Upload 4 files

.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+assets/highlight.jpg filter=lfs diff=lfs merge=lfs -text
+assets/seg.jpg filter=lfs diff=lfs merge=lfs -text
+assets/teaser.jpg filter=lfs diff=lfs merge=lfs -text

data_preparation/README.md

@@ -0,0 +1,82 @@
+# Segmentation Map
+![teaser](../assets/seg.jpg) 
+
+The code is located in `vitonhd_seg.py`, and the parameters include
+
+```python
+## Dataset storage location
+parser.add_argument('--dataset_dir', type=str, default='/data/extern/vition-HD')
+# The required data includes:"densepose"、"image-parse-agnostic-v3.2"、"warped_mask"
+# Among them, warped_mask can be downloaded, with the file name sample and structure as follows:
+"sample/{test_paired/test_unpaired/train_paired}/mask"
+# Download the warped_mask to the dataset_dir directory
+"""
+The content that dataset_dir needs to include is as follows:
+dataset_dir
+|-- sample
+|-- train
+|-- test
+Among them, both train and test contain:
+|-- image-parse-agnostic-v3.2
+|-- densepose
+The sample directory contains:
+sample
+|-- test_paired
+|   `-- mask
+|-- test_unpaired
+    |   `-- mask
+`-- train_paired
+    `-- mask
+"""
+
+## Splitting dataset txt name
+parser.add_argument('--dataset_list', type=str, default='train_pairs_1018new.txt')
+# Save the position of the dataset sequence txt for train and test, where the internal content format of txt is: img cloth mode, for example:
+"""
+12999_00.jpg 12999_00.jpg
+"""
+# Dataset splitting txt needs to be saved in the dataset_dir directory
+
+## dataset_mode
+parser.add_argument('--dataset_mode', type=str, default='test')
+## paired
+parser.add_argument('--paired', type=str, default='unpaired')
+## Save location
+parser.add_argument('--save_dir', type=str, default='./results/')
+"""
+The file structure after saving all file outputs is:
+results/
+|-- train
+|   `-- warped_paired
+|-- test
+|   `-- warped_paired
+|   `-- warped_unpaired
+"""
+```
+
+The densepose images can be downloaded at: [Baidu Cloud](https://pan.baidu.com/s/13sRu-KVUdUUwwG-FfnSrBQ?pwd=kf0a). The warped mask is generated from the [GP-VTON](https://github.com/xiezhy6/GP-VTON.git). The other data sources is based on the [VITON-HD](https://github.com/shadow2496/VITON-HD) dataset.
+
+Data processing can run the script `vitonhd_seg.sh`, which requires three parameters. The first parameter is **dataset_list**, the second parameter is train/test, and the third parameter is **paid/unpaired**. For example:
+
+```bash
+bash vitonhd_seg.sh test_pairs.txt test unpaired
+```
+
+Before running the script, it is necessary to modify the path corresponding to the script `vitonhd_seg.sh` to the path of one's own computer based on the local directory.
+
+# Highlighting Map	
+![teaser](../assets/highlight.jpg) 
+
+The code is located in `vitonhd_highlight.py`, and the parameters include
+```python
+## warped clothes dir
+parser.add_argument('--warped_path', type=str, default='/home/ock/aigc/GP-VTON-main/sample/viton_hd/train_paired/warped')
+## warped masks dir
+parser.add_argument('--mask_path', type=str, default='/home/ock/aigc/GP-VTON-main/sample/viton_hd/train_paired/mask')
+## output dir
+parser.add_argument('--output_folder', type=str, default='/home/ock/aigc/Try-On-old/highlight/train')
+```
+The warped cloth and mask pair is generated from the [GP-VTON](https://github.com/xiezhy6/GP-VTON.git). Data processing can run the file `vitonhd_highlight.py` . For example:
+```
+python  data_preparation/vitonhd_highlight.py  --warped_path A --mask_path B --output_folder C
+```