remove useless gif, test=document_fix (#7463)

README_cn.md

@@ -18,7 +18,7 @@
 
 
 <div  align="center">
-  <img src="docs/images/ppdet.gif" width="800"/>
+  <img src="https://user-images.githubusercontent.com/22989727/205581915-aa8d6bee-5624-4aec-8059-76b5ebaf96f1.gif" width="800"/>
 
 </div>
 

README_en.md

@@ -17,7 +17,7 @@
 </div>
 
 <div  align="center">
-  <img src="docs/images/ppdet.gif" width="800"/>
+  <img src="https://user-images.githubusercontent.com/22989727/205581915-aa8d6bee-5624-4aec-8059-76b5ebaf96f1.gif" width="800"/>
 
 </div>
 

configs/keypoint/README.md

@@ -3,7 +3,7 @@
 # 关键点检测系列模型
 
 <div align="center">
-  <img src="./football_keypoint.gif" width='800'/>
+  <img src="https://user-images.githubusercontent.com/22989727/205551833-a891a790-73c6-43cb-84f9-91553e9ef27b.gif" width='800'/>
 </div>
 
 ## 目录

configs/keypoint/README_en.md

@@ -30,7 +30,7 @@ The keypoint detection part in PaddleDetection follows the state-of-the-art algo
 At the same time, PaddleDetection provides a self-developed real-time keypoint detection model [PP-TinyPose](./tiny_pose/README_en.md) optimized for mobile devices.
 
 <div align="center">
-  <img src="./football_keypoint.gif" width='800'/>
+  <img src="https://user-images.githubusercontent.com/22989727/205551833-a891a790-73c6-43cb-84f9-91553e9ef27b.gif" width='800'/>
 </div>
 
 ## Model Recommendation

configs/keypoint/tiny_pose/README.md

@@ -32,7 +32,7 @@ PP-TinyPose是PaddleDetecion针对移动端设备优化的实时关键点检测
 - [Android Fitness Demo](https://github.com/zhiboniu/pose_demo_android)  基于PP-TinyPose, 高效实现健身校准与计数功能。
 
 <div align="center">
-  <img src="../../../docs/images/fitness_demo.gif" width='636'/>
+  <img src="https://user-images.githubusercontent.com/22989727/205545098-fe6515af-3f1d-4303-bb4d-6e2141e42e2c.gif" width='636'/>
 </div>
 
 - 欢迎扫码快速体验

configs/keypoint/tiny_pose/README_en.md

@@ -24,7 +24,7 @@ If you want to deploy it on the mobile devives, you also need:
 - [Android Fitness Demo](https://github.com/zhiboniu/pose_demo_android) based on PP-TinyPose, which efficiently implements fitness calibration and counting.
 
 <div align="center">
-  <img src="../../../docs/images/fitness_demo.gif" width='636'/>
+  <img src="https://user-images.githubusercontent.com/22989727/205545098-fe6515af-3f1d-4303-bb4d-6e2141e42e2c.gif" width='636'/>
 </div>
 
 - Welcome to scan the QR code for quick experience.

configs/mot/README.md

@@ -45,7 +45,7 @@ PP-Tracking单镜头跟踪采用的方案是[FairMOT](./fairmot)，跨镜头跟
 </div>
 
 <div width="1000" align="center">
-  <img src="../../docs/images/pptracking-demo.gif"/>
+  <img src="https://user-images.githubusercontent.com/22989727/205546999-f847183d-73e5-4abe-9896-ce6a245efc79.gif"/>
   <br>
   视频来源：VisDrone和BDD100K公开数据集</div>
 </div>

configs/mot/README_en.md

@@ -40,7 +40,7 @@ PP-Tracking supports GUI predict and deployment. Please refer to this [doc](http
 </div>
 
