diff --git a/docs/en/advanced_guides/codecs.md b/docs/en/advanced_guides/codecs.md index 610bd83a57..7c98ba31d9 100644 --- a/docs/en/advanced_guides/codecs.md +++ b/docs/en/advanced_guides/codecs.md @@ -8,7 +8,7 @@ MMPose 1.0 introduced a new module **Codec** to integrate the encoding and decod Here is a diagram to show where the `Codec` is: -![codec-en](https://user-images.githubusercontent.com/13503330/187112635-c01f13d1-a07e-420f-be50-3b8818524dec.png) +![pose_estimator_en](https://github.com/open-mmlab/mmpose/assets/13503330/0764baab-41c7-4a1d-ab64-5d7f9dfc8eec) A typical codec consists of two parts: diff --git a/docs/en/advanced_guides/implement_new_models.md b/docs/en/advanced_guides/implement_new_models.md index 8d2809421e..da46a99e39 100644 --- a/docs/en/advanced_guides/implement_new_models.md +++ b/docs/en/advanced_guides/implement_new_models.md @@ -31,7 +31,7 @@ You can refer to the following flow chart to locate the module you need to imple In pose estimatiors, we will define the inference process of a model, and decode the model output results in `predict()`, first transform it from `output space` to `input image space` using the [codec](./codecs.md), and then combine the meta information to transform to `original image space`. -![pose_estimator_en](https://github.com/open-mmlab/mmpose/assets/13503330/48c3813e-b977-4215-b5bc-e7379cfd2bce) +![pose_estimator_en](https://github.com/open-mmlab/mmpose/assets/13503330/0764baab-41c7-4a1d-ab64-5d7f9dfc8eec) Currently, MMPose supports the following types of pose estimator: diff --git a/docs/en/guide_to_framework.md b/docs/en/guide_to_framework.md index bb1efed08f..fceb37a142 100644 --- a/docs/en/guide_to_framework.md +++ b/docs/en/guide_to_framework.md @@ -298,7 +298,7 @@ In a keypoint detection task, data will be transformed among three scale spaces: Here is a diagram to show the workflow of data transformation among the three scale spaces: -![migration-en](https://user-images.githubusercontent.com/13503330/187190213-cad87b5f-0a95-4f1f-b722-15896914ded4.png) +![tour_en](https://github.com/open-mmlab/mmpose/assets/13503330/e82710e6-4181-4eb0-8185-7075b43dbec3) In MMPose, the modules used for data transformation are under `[$MMPOSE/mmpose/datasets/transforms](https://github.com/open-mmlab/mmpose/tree/main/mmpose/datasets/transforms)`, and their workflow is shown as follows: diff --git a/docs/zh_cn/advanced_guides/codecs.md b/docs/zh_cn/advanced_guides/codecs.md index 85d4d2e54b..60c588a239 100644 --- a/docs/zh_cn/advanced_guides/codecs.md +++ b/docs/zh_cn/advanced_guides/codecs.md @@ -8,7 +8,7 @@ MMPose 1.0 中引入了新模块 **编解码器(Codec)** ,将关键点数 编解码器在工作流程中所处的位置如下所示: -![codec-cn](https://user-images.githubusercontent.com/13503330/187829784-4d5939de-97d7-43cc-b934-c6d17c02d589.png) +![pose_estimator_cn](https://github.com/open-mmlab/mmpose/assets/13503330/0c048f66-b889-4268-937f-71b8753b505f) 一个编解码器主要包含两个部分: diff --git a/docs/zh_cn/advanced_guides/implement_new_models.md b/docs/zh_cn/advanced_guides/implement_new_models.md index e233850277..d3ed96bd37 100644 --- a/docs/zh_cn/advanced_guides/implement_new_models.md +++ b/docs/zh_cn/advanced_guides/implement_new_models.md @@ -30,7 +30,7 @@ mmpose 在姿态估计范式中,我们会定义一个模型的推理流程,并在 `predict()` 中对模型输出结果进行解码,先将其从 `输出尺度空间` 用 [编解码器](./codecs.md) 变换到 `输入图片空间`,然后再结合元信息变换到 `原始图片空间`。 -![image](https://github.com/open-mmlab/mmpose/assets/13503330/e3e700ac-a047-4cff-9017-67f83676b8cb) +![pose_estimator_cn](https://github.com/open-mmlab/mmpose/assets/13503330/0c048f66-b889-4268-937f-71b8753b505f) 当前 MMPose 已支持以下几类算法范式: diff --git a/docs/zh_cn/guide_to_framework.md b/docs/zh_cn/guide_to_framework.md index 132233fd5d..b4c44192a5 100644 --- a/docs/zh_cn/guide_to_framework.md +++ b/docs/zh_cn/guide_to_framework.md @@ -296,7 +296,7 @@ test_pipeline = [ 数据在三个空间中变换的流程如图所示: -![migration-cn](https://user-images.githubusercontent.com/13503330/187831574-13804daf-f498-47c2-ba43-64b8e6ffe3dd.png) +![tour_cn](https://github.com/open-mmlab/mmpose/assets/13503330/4c989d86-e824-49ea-9ba8-b3978548db37) 在MMPose中,数据变换所需要的模块在 `[$MMPOSE/mmpose/datasets/transforms](https://github.com/open-mmlab/mmpose/tree/main/mmpose/datasets/transforms)` 目录下,它们的工作流程如图所示: