mirror of
https://github.com/microsoft/OmniParser.git
synced 2025-02-18 03:18:33 +03:00
v2 pre-release; merge demo
This commit is contained in:
yadonglu
4
.gitignore
vendored
4
.gitignore
vendored
@@ -6,3 +6,7 @@ weights/icon_detect_v1_5_2/
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.gradio
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__pycache__/
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debug.ipynb
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util/__pycache__/
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index.html?linkid=2289031
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wget-log
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weights/icon_caption_florence_v2/
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18
README.md
18
README.md
@@ -7,11 +7,13 @@
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[](https://arxiv.org/abs/2408.00203)
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[](https://opensource.org/licenses/MIT)
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📢 [[Project Page](https://microsoft.github.io/OmniParser/)] [[Blog Post](https://www.microsoft.com/en-us/research/articles/omniparser-for-pure-vision-based-gui-agent/)] [[Models](https://huggingface.co/microsoft/OmniParser)] [huggingface space](https://huggingface.co/spaces/microsoft/OmniParser)
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📢 [[Project Page](https://microsoft.github.io/OmniParser/)] [[Blog Post](https://www.microsoft.com/en-us/research/articles/omniparser-for-pure-vision-based-gui-agent/)] [[Models V2](https://huggingface.co/microsoft/OmniParser-v2.0)] [[Models](https://huggingface.co/microsoft/OmniParser)] [[huggingface space](https://huggingface.co/spaces/microsoft/OmniParser)]
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**OmniParser** is a comprehensive method for parsing user interface screenshots into structured and easy-to-understand elements, which significantly enhances the ability of GPT-4V to generate actions that can be accurately grounded in the corresponding regions of the interface.
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## News
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- [2025/2] We release V2 [checkpoints](https://huggingface.co/microsoft/OmniParser-v2.0)
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- [2025/2] We introduce OmniTool: Control a Windows 11 VM with OmniParser + your vision model of choice. OmniTool supports out of the box the following large language models - OpenAI (4o/o1/o3-mini), DeepSeek (R1), Qwen (2.5VL) or Anthropic Computer Use.
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- [2025/1] V2 is coming. We achieve new state of the art results 39.5% on the new grounding benchmark [Screen Spot Pro](https://github.com/likaixin2000/ScreenSpot-Pro-GUI-Grounding/tree/main) with OmniParser v2 (will be released soon)! Read more details [here](https://github.com/microsoft/OmniParser/tree/master/docs/Evaluation.md).
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- [2024/11] We release an updated version, OmniParser V1.5 which features 1) more fine grained/small icon detection, 2) prediction of whether each screen element is interactable or not. Examples in the demo.ipynb.
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- [2024/10] OmniParser was the #1 trending model on huggingface model hub (starting 10/29/2024).
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@@ -27,6 +29,13 @@ conda activate omni
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pip install -r requirements.txt
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```
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Ensure you have the V2 weights downloaded in weights folder (ensure caption weights folder is called icon_caption_florence). If not download them with:
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```
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rm -rf weights/icon_detect weights/icon_caption weights/icon_caption_florence
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for f in icon_detect/{train_args.yaml,model.pt,model.yaml} icon_caption/{config.json,generation_config.json,model.safetensors}; do huggingface-cli download microsoft/OmniParser-v2.0 "$f" --local-dir weights; done
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mv weights/icon_caption weights/icon_caption_florence
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```
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<!-- ## [deprecated]
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Then download the model ckpts files in: https://huggingface.co/microsoft/OmniParser, and put them under weights/, default folder structure is: weights/icon_detect, weights/icon_caption_florence, weights/icon_caption_blip2.
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For v1:
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@@ -36,7 +45,7 @@ python weights/convert_safetensor_to_pt.py
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For v1.5:
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download 'model_v1_5.pt' from https://huggingface.co/microsoft/OmniParser/tree/main/icon_detect_v1_5, make a new dir: weights/icon_detect_v1_5, and put it inside the folder. No weight conversion is needed.
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```
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``` -->
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## Examples:
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We put together a few simple examples in the demo.ipynb.
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@@ -44,10 +53,7 @@ We put together a few simple examples in the demo.ipynb.
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## Gradio Demo
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To run gradio demo, simply run:
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```python
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# For v1
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python gradio_demo.py --icon_detect_model weights/icon_detect/best.pt --icon_caption_model florence2
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# For v1.5
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python gradio_demo.py --icon_detect_model weights/icon_detect_v1_5/model_v1_5.pt --icon_caption_model florence2
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python gradio_demo.py
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```
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## Model Weights License
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1625
demo.ipynb
1625
demo.ipynb
File diff suppressed because one or more lines are too long
@@ -8,12 +8,13 @@ import io
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import base64, os
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from utils import check_ocr_box, get_yolo_model, get_caption_model_processor, get_som_labeled_img
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from util.utils import check_ocr_box, get_yolo_model, get_caption_model_processor, get_som_labeled_img
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import torch
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from PIL import Image
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import argparse
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yolo_model = get_yolo_model(model_path='weights/icon_detect/model.pt')
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caption_model_processor = get_caption_model_processor(model_name="florence2", model_name_or_path="weights/icon_caption_florence")
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# caption_model_processor = get_caption_model_processor(model_name="blip2", model_name_or_path="weights/icon_caption_blip2")
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MARKDOWN = """
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# OmniParser for Pure Vision Based General GUI Agent 🔥
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@@ -36,9 +37,9 @@ def process(
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box_threshold,
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iou_threshold,
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use_paddleocr,
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imgsz,
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icon_process_batch_size,
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imgsz
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) -> Optional[Image.Image]:
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image_save_path = 'imgs/saved_image_demo.png'
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image_input.save(image_save_path)
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image = Image.open(image_save_path)
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@@ -54,27 +55,13 @@ def process(
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ocr_bbox_rslt, is_goal_filtered = check_ocr_box(image_save_path, display_img = False, output_bb_format='xyxy', goal_filtering=None, easyocr_args={'paragraph': False, 'text_threshold':0.9}, use_paddleocr=use_paddleocr)
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text, ocr_bbox = ocr_bbox_rslt
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# print('prompt:', prompt)
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dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image_save_path, yolo_model, BOX_TRESHOLD = box_threshold, output_coord_in_ratio=True, ocr_bbox=ocr_bbox,draw_bbox_config=draw_bbox_config, caption_model_processor=caption_model_processor, ocr_text=text,iou_threshold=iou_threshold, imgsz=imgsz, batch_size=icon_process_batch_size)
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dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image_save_path, yolo_model, BOX_TRESHOLD = box_threshold, output_coord_in_ratio=True, ocr_bbox=ocr_bbox,draw_bbox_config=draw_bbox_config, caption_model_processor=caption_model_processor, ocr_text=text,iou_threshold=iou_threshold, imgsz=imgsz,)
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image = Image.open(io.BytesIO(base64.b64decode(dino_labled_img)))
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print('finish processing')
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# parsed_content_list = '\n'.join(parsed_content_list)
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parsed_content_list = '\n'.join([f'type: {x['type']}, content: {x["content"]}, interactivity: {x["interactivity"]}' for x in parsed_content_list])
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parsed_content_list = '\n'.join([f'icon {i}: ' + str(v) for i,v in enumerate(parsed_content_list)])
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# parsed_content_list = str(parsed_content_list)
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return image, str(parsed_content_list)
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parser = argparse.ArgumentParser(description='Process model paths and names.')
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parser.add_argument('--icon_detect_model', type=str, required=True, default='weights/icon_detect/best.pt', help='Path to the YOLO model weights')
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parser.add_argument('--icon_caption_model', type=str, required=True, default='florence2', help='Name of the caption model')
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args = parser.parse_args()
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icon_detect_model, icon_caption_model = args.icon_detect_model, args.icon_caption_model
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yolo_model = get_yolo_model(model_path=icon_detect_model)
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if icon_caption_model == 'florence2':
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caption_model_processor = get_caption_model_processor(model_name="florence2", model_name_or_path="weights/icon_caption_florence")
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elif icon_caption_model == 'blip2':
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caption_model_processor = get_caption_model_processor(model_name="blip2", model_name_or_path="weights/icon_caption_blip2")
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with gr.Blocks() as demo:
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gr.Markdown(MARKDOWN)
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with gr.Row():
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@@ -88,11 +75,9 @@ with gr.Blocks() as demo:
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iou_threshold_component = gr.Slider(
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label='IOU Threshold', minimum=0.01, maximum=1.0, step=0.01, value=0.1)
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use_paddleocr_component = gr.Checkbox(
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label='Use PaddleOCR', value=False)
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label='Use PaddleOCR', value=True)
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imgsz_component = gr.Slider(
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label='Icon Detect Image Size', minimum=640, maximum=3200, step=32, value=1920)
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icon_process_batch_size_component = gr.Slider(
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label='Icon Process Batch Size', minimum=1, maximum=256, step=1, value=64)
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label='Icon Detect Image Size', minimum=640, maximum=1920, step=32, value=640)
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submit_button_component = gr.Button(
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value='Submit', variant='primary')
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with gr.Column():
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@@ -106,16 +91,10 @@ with gr.Blocks() as demo:
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box_threshold_component,
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iou_threshold_component,
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use_paddleocr_component,
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imgsz_component,
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icon_process_batch_size_component
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imgsz_component
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],
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outputs=[image_output_component, text_output_component]
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)
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# demo.launch(debug=False, show_error=True, share=True)
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demo.launch(share=True, server_port=7861, server_name='0.0.0.0')
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# python gradio_demo.py --icon_detect_model weights/icon_detect/best.pt --icon_caption_model florence2
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# python gradio_demo.py --icon_detect_model weights/icon_detect_v1_5/model_v1_5.pt --icon_caption_model florence2
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BIN
imgs/demo_image.jpg
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imgs/demo_image.jpg
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imgs/demo_image_som.jpg
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imgs/demo_image_som.jpg
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imgs/gradioicon.png
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imgs/gradioicon.png
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imgs/header_bar.png
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imgs/header_bar.png
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imgs/header_bar_thin.png
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imgs/mobile.png
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imgs/mobile.png
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imgs/omniboxicon.png
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imgs/omniboxicon.png
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imgs/omniparsericon.png
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imgs/som_overlaid_omni.png
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imgs/som_overlaid_omni.png
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@@ -1,60 +0,0 @@
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from utils import get_som_labeled_img, check_ocr_box, get_caption_model_processor, get_dino_model, get_yolo_model
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import torch
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from ultralytics import YOLO
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from PIL import Image
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from typing import Dict, Tuple, List
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import io
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import base64
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config = {
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'som_model_path': 'finetuned_icon_detect.pt',
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'device': 'cpu',
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'caption_model_path': 'Salesforce/blip2-opt-2.7b',
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'draw_bbox_config': {
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'text_scale': 0.8,
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'text_thickness': 2,
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'text_padding': 3,
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'thickness': 3,
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},
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'BOX_TRESHOLD': 0.05
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}
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class Omniparser(object):
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def __init__(self, config: Dict):
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self.config = config
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self.som_model = get_yolo_model(model_path=config['som_model_path'])
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# self.caption_model_processor = get_caption_model_processor(config['caption_model_path'], device=cofig['device'])
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# self.caption_model_processor['model'].to(torch.float32)
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def parse(self, image_path: str):
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print('Parsing image:', image_path)
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ocr_bbox_rslt, is_goal_filtered = check_ocr_box(image_path, display_img = False, output_bb_format='xyxy', goal_filtering=None, easyocr_args={'paragraph': False, 'text_threshold':0.9})
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text, ocr_bbox = ocr_bbox_rslt
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draw_bbox_config = self.config['draw_bbox_config']
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BOX_TRESHOLD = self.config['BOX_TRESHOLD']
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dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image_path, self.som_model, BOX_TRESHOLD = BOX_TRESHOLD, output_coord_in_ratio=False, ocr_bbox=ocr_bbox,draw_bbox_config=draw_bbox_config, caption_model_processor=None, ocr_text=text,use_local_semantics=False)
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image = Image.open(io.BytesIO(base64.b64decode(dino_labled_img)))
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# formating output
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return_list = [{'from': 'omniparser', 'shape': {'x':coord[0], 'y':coord[1], 'width':coord[2], 'height':coord[3]},
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'text': parsed_content_list[i].split(': ')[1], 'type':'text'} for i, (k, coord) in enumerate(label_coordinates.items()) if i < len(parsed_content_list)]
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return_list.extend(
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[{'from': 'omniparser', 'shape': {'x':coord[0], 'y':coord[1], 'width':coord[2], 'height':coord[3]},
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'text': 'None', 'type':'icon'} for i, (k, coord) in enumerate(label_coordinates.items()) if i >= len(parsed_content_list)]
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)
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return [image, return_list]
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parser = Omniparser(config)
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image_path = 'examples/pc_1.png'
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# time the parser
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import time
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s = time.time()
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image, parsed_content_list = parser.parse(image_path)
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device = config['device']
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print(f'Time taken for Omniparser on {device}:', time.time() - s)
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1
omnitool/gradio/.gitignore
vendored
Normal file
1
omnitool/gradio/.gitignore
vendored
Normal file
@@ -0,0 +1 @@
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tmp/
|
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162
omnitool/gradio/agent/anthropic_agent.py
Normal file
162
omnitool/gradio/agent/anthropic_agent.py
Normal file
@@ -0,0 +1,162 @@
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"""
|
||||
Agentic sampling loop that calls the Anthropic API and local implenmentation of anthropic-defined computer use tools.
|
||||
"""
|
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import asyncio
|
||||
import platform
|
||||
from collections.abc import Callable
|
||||
from datetime import datetime
|
||||
from enum import StrEnum
|
||||
from typing import Any, cast
|
||||
|
||||
from anthropic import Anthropic, AnthropicBedrock, AnthropicVertex, APIResponse
|
||||
from anthropic.types import (
|
||||
ToolResultBlockParam,
|
||||
)
|
||||
from anthropic.types.beta import (
|
||||
BetaContentBlock,
|
||||
BetaContentBlockParam,
|
||||
BetaImageBlockParam,
|
||||
BetaMessage,
|
||||
BetaMessageParam,
|
||||
BetaTextBlockParam,
|
||||
BetaToolResultBlockParam,
|
||||
)
|
||||
from anthropic.types import TextBlock
|
||||
from anthropic.types.beta import BetaMessage, BetaTextBlock, BetaToolUseBlock
|
||||
|
||||
from tools import ComputerTool, ToolCollection, ToolResult
|
||||
|
||||
from PIL import Image
|
||||
from io import BytesIO
|
||||
import gradio as gr
|
||||
from typing import Dict
|
||||
|
||||
BETA_FLAG = "computer-use-2024-10-22"
|
||||
|
||||
class APIProvider(StrEnum):
|
||||
ANTHROPIC = "anthropic"
|
||||
BEDROCK = "bedrock"
|
||||
VERTEX = "vertex"
|
||||
|
||||
SYSTEM_PROMPT = f"""<SYSTEM_CAPABILITY>
|
||||
* You are utilizing a Windows system with internet access.
|
||||
* The current date is {datetime.today().strftime('%A, %B %d, %Y')}.
|
||||
</SYSTEM_CAPABILITY>
|
||||
"""
|
||||
|
||||
class AnthropicActor:
|
||||
def __init__(
|
||||
self,
|
||||
model: str,
|
||||
provider: APIProvider,
|
||||
api_key: str,
|
||||
api_response_callback: Callable[[APIResponse[BetaMessage]], None],
|
||||
max_tokens: int = 4096,
|
||||
only_n_most_recent_images: int | None = None,
|
||||
print_usage: bool = True,
|
||||
):
|
||||
self.model = model
|
||||
self.provider = provider
|
||||
self.api_key = api_key
|
||||
self.api_response_callback = api_response_callback
|
||||
self.max_tokens = max_tokens
|
||||
self.only_n_most_recent_images = only_n_most_recent_images
|
||||
|
||||
self.tool_collection = ToolCollection(ComputerTool())
|
||||
|
||||
self.system = SYSTEM_PROMPT
|
||||
|
||||
self.total_token_usage = 0
|
||||
self.total_cost = 0
|
||||
self.print_usage = print_usage
|
||||
|
||||
# Instantiate the appropriate API client based on the provider
|
||||
if provider == APIProvider.ANTHROPIC:
|
||||
self.client = Anthropic(api_key=api_key)
|
||||
elif provider == APIProvider.VERTEX:
|
||||
self.client = AnthropicVertex()
|
||||
elif provider == APIProvider.BEDROCK:
|
||||
self.client = AnthropicBedrock()
|
||||
|
||||
def __call__(
|
||||
self,
|
||||
*,
|
||||
messages: list[BetaMessageParam]
|
||||
):
|
||||
"""
|
||||
Generate a response given history messages.
|
||||
"""
|
||||
if self.only_n_most_recent_images:
|
||||
_maybe_filter_to_n_most_recent_images(messages, self.only_n_most_recent_images)
|
||||
|
||||
# Call the API synchronously
|
||||
raw_response = self.client.beta.messages.with_raw_response.create(
|
||||
max_tokens=self.max_tokens,
|
||||
messages=messages,
|
||||
model=self.model,
|
||||
system=self.system,
|
||||
tools=self.tool_collection.to_params(),
|
||||
betas=["computer-use-2024-10-22"],
|
||||
)
|
||||
|
||||
self.api_response_callback(cast(APIResponse[BetaMessage], raw_response))
|
||||
|
||||
response = raw_response.parse()
|
||||
print(f"AnthropicActor response: {response}")
|
||||
|
||||
self.total_token_usage += response.usage.input_tokens + response.usage.output_tokens
|
||||
self.total_cost += (response.usage.input_tokens * 3 / 1000000 + response.usage.output_tokens * 15 / 1000000)
|
||||
|
||||
if self.print_usage:
|
||||
print(f"Claude total token usage so far: {self.total_token_usage}, total cost so far: $USD{self.total_cost}")
|
||||
|
||||
return response
|
||||
|
||||
|
||||
def _maybe_filter_to_n_most_recent_images(
|
||||
messages: list[BetaMessageParam],
|
||||
images_to_keep: int,
|
||||
min_removal_threshold: int = 10,
|
||||
):
|
||||
"""
|
||||
With the assumption that images are screenshots that are of diminishing value as
|
||||
the conversation progresses, remove all but the final `images_to_keep` tool_result
|
||||
images in place, with a chunk of min_removal_threshold to reduce the amount we
|
||||
break the implicit prompt cache.
|
||||
"""
|
||||
if images_to_keep is None:
|
||||
return messages
|
||||
|
||||
tool_result_blocks = cast(
|
||||
list[ToolResultBlockParam],
|
||||
[
|
||||
item
|
||||
for message in messages
|
||||
for item in (
|
||||
message["content"] if isinstance(message["content"], list) else []
|
||||
)
|
||||
if isinstance(item, dict) and item.get("type") == "tool_result"
|
||||
],
|
||||
)
|
||||
|
||||
total_images = sum(
|
||||
1
|
||||
for tool_result in tool_result_blocks
|
||||
for content in tool_result.get("content", [])
|
||||
if isinstance(content, dict) and content.get("type") == "image"
|
||||
)
|
||||
|
||||
images_to_remove = total_images - images_to_keep
|
||||
# for better cache behavior, we want to remove in chunks
|
||||
images_to_remove -= images_to_remove % min_removal_threshold
|
||||
|
||||
for tool_result in tool_result_blocks:
|
||||
if isinstance(tool_result.get("content"), list):
|
||||
new_content = []
|
||||
for content in tool_result.get("content", []):
|
||||
if isinstance(content, dict) and content.get("type") == "image":
|
||||
if images_to_remove > 0:
|
||||
images_to_remove -= 1
|
||||
continue
|
||||
new_content.append(content)
|
||||
tool_result["content"] = new_content
|
||||
59
omnitool/gradio/agent/llm_utils/groqclient.py
Normal file
59
omnitool/gradio/agent/llm_utils/groqclient.py
Normal file
@@ -0,0 +1,59 @@
|
||||
from groq import Groq
|
||||
import os
|
||||
from .utils import is_image_path
|
||||
|
||||
def run_groq_interleaved(messages: list, system: str, model_name: str, api_key: str, max_tokens=256, temperature=0.6):
|
||||
"""
|
||||
Run a chat completion through Groq's API, ignoring any images in the messages.
|
||||
"""
|
||||
api_key = api_key or os.environ.get("GROQ_API_KEY")
|
||||
if not api_key:
|
||||
raise ValueError("GROQ_API_KEY is not set")
|
||||
|
||||
client = Groq(api_key=api_key)
|
||||
# avoid using system messages for R1
|
||||
final_messages = [{"role": "user", "content": system}]
|
||||
|
||||
if isinstance(messages, list):
|
||||
for item in messages:
|
||||
if isinstance(item, dict):
|
||||
# For dict items, concatenate all text content, ignoring images
|
||||
text_contents = []
|
||||
for cnt in item["content"]:
|
||||
if isinstance(cnt, str):
|
||||
if not is_image_path(cnt): # Skip image paths
|
||||
text_contents.append(cnt)
|
||||
else:
|
||||
text_contents.append(str(cnt))
|
||||
|
||||
if text_contents: # Only add if there's text content
|
||||
message = {"role": "user", "content": " ".join(text_contents)}
|
||||
final_messages.append(message)
|
||||
else: # str
|
||||
message = {"role": "user", "content": item}
|
||||
final_messages.append(message)
|
||||
|
||||
elif isinstance(messages, str):
|
||||
final_messages.append({"role": "user", "content": messages})
|
||||
|
||||
try:
|
||||
completion = client.chat.completions.create(
|
||||
model="deepseek-r1-distill-llama-70b",
|
||||
messages=final_messages,
|
||||
temperature=0.6,
|
||||
max_completion_tokens=max_tokens,
|
||||
top_p=0.95,
|
||||
stream=False,
|
||||
reasoning_format="raw"
|
||||
)
|
||||
|
||||
response = completion.choices[0].message.content
|
||||
final_answer = response.split('</think>\n')[-1] if '</think>' in response else response
|
||||
final_answer = final_answer.replace("<output>", "").replace("</output>", "")
|
||||
token_usage = completion.usage.total_tokens
|
||||
|
||||
return final_answer, token_usage
|
||||
except Exception as e:
|
||||
print(f"Error in interleaved Groq: {e}")
|
||||
|
||||
return str(e), 0
|
||||
62
omnitool/gradio/agent/llm_utils/oaiclient.py
Normal file
62
omnitool/gradio/agent/llm_utils/oaiclient.py
Normal file
@@ -0,0 +1,62 @@
|
||||
import os
|
||||
import logging
|
||||
import base64
|
||||
import requests
|
||||
from .utils import is_image_path, encode_image
|
||||
|
||||
def run_oai_interleaved(messages: list, system: str, model_name: str, api_key: str, max_tokens=256, temperature=0, provider_base_url: str = "https://api.openai.com/v1"):
|
||||
headers = {"Content-Type": "application/json",
|
||||
"Authorization": f"Bearer {api_key}"}
|
||||
final_messages = [{"role": "system", "content": system}]
|
||||
|
||||
if type(messages) == list:
|
||||
for item in messages:
|
||||
contents = []
|
||||
if isinstance(item, dict):
|
||||
for cnt in item["content"]:
|
||||
if isinstance(cnt, str):
|
||||
if is_image_path(cnt) and 'o3-mini' not in model_name:
|
||||
# 03 mini does not support images
|
||||
base64_image = encode_image(cnt)
|
||||
content = {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}}
|
||||
else:
|
||||
content = {"type": "text", "text": cnt}
|
||||
else:
|
||||
# in this case it is a text block from anthropic
|
||||
content = {"type": "text", "text": str(cnt)}
|
||||
|
||||
contents.append(content)
|
||||
|
||||
message = {"role": 'user', "content": contents}
|
||||
else: # str
|
||||
contents.append({"type": "text", "text": item})
|
||||
message = {"role": "user", "content": contents}
|
||||
|
||||
final_messages.append(message)
|
||||
|
||||
|
||||
elif isinstance(messages, str):
|
||||
final_messages = [{"role": "user", "content": messages}]
|
||||
|
||||
payload = {
|
||||
"model": model_name,
|
||||
"messages": final_messages,
|
||||
}
|
||||
if 'o1' in model_name or 'o3-mini' in model_name:
|
||||
payload['reasoning_effort'] = 'low'
|
||||
payload['max_completion_tokens'] = max_tokens
|
||||
else:
|
||||
payload['max_tokens'] = max_tokens
|
||||
|
||||
response = requests.post(
|
||||
f"{provider_base_url}/chat/completions", headers=headers, json=payload
|
||||
)
|
||||
|
||||
|
||||
try:
|
||||
text = response.json()['choices'][0]['message']['content']
|
||||
token_usage = int(response.json()['usage']['total_tokens'])
|
||||
return text, token_usage
|
||||
except Exception as e:
|
||||
print(f"Error in interleaved openAI: {e}. This may due to your invalid API key. Please check the response: {response.json()} ")
|
||||
return response.json()
|
||||
44
omnitool/gradio/agent/llm_utils/omniparserclient.py
Normal file
44
omnitool/gradio/agent/llm_utils/omniparserclient.py
Normal file
@@ -0,0 +1,44 @@
|
||||
import requests
|
||||
import base64
|
||||
from pathlib import Path
|
||||
from tools.screen_capture import get_screenshot
|
||||
from agent.llm_utils.utils import encode_image
|
||||
|
||||
OUTPUT_DIR = "./tmp/outputs"
|
||||
|
||||
class OmniParserClient:
|
||||
def __init__(self,
|
||||
url: str) -> None:
|
||||
self.url = url
|
||||
|
||||
def __call__(self,):
|
||||
screenshot, screenshot_path = get_screenshot()
|
||||
screenshot_path = str(screenshot_path)
|
||||
image_base64 = encode_image(screenshot_path)
|
||||
response = requests.post(self.url, json={"base64_image": image_base64})
|
||||
response_json = response.json()
|
||||
print('omniparser latency:', response_json['latency'])
|
||||
|
||||
som_image_data = base64.b64decode(response_json['som_image_base64'])
|
||||
screenshot_path_uuid = Path(screenshot_path).stem.replace("screenshot_", "")
|
||||
som_screenshot_path = f"{OUTPUT_DIR}/screenshot_som_{screenshot_path_uuid}.png"
|
||||
with open(som_screenshot_path, "wb") as f:
|
||||
f.write(som_image_data)
|
||||
|
||||
response_json['width'] = screenshot.size[0]
|
||||
response_json['height'] = screenshot.size[1]
|
||||
response_json['original_screenshot_base64'] = image_base64
|
||||
response_json['screenshot_uuid'] = screenshot_path_uuid
|
||||
response_json = self.reformat_messages(response_json)
|
||||
return response_json
|
||||
|
||||
def reformat_messages(self, response_json: dict):
|
||||
screen_info = ""
|
||||
for idx, element in enumerate(response_json["parsed_content_list"]):
|
||||
element['idx'] = idx
|
||||
if element['type'] == 'text':
|
||||
screen_info += f'ID: {idx}, Text: {element["content"]}\n'
|
||||
elif element['type'] == 'icon':
|
||||
screen_info += f'ID: {idx}, Icon: {element["content"]}\n'
|
||||
response_json['screen_info'] = screen_info
|
||||
return response_json
|
||||
13
omnitool/gradio/agent/llm_utils/utils.py
Normal file
13
omnitool/gradio/agent/llm_utils/utils.py
Normal file
@@ -0,0 +1,13 @@
|
||||
import base64
|
||||
|
||||
def is_image_path(text):
|
||||
image_extensions = (".jpg", ".jpeg", ".png", ".gif", ".bmp", ".tiff", ".tif")
|
||||
if text.endswith(image_extensions):
|
||||
return True
|
||||
else:
|
||||
return False
|
||||
|
||||
def encode_image(image_path):
|
||||
"""Encode image file to base64."""
|
||||
with open(image_path, "rb") as image_file:
|
||||
return base64.b64encode(image_file.read()).decode("utf-8")
|
||||
353
omnitool/gradio/agent/vlm_agent.py
Normal file
353
omnitool/gradio/agent/vlm_agent.py
Normal file
@@ -0,0 +1,353 @@
|
||||
import json
|
||||
from collections.abc import Callable
|
||||
from typing import cast, Callable
|
||||
import uuid
|
||||
from PIL import Image, ImageDraw
|
||||
import base64
|
||||
from io import BytesIO
|
||||
|
||||
from anthropic import APIResponse
|
||||
from anthropic.types import ToolResultBlockParam
|
||||
from anthropic.types.beta import BetaMessage, BetaTextBlock, BetaToolUseBlock, BetaMessageParam, BetaUsage
|
||||
|
||||
from agent.llm_utils.oaiclient import run_oai_interleaved
|
||||
from agent.llm_utils.groqclient import run_groq_interleaved
|
||||
from agent.llm_utils.utils import is_image_path
|
||||
import time
|
||||
import re
|
||||
|
||||
OUTPUT_DIR = "./tmp/outputs"
|
||||
|
||||
def extract_data(input_string, data_type):
|
||||
# Regular expression to extract content starting from '```python' until the end if there are no closing backticks
|
||||
pattern = f"```{data_type}" + r"(.*?)(```|$)"
|
||||
# Extract content
|
||||
# re.DOTALL allows '.' to match newlines as well
|
||||
matches = re.findall(pattern, input_string, re.DOTALL)
|
||||
# Return the first match if exists, trimming whitespace and ignoring potential closing backticks
|
||||
return matches[0][0].strip() if matches else input_string
|
||||
|
||||
class VLMAgent:
|
||||
def __init__(
|
||||
self,
|
||||
model: str,
|
||||
provider: str,
|
||||
api_key: str,
|
||||
output_callback: Callable,
|
||||
api_response_callback: Callable,
|
||||
max_tokens: int = 4096,
|
||||
only_n_most_recent_images: int | None = None,
|
||||
print_usage: bool = True,
|
||||
):
|
||||
if model == "omniparser + gpt-4o":
|
||||
self.model = "gpt-4o-2024-11-20"
|
||||
elif model == "omniparser + R1":
|
||||
self.model = "deepseek-r1-distill-llama-70b"
|
||||
elif model == "omniparser + qwen2.5vl":
|
||||
self.model = "qwen2.5-vl-72b-instruct"
|
||||
elif model == "omniparser + o1":
|
||||
self.model = "o1"
|
||||
elif model == "omniparser + o3-mini":
|
||||
self.model = "o3-mini"
|
||||
else:
|
||||
raise ValueError(f"Model {model} not supported")
|
||||
|
||||
|
||||
self.provider = provider
|
||||
self.api_key = api_key
|
||||
self.api_response_callback = api_response_callback
|
||||
self.max_tokens = max_tokens
|
||||
self.only_n_most_recent_images = only_n_most_recent_images
|
||||
self.output_callback = output_callback
|
||||
|
||||
self.print_usage = print_usage
|
||||
self.total_token_usage = 0
|
||||
self.total_cost = 0
|
||||
self.step_count = 0
|
||||
|
||||
self.system = ''
|
||||
|
||||
def __call__(self, messages: list, parsed_screen: list[str, list, dict]):
|
||||
self.step_count += 1
|
||||
image_base64 = parsed_screen['original_screenshot_base64']
|
||||
latency_omniparser = parsed_screen['latency']
|
||||
self.output_callback(f'-- Step {self.step_count}: --', sender="bot")
|
||||
screen_info = str(parsed_screen['screen_info'])
|
||||
screenshot_uuid = parsed_screen['screenshot_uuid']
|
||||
screen_width, screen_height = parsed_screen['width'], parsed_screen['height']
|
||||
|
||||
boxids_and_labels = parsed_screen["screen_info"]
|
||||
system = self._get_system_prompt(boxids_and_labels)
|
||||
|
||||
# drop looping actions msg, byte image etc
|
||||
planner_messages = messages
|
||||
_remove_som_images(planner_messages)
|
||||
_maybe_filter_to_n_most_recent_images(planner_messages, self.only_n_most_recent_images)
|
||||
|
||||
if isinstance(planner_messages[-1], dict):
|
||||
if not isinstance(planner_messages[-1]["content"], list):
|
||||
planner_messages[-1]["content"] = [planner_messages[-1]["content"]]
|
||||
planner_messages[-1]["content"].append(f"{OUTPUT_DIR}/screenshot_{screenshot_uuid}.png")
|
||||
planner_messages[-1]["content"].append(f"{OUTPUT_DIR}/screenshot_som_{screenshot_uuid}.png")
|
||||
|
||||
start = time.time()
|
||||
if "gpt" in self.model or "o1" in self.model or "o3-mini" in self.model:
|
||||
vlm_response, token_usage = run_oai_interleaved(
|
||||
messages=planner_messages,
|
||||
system=system,
|
||||
model_name=self.model,
|
||||
api_key=self.api_key,
|
||||
max_tokens=self.max_tokens,
|
||||
provider_base_url="https://api.openai.com/v1",
|
||||
temperature=0,
|
||||
)
|
||||
print(f"oai token usage: {token_usage}")
|
||||
self.total_token_usage += token_usage
|
||||
if 'gpt' in self.model:
|
||||
self.total_cost += (token_usage * 2.5 / 1000000) # https://openai.com/api/pricing/
|
||||
elif 'o1' in self.model:
|
||||
self.total_cost += (token_usage * 15 / 1000000) # https://openai.com/api/pricing/
|
||||
elif 'o3-mini' in self.model:
|
||||
self.total_cost += (token_usage * 1.1 / 1000000) # https://openai.com/api/pricing/
|
||||
elif "r1" in self.model:
|
||||
vlm_response, token_usage = run_groq_interleaved(
|
||||
messages=planner_messages,
|
||||
system=system,
|
||||
model_name=self.model,
|
||||
api_key=self.api_key,
|
||||
max_tokens=self.max_tokens,
|
||||
)
|
||||
print(f"groq token usage: {token_usage}")
|
||||
self.total_token_usage += token_usage
|
||||
self.total_cost += (token_usage * 0.99 / 1000000)
|
||||
elif "qwen" in self.model:
|
||||
vlm_response, token_usage = run_oai_interleaved(
|
||||
messages=planner_messages,
|
||||
system=system,
|
||||
model_name=self.model,
|
||||
api_key=self.api_key,
|
||||
max_tokens=min(2048, self.max_tokens),
|
||||
provider_base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
|
||||
temperature=0,
|
||||
)
|
||||
print(f"qwen token usage: {token_usage}")
|
||||
self.total_token_usage += token_usage
|
||||
self.total_cost += (token_usage * 2.2 / 1000000) # https://help.aliyun.com/zh/model-studio/getting-started/models?spm=a2c4g.11186623.0.0.74b04823CGnPv7#fe96cfb1a422a
|
||||
else:
|
||||
raise ValueError(f"Model {self.model} not supported")
|
||||
latency_vlm = time.time() - start
|
||||
self.output_callback(f"LLM: {latency_vlm:.2f}s, OmniParser: {latency_omniparser:.2f}s", sender="bot")
|
||||
|
||||
print(f"{vlm_response}")
|
||||
|
||||
if self.print_usage:
|
||||
print(f"Total token so far: {self.total_token_usage}. Total cost so far: $USD{self.total_cost:.5f}")
|
||||
|
||||
vlm_response_json = extract_data(vlm_response, "json")
|
||||
vlm_response_json = json.loads(vlm_response_json)
|
||||
|
||||
img_to_show_base64 = parsed_screen["som_image_base64"]
|
||||
if "Box ID" in vlm_response_json:
|
||||
try:
|
||||
bbox = parsed_screen["parsed_content_list"][int(vlm_response_json["Box ID"])]["bbox"]
|
||||
vlm_response_json["box_centroid_coordinate"] = [int((bbox[0] + bbox[2]) / 2 * screen_width), int((bbox[1] + bbox[3]) / 2 * screen_height)]
|
||||
img_to_show_data = base64.b64decode(img_to_show_base64)
|
||||
img_to_show = Image.open(BytesIO(img_to_show_data))
|
||||
|
||||
draw = ImageDraw.Draw(img_to_show)
|
||||
x, y = vlm_response_json["box_centroid_coordinate"]
|
||||
radius = 10
|
||||
draw.ellipse((x - radius, y - radius, x + radius, y + radius), fill='red')
|
||||
draw.ellipse((x - radius*3, y - radius*3, x + radius*3, y + radius*3), fill=None, outline='red', width=2)
|
||||
|
||||
buffered = BytesIO()
|
||||
img_to_show.save(buffered, format="PNG")
|
||||
img_to_show_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8")
|
||||
except:
|
||||
print(f"Error parsing: {vlm_response_json}")
|
||||
pass
|
||||
self.output_callback(f'<img src="data:image/png;base64,{img_to_show_base64}">', sender="bot")
|
||||
self.output_callback(
|
||||
f'<details>'
|
||||
f' <summary>Parsed Screen elemetns by OmniParser</summary>'
|
||||
f' <pre>{screen_info}</pre>'
|
||||
f'</details>',
|
||||
sender="bot"
|
||||
)
|
||||
vlm_plan_str = ""
|
||||
for key, value in vlm_response_json.items():
|
||||
if key == "Reasoning":
|
||||
vlm_plan_str += f'{value}'
|
||||
else:
|
||||
vlm_plan_str += f'\n{key}: {value}'
|
||||
|
||||
# construct the response so that anthropicExcutor can execute the tool
|
||||
response_content = [BetaTextBlock(text=vlm_plan_str, type='text')]
|
||||
if 'box_centroid_coordinate' in vlm_response_json:
|
||||
move_cursor_block = BetaToolUseBlock(id=f'toolu_{uuid.uuid4()}',
|
||||
input={'action': 'mouse_move', 'coordinate': vlm_response_json["box_centroid_coordinate"]},
|
||||
name='computer', type='tool_use')
|
||||
response_content.append(move_cursor_block)
|
||||
|
||||
if vlm_response_json["Next Action"] == "None":
|
||||
print("Task paused/completed.")
|
||||
elif vlm_response_json["Next Action"] == "type":
|
||||
sim_content_block = BetaToolUseBlock(id=f'toolu_{uuid.uuid4()}',
|
||||
input={'action': vlm_response_json["Next Action"], 'text': vlm_response_json["value"]},
|
||||
name='computer', type='tool_use')
|
||||
response_content.append(sim_content_block)
|
||||
else:
|
||||
sim_content_block = BetaToolUseBlock(id=f'toolu_{uuid.uuid4()}',
|
||||
input={'action': vlm_response_json["Next Action"]},
|
||||
name='computer', type='tool_use')
|
||||
response_content.append(sim_content_block)
|
||||
response_message = BetaMessage(id=f'toolu_{uuid.uuid4()}', content=response_content, model='', role='assistant', type='message', stop_reason='tool_use', usage=BetaUsage(input_tokens=0, output_tokens=0))
|
||||
return response_message, vlm_response_json
|
||||
|
||||
def _api_response_callback(self, response: APIResponse):
|
||||
self.api_response_callback(response)
|
||||
|
||||
def _get_system_prompt(self, screen_info: str = ""):
|
||||
main_section = f"""
|
||||
You are using a Windows device.
|
||||
You are able to use a mouse and keyboard to interact with the computer based on the given task and screenshot.
|
||||
You can only interact with the desktop GUI (no terminal or application menu access).
|
||||
|
||||
You may be given some history plan and actions, this is the response from the previous loop.
|
||||
You should carefully consider your plan base on the task, screenshot, and history actions.
|
||||
|
||||
Here is the list of all detected bounding boxes by IDs on the screen and their description:{screen_info}
|
||||
|
||||
Your available "Next Action" only include:
|
||||
- type: types a string of text.
|
||||
- left_click: move mouse to box id and left clicks.
|
||||
- right_click: move mouse to box id and right clicks.
|
||||
- double_click: move mouse to box id and double clicks.
|
||||
- hover: move mouse to box id.
|
||||
- scroll_up: scrolls the screen up to view previous content.
|
||||
- scroll_down: scrolls the screen down, when the desired button is not visible, or you need to see more content.
|
||||
- wait: waits for 1 second for the device to load or respond.
|
||||
|
||||
Based on the visual information from the screenshot image and the detected bounding boxes, please determine the next action, the Box ID you should operate on (if action is one of 'type', 'hover', 'scroll_up', 'scroll_down', 'wait', there should be no Box ID field), and the value (if the action is 'type') in order to complete the task.
|
||||
|
||||
Output format:
|
||||
```json
|
||||
{{
|
||||
"Reasoning": str, # describe what is in the current screen, taking into account the history, then describe your step-by-step thoughts on how to achieve the task, choose one action from available actions at a time.
|
||||
"Next Action": "action_type, action description" | "None" # one action at a time, describe it in short and precisely.
|
||||
"Box ID": n,
|
||||
"value": "xxx" # only provide value field if the action is type, else don't include value key
|
||||
}}
|
||||
```
|
||||
|
||||
One Example:
|
||||
```json
|
||||
{{
|
||||
"Reasoning": "The current screen shows google result of amazon, in previous action I have searched amazon on google. Then I need to click on the first search results to go to amazon.com.",
|
||||
"Next Action": "left_click",
|
||||
"Box ID": m
|
||||
}}
|
||||
```
|
||||
|
||||
Another Example:
|
||||
```json
|
||||
{{
|
||||
"Reasoning": "The current screen shows the front page of amazon. There is no previous action. Therefore I need to type "Apple watch" in the search bar.",
|
||||
"Next Action": "type",
|
||||
"Box ID": n,
|
||||
"value": "Apple watch"
|
||||
}}
|
||||
```
|
||||
|
||||
Another Example:
|
||||
```json
|
||||
{{
|
||||
"Reasoning": "The current screen does not show 'submit' button, I need to scroll down to see if the button is available.",
|
||||
"Next Action": "scroll_down",
|
||||
}}
|
||||
```
|
||||
|
||||
IMPORTANT NOTES:
|
||||
1. You should only give a single action at a time.
|
||||
|
||||
"""
|
||||
thinking_model = "r1" in self.model
|
||||
if not thinking_model:
|
||||
main_section += """
|
||||
2. You should give an analysis to the current screen, and reflect on what has been done by looking at the history, then describe your step-by-step thoughts on how to achieve the task.
|
||||
|
||||
"""
|
||||
else:
|
||||
main_section += """
|
||||
2. In <think> XML tags give an analysis to the current screen, and reflect on what has been done by looking at the history, then describe your step-by-step thoughts on how to achieve the task. In <output> XML tags put the next action prediction JSON.
|
||||
|
||||
"""
|
||||
main_section += """
|
||||
3. Attach the next action prediction in the "Next Action".
|
||||
4. You should not include other actions, such as keyboard shortcuts.
|
||||
5. When the task is completed, don't complete additional actions. You should say "Next Action": "None" in the json field.
|
||||
6. The tasks involve buying multiple products or navigating through multiple pages. You should break it into subgoals and complete each subgoal one by one in the order of the instructions.
|
||||
7. avoid choosing the same action/elements multiple times in a row, if it happens, reflect to yourself, what may have gone wrong, and predict a different action.
|
||||
8. If you are prompted with login information page or captcha page, or you think it need user's permission to do the next action, you should say "Next Action": "None" in the json field.
|
||||
"""
|
||||
|
||||
return main_section
|
||||
|
||||
def _remove_som_images(messages):
|
||||
for msg in messages:
|
||||
msg_content = msg["content"]
|
||||
if isinstance(msg_content, list):
|
||||
msg["content"] = [
|
||||
cnt for cnt in msg_content
|
||||
if not (isinstance(cnt, str) and 'som' in cnt and is_image_path(cnt))
|
||||
]
|
||||
|
||||
|
||||
def _maybe_filter_to_n_most_recent_images(
|
||||
messages: list[BetaMessageParam],
|
||||
images_to_keep: int,
|
||||
min_removal_threshold: int = 10,
|
||||
):
|
||||
"""
|
||||
With the assumption that images are screenshots that are of diminishing value as
|
||||
the conversation progresses, remove all but the final `images_to_keep` tool_result
|
||||
images in place
|
||||
"""
|
||||
if images_to_keep is None:
|
||||
return messages
|
||||
|
||||
total_images = 0
|
||||
for msg in messages:
|
||||
for cnt in msg.get("content", []):
|
||||
if isinstance(cnt, str) and is_image_path(cnt):
|
||||
total_images += 1
|
||||
elif isinstance(cnt, dict) and cnt.get("type") == "tool_result":
|
||||
for content in cnt.get("content", []):
|
||||
if isinstance(content, dict) and content.get("type") == "image":
|
||||
total_images += 1
|
||||
|
||||
images_to_remove = total_images - images_to_keep
|
||||
|
||||
for msg in messages:
|
||||
msg_content = msg["content"]
|
||||
if isinstance(msg_content, list):
|
||||
new_content = []
|
||||
for cnt in msg_content:
|
||||
# Remove images from SOM or screenshot as needed
|
||||
if isinstance(cnt, str) and is_image_path(cnt):
|
||||
if images_to_remove > 0:
|
||||
images_to_remove -= 1
|
||||
continue
|
||||
# VLM shouldn't use anthropic screenshot tool so shouldn't have these but in case it does, remove as needed
|
||||
elif isinstance(cnt, dict) and cnt.get("type") == "tool_result":
|
||||
new_tool_result_content = []
|
||||
for tool_result_entry in cnt.get("content", []):
|
||||
if isinstance(tool_result_entry, dict) and tool_result_entry.get("type") == "image":
|
||||
if images_to_remove > 0:
|
||||
images_to_remove -= 1
|
||||
continue
|
||||
new_tool_result_content.append(tool_result_entry)
|
||||
cnt["content"] = new_tool_result_content
|
||||
# Append fixed content to current message's content list
|
||||
new_content.append(cnt)
|
||||
msg["content"] = new_content
|
||||
426
omnitool/gradio/app.py
Normal file
426
omnitool/gradio/app.py
Normal file
@@ -0,0 +1,426 @@
|
||||
"""
|
||||
python app.py --windows_host_url localhost:8006 --omniparser_server_url localhost:8000
|
||||
"""
|
||||
|
||||
import os
|
||||
from datetime import datetime
|
||||
from enum import StrEnum
|
||||
from functools import partial
|
||||
from pathlib import Path
|
||||
from typing import cast
|
||||
import argparse
|
||||
import gradio as gr
|
||||
from anthropic import APIResponse
|
||||
from anthropic.types import TextBlock
|
||||
from anthropic.types.beta import BetaMessage, BetaTextBlock, BetaToolUseBlock
|
||||
from anthropic.types.tool_use_block import ToolUseBlock
|
||||
from loop import (
|
||||
APIProvider,
|
||||
sampling_loop_sync,
|
||||
)
|
||||
from tools import ToolResult
|
||||
import requests
|
||||
from requests.exceptions import RequestException
|
||||
import base64
|
||||
|
||||
CONFIG_DIR = Path("~/.anthropic").expanduser()
|
||||
API_KEY_FILE = CONFIG_DIR / "api_key"
|
||||
|
||||
INTRO_TEXT = '''
|
||||
OmniParser lets you turn any vision-langauge model into an AI agent. We currently support **OpenAI (4o/o1/o3-mini), DeepSeek (R1), Qwen (2.5VL) or Anthropic Computer Use (Sonnet).**
|
||||
|
||||
Type a message and press submit to start OmniTool. Press stop to pause, and press the trash icon in the chat to clear the message history.
|
||||
'''
|
||||
|
||||
def parse_arguments():
|
||||
|
||||
parser = argparse.ArgumentParser(description="Gradio App")
|
||||
parser.add_argument("--windows_host_url", type=str, default='localhost:8006')
|
||||
parser.add_argument("--omniparser_server_url", type=str, default="localhost:8000")
|
||||
return parser.parse_args()
|
||||
args = parse_arguments()
|
||||
|
||||
|
||||
class Sender(StrEnum):
|
||||
USER = "user"
|
||||
BOT = "assistant"
|
||||
TOOL = "tool"
|
||||
|
||||
|
||||
def setup_state(state):
|
||||
if "messages" not in state:
|
||||
state["messages"] = []
|
||||
if "model" not in state:
|
||||
state["model"] = "omniparser + gpt-4o"
|
||||
if "provider" not in state:
|
||||
state["provider"] = "openai"
|
||||
if "openai_api_key" not in state: # Fetch API keys from environment variables
|
||||
state["openai_api_key"] = os.getenv("OPENAI_API_KEY", "")
|
||||
if "anthropic_api_key" not in state:
|
||||
state["anthropic_api_key"] = os.getenv("ANTHROPIC_API_KEY", "")
|
||||
if "api_key" not in state:
|
||||
state["api_key"] = ""
|
||||
if "auth_validated" not in state:
|
||||
state["auth_validated"] = False
|
||||
if "responses" not in state:
|
||||
state["responses"] = {}
|
||||
if "tools" not in state:
|
||||
state["tools"] = {}
|
||||
if "only_n_most_recent_images" not in state:
|
||||
state["only_n_most_recent_images"] = 2
|
||||
if 'chatbot_messages' not in state:
|
||||
state['chatbot_messages'] = []
|
||||
if 'stop' not in state:
|
||||
state['stop'] = False
|
||||
|
||||
async def main(state):
|
||||
"""Render loop for Gradio"""
|
||||
setup_state(state)
|
||||
return "Setup completed"
|
||||
|
||||
def validate_auth(provider: APIProvider, api_key: str | None):
|
||||
if provider == APIProvider.ANTHROPIC:
|
||||
if not api_key:
|
||||
return "Enter your Anthropic API key to continue."
|
||||
if provider == APIProvider.BEDROCK:
|
||||
import boto3
|
||||
|
||||
if not boto3.Session().get_credentials():
|
||||
return "You must have AWS credentials set up to use the Bedrock API."
|
||||
if provider == APIProvider.VERTEX:
|
||||
import google.auth
|
||||
from google.auth.exceptions import DefaultCredentialsError
|
||||
|
||||
if not os.environ.get("CLOUD_ML_REGION"):
|
||||
return "Set the CLOUD_ML_REGION environment variable to use the Vertex API."
|
||||
try:
|
||||
google.auth.default(scopes=["https://www.googleapis.com/auth/cloud-platform"])
|
||||
except DefaultCredentialsError:
|
||||
return "Your google cloud credentials are not set up correctly."
|
||||
|
||||
def load_from_storage(filename: str) -> str | None:
|
||||
"""Load data from a file in the storage directory."""
|
||||
try:
|
||||
file_path = CONFIG_DIR / filename
|
||||
if file_path.exists():
|
||||
data = file_path.read_text().strip()
|
||||
if data:
|
||||
return data
|
||||
except Exception as e:
|
||||
print(f"Debug: Error loading {filename}: {e}")
|
||||
return None
|
||||
|
||||
def save_to_storage(filename: str, data: str) -> None:
|
||||
"""Save data to a file in the storage directory."""
|
||||
try:
|
||||
CONFIG_DIR.mkdir(parents=True, exist_ok=True)
|
||||
file_path = CONFIG_DIR / filename
|
||||
file_path.write_text(data)
|
||||
# Ensure only user can read/write the file
|
||||
file_path.chmod(0o600)
|
||||
except Exception as e:
|
||||
print(f"Debug: Error saving {filename}: {e}")
|
||||
|
||||
def _api_response_callback(response: APIResponse[BetaMessage], response_state: dict):
|
||||
response_id = datetime.now().isoformat()
|
||||
response_state[response_id] = response
|
||||
|
||||
def _tool_output_callback(tool_output: ToolResult, tool_id: str, tool_state: dict):
|
||||
tool_state[tool_id] = tool_output
|
||||
|
||||
def chatbot_output_callback(message, chatbot_state, hide_images=False, sender="bot"):
|
||||
def _render_message(message: str | BetaTextBlock | BetaToolUseBlock | ToolResult, hide_images=False):
|
||||
|
||||
print(f"_render_message: {str(message)[:100]}")
|
||||
|
||||
if isinstance(message, str):
|
||||
return message
|
||||
|
||||
is_tool_result = not isinstance(message, str) and (
|
||||
isinstance(message, ToolResult)
|
||||
or message.__class__.__name__ == "ToolResult"
|
||||
)
|
||||
if not message or (
|
||||
is_tool_result
|
||||
and hide_images
|
||||
and not hasattr(message, "error")
|
||||
and not hasattr(message, "output")
|
||||
): # return None if hide_images is True
|
||||
return
|
||||
# render tool result
|
||||
if is_tool_result:
|
||||
message = cast(ToolResult, message)
|
||||
if message.output:
|
||||
return message.output
|
||||
if message.error:
|
||||
return f"Error: {message.error}"
|
||||
if message.base64_image and not hide_images:
|
||||
# somehow can't display via gr.Image
|
||||
# image_data = base64.b64decode(message.base64_image)
|
||||
# return gr.Image(value=Image.open(io.BytesIO(image_data)))
|
||||
return f'<img src="data:image/png;base64,{message.base64_image}">'
|
||||
|
||||
elif isinstance(message, BetaTextBlock) or isinstance(message, TextBlock):
|
||||
return f"Analysis: {message.text}"
|
||||
elif isinstance(message, BetaToolUseBlock) or isinstance(message, ToolUseBlock):
|
||||
# return f"Tool Use: {message.name}\nInput: {message.input}"
|
||||
return f"Next I will perform the following action: {message.input}"
|
||||
else:
|
||||
return message
|
||||
|
||||
def _truncate_string(s, max_length=500):
|
||||
"""Truncate long strings for concise printing."""
|
||||
if isinstance(s, str) and len(s) > max_length:
|
||||
return s[:max_length] + "..."
|
||||
return s
|
||||
# processing Anthropic messages
|
||||
message = _render_message(message, hide_images)
|
||||
|
||||
if sender == "bot":
|
||||
chatbot_state.append((None, message))
|
||||
else:
|
||||
chatbot_state.append((message, None))
|
||||
|
||||
# Create a concise version of the chatbot state for printing
|
||||
concise_state = [(_truncate_string(user_msg), _truncate_string(bot_msg))
|
||||
for user_msg, bot_msg in chatbot_state]
|
||||
# print(f"chatbot_output_callback chatbot_state: {concise_state} (truncated)")
|
||||
|
||||
def valid_params(user_input, state):
|
||||
"""Validate all requirements and return a list of error messages."""
|
||||
errors = []
|
||||
|
||||
for server_name, url in [('Windows Host', 'localhost:5000'), ('OmniParser Server', args.omniparser_server_url)]:
|
||||
try:
|
||||
url = f'http://{url}/probe'
|
||||
response = requests.get(url, timeout=3)
|
||||
if response.status_code != 200:
|
||||
errors.append(f"{server_name} is not responding")
|
||||
except RequestException as e:
|
||||
errors.append(f"{server_name} is not responding")
|
||||
|
||||
if not state["api_key"].strip():
|
||||
errors.append("LLM API Key is not set")
|
||||
|
||||
if not user_input:
|
||||
errors.append("no computer use request provided")
|
||||
|
||||
return errors
|
||||
|
||||
def process_input(user_input, state):
|
||||
# Reset the stop flag
|
||||
if state["stop"]:
|
||||
state["stop"] = False
|
||||
|
||||
errors = valid_params(user_input, state)
|
||||
if errors:
|
||||
raise gr.Error("Validation errors: " + ", ".join(errors))
|
||||
|
||||
# Append the user message to state["messages"]
|
||||
state["messages"].append(
|
||||
{
|
||||
"role": Sender.USER,
|
||||
"content": [TextBlock(type="text", text=user_input)],
|
||||
}
|
||||
)
|
||||
|
||||
# Append the user's message to chatbot_messages with None for the assistant's reply
|
||||
state['chatbot_messages'].append((user_input, None))
|
||||
yield state['chatbot_messages'] # Yield to update the chatbot UI with the user's message
|
||||
|
||||
print("state")
|
||||
print(state)
|
||||
|
||||
# Run sampling_loop_sync with the chatbot_output_callback
|
||||
for loop_msg in sampling_loop_sync(
|
||||
model=state["model"],
|
||||
provider=state["provider"],
|
||||
messages=state["messages"],
|
||||
output_callback=partial(chatbot_output_callback, chatbot_state=state['chatbot_messages'], hide_images=False),
|
||||
tool_output_callback=partial(_tool_output_callback, tool_state=state["tools"]),
|
||||
api_response_callback=partial(_api_response_callback, response_state=state["responses"]),
|
||||
api_key=state["api_key"],
|
||||
only_n_most_recent_images=state["only_n_most_recent_images"],
|
||||
max_tokens=16384,
|
||||
omniparser_url=args.omniparser_server_url
|
||||
):
|
||||
if loop_msg is None or state.get("stop"):
|
||||
yield state['chatbot_messages']
|
||||
print("End of task. Close the loop.")