 <div width="1000" align="center">
-  <img src="../../docs/images/pptracking-demo.gif"/>
+  <img src="https://user-images.githubusercontent.com/22989727/205546999-f847183d-73e5-4abe-9896-ce6a245efc79.gif"/>
   <br>
   video source：VisDrone, BDD100K dataset</div>
 </div>

configs/mot/headtracking21/README_cn.md

@@ -6,7 +6,7 @@
 现有行人跟踪器对高人群密度场景表现不佳，人头跟踪更适用于密集场景的跟踪。
 [HT-21](https://motchallenge.net/data/Head_Tracking_21)是一个高人群密度拥挤场景的人头跟踪数据集，场景包括不同的光线和环境条件下的拥挤的室内和室外场景，所有序列的帧速率都是25fps。
 <div align="center">
-  <img src="../../../docs/images/ht_fairmot.gif" width='800'/>
+  <img src="https://user-images.githubusercontent.com/22989727/205540742-820984c2-8920-467a-bdde-faea421018c5.gif" width='800'/>
 </div>
 
 ## 模型库

configs/mot/jde/README.md

@@ -22,7 +22,7 @@ PP-Tracking supports two paradigms: single camera tracking (MOT) and multi-camer
 PP-tracking provides an AI studio public project tutorial. Please refer to this [tutorial](https://aistudio.baidu.com/aistudio/projectdetail/3022582).
 
 <div align="center">
-  <img src="../../../docs/images/mot16_jde.gif" width=500 />
+  <img src="https://user-images.githubusercontent.com/22989727/205540305-457d48bf-e9ec-4f28-896c-64c870126e05.gif" width=500 />
 </div>
 
 ## Model Zoo

configs/mot/jde/README_cn.md

@@ -20,7 +20,7 @@ PP-Tracking支持单镜头跟踪(MOT)和跨镜头跟踪(MTMCT)两种模式，针
 PP-Tracking 提供了AI Studio公开项目案例，教程请参考[PP-Tracking之手把手玩转多目标跟踪](https://aistudio.baidu.com/aistudio/projectdetail/3022582)。
 
 <div align="center">
-  <img src="../../../docs/images/mot16_jde.gif" width=500 />
+  <img src="https://user-images.githubusercontent.com/22989727/205540305-457d48bf-e9ec-4f28-896c-64c870126e05.gif" width=500 />
 </div>
 
 ## 模型库

deploy/pipeline/docs/tutorials/pphuman_action.md

@@ -17,7 +17,7 @@
 应用行为：摔倒识别
 
 <div align="center">
-  <img src="../images/action.gif" width='1000'/>
+  <img src="https://user-images.githubusercontent.com/22989727/205582385-08a1b6ae-9b1b-465a-ac25-d6427571eb56.gif" width='600'/><br>
   <center>数据来源及版权归属：天覆科技，感谢提供并开源实际场景数据，仅限学术研究使用</center>
 </div>
 
@@ -88,7 +88,7 @@ SKELETON_ACTION: # 基于骨骼点的行为识别模型配置
 应用行为：打电话识别
 
 <div align="center">
-  <img src="../images/calling.gif" width='1000'/>
+  <img src="https://user-images.githubusercontent.com/22989727/205596971-d92fd24e-977a-4742-91cc-ce5b4802473c.gif" width='600'/><br>
   <center>数据来源及版权归属：天覆科技，感谢提供并开源实际场景数据，仅限学术研究使用</center>
 </div>
 
@@ -145,7 +145,7 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pph
 应用行为：吸烟识别
 
 <div align="center">
-  <img src="../images/smoking.gif" width='1000'/>
+  <img src="https://user-images.githubusercontent.com/22989727/205599300-380c3805-63d6-43cc-9b77-2687b1328d7b.gif" width='600'/><br>
   <center>数据来源及版权归属：天覆科技，感谢提供并开源实际场景数据，仅限学术研究使用</center>
 </div>
 
@@ -198,7 +198,7 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pph
 应用行为：闯入识别
 
 <div align="center">
-  <img src="https://user-images.githubusercontent.com/22989727/178769370-03ab1965-cfd1-401b-9902-82620a06e43c.gif" width='1000'/>
+  <img src="https://user-images.githubusercontent.com/22989727/178769370-03ab1965-cfd1-401b-9902-82620a06e43c.gif" width='600'/>
 </div>
 
