结构体
Detection 目标检测，输入图像，检测图像中物体位置，并返回检测框坐标及类别和置信度 DetectionResult
Segmentation 语义分割，输入图像，给出图像中每个像素的分类及置信度 SegmentationResult
Classification 图像分类，输入图像，给出图像的分类结果和置信度 ClassifyResult
FaceDetection 人脸检测，输入图像，检测图像中人脸位置，并返回检测框坐标及人脸关键点 FaceDetectionResult
KeypointDetection 关键点检测，输入图像，返回图像中人物行为的各个关键点坐标和置信度 KeyPointDetectionResult
FaceRecognition 人脸识别，输入图像，返回可用于相似度计算的人脸特征的embedding FaceRecognitionResult
Matting 抠图，输入图像，返回图片的前景每个像素点的Alpha值 MattingResult
OCR 文本框检测，分类，文本框内容识别，输入图像，返回文本框坐标，文本框的方向类别以及框内的文本内容 OCRResult

FastDeploy API设计

视觉模型具有较有统一任务范式，在设计API时（包括C++/Python），FastDeploy将视觉模型的部署拆分为四个步骤

  • 模型加载
  • 图像预处理
  • 模型推理
  • 推理结果后处理

FastDeploy针对飞桨的视觉套件，以及外部热门模型，提供端到端的部署服务，用户只需准备模型，按以下步骤即可完成整个模型的部署

  • 加载模型
  • 调用predict接口

FastDeploy在各视觉模型部署时，也支持一键切换后端推理引擎，详情参阅如何切换模型推理引擎。