|
||||
break
|
||||
|
||||
yield state['chatbot_messages'] # Yield the updated chatbot_messages to update the chatbot UI
|
||||
|
||||
def stop_app(state):
|
||||
state["stop"] = True
|
||||
return "App stopped"
|
||||
|
||||
def get_header_image_base64():
|
||||
try:
|
||||
# Get the absolute path to the image relative to this script
|
||||
script_dir = Path(__file__).parent
|
||||
image_path = script_dir.parent.parent / "imgs" / "header_bar_thin.png"
|
||||
|
||||
with open(image_path, "rb") as image_file:
|
||||
encoded_string = base64.b64encode(image_file.read()).decode()
|
||||
return f'data:image/png;base64,{encoded_string}'
|
||||
except Exception as e:
|
||||
print(f"Failed to load header image: {e}")
|
||||
return None
|
||||
|
||||
with gr.Blocks(theme=gr.themes.Default()) as demo:
|
||||
gr.HTML("""
|
||||
<style>
|
||||
.no-padding {
|
||||
padding: 0 !important;
|
||||
}
|
||||
.no-padding > div {
|
||||
padding: 0 !important;
|
||||
}
|
||||
.markdown-text p {
|
||||
font-size: 18px; /* Adjust the font size as needed */
|
||||
}
|
||||
</style>
|
||||
""")
|
||||
state = gr.State({})
|
||||
|
||||
setup_state(state.value)
|
||||
|
||||
header_image = get_header_image_base64()
|
||||
if header_image:
|
||||
gr.HTML(f'<img src="{header_image}" alt="OmniTool Header" width="100%">', elem_classes="no-padding")
|
||||
gr.HTML('<h1 style="text-align: center; font-weight: normal;">Omni<span style="font-weight: bold;">Tool</span></h1>')
|
||||
else:
|
||||
gr.Markdown("# OmniTool")
|
||||
|
||||
if not os.getenv("HIDE_WARNING", False):
|
||||
gr.Markdown(INTRO_TEXT, elem_classes="markdown-text")
|
||||
|
||||
|
||||
with gr.Accordion("Settings", open=True):
|
||||
with gr.Row():
|
||||
with gr.Column():
|
||||
model = gr.Dropdown(
|
||||
label="Model",
|
||||
choices=["omniparser + gpt-4o", "omniparser + o1", "omniparser + o3-mini", "omniparser + R1", "omniparser + qwen2.5vl", "claude-3-5-sonnet-20241022"],
|
||||
value="omniparser + gpt-4o",
|
||||
interactive=True,
|
||||
)
|
||||
with gr.Column():
|
||||
only_n_images = gr.Slider(
|
||||
label="N most recent screenshots",
|
||||
minimum=0,
|
||||
maximum=10,
|
||||
step=1,
|
||||
value=2,
|
||||
interactive=True
|
||||
)
|
||||
with gr.Row():
|
||||
with gr.Column(1):
|
||||
provider = gr.Dropdown(
|
||||
label="API Provider",
|
||||
choices=[option.value for option in APIProvider],
|
||||
value="openai",
|
||||
interactive=False,
|
||||
)
|
||||
with gr.Column(2):
|
||||
api_key = gr.Textbox(
|
||||
label="API Key",
|
||||
type="password",
|
||||
value=state.value.get("api_key", ""),
|
||||
placeholder="Paste your API key here",
|
||||
interactive=True,
|
||||
)
|
||||
|
||||
with gr.Row():
|
||||
with gr.Column(scale=8):
|
||||
chat_input = gr.Textbox(show_label=False, placeholder="Type a message to send to Omniparser + X ...", container=False)
|
||||
with gr.Column(scale=1, min_width=50):
|
||||
submit_button = gr.Button(value="Send", variant="primary")
|
||||
with gr.Column(scale=1, min_width=50):
|
||||
stop_button = gr.Button(value="Stop", variant="secondary")
|
||||
|
||||
with gr.Row():
|
||||
with gr.Column(scale=1):
|
||||
chatbot = gr.Chatbot(label="Chatbot History", autoscroll=True, height=580)
|
||||
with gr.Column(scale=3):
|
||||
iframe = gr.HTML(
|
||||
f'<iframe src="http://{args.windows_host_url}/vnc.html?view_only=1&autoconnect=1&resize=scale" width="100%" height="580" allow="fullscreen"></iframe>',
|
||||
container=False,
|
||||
elem_classes="no-padding"
|
||||
)
|
||||
|
||||
def update_model(model_selection, state):
|
||||
state["model"] = model_selection
|
||||
print(f"Model updated to: {state['model']}")
|
||||
|
||||
if model_selection == "claude-3-5-sonnet-20241022":
|
||||
provider_choices = [option.value for option in APIProvider if option.value != "openai"]
|
||||
elif model_selection in set(["omniparser + gpt-4o", "omniparser + o1", "omniparser + o3-mini"]):
|
||||
provider_choices = ["openai"]
|
||||
elif model_selection == "omniparser + R1":
|
||||
provider_choices = ["groq"]
|
||||
elif model_selection == "omniparser + qwen2.5vl":
|
||||
provider_choices = ["dashscope"]
|
||||
else:
|
||||
provider_choices = [option.value for option in APIProvider]
|
||||
default_provider_value = provider_choices[0]
|
||||
|
||||
provider_interactive = len(provider_choices) > 1
|
||||
api_key_placeholder = f"{default_provider_value.title()} API Key"
|
||||
|
||||
# Update state
|
||||
state["provider"] = default_provider_value
|
||||
state["api_key"] = state.get(f"{default_provider_value}_api_key", "")
|
||||
|
||||
# Calls to update other components UI
|
||||
provider_update = gr.update(
|
||||
choices=provider_choices,
|
||||
value=default_provider_value,
|
||||
interactive=provider_interactive
|
||||
)
|
||||
api_key_update = gr.update(
|
||||
placeholder=api_key_placeholder,
|
||||
value=state["api_key"]
|
||||
)
|
||||
|
||||
return provider_update, api_key_update
|
||||
|
||||
def update_only_n_images(only_n_images_value, state):
|
||||
state["only_n_most_recent_images"] = only_n_images_value
|
||||
|
||||
def update_provider(provider_value, state):
|
||||
# Update state
|
||||
state["provider"] = provider_value
|
||||
state["api_key"] = state.get(f"{provider_value}_api_key", "")
|
||||
|
||||
# Calls to update other components UI
|
||||
api_key_update = gr.update(
|
||||
placeholder=f"{provider_value.title()} API Key",
|
||||
value=state["api_key"]
|
||||
)
|
||||
return api_key_update
|
||||
|
||||
def update_api_key(api_key_value, state):
|
||||
state["api_key"] = api_key_value
|
||||
state[f'{state["provider"]}_api_key'] = api_key_value
|
||||
|
||||
def clear_chat(state):
|
||||
# Reset message-related state
|
||||
state["messages"] = []
|
||||
state["responses"] = {}
|
||||
state["tools"] = {}
|
||||
state['chatbot_messages'] = []
|
||||
return state['chatbot_messages']
|
||||
|
||||
model.change(fn=update_model, inputs=[model, state], outputs=[provider, api_key])
|
||||
only_n_images.change(fn=update_only_n_images, inputs=[only_n_images, state], outputs=None)
|
||||
provider.change(fn=update_provider, inputs=[provider, state], outputs=api_key)
|
||||
api_key.change(fn=update_api_key, inputs=[api_key, state], outputs=None)
|
||||
chatbot.clear(fn=clear_chat, inputs=[state], outputs=[chatbot])
|
||||
|
||||
submit_button.click(process_input, [chat_input, state], chatbot)
|
||||
stop_button.click(stop_app, [state], None)
|
||||
|
||||
if __name__ == "__main__":
|
||||
demo.launch(server_name="0.0.0.0", server_port=7888)
|
||||
132
omnitool/gradio/executor/anthropic_executor.py
Normal file
132
omnitool/gradio/executor/anthropic_executor.py
Normal file
@@ -0,0 +1,132 @@
|
||||
import asyncio
|
||||
from typing import Any, Dict, cast
|
||||
from collections.abc import Callable
|
||||
from anthropic.types.beta import (
|
||||
BetaContentBlock,
|
||||
BetaContentBlockParam,
|
||||
BetaImageBlockParam,
|
||||
BetaMessage,
|
||||
BetaMessageParam,
|
||||
BetaTextBlockParam,
|
||||
BetaToolResultBlockParam,
|
||||
)
|
||||
from anthropic.types import TextBlock
|
||||
from anthropic.types.beta import BetaMessage, BetaTextBlock, BetaToolUseBlock
|
||||
from tools import ComputerTool, ToolCollection, ToolResult
|
||||
|
||||
|
||||
class AnthropicExecutor:
|
||||
def __init__(
|
||||
self,
|
||||
output_callback: Callable[[BetaContentBlockParam], None],
|
||||
tool_output_callback: Callable[[Any, str], None],
|
||||
):
|
||||
self.tool_collection = ToolCollection(
|
||||
ComputerTool()
|
||||
)
|
||||
self.output_callback = output_callback
|
||||
self.tool_output_callback = tool_output_callback
|
||||
|
||||
def __call__(self, response: BetaMessage, messages: list[BetaMessageParam]):
|
||||
new_message = {
|
||||
"role": "assistant",
|
||||
"content": cast(list[BetaContentBlockParam], response.content),
|
||||
}
|
||||
if new_message not in messages:
|
||||
messages.append(new_message)
|
||||
else:
|
||||
print("new_message already in messages, there are duplicates.")
|
||||
|
||||
tool_result_content: list[BetaToolResultBlockParam] = []
|
||||
for content_block in cast(list[BetaContentBlock], response.content):
|
||||
self.output_callback(content_block, sender="bot")
|
||||
# Execute the tool
|
||||
if content_block.type == "tool_use":
|
||||
# Run the asynchronous tool execution in a synchronous context
|
||||
result = asyncio.run(self.tool_collection.run(
|
||||
name=content_block.name,
|
||||
tool_input=cast(dict[str, Any], content_block.input),
|
||||
))
|
||||
|
||||
self.output_callback(result, sender="bot")
|
||||
|
||||
tool_result_content.append(
|
||||
_make_api_tool_result(result, content_block.id)
|
||||
)
|
||||
self.tool_output_callback(result, content_block.id)
|
||||
|
||||
# Craft messages based on the content_block
|
||||
# Note: to display the messages in the gradio, you should organize the messages in the following way (user message, bot message)
|
||||
|
||||
display_messages = _message_display_callback(messages)
|
||||
# display_messages = []
|
||||
|
||||
# Send the messages to the gradio
|
||||
for user_msg, bot_msg in display_messages:
|
||||
# yield [user_msg, bot_msg], tool_result_content
|
||||
yield [None, None], tool_result_content
|
||||
|
||||
if not tool_result_content:
|
||||
return messages
|
||||
|
||||
return tool_result_content
|
||||
|
||||
def _message_display_callback(messages):
|
||||
display_messages = []
|
||||
for msg in messages:
|
||||
try:
|
||||
if isinstance(msg["content"][0], TextBlock):
|
||||
display_messages.append((msg["content"][0].text, None)) # User message
|
||||
elif isinstance(msg["content"][0], BetaTextBlock):
|
||||
display_messages.append((None, msg["content"][0].text)) # Bot message
|
||||
elif isinstance(msg["content"][0], BetaToolUseBlock):
|
||||
display_messages.append((None, f"Tool Use: {msg['content'][0].name}\nInput: {msg['content'][0].input}")) # Bot message
|
||||
elif isinstance(msg["content"][0], Dict) and msg["content"][0]["content"][-1]["type"] == "image":
|
||||
display_messages.append((None, f'<img src="data:image/png;base64,{msg["content"][0]["content"][-1]["source"]["data"]}">')) # Bot message
|
||||
else:
|
||||
print(msg["content"][0])
|
||||
except Exception as e:
|
||||
print("error", e)
|
||||
pass
|
||||
return display_messages
|
||||
|
||||
def _make_api_tool_result(
|
||||
result: ToolResult, tool_use_id: str
|
||||
) -> BetaToolResultBlockParam:
|
||||
"""Convert an agent ToolResult to an API ToolResultBlockParam."""
|
||||
tool_result_content: list[BetaTextBlockParam | BetaImageBlockParam] | str = []
|
||||
is_error = False
|
||||
if result.error:
|
||||
is_error = True
|
||||
tool_result_content = _maybe_prepend_system_tool_result(result, result.error)
|
||||
else:
|
||||
if result.output:
|
||||
tool_result_content.append(
|
||||
{
|
||||
"type": "text",
|
||||
"text": _maybe_prepend_system_tool_result(result, result.output),
|
||||
}
|
||||
)
|
||||
if result.base64_image:
|
||||
tool_result_content.append(
|
||||
{
|
||||
"type": "image",
|
||||
"source": {
|
||||
"type": "base64",
|
||||
"media_type": "image/png",
|
||||
"data": result.base64_image,
|
||||
},
|
||||
}
|
||||
)
|
||||
return {
|
||||
"type": "tool_result",
|
||||
"content": tool_result_content,
|
||||
"tool_use_id": tool_use_id,
|
||||
"is_error": is_error,
|
||||
}
|
||||
|
||||
|
||||
def _maybe_prepend_system_tool_result(result: ToolResult, result_text: str):
|
||||
if result.system:
|
||||
result_text = f"<system>{result.system}</system>\n{result_text}"
|
||||
return result_text
|
||||
114
omnitool/gradio/loop.py
Normal file
114
omnitool/gradio/loop.py
Normal file
@@ -0,0 +1,114 @@
|
||||
"""
|
||||
Agentic sampling loop that calls the Anthropic API and local implenmentation of anthropic-defined computer use tools.
|
||||
"""
|
||||
from collections.abc import Callable
|
||||
from enum import StrEnum
|
||||
|
||||
from anthropic import APIResponse
|
||||
from anthropic.types import (
|
||||
TextBlock,
|
||||
)
|
||||
from anthropic.types.beta import (
|
||||
BetaContentBlock,
|
||||
BetaMessage,
|
||||
BetaMessageParam
|
||||
)
|
||||
from tools import ToolResult
|
||||
|
||||
from agent.llm_utils.omniparserclient import OmniParserClient
|
||||
from agent.anthropic_agent import AnthropicActor
|
||||
from agent.vlm_agent import VLMAgent
|
||||
from executor.anthropic_executor import AnthropicExecutor
|
||||
|
||||
BETA_FLAG = "computer-use-2024-10-22"
|
||||
|
||||
class APIProvider(StrEnum):
|
||||
ANTHROPIC = "anthropic"
|
||||
BEDROCK = "bedrock"
|
||||
VERTEX = "vertex"
|
||||
OPENAI = "openai"
|
||||
|
||||
|
||||
PROVIDER_TO_DEFAULT_MODEL_NAME: dict[APIProvider, str] = {
|
||||
APIProvider.ANTHROPIC: "claude-3-5-sonnet-20241022",
|
||||
APIProvider.BEDROCK: "anthropic.claude-3-5-sonnet-20241022-v2:0",
|
||||
APIProvider.VERTEX: "claude-3-5-sonnet-v2@20241022",
|
||||
APIProvider.OPENAI: "gpt-4o",
|
||||
}
|
||||
|
||||
def sampling_loop_sync(
|
||||
*,
|
||||
model: str,
|
||||
provider: APIProvider | None,
|
||||
messages: list[BetaMessageParam],
|
||||
output_callback: Callable[[BetaContentBlock], None],
|
||||
tool_output_callback: Callable[[ToolResult, str], None],
|
||||
api_response_callback: Callable[[APIResponse[BetaMessage]], None],
|
||||
api_key: str,
|
||||
only_n_most_recent_images: int | None = 2,
|
||||
max_tokens: int = 4096,
|
||||
omniparser_url: str
|
||||
):
|
||||
"""
|
||||
Synchronous agentic sampling loop for the assistant/tool interaction of computer use.
|
||||
"""
|
||||
print('in sampling_loop_sync, model:', model)
|
||||
omniparser_client = OmniParserClient(url=f"http://{omniparser_url}/parse/")
|
||||
if model == "claude-3-5-sonnet-20241022":
|
||||
# Register Actor and Executor
|
||||
actor = AnthropicActor(
|
||||
model=model,
|
||||
provider=provider,
|
||||
api_key=api_key,
|
||||
api_response_callback=api_response_callback,
|
||||
max_tokens=max_tokens,
|
||||
only_n_most_recent_images=only_n_most_recent_images
|
||||
)
|
||||
elif model in set(["omniparser + gpt-4o", "omniparser + o1", "omniparser + o3-mini", "omniparser + R1", "omniparser + qwen2.5vl"]):
|
||||
actor = VLMAgent(
|
||||
model=model,
|
||||
provider=provider,
|
||||
api_key=api_key,
|
||||
api_response_callback=api_response_callback,
|
||||
output_callback=output_callback,
|
||||
max_tokens=max_tokens,
|
||||
only_n_most_recent_images=only_n_most_recent_images
|
||||
)
|
||||
else:
|
||||
raise ValueError(f"Model {model} not supported")
|
||||
executor = AnthropicExecutor(
|
||||
output_callback=output_callback,
|
||||
tool_output_callback=tool_output_callback,
|
||||
)
|
||||
print(f"Model Inited: {model}, Provider: {provider}")
|
||||
|
||||
tool_result_content = None
|
||||
|
||||
print(f"Start the message loop. User messages: {messages}")
|
||||
|
||||
if model == "claude-3-5-sonnet-20241022": # Anthropic loop
|
||||
while True:
|
||||
parsed_screen = omniparser_client() # parsed_screen: {"som_image_base64": dino_labled_img, "parsed_content_list": parsed_content_list, "screen_info"}
|
||||
screen_info_block = TextBlock(text='Below is the structured accessibility information of the current UI screen, which includes text and icons you can operate on, take these information into account when you are making the prediction for the next action. Note you will still need to take screenshot to get the image: \n' + parsed_screen['screen_info'], type='text')
|
||||
screen_info_dict = {"role": "user", "content": [screen_info_block]}
|
||||
messages.append(screen_info_dict)
|
||||
tools_use_needed = actor(messages=messages)
|
||||
|
||||
for message, tool_result_content in executor(tools_use_needed, messages):
|
||||
yield message
|
||||
|
||||
if not tool_result_content:
|
||||
return messages
|
||||
|
||||
messages.append({"content": tool_result_content, "role": "user"})
|
||||
|
||||
elif model in set(["omniparser + gpt-4o", "omniparser + o1", "omniparser + o3-mini", "omniparser + R1", "omniparser + qwen2.5vl"]):
|
||||
while True:
|
||||
parsed_screen = omniparser_client()
|
||||
tools_use_needed, vlm_response_json = actor(messages=messages, parsed_screen=parsed_screen)
|
||||
|
||||
for message, tool_result_content in executor(tools_use_needed, messages):
|
||||
yield message
|
||||
|
||||
if not tool_result_content:
|
||||
return messages
|
||||
11
omnitool/gradio/tools/__init__.py
Normal file
11
omnitool/gradio/tools/__init__.py
Normal file
@@ -0,0 +1,11 @@
|
||||
from .base import ToolResult
|
||||
from .collection import ToolCollection
|
||||
from .computer import ComputerTool
|
||||
from .screen_capture import get_screenshot
|
||||
|
||||
__ALL__ = [
|
||||
ComputerTool,
|
||||
ToolCollection,
|
||||
ToolResult,
|
||||
get_screenshot,
|
||||
]
|
||||
65
omnitool/gradio/tools/base.py
Normal file
65
omnitool/gradio/tools/base.py
Normal file
@@ -0,0 +1,65 @@
|
||||
from abc import ABCMeta, abstractmethod
|
||||
from dataclasses import dataclass, fields, replace
|
||||
from typing import Any
|
||||
|
||||
from anthropic.types.beta import BetaToolUnionParam
|
||||
|
||||
|
||||
class BaseAnthropicTool(metaclass=ABCMeta):
|
||||
"""Abstract base class for Anthropic-defined tools."""
|
||||
|
||||
@abstractmethod
|
||||
def __call__(self, **kwargs) -> Any:
|
||||
"""Executes the tool with the given arguments."""
|
||||
...
|
||||
|
||||
@abstractmethod
|
||||
def to_params(
|
||||
self,
|
||||
) -> BetaToolUnionParam:
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
@dataclass(kw_only=True, frozen=True)
|
||||
class ToolResult:
|
||||
"""Represents the result of a tool execution."""
|
||||
|
||||
output: str | None = None
|
||||
error: str | None = None
|
||||
base64_image: str | None = None
|
||||
system: str | None = None
|
||||
|
||||
def __bool__(self):
|
||||
return any(getattr(self, field.name) for field in fields(self))
|
||||
|
||||
def __add__(self, other: "ToolResult"):
|
||||
def combine_fields(
|
||||
field: str | None, other_field: str | None, concatenate: bool = True
|
||||
):
|
||||
if field and other_field:
|
||||
if concatenate:
|
||||
return field + other_field
|
||||
raise ValueError("Cannot combine tool results")
|
||||
return field or other_field
|
||||
|
||||
return ToolResult(
|
||||
output=combine_fields(self.output, other.output),
|
||||
error=combine_fields(self.error, other.error),
|
||||
base64_image=combine_fields(self.base64_image, other.base64_image, False),
|
||||
system=combine_fields(self.system, other.system),
|
||||
)
|
||||
|
||||
def replace(self, **kwargs):
|
||||
"""Returns a new ToolResult with the given fields replaced."""
|
||||
return replace(self, **kwargs)
|
||||
|
||||
|
||||
class ToolFailure(ToolResult):
|
||||
"""A ToolResult that represents a failure."""
|
||||
|
||||
|
||||
class ToolError(Exception):
|
||||
"""Raised when a tool encounters an error."""
|
||||
|
||||
def __init__(self, message):
|
||||
self.message = message
|
||||
34
omnitool/gradio/tools/collection.py
Normal file
34
omnitool/gradio/tools/collection.py
Normal file
@@ -0,0 +1,34 @@
|
||||
"""Collection classes for managing multiple tools."""
