Update StableSigner3 repository structure and add Git LFS tracking

.gitattributes

@@ -10,3 +10,5 @@
 *.avi filter=lfs diff=lfs merge=lfs -text
 *.mov filter=lfs diff=lfs merge=lfs -text
 *.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,170 +1,14 @@
-# fast_dwpose
+# For developers
 
-The goal of Easy DWPose is to provide a generic, reliable, and easy-to-use interface for making skeletons for ControlNet.
+## tutorial00 Use DWpose to preprocess the original sign language video
 
-SF do some improve for [easy-dwpose](https://github.com/reallyigor/easy_dwpose), named it fast-dwpose.
+## tutorial01 prepare pose dict
 
-## Installation
+## tutorial02 get pose video from text input
 
-### PIP
+## tutorial03 render pose video
 
-```bash
-pip install easy-dwpose
-```
 
-## Quickstart
+# For Users
 
-### In you own .py scrip or in Jupyter
-
-```python
-import torch
-from PIL import Image
-import numpy as np
-import json
-
-from easy_dwpose import DWposeDetector
-
-#####---------Setup init
-# You can use a different GPU, e.g. "cuda:1"
-device = "cuda:0" if torch.cuda.is_available() else "cpu"
-detector = DWposeDetector(device=device)
-input_image = Image.open("assets/pose.png").convert("RGB")
-
-
-#####---------Get both the skeleton image
-# SF: skeleton should be a kind of img 
-skeleton = detector(input_image, output_type="pil", include_hands=True, include_face=True)
-# Save the skeleton image
-skeleton.save("skeleton.png")
-
-
-#####---------Get pose data
-# SF: pose_data should be numpy/tensor
-# # This returns the dictionary
-pose_data = detector(input_image, draw_pose=False)
-
-# Save the skeleton pose information:
-# Option 1: Save as NPY file
-np.save('pose_data.npy', pose_data)
-
-# Option 2: Save as NPZ file
-np.savez('pose_data.npz', **pose_data)
-
-# Option 3: Save as JSON file
-# Convert numpy arrays to lists for JSON serialization
-pose_data_json = {k: v.tolist() if isinstance(v, np.ndarray) else v for k, v in pose_data.items()}
-with open('pose_data.json', 'w') as f:
-    json.dump(pose_data_json, f)
-```
-
-<table align="center">
-    <tr>
-      <th align="center">Input</th>
-      <th align="center">Output</th>
-    </tr>
-    <tr>
-        <td align="center">
-          <br />
-          <img src="./assets/pose.png"/>
-        </td>
-        <td align="center">
-          <br/>
-          <img src="./assets/skeleton.png"/>
-        </td>
-    </tr>
-</table>
-
-### On a video
-
-```bash
-python scripts/inference_on_video.py --input assets/dance.mp4 --output_path result.mp4
-```
-
-<table align="center">
-    <tr>
-      <th align="center">Input</th>
-      <th align="center">Output</th>
-    </tr>
-    <tr>
-        <td align="center">
-          <br />
-          <img src="./assets/dance.gif"/>
-        </td>
-        <td align="center">
-          <br/>
-          <img src="./assets/skeleton.gif"/>
-        </td>
-    </tr>
-</table>
-
-### On a folder of images
-
-```bash
-python scripts/inference_on_folder.py --input assets/ --output_path results/
-```
-
-### Easy-DWPose Custom skeleton drawing
-
-By default, we use standart skeleton drawing function but several projects change it (e.g. [MusePose](https://github.com/TMElyralab/MusePose)). Modify it or write your own from scratch!
-
-```python
-from PIL import Image
-from easy_dwpose import DWposeDetector
-from easy_dwpose.draw.musepose import draw_pose as draw_pose_musepose
-
-detector = DWposeDetector(device="cpu")
-input_image = Image.open("assets/pose.png").convert("RGB")
-
-skeleton = detector(input_image, output_type="pil", draw_pose=draw_pose_musepose, draw_face=False)
-skeleton.save("skeleton.png")
-```
-
-### SF Custom skeleton drawing
-
-I prefer ControlNext style, I have developed a visualization method and placed it in `./easy_dwpose/draw/controlnext.py`. It does not integrate with easy dwpose and the calling method is slightly different:
-
-```python
-import torch
-from PIL import Image
-import numpy as np
-import json
-
-from easy_dwpose import DWposeDetector
-from easy_dwpose.draw.controlnext import draw_pose, process_pose_data 
-
-#####---------Setup init
-# You can use a different GPU, e.g. "cuda:1"
-device = "cuda:0" if torch.cuda.is_available() else "cpu"
-detector = DWposeDetector(device=device)
-input_image = Image.open("assets/pose.png").convert("RGB")
-
-#####---------Custom ControlNext drawing style
-# Get pose data for custom drawing
-pose_data = detector(input_image, draw_pose=False)
-
-# Get image dimensions
-width, height = input_image.size
-
-# Process the pose data for custom drawing
-processed_pred = process_pose_data(pose_data, height, width)
-
-# Draw pose using custom ControlNext style
-vis_img = draw_pose(
-    pose=processed_pred,
-    H=height,
-    W=width,
-    include_body=True,
-    include_hand=True,
-    include_face=True
-)
-
-# Convert to PIL Image and save (vis_img is in CHW format)
-custom_skeleton = Image.fromarray(vis_img.transpose(1, 2, 0))
-custom_skeleton.save("skeleton_controlnext.png")
-```
-
-## Acknowledgement
-
-We thank the original authors of the [DWPose](https://github.com/IDEA-Research/DWPose) for their incredible models!
-
-Thanks for open-sourcing!
+## start00 just start as a user