 具体使用请参照[PP-Human检测跟踪模块](pphuman_mot.md)的`5. 区域闯入判断和计数`。
@@ -213,7 +213,7 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pph
 应用行为：打架识别
 
 <div align="center">
-  <img src="../images/fight_demo.gif" width='1000'/>
+  <img src="https://user-images.githubusercontent.com/22989727/205597198-8b4333b3-6c39-472c-a25c-018dac908867.gif" width='600'/><br>
   <center>数据来源及版权归属：Surveillance Camera Fight Dataset。</center>
 </div>
 

deploy/pipeline/docs/tutorials/pphuman_action_en.md

@@ -36,7 +36,7 @@ Note:
 
 ## Skeleton-based action recognition -- falling detection
 
-<div align="center">  <img src="../images/action.gif" width='1000'/> <center>Data source and copyright owner：Skyinfor
+<div align="center">  <img src="https://user-images.githubusercontent.com/22989727/205582385-08a1b6ae-9b1b-465a-ac25-d6427571eb56.gif" width='600'/><br> <center>Data source and copyright owner：Skyinfor
 Technology. Thanks for the provision of actual scenario data, which are only
 used for academic research here. </center>
 
@@ -101,7 +101,7 @@ SKELETON_ACTION: # Config for skeleton-based action recognition model
 
 ## Image-Classification-Based Action Recognition -- Calling Recognition
 
-<div align="center">  <img src="../images/calling.gif" width='1000'/> <center>Data source and copyright owner：Skyinfor
+<div align="center">  <img src="https://user-images.githubusercontent.com/22989727/205596971-d92fd24e-977a-4742-91cc-ce5b4802473c.gif" width='600'/><br> <center>Data source and copyright owner：Skyinfor
 Technology. Thanks for the provision of actual scenario data, which are only
 used for academic research here. </center>
 
@@ -148,7 +148,7 @@ ID_BASED_CLSACTION: # config for classfication-based action recognition model
 
 ## Detection-based Action Recognition -- Smoking Detection
 
-<div align="center">  <img src="../images/smoking.gif" width='1000'/> <center>Data source and copyright owner：Skyinfor
+<div align="center">  <img src="https://user-images.githubusercontent.com/22989727/205599300-380c3805-63d6-43cc-9b77-2687b1328d7b.gif" width='600'/><br> <center>Data source and copyright owner：Skyinfor
 Technology. Thanks for the provision of actual scenario data, which are only
 used for academic research here. </center>
 
@@ -240,8 +240,8 @@ VIDEO_ACTION:  # Config for detection-based action recognition model
 
 The result is shown as follow:
 
-<div width="1000" align="center">
-  <img src="../images/fight_demo.gif"/>
+<div width="600" align="center">
+  <img src="https://user-images.githubusercontent.com/22989727/205597198-8b4333b3-6c39-472c-a25c-018dac908867.gif"/>
 </div>
 
 Data source and copyright owner: Surveillance Camera Fight Dataset.

deploy/pipeline/docs/tutorials/pphuman_attribute.md

@@ -69,8 +69,8 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pph
 
 测试效果如下：
 
-<div width="1000" align="center">
-  <img src="../images/attribute.gif"/>
+<div width="600" align="center">
+  <img src="https://user-images.githubusercontent.com/22989727/205597518-7a602bd5-e643-44a1-a4ca-03c9ffecd918.gif"/>
 </div>
 