|
||||
|
||||
from typing import Any
|
||||
|
||||
from anthropic.types.beta import BetaToolUnionParam
|
||||
|
||||
from .base import (
|
||||
BaseAnthropicTool,
|
||||
ToolError,
|
||||
ToolFailure,
|
||||
ToolResult,
|
||||
)
|
||||
|
||||
|
||||
class ToolCollection:
|
||||
"""A collection of anthropic-defined tools."""
|
||||
|
||||
def __init__(self, *tools: BaseAnthropicTool):
|
||||
self.tools = tools
|
||||
self.tool_map = {tool.to_params()["name"]: tool for tool in tools}
|
||||
|
||||
def to_params(
|
||||
self,
|
||||
) -> list[BetaToolUnionParam]:
|
||||
return [tool.to_params() for tool in self.tools]
|
||||
|
||||
async def run(self, *, name: str, tool_input: dict[str, Any]) -> ToolResult:
|
||||
tool = self.tool_map.get(name)
|
||||
if not tool:
|
||||
return ToolFailure(error=f"Tool {name} is invalid")
|
||||
try:
|
||||
return await tool(**tool_input)
|
||||
except ToolError as e:
|
||||
return ToolFailure(error=e.message)
|
||||
329
omnitool/gradio/tools/computer.py
Normal file
329
omnitool/gradio/tools/computer.py
Normal file
@@ -0,0 +1,329 @@
|
||||
import base64
|
||||
import time
|
||||
from enum import StrEnum
|
||||
from typing import Literal, TypedDict
|
||||
|
||||
from PIL import Image
|
||||
|
||||
from anthropic.types.beta import BetaToolComputerUse20241022Param
|
||||
|
||||
from .base import BaseAnthropicTool, ToolError, ToolResult
|
||||
from .screen_capture import get_screenshot
|
||||
import requests
|
||||
import re
|
||||
|
||||
OUTPUT_DIR = "./tmp/outputs"
|
||||
|
||||
TYPING_DELAY_MS = 12
|
||||
TYPING_GROUP_SIZE = 50
|
||||
|
||||
Action = Literal[
|
||||
"key",
|
||||
"type",
|
||||
"mouse_move",
|
||||
"left_click",
|
||||
"left_click_drag",
|
||||
"right_click",
|
||||
"middle_click",
|
||||
"double_click",
|
||||
"screenshot",
|
||||
"cursor_position",
|
||||
"hover",
|
||||
"wait"
|
||||
]
|
||||
|
||||
|
||||
class Resolution(TypedDict):
|
||||
width: int
|
||||
height: int
|
||||
|
||||
|
||||
MAX_SCALING_TARGETS: dict[str, Resolution] = {
|
||||
"XGA": Resolution(width=1024, height=768), # 4:3
|
||||
"WXGA": Resolution(width=1280, height=800), # 16:10
|
||||
"FWXGA": Resolution(width=1366, height=768), # ~16:9
|
||||
}
|
||||
|
||||
|
||||
class ScalingSource(StrEnum):
|
||||
COMPUTER = "computer"
|
||||
API = "api"
|
||||
|
||||
|
||||
class ComputerToolOptions(TypedDict):
|
||||
display_height_px: int
|
||||
display_width_px: int
|
||||
display_number: int | None
|
||||
|
||||
|
||||
def chunks(s: str, chunk_size: int) -> list[str]:
|
||||
return [s[i : i + chunk_size] for i in range(0, len(s), chunk_size)]
|
||||
|
||||
class ComputerTool(BaseAnthropicTool):
|
||||
"""
|
||||
A tool that allows the agent to interact with the screen, keyboard, and mouse of the current computer.
|
||||
Adapted for Windows using 'pyautogui'.
|
||||
"""
|
||||
|
||||
name: Literal["computer"] = "computer"
|
||||
api_type: Literal["computer_20241022"] = "computer_20241022"
|
||||
width: int
|
||||
height: int
|
||||
display_num: int | None
|
||||
|
||||
_screenshot_delay = 2.0
|
||||
_scaling_enabled = True
|
||||
|
||||
@property
|
||||
def options(self) -> ComputerToolOptions:
|
||||
width, height = self.scale_coordinates(
|
||||
ScalingSource.COMPUTER, self.width, self.height
|
||||
)
|
||||
return {
|
||||
"display_width_px": width,
|
||||
"display_height_px": height,
|
||||
"display_number": self.display_num,
|
||||
}
|
||||
|
||||
def to_params(self) -> BetaToolComputerUse20241022Param:
|
||||
return {"name": self.name, "type": self.api_type, **self.options}
|
||||
|
||||
def __init__(self, is_scaling: bool = False):
|
||||
super().__init__()
|
||||
|
||||
# Get screen width and height using Windows command
|
||||
self.display_num = None
|
||||
self.offset_x = 0
|
||||
self.offset_y = 0
|
||||
self.is_scaling = is_scaling
|
||||
self.width, self.height = self.get_screen_size()
|
||||
print(f"screen size: {self.width}, {self.height}")
|
||||
|
||||
self.key_conversion = {"Page_Down": "pagedown",
|
||||
"Page_Up": "pageup",
|
||||
"Super_L": "win",
|
||||
"Escape": "esc"}
|
||||
|
||||
|
||||
async def __call__(
|
||||
self,
|
||||
*,
|
||||
action: Action,
|
||||
text: str | None = None,
|
||||
coordinate: tuple[int, int] | None = None,
|
||||
**kwargs,
|
||||
):
|
||||
print(f"action: {action}, text: {text}, coordinate: {coordinate}, is_scaling: {self.is_scaling}")
|
||||
if action in ("mouse_move", "left_click_drag"):
|
||||
if coordinate is None:
|
||||
raise ToolError(f"coordinate is required for {action}")
|
||||
if text is not None:
|
||||
raise ToolError(f"text is not accepted for {action}")
|
||||
if not isinstance(coordinate, (list, tuple)) or len(coordinate) != 2:
|
||||
raise ToolError(f"{coordinate} must be a tuple of length 2")
|
||||
# if not all(isinstance(i, int) and i >= 0 for i in coordinate):
|
||||
if not all(isinstance(i, int) for i in coordinate):
|
||||
raise ToolError(f"{coordinate} must be a tuple of non-negative ints")
|
||||
|
||||
if self.is_scaling:
|
||||
x, y = self.scale_coordinates(
|
||||
ScalingSource.API, coordinate[0], coordinate[1]
|
||||
)
|
||||
else:
|
||||
x, y = coordinate
|
||||
|
||||
# print(f"scaled_coordinates: {x}, {y}")
|
||||
# print(f"offset: {self.offset_x}, {self.offset_y}")
|
||||
|
||||
# x += self.offset_x # TODO - check if this is needed
|
||||
# y += self.offset_y
|
||||
|
||||
print(f"mouse move to {x}, {y}")
|
||||
|
||||
if action == "mouse_move":
|
||||
self.send_to_vm(f"pyautogui.moveTo({x}, {y})")
|
||||
return ToolResult(output=f"Moved mouse to ({x}, {y})")
|
||||
elif action == "left_click_drag":
|
||||
current_x, current_y = self.send_to_vm("pyautogui.position()")
|
||||
self.send_to_vm(f"pyautogui.dragTo({x}, {y}, duration=0.5)")
|
||||
return ToolResult(output=f"Dragged mouse from ({current_x}, {current_y}) to ({x}, {y})")
|
||||
|
||||
if action in ("key", "type"):
|
||||
if text is None:
|
||||
raise ToolError(f"text is required for {action}")
|
||||
if coordinate is not None:
|
||||
raise ToolError(f"coordinate is not accepted for {action}")
|
||||
if not isinstance(text, str):
|
||||
raise ToolError(output=f"{text} must be a string")
|
||||
|
||||
if action == "key":
|
||||
# Handle key combinations
|
||||
keys = text.split('+')
|
||||
for key in keys:
|
||||
key = self.key_conversion.get(key.strip(), key.strip())
|
||||
key = key.lower()
|
||||
self.send_to_vm(f"pyautogui.keyDown('{key}')") # Press down each key
|
||||
for key in reversed(keys):
|
||||
key = self.key_conversion.get(key.strip(), key.strip())
|
||||
key = key.lower()
|
||||
self.send_to_vm(f"pyautogui.keyUp('{key}')") # Release each key in reverse order
|
||||
return ToolResult(output=f"Pressed keys: {text}")
|
||||
|
||||
elif action == "type":
|
||||
# default click before type TODO: check if this is needed
|
||||
self.send_to_vm("pyautogui.click()")
|
||||
self.send_to_vm(f"pyautogui.typewrite('{text}', interval={TYPING_DELAY_MS / 1000})")
|
||||
self.send_to_vm("pyautogui.press('enter')")
|
||||
screenshot_base64 = (await self.screenshot()).base64_image
|
||||
return ToolResult(output=text, base64_image=screenshot_base64)
|
||||
|
||||
if action in (
|
||||
"left_click",
|
||||
"right_click",
|
||||
"double_click",
|
||||
"middle_click",
|
||||
"screenshot",
|
||||
"cursor_position",
|
||||
"left_press",
|
||||
):
|
||||
if text is not None:
|
||||
raise ToolError(f"text is not accepted for {action}")
|
||||
if coordinate is not None:
|
||||
raise ToolError(f"coordinate is not accepted for {action}")
|
||||
|
||||
if action == "screenshot":
|
||||
return await self.screenshot()
|
||||
elif action == "cursor_position":
|
||||
x, y = self.send_to_vm("pyautogui.position()")
|
||||
x, y = self.scale_coordinates(ScalingSource.COMPUTER, x, y)
|
||||
return ToolResult(output=f"X={x},Y={y}")
|
||||
else:
|
||||
if action == "left_click":
|
||||
self.send_to_vm("pyautogui.click()")
|
||||
elif action == "right_click":
|
||||
self.send_to_vm("pyautogui.rightClick()")
|
||||
elif action == "middle_click":
|
||||
self.send_to_vm("pyautogui.middleClick()")
|
||||
elif action == "double_click":
|
||||
self.send_to_vm("pyautogui.doubleClick()")
|
||||
elif action == "left_press":
|
||||
self.send_to_vm("pyautogui.mouseDown()")
|
||||
time.sleep(1)
|
||||
self.send_to_vm("pyautogui.mouseUp()")
|
||||
return ToolResult(output=f"Performed {action}")
|
||||
if action in ("scroll_up", "scroll_down"):
|
||||
if action == "scroll_up":
|
||||
self.send_to_vm("pyautogui.scroll(100)")
|
||||
elif action == "scroll_down":
|
||||
self.send_to_vm("pyautogui.scroll(-100)")
|
||||
return ToolResult(output=f"Performed {action}")
|
||||
if action == "hover":
|
||||
return ToolResult(output=f"Performed {action}")
|
||||
if action == "wait":
|
||||
time.sleep(1)
|
||||
return ToolResult(output=f"Performed {action}")
|
||||
raise ToolError(f"Invalid action: {action}")
|
||||
|
||||
def send_to_vm(self, action: str):
|
||||
"""
|
||||
Executes a python command on the server. Only return tuple of x,y when action is "pyautogui.position()"
|
||||
"""
|
||||
prefix = "import pyautogui; pyautogui.FAILSAFE = False;"
|
||||
command_list = ["python", "-c", f"{prefix} {action}"]
|
||||
parse = action == "pyautogui.position()"
|
||||
if parse:
|
||||
command_list[-1] = f"{prefix} print({action})"
|
||||
|
||||
try:
|
||||
print(f"sending to vm: {command_list}")
|
||||
response = requests.post(
|
||||
f"http://localhost:5000/execute",
|
||||
headers={'Content-Type': 'application/json'},
|
||||
json={"command": command_list},
|
||||
timeout=90
|
||||
)
|
||||
time.sleep(0.7) # avoid async error as actions take time to complete
|
||||
print(f"action executed")
|
||||
if response.status_code != 200:
|
||||
raise ToolError(f"Failed to execute command. Status code: {response.status_code}")
|
||||
if parse:
|
||||
output = response.json()['output'].strip()
|
||||
match = re.search(r'Point\(x=(\d+),\s*y=(\d+)\)', output)
|
||||
if not match:
|
||||
raise ToolError(f"Could not parse coordinates from output: {output}")
|
||||
x, y = map(int, match.groups())
|
||||
return x, y
|
||||
except requests.exceptions.RequestException as e:
|
||||
raise ToolError(f"An error occurred while trying to execute the command: {str(e)}")
|
||||
|
||||
async def screenshot(self):
|
||||
if not hasattr(self, 'target_dimension'):
|
||||
screenshot = self.padding_image(screenshot)
|
||||
self.target_dimension = MAX_SCALING_TARGETS["WXGA"]
|
||||
width, height = self.target_dimension["width"], self.target_dimension["height"]
|
||||
screenshot, path = get_screenshot(resize=True, target_width=width, target_height=height)
|
||||
time.sleep(0.7) # avoid async error as actions take time to complete
|
||||
return ToolResult(base64_image=base64.b64encode(path.read_bytes()).decode())
|
||||
|
||||
def padding_image(self, screenshot):
|
||||
"""Pad the screenshot to 16:10 aspect ratio, when the aspect ratio is not 16:10."""
|
||||
_, height = screenshot.size
|
||||
new_width = height * 16 // 10
|
||||
|
||||
padding_image = Image.new("RGB", (new_width, height), (255, 255, 255))
|
||||
# padding to top left
|
||||
padding_image.paste(screenshot, (0, 0))
|
||||
return padding_image
|
||||
|
||||
def scale_coordinates(self, source: ScalingSource, x: int, y: int):
|
||||
"""Scale coordinates to a target maximum resolution."""
|
||||
if not self._scaling_enabled:
|
||||
return x, y
|
||||
ratio = self.width / self.height
|
||||
target_dimension = None
|
||||
|
||||
for target_name, dimension in MAX_SCALING_TARGETS.items():
|
||||
# allow some error in the aspect ratio - not ratios are exactly 16:9
|
||||
if abs(dimension["width"] / dimension["height"] - ratio) < 0.02:
|
||||
if dimension["width"] < self.width:
|
||||
target_dimension = dimension
|
||||
self.target_dimension = target_dimension
|
||||
# print(f"target_dimension: {target_dimension}")
|
||||
break
|
||||
|
||||
if target_dimension is None:
|
||||
# TODO: currently we force the target to be WXGA (16:10), when it cannot find a match
|
||||
target_dimension = MAX_SCALING_TARGETS["WXGA"]
|
||||
self.target_dimension = MAX_SCALING_TARGETS["WXGA"]
|
||||
|
||||
# should be less than 1
|
||||
x_scaling_factor = target_dimension["width"] / self.width
|
||||
y_scaling_factor = target_dimension["height"] / self.height
|
||||
if source == ScalingSource.API:
|
||||
if x > self.width or y > self.height:
|
||||
raise ToolError(f"Coordinates {x}, {y} are out of bounds")
|
||||
# scale up
|
||||
return round(x / x_scaling_factor), round(y / y_scaling_factor)
|
||||
# scale down
|
||||
return round(x * x_scaling_factor), round(y * y_scaling_factor)
|
||||
|
||||
def get_screen_size(self):
|
||||
"""Return width and height of the screen"""
|
||||
try:
|
||||
response = requests.post(
|
||||
f"http://localhost:5000/execute",
|
||||
headers={'Content-Type': 'application/json'},
|
||||
json={"command": ["python", "-c", "import pyautogui; print(pyautogui.size())"]},
|
||||
timeout=90
|
||||
)
|
||||
if response.status_code != 200:
|
||||
raise ToolError(f"Failed to get screen size. Status code: {response.status_code}")
|
||||
|
||||
output = response.json()['output'].strip()
|
||||
match = re.search(r'Size\(width=(\d+),\s*height=(\d+)\)', output)
|
||||
if not match:
|
||||
raise ToolError(f"Could not parse screen size from output: {output}")
|
||||
width, height = map(int, match.groups())
|
||||
return width, height
|
||||
except requests.exceptions.RequestException as e:
|
||||
raise ToolError(f"An error occurred while trying to get screen size: {str(e)}")
|
||||
29
omnitool/gradio/tools/screen_capture.py
Normal file
29
omnitool/gradio/tools/screen_capture.py
Normal file
@@ -0,0 +1,29 @@
|
||||
from pathlib import Path
|
||||
from uuid import uuid4
|
||||
import requests
|
||||
from PIL import Image
|
||||
from .base import BaseAnthropicTool, ToolError
|
||||
from io import BytesIO
|
||||
|
||||
OUTPUT_DIR = "./tmp/outputs"
|
||||
|
||||
def get_screenshot(resize: bool = False, target_width: int = 1920, target_height: int = 1080):
|
||||
"""Capture screenshot by requesting from HTTP endpoint - returns native resolution unless resized"""
|
||||
output_dir = Path(OUTPUT_DIR)
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
path = output_dir / f"screenshot_{uuid4().hex}.png"
|
||||
|
||||
try:
|
||||
response = requests.get('http://localhost:5000/screenshot')
|
||||
if response.status_code != 200:
|
||||
raise ToolError(f"Failed to capture screenshot: HTTP {response.status_code}")
|
||||
|
||||
# (1280, 800)
|
||||
screenshot = Image.open(BytesIO(response.content))
|
||||
|
||||
if resize and screenshot.size != (target_width, target_height):
|
||||
screenshot = screenshot.resize((target_width, target_height))
|
||||
screenshot.save(path)
|
||||
return screenshot, path
|
||||
except Exception as e:
|
||||
raise ToolError(f"Failed to capture screenshot: {str(e)}")
|
||||
4
omnitool/omnibox/.gitignore
vendored
Normal file
4
omnitool/omnibox/.gitignore
vendored
Normal file
@@ -0,0 +1,4 @@
|
||||
vm/win11iso/custom.iso
|
||||
vm/win11storage
|
||||
vm/win11setup/setupscripts/firstboot_log.txt
|
||||
vm/win11setup/setupscripts/server/server.log
|
||||
48
omnitool/omnibox/Dockerfile
Normal file
48
omnitool/omnibox/Dockerfile
Normal file
@@ -0,0 +1,48 @@
|
||||
ARG VERSION_ARG="latest"
|
||||
FROM scratch AS build-amd64
|
||||
|
||||
COPY --from=qemux/qemu-docker:6.08 / /
|
||||
|
||||
ARG DEBCONF_NOWARNINGS="yes"
|
||||
ARG DEBIAN_FRONTEND="noninteractive"
|
||||
ARG DEBCONF_NONINTERACTIVE_SEEN="true"
|
||||
|
||||
RUN set -eu && \
|
||||
apt-get update && \
|
||||
apt-get --no-install-recommends -y install \
|
||||
bc \
|
||||
jq \
|
||||
curl \
|
||||
7zip \
|
||||
wsdd \
|
||||
samba \
|
||||
xz-utils \
|
||||
wimtools \
|
||||
dos2unix \
|
||||
cabextract \
|
||||
genisoimage \
|
||||
libxml2-utils \
|
||||
libarchive-tools && \
|
||||
apt-get clean && \
|
||||
rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
|
||||
|
||||
COPY --chmod=755 ./vm/buildcontainer /run/
|
||||
RUN dos2unix /run/*
|
||||
|
||||
COPY --chmod=755 ./vm/win11def /run/assets
|
||||
RUN dos2unix /run/assets/*
|
||||
|
||||
ADD --chmod=755 https://raw.githubusercontent.com/christgau/wsdd/v0.8/src/wsdd.py /usr/sbin/wsdd
|
||||
ADD --chmod=664 https://github.com/qemus/virtiso-whql/releases/download/v1.9.43-0/virtio-win-1.9.43.tar.xz /drivers.txz
|
||||
|
||||
FROM dockurr/windows-arm:${VERSION_ARG} AS build-arm64
|
||||
FROM build-${TARGETARCH}
|
||||
|
||||
ARG VERSION_ARG="0.00"
|
||||
RUN echo "$VERSION_ARG" > /run/version
|
||||
|
||||
EXPOSE 8006 3389
|
||||
|
||||
ENV VERSION="win11e"
|
||||
|
||||
ENTRYPOINT ["/usr/bin/tini", "-s", "/run/entry.sh"]
|
||||
23
omnitool/omnibox/compose.yml
Normal file
23
omnitool/omnibox/compose.yml
Normal file
@@ -0,0 +1,23 @@
|
||||
services:
|
||||
windows:
|
||||
image: windows-local
|
||||
container_name: omni-windows
|
||||
privileged: true
|
||||
environment:
|
||||
RAM_SIZE: "8G"
|
||||
CPU_CORES: "4"
|
||||
DISK_SIZE: "20G"
|
||||
devices:
|
||||
- /dev/kvm
|
||||
- /dev/net/tun
|
||||
cap_add:
|
||||
- NET_ADMIN
|
||||
ports:
|
||||
- 8006:8006 # Web Viewer access
|
||||
- 5000:5000 # Computer control server
|
||||
volumes:
|
||||
- ./vm/win11iso/custom.iso:/custom.iso
|
||||
- ./vm/win11setup/firstboot:/oem
|
||||
- ./vm/win11setup/setupscripts:/data
|
||||
- ./vm/win11storage:/storage
|
||||
|
||||
70
omnitool/omnibox/scripts/manage_vm.ps1
Normal file
70
omnitool/omnibox/scripts/manage_vm.ps1
Normal file
@@ -0,0 +1,70 @@
|
||||
function Create-VM {
|
||||
if (-not (docker images windows-local -q)) {
|
||||
Write-Host "Image not found locally. Building..."
