From 269d06aa40c7b23b21bcb4c4427dd868396b03e7 Mon Sep 17 00:00:00 2001
From: WEI CHEN <77597327+Wei-Chen-hub@users.noreply.github.com>
Date: Tue, 22 Aug 2023 12:34:11 +0800
Subject: [PATCH] Update convert_datasets.md

---
 docs/convert_datasets.md | 85 ++++++++++++++++++++++++++++++++++++----
 1 file changed, 78 insertions(+), 7 deletions(-)

diff --git a/docs/convert_datasets.md b/docs/convert_datasets.md
index 033dcd11..960f0485 100644
--- a/docs/convert_datasets.md
+++ b/docs/convert_datasets.md
@@ -1,11 +1,82 @@
-## Convert Datasets to HumanData
-This page describes how to process various datasets to current HumanData.
+# Convert Datasets to HumanData and Use in Training
+**This page describes how to use the various HumanData flies** as well as (only if you are interested) how to process various datasets to current HumanData.
 
-### Overview - Current Supported Datasets
+## Introduction & Settings
+1. How can I read image from HumanData image path?
+   
+   In HumanData file, image path denotes the relative path within the dataset. In this example:
+   
+    ```
+    your_path_to_dataset = '/mnt/d'
+    dataset_name = 'egobody'
+    image_path = HumanData['image_path'][100]  # for example
+    image_real_path = os.path.join(your_path_to_dataset, dataset_name, image_path)
+    image = cv2.imread(image_real_path)
+    ``` 
+2. For using HumanData only (not including converting), you just need to do several changes to the file structues on the originally downloaded datasets. The converting process and file included will be marked **"Converter"**.
+3. Ideal size of HumanData file is less than 2GB, given loading it uses 10x memory, therefore some huge datasets are split in several files.
+4. (For convert only) Install "convertors" branch and set the working directory as below.
+    
+     ```
+     git clone -b convertors https://github.com/open-mmlab/mmhuman3d.git
+     cd /Your_path_to_mmhuman3d/mmhuman3d  # e.g. /mnt/d/zoehuman/mmhuman3d
+     ```
+5. (For convert only) The usage of convert_datasets.py
+     ```
+     python tools/convert_datasets.py \
+      --datasets moyo \ 
+      --root_path /mnt/d/datasets \
+      --output_path /mnt/d/datasets/moyo/output \
+      --modes train
+     # dataset folder should be os.path.join(root_path, datasets)
+     # output path is the folder which sotres output HumanData
+     ``` 
+
+## Overview - Current Supported Datasets
 - MSCOCO (Neural Annot)
 
-### Subsection 1 - Neural Annot Datasets
-#### MSCOCO
-**STEP 1:**
-  Download from Nerual Annot Homepage
+## Subsection 1 - Neural Annot Datasets
+Overall: Download from [Nerual Annot Homepage](https://github.com/mks0601/NeuralAnnot_RELEASE/blob/main/README.md)
+<details>
+<summary>MSCOCO</summary>
+  
+**Step 1 - Only Step for using HumanData** 
+
+Download and rearrange the file structure as below:
+  
+    ```
+    D:\datasets\mscoco\
+    │
+    ├── annotations\
+    │   ├──MSCOCO_train_SMPLX.json
+    │   ├──MSCOCO_train_SMPLX_all_NeuralAnnot.json
+    │   ├──coco_wholebody_train_v1.0.json
+    │   ├──coco_wholebody_train_v1.0_reformat.json  # Optional
+    │   └──coco_wholebody_val_v1.0.json
+    │
+    ├── images\
+    │   │
+    │   ├── train2017\
+    │   │
+    │   └── val2017\
+    ```
+**Step 2 (Converter) - Preprocess coco annotations**
+
+This process converts the coco annotation json to faciliate sorting ids.
+    ```
+    python tools/preprocess/neural_annot.py --dataset_path /YOUR_PATH/mscoco
+    ```
     
+**Step 3 (Converter) - Convert Dataset**
+```
+python tools/convert_datasets.py \
+    --datasets mscoco \
+    --root_path /mnt/d/datasets \
+    --output_path /mnt/d/datasets/mscoco/output \
+    --modes train
+```
+</details>
+
+<details>
+<summary>MPII</summary>
+</details>