 数据来源及版权归属：天覆科技，感谢提供并开源实际场景数据，仅限学术研究使用

deploy/pipeline/docs/tutorials/pphuman_attribute_en.md

@@ -65,7 +65,7 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pph
 
 The test result is：
 
-<div width="1000" align="center">
+<div width="600" align="center">
   <img src="https://user-images.githubusercontent.com/48054808/159898428-5bda0831-7249-4889-babd-9165f26f664d.gif"/>
 </div>
 

deploy/pipeline/docs/tutorials/pphuman_mot.md

@@ -57,8 +57,8 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pph
 
 测试效果如下：
 
-<div width="1000" align="center">
-  <img src="../images/mot.gif"/>
+<div width="600" align="center">
+  <img src="https://user-images.githubusercontent.com/22989727/205599943-8da89ce8-f6d1-47e5-adc8-6d199b76d167.gif"/>
 </div>
 
 数据来源及版权归属：天覆科技，感谢提供并开源实际场景数据，仅限学术研究使用
@@ -90,7 +90,7 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pph
 测试效果如下：
 
 <div align="center">
-  <img src="https://user-images.githubusercontent.com/22989727/178769370-03ab1965-cfd1-401b-9902-82620a06e43c.gif" width='1000'/>
+  <img src="https://user-images.githubusercontent.com/22989727/178769370-03ab1965-cfd1-401b-9902-82620a06e43c.gif" width='600'/>
 </div>
 
 ## 方案说明

deploy/pipeline/docs/tutorials/pphuman_mot_en.md

@@ -59,8 +59,8 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pph
 
 The test result is：
 
-<div width="1000" align="center">
-  <img src="../images/mot.gif"/>
+<div width="600" align="center">
+  <img src="https://user-images.githubusercontent.com/22989727/205599943-8da89ce8-f6d1-47e5-adc8-6d199b76d167.gif"/>
 </div>
 
 Data source and copyright owner：Skyinfor Technology. Thanks for the provision of actual scenario data, which are only used for academic research here.
@@ -90,7 +90,7 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_pph
 The test result is：
 
 <div align="center">
-  <img src="https://user-images.githubusercontent.com/22989727/178769370-03ab1965-cfd1-401b-9902-82620a06e43c.gif" width='1000'/>
+  <img src="https://user-images.githubusercontent.com/22989727/178769370-03ab1965-cfd1-401b-9902-82620a06e43c.gif" width='600'/>
 </div>
 
 

deploy/pipeline/docs/tutorials/pphuman_mtmct.md

@@ -58,13 +58,13 @@ python3 deploy/pipeline/pipeline.py
 ### 示例效果
 
 - camera 1:
-<div width="1080" align="center">
-  <img src="../images/c1.gif"/>
+<div width="600" align="center">
+  <img src="https://user-images.githubusercontent.com/22989727/205595795-fd859feb-8218-450f-a109-91c27713a662.gif"/>
 </div>
 
 - camera 2:
-<div width="1080" align="center">
-  <img src="../images/c2.gif"/>
+<div width="600" align="center">
+  <img src="https://user-images.githubusercontent.com/22989727/205595826-18ab5f0e-a572-4950-a502-96e6eb904a1e.gif"/>
 </div>
 
 

deploy/pipeline/docs/tutorials/pphuman_mtmct_en.md

@@ -61,13 +61,13 @@ Under the above circumstances, the REID model used in MTMCT integrates open-sour
 ### Example
 
 - camera 1:
-<div width="1080" align="center">
-  <img src="../images/c1.gif"/>
+<div width="600" align="center">
+  <img src="https://user-images.githubusercontent.com/22989727/205595795-fd859feb-8218-450f-a109-91c27713a662.gif"/>
 </div>
 
 - camera 2:
-<div width="1080" align="center">
-  <img src="../images/c2.gif"/>
+<div width="600" align="center">
+  <img src="https://user-images.githubusercontent.com/22989727/205595826-18ab5f0e-a572-4950-a502-96e6eb904a1e.gif"/>
 </div>
 
 

deploy/pipeline/docs/tutorials/ppvehicle_attribute.md

@@ -103,8 +103,8 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppv
 
 测试效果如下：
 
-<div width="1000" align="center">
-  <img src="../images/vehicle_attribute.gif"/>
+<div width="600" align="center">
+  <img src="https://user-images.githubusercontent.com/22989727/205599146-56abd72f-6e0a-4a21-bd11-f8bb421f2887.gif"/>
 </div>
 