|
||||
docker build -t windows-local ..
|
||||
} else {
|
||||
Write-Host "Image found locally. Skipping build."
|
||||
}
|
||||
|
||||
docker compose -f ../compose.yml up -d
|
||||
|
||||
while ($true) {
|
||||
try {
|
||||
$response = Invoke-WebRequest -Uri "http://localhost:5000/probe" -Method GET -UseBasicParsing
|
||||
if ($response.StatusCode -eq 200) {
|
||||
break
|
||||
}
|
||||
} catch {
|
||||
Write-Host "Waiting for a response from the computer control server. When first building the VM storage folder this can take a while..."
|
||||
Start-Sleep -Seconds 5
|
||||
}
|
||||
}
|
||||
|
||||
Write-Host "VM + server is up and running!"
|
||||
}
|
||||
|
||||
function Start-LocalVM {
|
||||
Write-Host "Starting VM..."
|
||||
docker compose -f ../compose.yml start
|
||||
while ($true) {
|
||||
try {
|
||||
$response = Invoke-WebRequest -Uri "http://localhost:5000/probe" -Method GET -UseBasicParsing
|
||||
if ($response.StatusCode -eq 200) {
|
||||
break
|
||||
}
|
||||
} catch {
|
||||
Write-Host "Waiting for a response from the computer control server"
|
||||
Start-Sleep -Seconds 5
|
||||
}
|
||||
}
|
||||
Write-Host "VM started"
|
||||
}
|
||||
|
||||
function Stop-LocalVM {
|
||||
Write-Host "Stopping VM..."
|
||||
docker compose -f ../compose.yml stop
|
||||
Write-Host "VM stopped"
|
||||
}
|
||||
|
||||
function Remove-VM {
|
||||
Write-Host "Removing VM and associated containers..."
|
||||
docker compose -f ../compose.yml down
|
||||
Write-Host "VM removed"
|
||||
}
|
||||
|
||||
if (-not $args[0]) {
|
||||
Write-Host "Usage: $($MyInvocation.MyCommand.Name) [create|start|stop|delete]"
|
||||
exit 1
|
||||
}
|
||||
|
||||
switch ($args[0]) {
|
||||
"create" { Create-VM }
|
||||
"start" { Start-LocalVM }
|
||||
"stop" { Stop-LocalVM }
|
||||
"delete" { Remove-VM }
|
||||
default {
|
||||
Write-Host "Invalid option: $($args[0])"
|
||||
Write-Host "Usage: $($MyInvocation.MyCommand.Name) [create|start|stop|delete]"
|
||||
exit 1
|
||||
}
|
||||
}
|
||||
77
omnitool/omnibox/scripts/manage_vm.sh
Executable file
77
omnitool/omnibox/scripts/manage_vm.sh
Executable file
@@ -0,0 +1,77 @@
|
||||
#!/bin/bash
|
||||
|
||||
create_vm() {
|
||||
if ! docker images windows-local -q | grep -q .; then
|
||||
echo "Image not found locally. Building..."
|
||||
docker build -t windows-local ..
|
||||
else
|
||||
echo "Image found locally. Skipping build."
|
||||
fi
|
||||
|
||||
docker compose -f ../compose.yml up -d
|
||||
|
||||
# Wait for the VM to start up
|
||||
while true; do
|
||||
response=$(curl --write-out '%{http_code}' --silent --output /dev/null localhost:5000/probe)
|
||||
if [ $response -eq 200 ]; then
|
||||
break
|
||||
fi
|
||||
echo "Waiting for a response from the computer control server. When first building the VM storage folder this can take a while..."
|
||||
sleep 5
|
||||
done
|
||||
|
||||
echo "VM + server is up and running!"
|
||||
}
|
||||
|
||||
start_vm() {
|
||||
echo "Starting VM..."
|
||||
docker compose -f ../compose.yml start
|
||||
while true; do
|
||||
response=$(curl --write-out '%{http_code}' --silent --output /dev/null localhost:5000/probe)
|
||||
if [ $response -eq 200 ]; then
|
||||
break
|
||||
fi
|
||||
echo "Waiting for a response from the computer control server"
|
||||
sleep 5
|
||||
done
|
||||
echo "VM started"
|
||||
}
|
||||
|
||||
stop_vm() {
|
||||
echo "Stopping VM..."
|
||||
docker compose -f ../compose.yml stop
|
||||
echo "VM stopped"
|
||||
}
|
||||
|
||||
delete_vm() {
|
||||
echo "Removing VM and associated containers..."
|
||||
docker compose -f ../compose.yml down
|
||||
echo "VM removed"
|
||||
}
|
||||
|
||||
# Check if control parameter is provided
|
||||
if [ -z "$1" ]; then
|
||||
echo "Usage: $0 [create|start|stop|delete]"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# Execute the appropriate function based on the control parameter
|
||||
case "$1" in
|
||||
"create")
|
||||
create_vm
|
||||
;;
|
||||
"start")
|
||||
start_vm
|
||||
;;
|
||||
"stop")
|
||||
stop_vm
|
||||
;;
|
||||
"delete")
|
||||
delete_vm
|
||||
;;
|
||||
*)
|
||||
echo "Invalid option: $1"
|
||||
echo "Usage: $0 [create|start|stop|delete]"
|
||||
exit 1
|
||||
;;
|
||||
esac
|
||||
410
omnitool/omnibox/vm/buildcontainer/define.sh
Normal file
410
omnitool/omnibox/vm/buildcontainer/define.sh
Normal file
@@ -0,0 +1,410 @@
|
||||
#!/usr/bin/env bash
|
||||
set -Eeuo pipefail
|
||||
|
||||
: "${WIDTH:=""}"
|
||||
: "${HEIGHT:=""}"
|
||||
: "${VERIFY:=""}"
|
||||
: "${REGION:=""}"
|
||||
: "${MANUAL:=""}"
|
||||
: "${REMOVE:=""}"
|
||||
: "${VERSION:=""}"
|
||||
: "${DETECTED:=""}"
|
||||
: "${KEYBOARD:=""}"
|
||||
: "${LANGUAGE:=""}"
|
||||
: "${USERNAME:=""}"
|
||||
: "${PASSWORD:=""}"
|
||||
|
||||
MIRRORS=4
|
||||
PLATFORM="x64"
|
||||
|
||||
parseVersion() {
|
||||
|
||||
if [[ "${VERSION}" == \"*\" || "${VERSION}" == \'*\' ]]; then
|
||||
VERSION="${VERSION:1:-1}"
|
||||
fi
|
||||
|
||||
[ -z "$VERSION" ] && VERSION="win11"
|
||||
|
||||
case "${VERSION,,}" in
|
||||
"11" | "11p" | "win11" | "pro11" | "win11p" | "windows11" | "windows 11" )
|
||||
VERSION="win11x64"
|
||||
;;
|
||||
"11e" | "win11e" | "windows11e" | "windows 11e" | "win11x64-enterprise-eval" )
|
||||
VERSION="win11x64-enterprise-eval"
|
||||
;;
|
||||
esac
|
||||
|
||||
return 0
|
||||
}
|
||||
|
||||
getLanguage() {
|
||||
|
||||
local id="$1"
|
||||
local ret="$2"
|
||||
local lang=""
|
||||
local desc=""
|
||||
local culture=""
|
||||
|
||||
case "${id,,}" in
|
||||
"ar" | "ar-"* )
|
||||
lang="Arabic"
|
||||
desc="$lang"
|
||||
culture="ar-SA" ;;
|
||||
"bg" | "bg-"* )
|
||||
lang="Bulgarian"
|
||||
desc="$lang"
|
||||
culture="bg-BG" ;;
|
||||
"cs" | "cs-"* | "cz" | "cz-"* )
|
||||
lang="Czech"
|
||||
desc="$lang"
|
||||
culture="cs-CZ" ;;
|
||||
"da" | "da-"* | "dk" | "dk-"* )
|
||||
lang="Danish"
|
||||
desc="$lang"
|
||||
culture="da-DK" ;;
|
||||
"de" | "de-"* )
|
||||
lang="German"
|
||||
desc="$lang"
|
||||
culture="de-DE" ;;
|
||||
"el" | "el-"* | "gr" | "gr-"* )
|
||||
lang="Greek"
|
||||
desc="$lang"
|
||||
culture="el-GR" ;;
|
||||
"gb" | "en-gb" )
|
||||
lang="English International"
|
||||
desc="English"
|
||||
culture="en-GB" ;;
|
||||
"en" | "en-"* )
|
||||
lang="English"
|
||||
desc="English"
|
||||
culture="en-US" ;;
|
||||
"mx" | "es-mx" )
|
||||
lang="Spanish (Mexico)"
|
||||
desc="Spanish"
|
||||
culture="es-MX" ;;
|
||||
"es" | "es-"* )
|
||||
lang="Spanish"
|
||||
desc="$lang"
|
||||
culture="es-ES" ;;
|
||||
"et" | "et-"* )
|
||||
lang="Estonian"
|
||||
desc="$lang"
|
||||
culture="et-EE" ;;
|
||||
"fi" | "fi-"* )
|
||||
lang="Finnish"
|
||||
desc="$lang"
|
||||
culture="fi-FI" ;;
|
||||
"ca" | "fr-ca" )
|
||||
lang="French Canadian"
|
||||
desc="French"
|
||||
culture="fr-CA" ;;
|
||||
"fr" | "fr-"* )
|
||||
lang="French"
|
||||
desc="$lang"
|
||||
culture="fr-FR" ;;
|
||||
"he" | "he-"* | "il" | "il-"* )
|
||||
lang="Hebrew"
|
||||
desc="$lang"
|
||||
culture="he-IL" ;;
|
||||
"hr" | "hr-"* | "cr" | "cr-"* )
|
||||
lang="Croatian"
|
||||
desc="$lang"
|
||||
culture="hr-HR" ;;
|
||||
"hu" | "hu-"* )
|
||||
lang="Hungarian"
|
||||
desc="$lang"
|
||||
culture="hu-HU" ;;
|
||||
"it" | "it-"* )
|
||||
lang="Italian"
|
||||
desc="$lang"
|
||||
culture="it-IT" ;;
|
||||
"ja" | "ja-"* | "jp" | "jp-"* )
|
||||
lang="Japanese"
|
||||
desc="$lang"
|
||||
culture="ja-JP" ;;
|
||||
"ko" | "ko-"* | "kr" | "kr-"* )
|
||||
lang="Korean"
|
||||
desc="$lang"
|
||||
culture="ko-KR" ;;
|
||||
"lt" | "lt-"* )
|
||||
lang="Lithuanian"
|
||||
desc="$lang"
|
||||
culture="lv-LV" ;;
|
||||
"lv" | "lv-"* )
|
||||
lang="Latvian"
|
||||
desc="$lang"
|
||||
culture="lt-LT" ;;
|
||||
"nb" | "nb-"* |"nn" | "nn-"* | "no" | "no-"* )
|
||||
lang="Norwegian"
|
||||
desc="$lang"
|
||||
culture="nb-NO" ;;
|
||||
"nl" | "nl-"* )
|
||||
lang="Dutch"
|
||||
desc="$lang"
|
||||
culture="nl-NL" ;;
|
||||
"pl" | "pl-"* )
|
||||
lang="Polish"
|
||||
desc="$lang"
|
||||
culture="pl-PL" ;;
|
||||
"br" | "pt-br" )
|
||||
lang="Brazilian Portuguese"
|
||||
desc="Portuguese"
|
||||
culture="pt-BR" ;;
|
||||
"pt" | "pt-"* )
|
||||
lang="Portuguese"
|
||||
desc="$lang"
|
||||
culture="pt-BR" ;;
|
||||
"ro" | "ro-"* )
|
||||
lang="Romanian"
|
||||
desc="$lang"
|
||||
culture="ro-RO" ;;
|
||||
"ru" | "ru-"* )
|
||||
lang="Russian"
|
||||
desc="$lang"
|
||||
culture="ru-RU" ;;
|
||||
"sk" | "sk-"* )
|
||||
lang="Slovak"
|
||||
desc="$lang"
|
||||
culture="sk-SK" ;;
|
||||
"sl" | "sl-"* | "si" | "si-"* )
|
||||
lang="Slovenian"
|
||||
desc="$lang"
|
||||
culture="sl-SI" ;;
|
||||
"sr" | "sr-"* )
|
||||
lang="Serbian Latin"
|
||||
desc="Serbian"
|
||||
culture="sr-Latn-RS" ;;
|
||||
"sv" | "sv-"* | "se" | "se-"* )
|
||||
lang="Swedish"
|
||||
desc="$lang"
|
||||
culture="sv-SE" ;;
|
||||
"th" | "th-"* )
|
||||
lang="Thai"
|
||||
desc="$lang"
|
||||
culture="th-TH" ;;
|
||||
"tr" | "tr-"* )
|
||||
lang="Turkish"
|
||||
desc="$lang"
|
||||
culture="tr-TR" ;;
|
||||
"ua" | "ua-"* | "uk" | "uk-"* )
|
||||
lang="Ukrainian"
|
||||
desc="$lang"
|
||||
culture="uk-UA" ;;
|
||||
"hk" | "zh-hk" | "cn-hk" )
|
||||
lang="Chinese (Traditional)"
|
||||
desc="Chinese HK"
|
||||
culture="zh-TW" ;;
|
||||
"tw" | "zh-tw" | "cn-tw" )
|
||||
lang="Chinese (Traditional)"
|
||||
desc="Chinese TW"
|
||||
culture="zh-TW" ;;
|
||||
"zh" | "zh-"* | "cn" | "cn-"* )
|
||||
lang="Chinese (Simplified)"
|
||||
desc="Chinese"
|
||||
culture="zh-CN" ;;
|
||||
esac
|
||||
|
||||
case "${ret,,}" in
|
||||
"desc" ) echo "$desc" ;;
|
||||
"name" ) echo "$lang" ;;
|
||||
"culture" ) echo "$culture" ;;
|
||||
*) echo "$desc";;
|
||||
esac
|
||||
|
||||
return 0
|
||||
}
|
||||
|
||||
parseLanguage() {
|
||||
|
||||
REGION="${REGION//_/-/}"
|
||||
KEYBOARD="${KEYBOARD//_/-/}"
|
||||
LANGUAGE="${LANGUAGE//_/-/}"
|
||||
|
||||
[ -z "$LANGUAGE" ] && LANGUAGE="en"
|
||||
|
||||
case "${LANGUAGE,,}" in
|
||||
"arabic" | "arab" ) LANGUAGE="ar" ;;
|
||||
"bulgarian" | "bu" ) LANGUAGE="bg" ;;
|
||||
"chinese" | "cn" ) LANGUAGE="zh" ;;
|
||||
"croatian" | "cr" | "hrvatski" ) LANGUAGE="hr" ;;
|
||||
"czech" | "cz" | "cesky" ) LANGUAGE="cs" ;;
|
||||
"danish" | "dk" | "danske" ) LANGUAGE="da" ;;
|
||||
"dutch" | "nederlands" ) LANGUAGE="nl" ;;
|
||||
"english" | "gb" | "british" ) LANGUAGE="en" ;;
|
||||
"estonian" | "eesti" ) LANGUAGE="et" ;;
|
||||
"finnish" | "suomi" ) LANGUAGE="fi" ;;
|
||||
"french" | "français" | "francais" ) LANGUAGE="fr" ;;
|
||||
"german" | "deutsch" ) LANGUAGE="de" ;;
|
||||
"greek" | "gr" ) LANGUAGE="el" ;;
|
||||
"hebrew" | "il" ) LANGUAGE="he" ;;
|
||||
"hungarian" | "magyar" ) LANGUAGE="hu" ;;
|
||||
"italian" | "italiano" ) LANGUAGE="it" ;;
|
||||
"japanese" | "jp" ) LANGUAGE="ja" ;;
|
||||
"korean" | "kr" ) LANGUAGE="ko" ;;
|
||||
"latvian" | "latvijas" ) LANGUAGE="lv" ;;
|
||||
"lithuanian" | "lietuvos" ) LANGUAGE="lt" ;;
|
||||
"norwegian" | "no" | "nb" | "norsk" ) LANGUAGE="nn" ;;
|
||||
"polish" | "polski" ) LANGUAGE="pl" ;;
|
||||
"portuguese" | "pt" | "br" ) LANGUAGE="pt-br" ;;
|
||||
"português" | "portugues" ) LANGUAGE="pt-br" ;;
|
||||
"romanian" | "română" | "romana" ) LANGUAGE="ro" ;;
|
||||
"russian" | "ruski" ) LANGUAGE="ru" ;;
|
||||
"serbian" | "serbian latin" ) LANGUAGE="sr" ;;
|
||||
"slovak" | "slovenský" | "slovensky" ) LANGUAGE="sk" ;;
|
||||
"slovenian" | "si" | "slovenski" ) LANGUAGE="sl" ;;
|
||||
"spanish" | "espanol" | "español" ) LANGUAGE="es" ;;
|
||||
"swedish" | "se" | "svenska" ) LANGUAGE="sv" ;;
|
||||
"turkish" | "türk" | "turk" ) LANGUAGE="tr" ;;
|
||||
"thai" ) LANGUAGE="th" ;;
|
||||
"ukrainian" | "ua" ) LANGUAGE="uk" ;;
|
||||
esac
|
||||
|
||||
local culture
|
||||
culture=$(getLanguage "$LANGUAGE" "culture")
|
||||
[ -n "$culture" ] && return 0
|
||||
|
||||
error "Invalid LANGUAGE specified, value \"$LANGUAGE\" is not recognized!"
|
||||
return 1
|
||||
}
|
||||
|
||||
printVersion() {
|
||||
|
||||
local id="$1"
|
||||
local desc="$2"
|
||||
|
||||
case "${id,,}" in
|
||||
"win11"* ) desc="Windows 11" ;;
|
||||
esac
|
||||
|
||||
if [ -z "$desc" ]; then
|
||||
desc="Windows"
|
||||
[[ "${PLATFORM,,}" != "x64" ]] && desc+=" for ${PLATFORM}"
|
||||
fi
|
||||
|
||||
echo "$desc"
|
||||
return 0
|
||||
}
|
||||
|
||||
printEdition() {
|
||||
|
||||
local id="$1"
|
||||
local desc="$2"
|
||||
local result=""
|
||||
local edition=""
|
||||
|
||||
result=$(printVersion "$id" "x")
|
||||
[[ "$result" == "x" ]] && echo "$desc" && return 0
|
||||
|
||||
case "${id,,}" in
|
||||
*"-enterprise" )
|
||||
edition="Enterprise"
|
||||
;;
|
||||
*"-enterprise-eval" )
|
||||
edition="Enterprise (Evaluation)"
|
||||
;;
|
||||
esac
|
||||
|
||||
[ -n "$edition" ] && result+=" $edition"
|
||||
|
||||
echo "$result"
|
||||
return 0
|
||||
}
|
||||
|
||||
fromName() {
|
||||
|
||||
local id=""
|
||||
local name="$1"
|
||||
local arch="$2"
|
||||
|
||||
local add=""
|
||||
[[ "$arch" != "x64" ]] && add="$arch"
|
||||
|
||||
case "${name,,}" in
|
||||
*"windows 11"* ) id="win11${arch}" ;;
|
||||
esac
|
||||
|
||||
echo "$id"
|
||||
return 0
|
||||
}
|
||||
|
||||
getVersion() {
|
||||
|
||||
local id
|
||||
local name="$1"
|
||||
local arch="$2"
|
||||
|
||||
id=$(fromName "$name" "$arch")
|
||||
|
||||
case "${id,,}" in
|
||||
"win11"* )
|
||||
case "${name,,}" in
|
||||
*" enterprise evaluation"* ) id="$id-enterprise-eval" ;;
|
||||
*" enterprise"* ) id="$id-enterprise" ;;
|
||||
esac
|
||||
;;
|
||||
esac
|
||||
|
||||
echo "$id"
|
||||
return 0
|
||||
}
|
||||
|
||||
addFolder() {
|
||||
|
||||
local src="$1"
|
||||
local folder="/oem"
|
||||
|
||||
[ ! -d "$folder" ] && folder="/OEM"
|
||||
[ ! -d "$folder" ] && folder="$STORAGE/oem"
|
||||
[ ! -d "$folder" ] && folder="$STORAGE/OEM"
|
||||
[ ! -d "$folder" ] && return 0
|
||||
|
||||
local msg="Adding OEM folder to image..."
|
||||
info "$msg" && html "$msg"
|
||||
|
||||
local dest="$src/\$OEM\$/\$1/OEM"
|
||||
mkdir -p "$dest" || return 1
|
||||
cp -Lr "$folder/." "$dest" || return 1
|
||||
|
||||
local file
|
||||
file=$(find "$dest" -maxdepth 1 -type f -iname install.bat | head -n 1)
|
||||
[ -f "$file" ] && unix2dos -q "$file"
|
||||
|
||||
return 0
|
||||
}
|
||||
|
||||
# migrateFiles() {
|
||||
|
||||
# local base="$1"
|
||||
# local version="$2"
|
||||
# local file=""
|
||||
|
||||
# [ -f "$base" ] && return 0
|
||||
|
||||
# [[ "${version,,}" == "tiny10" ]] && file="tiny10_x64_23h2.iso"
|
||||
# [[ "${version,,}" == "tiny11" ]] && file="tiny11_2311_x64.iso"
|
||||
# [[ "${version,,}" == "core11" ]] && file="tiny11_core_x64_beta_1.iso"
|
||||
# [[ "${version,,}" == "winxpx86" ]] && file="en_windows_xp_professional_with_service_pack_3_x86_cd_x14-80428.iso"
|
||||
# [[ "${version,,}" == "winvistax64" ]] && file="en_windows_vista_sp2_x64_dvd_342267.iso"
|
||||
# [[ "${version,,}" == "win7x64" ]] && file="en_windows_7_enterprise_with_sp1_x64_dvd_u_677651.iso"
|
||||
|
||||
# [ ! -f "$STORAGE/$file" ] && return 0
|
||||
# mv -f "$STORAGE/$file" "$base" || return 1
|
||||
|
||||
# return 0
|
||||
# }
|
||||
|
||||
migrateFiles() {
|
||||
|
||||
local base="$1"
|
||||
local version="$2"
|
||||
local file=""
|
||||
|
||||
[ -f "$base" ] && return 0
|
||||
|
||||
[ ! -f "$STORAGE/$file" ] && return 0
|
||||
mv -f "$STORAGE/$file" "$base" || return 1
|
||||
|
||||
return 0
|
||||
}
|
||||
|
||||
return 0
|
||||
38
omnitool/omnibox/vm/buildcontainer/entry.sh
Normal file
38
omnitool/omnibox/vm/buildcontainer/entry.sh
Normal file
@@ -0,0 +1,38 @@
|
||||
#!/usr/bin/env bash
|
||||
set -Eeuo pipefail
|
||||
|
||||
: "${BOOT_MODE:="windows"}"
|
||||
|
||||
APP="OmniParser Windows"
|
||||
SUPPORT="https://github.com/microsoft/OmniParser"
|
||||
|
||||
cd /run
|
||||
|
||||
. reset.sh # Initialize system
|
||||
. define.sh # Define versions
|
||||
. install.sh # Run installation
|
||||
. disk.sh # Initialize disks
|
||||
. display.sh # Initialize graphics
|
||||
. network.sh # Initialize network
|
||||
. samba.sh # Configure samba
|
||||
. boot.sh # Configure boot
|
||||
. proc.sh # Initialize processor
|
||||
. power.sh # Configure shutdown
|
||||
. config.sh # Configure arguments
|
||||
|
||||
trap - ERR
|
||||
|
||||
version=$(qemu-system-x86_64 --version | head -n 1 | cut -d '(' -f 1 | awk '{ print $NF }')
|
||||
info "Booting ${APP}${BOOT_DESC} using QEMU v$version..."
|
||||
|
||||
{ qemu-system-x86_64 ${ARGS:+ $ARGS} >"$QEMU_OUT" 2>"$QEMU_LOG"; rc=$?; } || :
|
||||
(( rc != 0 )) && error "$(<"$QEMU_LOG")" && exit 15
|
||||
|
||||
terminal
|
||||
( sleep 30; boot ) &
|
||||
tail -fn +0 "$QEMU_LOG" 2>/dev/null &
|
||||
cat "$QEMU_TERM" 2> /dev/null | tee "$QEMU_PTY" &
|
||||
wait $! || :
|
||||
|
||||
sleep 1 & wait $!
|
||||
[ ! -f "$QEMU_END" ] && finish 0
|
||||
1024
omnitool/omnibox/vm/buildcontainer/install.sh
Normal file
1024
omnitool/omnibox/vm/buildcontainer/install.sh
Normal file
File diff suppressed because it is too large
Load Diff
223
omnitool/omnibox/vm/buildcontainer/power.sh
Normal file
223
omnitool/omnibox/vm/buildcontainer/power.sh
Normal file
@@ -0,0 +1,223 @@
|
||||
#!/usr/bin/env bash
|
||||
set -Eeuo pipefail
|
||||
|
||||
# Configure QEMU for graceful shutdown
|
||||
|
||||
QEMU_TERM=""
|
||||
QEMU_PORT=7100
|
||||
QEMU_TIMEOUT=110
|
||||
QEMU_DIR="/run/shm"
|
||||
QEMU_PID="$QEMU_DIR/qemu.pid"
|
||||
QEMU_PTY="$QEMU_DIR/qemu.pty"
|
||||
QEMU_LOG="$QEMU_DIR/qemu.log"
|
||||
QEMU_OUT="$QEMU_DIR/qemu.out"
|
||||
QEMU_END="$QEMU_DIR/qemu.end"
|
||||
|
||||
rm -f "$QEMU_DIR/qemu.*"
|
||||
touch "$QEMU_LOG"
|
||||
|
||||
_trap() {
|
||||
func="$1" ; shift
|
||||
for sig ; do
|
||||
trap "$func $sig" "$sig"
|
||||
done
|
||||
}
|
||||
|
||||
boot() {
|
||||
|
||||
[ -f "$QEMU_END" ] && return 0
|
||||
|
||||
if [ -s "$QEMU_PTY" ]; then
|
||||
if [ "$(stat -c%s "$QEMU_PTY")" -gt 7 ]; then
|
||||
local fail=""
|
||||
if [[ "${BOOT_MODE,,}" == "windows_legacy" ]]; then
|
||||
grep -Fq "No bootable device." "$QEMU_PTY" && fail="y"
|
||||
grep -Fq "BOOTMGR is missing" "$QEMU_PTY" && fail="y"
|
||||
fi
|
||||
if [ -z "$fail" ]; then
|
||||
info "Windows has started successfully. You can directly view the VM at http://localhost:8006/vnc.html?view_only=1&autoconnect=1&resize=scale. Wait until setup is complete before interacting manually."
|
||||
return 0
|
||||
fi
|
||||
fi
|
||||
fi
|
||||
|
||||
error "Timeout while waiting for QEMU to boot the machine!"
|
||||
|
||||
local pid
|
||||
pid=$(<"$QEMU_PID")
|
||||
{ kill -15 "$pid" || true; } 2>/dev/null
|
||||
|
||||
return 0
|
||||
}
|
||||
|
||||
ready() {
|
||||
|
||||
[ -f "$STORAGE/windows.boot" ] && return 0
|
||||
[ ! -s "$QEMU_PTY" ] && return 1
|
||||
|
||||
if [[ "${BOOT_MODE,,}" == "windows_legacy" ]]; then
|
||||
local last
|
||||
local bios="Booting from Hard"
|
||||
last=$(grep "^Booting.*" "$QEMU_PTY" | tail -1)
|
||||
[[ "${last,,}" != "${bios,,}"* ]] && return 1
|
||||
grep -Fq "No bootable device." "$QEMU_PTY" && return 1
|
||||
grep -Fq "BOOTMGR is missing" "$QEMU_PTY" && return 1
|
||||
return 0
|
||||
fi
|
||||
|
||||
local line="\"Windows Boot Manager\""
|
||||
grep -Fq "$line" "$QEMU_PTY" && return 0
|
||||
|
||||
return 1
|
||||
}
|
||||
|
||||
finish() {
|
||||
|
||||
local pid
|
||||
local reason=$1
|
||||
|
||||
touch "$QEMU_END"
|
||||
|
||||
if [ -s "$QEMU_PID" ]; then
|
||||
|
||||
pid=$(<"$QEMU_PID")
|
||||
error "Forcefully terminating Windows, reason: $reason..."
|
||||
{ kill -15 "$pid" || true; } 2>/dev/null
|
||||
|
||||
while isAlive "$pid"; do
|
||||
sleep 1
|
||||
# Workaround for zombie pid
|
||||
[ ! -s "$QEMU_PID" ] && break
|
||||
done
|
||||
fi
|
||||
|
||||
if [ ! -f "$STORAGE/windows.boot" ] && [ -f "$BOOT" ]; then
|
||||
# Remove CD-ROM ISO after install
|
||||
if ready; then
|
||||
touch "$STORAGE/windows.boot"
|
||||
if [[ "$REMOVE" != [Nn]* ]]; then
|
||||
rm -f "$BOOT" 2>/dev/null || true
|
||||
fi
|
||||
fi
|
||||
fi
|
||||
|
||||
pid="/var/run/tpm.pid"
|
||||
[ -s "$pid" ] && pKill "$(<"$pid")"
|
||||
|
||||
pid="/var/run/wsdd.pid"
|
||||
[ -s "$pid" ] && pKill "$(<"$pid")"
|
||||
|
||||
fKill "smbd"
|
||||
|
||||
closeNetwork
|
||||
|
||||
sleep 0.5
|
||||
echo "❯ Shutdown completed!"
|
||||
|
||||
exit "$reason"
|
||||
}
|
||||
|
||||
terminal() {
|
||||
|
||||
local dev=""
|
||||
|
||||
if [ -s "$QEMU_OUT" ]; then
|
||||
|
||||
local msg
|
||||
msg=$(<"$QEMU_OUT")
|
||||
|
||||
if [ -n "$msg" ]; then
|
||||
|
||||
if [[ "${msg,,}" != "char"* || "$msg" != *"serial0)" ]]; then
|
||||
echo "$msg"
|
||||
fi
|
||||
|
||||
dev="${msg#*/dev/p}"
|
||||
dev="/dev/p${dev%% *}"
|
||||
|
||||
fi
|
||||
fi
|
||||
|
||||
if [ ! -c "$dev" ]; then
|
||||
dev=$(echo 'info chardev' | nc -q 1 -w 1 localhost "$QEMU_PORT" | tr -d '\000')
|
||||
dev="${dev#*serial0}"
|
||||
dev="${dev#*pty:}"
|
||||
dev="${dev%%$'\n'*}"
|
||||
dev="${dev%%$'\r'*}"
|
||||
fi
|
||||
|
||||
if [ ! -c "$dev" ]; then
|
||||
error "Device '$dev' not found!"
|
||||
finish 34 && return 34
|
||||
fi
|
||||
|
||||
QEMU_TERM="$dev"
|
||||
return 0
|
||||
}
|
||||
|
||||
_graceful_shutdown() {
|
||||
|
||||
local code=$?
|
||||
|
||||
set +e
|
||||
|
||||
if [ -f "$QEMU_END" ]; then
|
||||
info "Received $1 while already shutting down..."
|
||||
return
|
||||
fi
|
||||
|
||||
touch "$QEMU_END"
|
||||
info "Received $1, sending ACPI shutdown signal..."
|
||||
|
||||
if [ ! -s "$QEMU_PID" ]; then
|
||||
error "QEMU PID file does not exist?"
|
||||
finish "$code" && return "$code"
|
||||
fi
|
||||
|
||||
local pid=""
|
||||
pid=$(<"$QEMU_PID")
|
||||
|
||||
if ! isAlive "$pid"; then
|
||||
error "QEMU process does not exist?"
|
||||
finish "$code" && return "$code"
|
||||
fi
|
||||
|
||||
if ! ready; then
|
||||
info "Cannot send ACPI signal during Windows setup, aborting..."
|
||||
finish "$code" && return "$code"
|
||||
fi
|
||||
|
||||
# Send ACPI shutdown signal
|
||||
echo 'system_powerdown' | nc -q 1 -w 1 localhost "${QEMU_PORT}" > /dev/null
|
||||
|
||||
local cnt=0
|
||||
while [ "$cnt" -lt "$QEMU_TIMEOUT" ]; do
|
||||
|
||||
sleep 1
|
||||
cnt=$((cnt+1))
|
||||
|
||||
! isAlive "$pid" && break
|
||||
# Workaround for zombie pid
|
||||
[ ! -s "$QEMU_PID" ] && break
|
||||
|
||||
info "Waiting for Windows to shutdown... ($cnt/$QEMU_TIMEOUT)"
|
||||
|
||||
# Send ACPI shutdown signal
|
||||
echo 'system_powerdown' | nc -q 1 -w 1 localhost "${QEMU_PORT}" > /dev/null
|
||||
|
||||
done
|
||||
|
||||
if [ "$cnt" -ge "$QEMU_TIMEOUT" ]; then
|
||||
error "Shutdown timeout reached, aborting..."
|
||||
fi
|
||||
|
||||
finish "$code" && return "$code"
|
||||
}
|
||||
|
||||
SERIAL="pty"
|
||||
MONITOR="telnet:localhost:$QEMU_PORT,server,nowait,nodelay"
|
||||
MONITOR+=" -daemonize -D $QEMU_LOG -pidfile $QEMU_PID"
|
||||
|
||||
_trap _graceful_shutdown SIGTERM SIGHUP SIGINT SIGABRT SIGQUIT
|
||||
|
||||
return 0
|
||||
109
omnitool/omnibox/vm/buildcontainer/samba.sh
Normal file
109
omnitool/omnibox/vm/buildcontainer/samba.sh
Normal file
@@ -0,0 +1,109 @@
|
||||
#!/usr/bin/env bash
|
||||
set -Eeuo pipefail
|
||||
|
||||
: "${SAMBA:="Y"}"
|
||||
|
||||
[[ "$SAMBA" == [Nn]* ]] && return 0
|
||||
[[ "$NETWORK" == [Nn]* ]] && return 0
|
||||
|
||||
hostname="host.lan"
|
||||
interface="dockerbridge"
|
||||
|
||||
if [[ "$DHCP" == [Yy1]* ]]; then
|
||||
hostname="$IP"
|
||||
interface="$VM_NET_DEV"
|
||||
fi
|
||||
|
||||
addShare() {
|
||||
local dir="$1"
|
||||
local name="$2"
|
||||
local comment="$3"
|
||||
|
||||
mkdir -p "$dir" || return 1
|
||||
|
||||
if [ -z "$(ls -A "$dir")" ]; then
|
||||
|
||||
chmod 777 "$dir"
|
||||
|
||||
{ echo "--------------------------------------------------------"
|
||||
echo " $APP"
|
||||
echo " For support visit $SUPPORT"
|
||||
echo "--------------------------------------------------------"
|
||||
echo ""
|
||||
echo "Using this folder you can share files with the host machine."
|
||||
echo ""
|
||||
echo "To change its location, include the following bind mount in your compose file:"
|
||||
echo ""
|
||||
echo " volumes:"
|
||||
echo " - \"/home/example:/${name,,}\""
|
||||
echo ""
|
||||
echo "Or in your run command:"
|
||||
echo ""
|
||||
echo " -v \"/home/example:/${name,,}\""
|
||||
echo ""
|
||||
echo "Replace the example path /home/example with the desired shared folder."
|
||||
echo ""
|
||||
} | unix2dos > "$dir/readme.txt"
|
||||
|
||||
fi
|
||||
|
||||
{ echo ""
|
||||
echo "[$name]"
|
||||
echo " path = $dir"
|
||||
echo " comment = $comment"
|
||||
echo " writable = yes"
|
||||
echo " guest ok = yes"
|
||||
echo " guest only = yes"
|
||||
echo " force user = root"
|
||||
echo " force group = root"
|
||||
} >> "/etc/samba/smb.conf"
|
||||
|
||||
return 0
|
||||
}
|
||||
|
||||
{ echo "[global]"
|
||||
echo " server string = Dockur"
|
||||
echo " netbios name = $hostname"
|
||||
echo " workgroup = WORKGROUP"
|
||||
echo " interfaces = $interface"
|
||||
echo " bind interfaces only = yes"
|
||||
echo " security = user"
|
||||
echo " guest account = nobody"
|
||||
echo " map to guest = Bad User"
|
||||
echo " server min protocol = NT1"
|
||||
echo ""
|
||||
echo " # disable printing services"
|
||||
echo " load printers = no"
|
||||
echo " printing = bsd"
|
||||
echo " printcap name = /dev/null"
|
||||
echo " disable spoolss = yes"
|
||||
} > "/etc/samba/smb.conf"
|
||||
|
||||
share="/data"
|
||||
[ ! -d "$share" ] && [ -d "$STORAGE/data" ] && share="$STORAGE/data"
|
||||
[ ! -d "$share" ] && [ -d "/shared" ] && share="/shared"
|
||||
[ ! -d "$share" ] && [ -d "$STORAGE/shared" ] && share="$STORAGE/shared"
|
||||
|
||||
addShare "$share" "Data" "Shared" || error "Failed to create shared folder!"
|
||||
|
||||
[ -d "/data2" ] && addShare "/data2" "Data2" "Shared"
|
||||
[ -d "/data3" ] && addShare "/data3" "Data3" "Shared"
|
||||
|
||||
if ! smbd; then
|
||||
error "Samba daemon failed to start!"