 ## 方案说明

deploy/pipeline/docs/tutorials/ppvehicle_attribute_en.md

@@ -105,8 +105,8 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppv
 
 The result is shown as follow:
 
-<div width="1000" align="center">
-  <img src="../images/vehicle_attribute.gif"/>
+<div width="600" align="center">
+  <img src="https://user-images.githubusercontent.com/22989727/205599146-56abd72f-6e0a-4a21-bd11-f8bb421f2887.gif"/>
 </div>
 
 

deploy/pipeline/docs/tutorials/ppvehicle_illegal_parking.md

@@ -74,8 +74,8 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/examples/infe
 
 测试效果如下：
 
-<div width="1000" align="center">
-  <img src="../images/illegal_parking_demo.gif"/>
+<div width="600" align="center">
+  <img src="https://user-images.githubusercontent.com/22989727/205598624-bcf5165c-990c-4fe4-8cde-eb1d45298d8f.gif"/>
 </div>
 
 可视化视频中左上角num后面的数值表示当前帧中车辆的数目；Total count表示画面中出现的车辆的总数，包括出现又消失的车辆。

deploy/pipeline/docs/tutorials/ppvehicle_illegal_parking_en.md

@@ -62,8 +62,8 @@ The parameter description:
 
 Test Result:
 
-<div width="1000" align="center">
-  <img src="../images/illegal_parking_demo.gif"/>
+<div width="600" align="center">
+  <img src="https://user-images.githubusercontent.com/22989727/205598624-bcf5165c-990c-4fe4-8cde-eb1d45298d8f.gif"/>
 </div>
 
 

deploy/pipeline/docs/tutorials/ppvehicle_mot.md

@@ -99,7 +99,7 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppv
 
 【效果展示】
 
-<div width="1000" align="center">
+<div width="600" align="center">
   <img src="../images/mot_vehicle.gif"/>
 </div>
 

deploy/pipeline/docs/tutorials/ppvehicle_mot_en.md

@@ -120,7 +120,7 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppv
 
 【Showcase】
 
-<div width="1000" align="center">
+<div width="600" align="center">
   <img src="../images/mot_vehicle.gif"/>
 </div>
 

deploy/pipeline/docs/tutorials/ppvehicle_plate.md

@@ -65,7 +65,7 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppv
 
 测试效果如下：
 
-<div width="1000" align="center">
+<div width="600" align="center">
   <img src="../images/ppvehicleplate.jpg"/>
 </div>
 

deploy/pipeline/docs/tutorials/ppvehicle_plate_en.md

@@ -68,7 +68,7 @@ python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppv
 
 The test results are as follows:
 
-<div width="1000" align="center">
+<div width="600" align="center">
   <img src="../images/ppvehicleplate.jpg"/>
 </div>
 

deploy/pptracking/README_cn.md

@@ -10,7 +10,7 @@ PP-Tracking支持单镜头跟踪(MOT)和跨镜头跟踪(MTMCT)两种模式，针
 </div>
 
 <div width="1000" align="center">
-  <img src="../../docs/images/pptracking-demo.gif"/>
+  <img src="https://user-images.githubusercontent.com/22989727/205546999-f847183d-73e5-4abe-9896-ce6a245efc79.gif"/>
   <br>
   视频来源：VisDrone和BDD100K公开数据集</div>
 </div>

deploy/pptracking/README_en.md

@@ -11,7 +11,7 @@ PP-Tracking supports two paradigms: single camera tracking (MOT) and multi-camer
 </div>
 
 <div width="1000" align="center">
-  <img src="../../docs/images/pptracking-demo.gif"/>
+  <img src="https://user-images.githubusercontent.com/22989727/205546999-f847183d-73e5-4abe-9896-ce6a245efc79.gif"/>
   <br>
   video source：VisDrone and BDD100K dataset</div>
 </div>