|
||||
smbd -i --debug-stdout || true
|
||||
fi
|
||||
|
||||
if [[ "${BOOT_MODE:-}" == "windows_legacy" ]]; then
|
||||
# Enable NetBIOS on Windows 7 and lower
|
||||
if ! nmbd; then
|
||||
error "NetBIOS daemon failed to start!"
|
||||
nmbd -i --debug-stdout || true
|
||||
fi
|
||||
else
|
||||
# Enable Web Service Discovery on Vista and up
|
||||
wsdd -i "$interface" -p -n "$hostname" &
|
||||
echo "$!" > /var/run/wsdd.pid
|
||||
fi
|
||||
|
||||
return 0
|
||||
462
omnitool/omnibox/vm/win11def/win11x64-enterprise-eval.xml
Normal file
462
omnitool/omnibox/vm/win11def/win11x64-enterprise-eval.xml
Normal file
@@ -0,0 +1,462 @@
|
||||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<unattend xmlns="urn:schemas-microsoft-com:unattend" xmlns:wcm="http://schemas.microsoft.com/WMIConfig/2002/State">
|
||||
<settings pass="windowsPE">
|
||||
<component name="Microsoft-Windows-International-Core-WinPE" processorArchitecture="amd64" publicKeyToken="31bf3856ad364e35" language="neutral" versionScope="nonSxS">
|
||||
<SetupUILanguage>
|
||||
<UILanguage>en-US</UILanguage>
|
||||
</SetupUILanguage>
|
||||
<InputLocale>0409:00000409</InputLocale>
|
||||
<SystemLocale>en-US</SystemLocale>
|
||||
<UILanguage>en-US</UILanguage>
|
||||
<UserLocale>en-US</UserLocale>
|
||||
</component>
|
||||
<component name="Microsoft-Windows-Setup" processorArchitecture="amd64" publicKeyToken="31bf3856ad364e35" language="neutral" versionScope="nonSxS">
|
||||
<DiskConfiguration>
|
||||
<Disk wcm:action="add">
|
||||
<DiskID>0</DiskID>
|
||||
<WillWipeDisk>true</WillWipeDisk>
|
||||
<CreatePartitions>
|
||||
<!-- System partition (ESP) -->
|
||||
<CreatePartition wcm:action="add">
|
||||
<Order>1</Order>
|
||||
<Type>EFI</Type>
|
||||
<Size>128</Size>
|
||||
</CreatePartition>
|
||||
<!-- Microsoft reserved partition (MSR) -->
|
||||
<CreatePartition wcm:action="add">
|
||||
<Order>2</Order>
|
||||
<Type>MSR</Type>
|
||||
<Size>128</Size>
|
||||
</CreatePartition>
|
||||
<!-- Windows partition -->
|
||||
<CreatePartition wcm:action="add">
|
||||
<Order>3</Order>
|
||||
<Type>Primary</Type>
|
||||
<Extend>true</Extend>
|
||||
</CreatePartition>
|
||||
</CreatePartitions>
|
||||
<ModifyPartitions>
|
||||
<!-- System partition (ESP) -->
|
||||
<ModifyPartition wcm:action="add">
|
||||
<Order>1</Order>
|
||||
<PartitionID>1</PartitionID>
|
||||
<Label>System</Label>
|
||||
<Format>FAT32</Format>
|
||||
</ModifyPartition>
|
||||
<!-- MSR partition does not need to be modified -->
|
||||
<ModifyPartition wcm:action="add">
|
||||
<Order>2</Order>
|
||||
<PartitionID>2</PartitionID>
|
||||
</ModifyPartition>
|
||||
<!-- Windows partition -->
|
||||
<ModifyPartition wcm:action="add">
|
||||
<Order>3</Order>
|
||||
<PartitionID>3</PartitionID>
|
||||
<Label>Windows</Label>
|
||||
<Letter>C</Letter>
|
||||
<Format>NTFS</Format>
|
||||
</ModifyPartition>
|
||||
</ModifyPartitions>
|
||||
</Disk>
|
||||
</DiskConfiguration>
|
||||
<ImageInstall>
|
||||
<OSImage>
|
||||
<InstallTo>
|
||||
<DiskID>0</DiskID>
|
||||
<PartitionID>3</PartitionID>
|
||||
</InstallTo>
|
||||
<InstallToAvailablePartition>false</InstallToAvailablePartition>
|
||||
</OSImage>
|
||||
</ImageInstall>
|
||||
<DynamicUpdate>
|
||||
<Enable>true</Enable>
|
||||
<WillShowUI>Never</WillShowUI>
|
||||
</DynamicUpdate>
|
||||
<UpgradeData>
|
||||
<Upgrade>false</Upgrade>
|
||||
<WillShowUI>Never</WillShowUI>
|
||||
</UpgradeData>
|
||||
<UserData>
|
||||
<AcceptEula>true</AcceptEula>
|
||||
<FullName>Docker</FullName>
|
||||
<Organization>Windows for Docker</Organization>
|
||||
</UserData>
|
||||
<EnableFirewall>false</EnableFirewall>
|
||||
<Diagnostics>
|
||||
<OptIn>false</OptIn>
|
||||
</Diagnostics>
|
||||
<RunSynchronous>
|
||||
<RunSynchronousCommand wcm:action="add">
|
||||
<Order>1</Order>
|
||||
<Path>reg.exe add "HKLM\SYSTEM\Setup\LabConfig" /v BypassTPMCheck /t REG_DWORD /d 1 /f</Path>
|
||||
</RunSynchronousCommand>
|
||||
<RunSynchronousCommand wcm:action="add">
|
||||
<Order>2</Order>
|
||||
<Path>reg.exe add "HKLM\SYSTEM\Setup\LabConfig" /v BypassSecureBootCheck /t REG_DWORD /d 1 /f</Path>
|
||||
</RunSynchronousCommand>
|
||||
<RunSynchronousCommand wcm:action="add">
|
||||
<Order>3</Order>
|
||||
<Path>reg.exe add "HKLM\SYSTEM\Setup\LabConfig" /v BypassRAMCheck /t REG_DWORD /d 1 /f</Path>
|
||||
</RunSynchronousCommand>
|
||||
<RunSynchronousCommand wcm:action="add">
|
||||
<Order>4</Order>
|
||||
<Path>reg.exe add "HKLM\SYSTEM\Setup\MoSetup" /v AllowUpgradesWithUnsupportedTPMOrCPU /t REG_DWORD /d 1 /f</Path>
|
||||
</RunSynchronousCommand>
|
||||
</RunSynchronous>
|
||||
</component>
|
||||
</settings>
|
||||
<settings pass="offlineServicing">
|
||||
<component name="Microsoft-Windows-LUA-Settings" processorArchitecture="amd64" publicKeyToken="31bf3856ad364e35" language="neutral" versionScope="nonSxS">
|
||||
<EnableLUA>false</EnableLUA>
|
||||
</component>
|
||||
</settings>
|
||||
<settings pass="generalize">
|
||||
<component name="Microsoft-Windows-PnPSysprep" processorArchitecture="amd64" publicKeyToken="31bf3856ad364e35" language="neutral" versionScope="nonSxS">
|
||||
<PersistAllDeviceInstalls>true</PersistAllDeviceInstalls>
|
||||
</component>
|
||||
<component name="Microsoft-Windows-Security-SPP" processorArchitecture="amd64" publicKeyToken="31bf3856ad364e35" language="neutral" versionScope="nonSxS">
|
||||
<SkipRearm>1</SkipRearm>
|
||||
</component>
|
||||
</settings>
|
||||
<settings pass="specialize">
|
||||
<component name="Microsoft-Windows-Security-SPP-UX" processorArchitecture="amd64" publicKeyToken="31bf3856ad364e35" language="neutral" versionScope="nonSxS">
|
||||
<SkipAutoActivation>true</SkipAutoActivation>
|
||||
</component>
|
||||
<component name="Microsoft-Windows-Shell-Setup" processorArchitecture="amd64" publicKeyToken="31bf3856ad364e35" language="neutral" versionScope="nonSxS">
|
||||
<ComputerName>*</ComputerName>
|
||||
<OEMInformation>
|
||||
<Manufacturer>Dockur</Manufacturer>
|
||||
<Model>Windows for Docker</Model>
|
||||
<SupportHours>24/7</SupportHours>
|
||||
<SupportPhone />
|
||||
<SupportProvider>Dockur</SupportProvider>
|
||||
<SupportURL>https://github.com/dockur/windows/issues</SupportURL>
|
||||
</OEMInformation>
|
||||
<OEMName>Windows for Docker</OEMName>
|
||||
</component>
|
||||
<component name="Microsoft-Windows-ErrorReportingCore" processorArchitecture="amd64" publicKeyToken="31bf3856ad364e35" language="neutral" versionScope="nonSxS">
|
||||
<DisableWER>1</DisableWER>
|
||||
</component>
|
||||
<component name="Microsoft-Windows-IE-InternetExplorer" processorArchitecture="amd64" publicKeyToken="31bf3856ad364e35" language="neutral" versionScope="nonSxS">
|
||||
<DisableAccelerators>true</DisableAccelerators>
|
||||
<DisableFirstRunWizard>true</DisableFirstRunWizard>
|
||||
<Home_Page>https://google.com</Home_Page>
|
||||
<Help_Page>about:blank</Help_Page>
|
||||
</component>
|
||||
<component name="Microsoft-Windows-IE-InternetExplorer" processorArchitecture="wow64" publicKeyToken="31bf3856ad364e35" language="neutral" versionScope="nonSxS">
|
||||
<DisableAccelerators>true</DisableAccelerators>
|
||||
<DisableFirstRunWizard>true</DisableFirstRunWizard>
|
||||
<Home_Page>https://google.com</Home_Page>
|
||||
<Help_Page>about:blank</Help_Page>
|
||||
</component>
|
||||
<component name="Microsoft-Windows-SQMApi" processorArchitecture="amd64" publicKeyToken="31bf3856ad364e35" language="neutral" versionScope="nonSxS">
|
||||
<CEIPEnabled>0</CEIPEnabled>
|
||||
</component>
|
||||
<component name="Microsoft-Windows-SystemRestore-Main" processorArchitecture="amd64" publicKeyToken="31bf3856ad364e35" language="neutral" versionScope="nonSxS">
|
||||
<DisableSR>1</DisableSR>
|
||||
</component>
|
||||
<component name="Microsoft-Windows-International-Core" processorArchitecture="amd64" publicKeyToken="31bf3856ad364e35" language="neutral" versionScope="nonSxS">
|
||||
<InputLocale>0409:00000409</InputLocale>
|
||||
<SystemLocale>en-US</SystemLocale>
|
||||
<UILanguage>en-US</UILanguage>
|
||||
<UserLocale>en-US</UserLocale>
|
||||
</component>
|
||||
<component name="Microsoft-Windows-Deployment" processorArchitecture="amd64" publicKeyToken="31bf3856ad364e35" language="neutral" versionScope="nonSxS">
|
||||
<RunSynchronous>
|
||||
<RunSynchronousCommand wcm:action="add">
|
||||
<Order>1</Order>
|
||||
<Path>reg.exe add "HKLM\SOFTWARE\Microsoft\Windows\CurrentVersion\OOBE" /v BypassNRO /t REG_DWORD /d 1 /f</Path>
|
||||
</RunSynchronousCommand>
|
||||
<RunSynchronousCommand wcm:action="add">
|
||||
<Order>2</Order>
|
||||
<Path>reg.exe load "HKU\mount" "C:\Users\Default\NTUSER.DAT"</Path>
|
||||
</RunSynchronousCommand>
|
||||
<RunSynchronousCommand wcm:action="add">
|
||||
<Order>3</Order>
|
||||
<Path>reg.exe add "HKU\mount\Software\Microsoft\Windows\CurrentVersion\ContentDeliveryManager" /v "ContentDeliveryAllowed" /t REG_DWORD /d 0 /f</Path>
|
||||
</RunSynchronousCommand>
|
||||
<RunSynchronousCommand wcm:action="add">
|
||||
<Order>4</Order>
|
||||
<Path>reg.exe add "HKU\mount\Software\Microsoft\Windows\CurrentVersion\ContentDeliveryManager" /v "FeatureManagementEnabled" /t REG_DWORD /d 0 /f</Path>
|
||||
</RunSynchronousCommand>
|
||||
<RunSynchronousCommand wcm:action="add">
|
||||
<Order>5</Order>
|
||||
<Path>reg.exe add "HKU\mount\Software\Microsoft\Windows\CurrentVersion\ContentDeliveryManager" /v "OEMPreInstalledAppsEnabled" /t REG_DWORD /d 0 /f</Path>
|
||||
</RunSynchronousCommand>
|
||||
<RunSynchronousCommand wcm:action="add">
|
||||
<Order>6</Order>
|
||||
<Path>reg.exe add "HKU\mount\Software\Microsoft\Windows\CurrentVersion\ContentDeliveryManager" /v "PreInstalledAppsEnabled" /t REG_DWORD /d 0 /f</Path>
|
||||
</RunSynchronousCommand>
|
||||
<RunSynchronousCommand wcm:action="add">
|
||||
<Order>7</Order>
|
||||
<Path>reg.exe add "HKU\mount\Software\Microsoft\Windows\CurrentVersion\ContentDeliveryManager" /v "PreInstalledAppsEverEnabled" /t REG_DWORD /d 0 /f</Path>
|
||||
</RunSynchronousCommand>
|
||||
<RunSynchronousCommand wcm:action="add">
|
||||
<Order>8</Order>
|
||||
<Path>reg.exe add "HKU\mount\Software\Microsoft\Windows\CurrentVersion\ContentDeliveryManager" /v "SilentInstalledAppsEnabled" /t REG_DWORD /d 0 /f</Path>
|
||||
</RunSynchronousCommand>
|
||||
<RunSynchronousCommand wcm:action="add">
|
||||
<Order>9</Order>
|
||||
<Path>reg.exe add "HKU\mount\Software\Microsoft\Windows\CurrentVersion\ContentDeliveryManager" /v "SoftLandingEnabled" /t REG_DWORD /d 0 /f</Path>
|
||||
</RunSynchronousCommand>
|
||||
<RunSynchronousCommand wcm:action="add">
|
||||
<Order>10</Order>
|
||||
<Path>reg.exe add "HKU\mount\Software\Microsoft\Windows\CurrentVersion\ContentDeliveryManager" /v "SubscribedContentEnabled" /t REG_DWORD /d 0 /f</Path>
|
||||
</RunSynchronousCommand>
|
||||
<RunSynchronousCommand wcm:action="add">
|
||||
<Order>11</Order>
|
||||
<Path>reg.exe add "HKU\mount\Software\Microsoft\Windows\CurrentVersion\ContentDeliveryManager" /v "SubscribedContent-310093Enabled" /t REG_DWORD /d 0 /f</Path>
|
||||
</RunSynchronousCommand>
|
||||
<RunSynchronousCommand wcm:action="add">
|
||||
<Order>12</Order>
|
||||
<Path>reg.exe add "HKU\mount\Software\Microsoft\Windows\CurrentVersion\ContentDeliveryManager" /v "SubscribedContent-338387Enabled" /t REG_DWORD /d 0 /f</Path>
|
||||
</RunSynchronousCommand>
|
||||
<RunSynchronousCommand wcm:action="add">
|
||||
<Order>13</Order>
|
||||
<Path>reg.exe add "HKU\mount\Software\Microsoft\Windows\CurrentVersion\ContentDeliveryManager" /v "SubscribedContent-338388Enabled" /t REG_DWORD /d 0 /f</Path>
|
||||
</RunSynchronousCommand>
|
||||
<RunSynchronousCommand wcm:action="add">
|
||||
<Order>14</Order>
|
||||
<Path>reg.exe add "HKU\mount\Software\Microsoft\Windows\CurrentVersion\ContentDeliveryManager" /v "SubscribedContent-338389Enabled" /t REG_DWORD /d 0 /f</Path>
|
||||
</RunSynchronousCommand>
|
||||
<RunSynchronousCommand wcm:action="add">
|
||||
<Order>15</Order>
|
||||
<Path>reg.exe add "HKU\mount\Software\Microsoft\Windows\CurrentVersion\ContentDeliveryManager" /v "SubscribedContent-338393Enabled" /t REG_DWORD /d 0 /f</Path>
|
||||
</RunSynchronousCommand>
|
||||
<RunSynchronousCommand wcm:action="add">
|
||||
<Order>16</Order>
|
||||
<Path>reg.exe add "HKU\mount\Software\Microsoft\Windows\CurrentVersion\ContentDeliveryManager" /v "SubscribedContent-353698Enabled" /t REG_DWORD /d 0 /f</Path>
|
||||
</RunSynchronousCommand>
|
||||
<RunSynchronousCommand wcm:action="add">
|
||||
<Order>17</Order>
|
||||
<Path>reg.exe add "HKU\mount\Software\Microsoft\Windows\CurrentVersion\ContentDeliveryManager" /v "SystemPaneSuggestionsEnabled" /t REG_DWORD /d 0 /f</Path>
|
||||
</RunSynchronousCommand>
|
||||
<RunSynchronousCommand wcm:action="add">
|
||||
<Order>18</Order>
|
||||
<Path>reg.exe add "HKU\mount\Software\Policies\Microsoft\Windows\CloudContent" /v "DisableCloudOptimizedContent" /t REG_DWORD /d 1 /f</Path>
|
||||
</RunSynchronousCommand>
|
||||
<RunSynchronousCommand wcm:action="add">
|
||||
<Order>19</Order>
|
||||
<Path>reg.exe add "HKU\mount\Software\Policies\Microsoft\Windows\CloudContent" /v "DisableWindowsConsumerFeatures" /t REG_DWORD /d 1 /f</Path>
|
||||
</RunSynchronousCommand>
|
||||
<RunSynchronousCommand wcm:action="add">
|
||||
<Order>20</Order>
|
||||
<Path>reg.exe add "HKU\mount\Software\Policies\Microsoft\Windows\CloudContent" /v "DisableConsumerAccountStateContent" /t REG_DWORD /d 1 /f</Path>
|
||||
</RunSynchronousCommand>
|
||||
<RunSynchronousCommand wcm:action="add">
|
||||
<Order>21</Order>
|
||||
<Path>reg.exe unload "HKU\mount"</Path>
|
||||
</RunSynchronousCommand>
|
||||
<RunSynchronousCommand wcm:action="add">
|
||||
<Order>22</Order>
|
||||
<Path>reg.exe add "HKLM\Software\Policies\Microsoft\Windows\CloudContent" /v "DisableCloudOptimizedContent" /t REG_DWORD /d 1 /f</Path>
|
||||
</RunSynchronousCommand>
|
||||
<RunSynchronousCommand wcm:action="add">
|
||||
<Order>23</Order>
|
||||
<Path>reg.exe add "HKLM\Software\Policies\Microsoft\Windows\CloudContent" /v "DisableWindowsConsumerFeatures" /t REG_DWORD /d 1 /f</Path>
|
||||
</RunSynchronousCommand>
|
||||
<RunSynchronousCommand wcm:action="add">
|
||||
<Order>24</Order>
|
||||
<Path>reg.exe add "HKLM\Software\Policies\Microsoft\Windows\CloudContent" /v "DisableConsumerAccountStateContent" /t REG_DWORD /d 1 /f</Path>
|
||||
</RunSynchronousCommand>
|
||||
<RunSynchronousCommand wcm:action="add">
|
||||
<Order>25</Order>
|
||||
<Path>reg.exe add "HKLM\SOFTWARE\Policies\Microsoft\Windows NT\CurrentVersion\NetworkList\Signatures\FirstNetwork" /v Category /t REG_DWORD /d 1 /f</Path>
|
||||
<Description>Set Network Location to Home</Description>
|
||||
</RunSynchronousCommand>
|
||||
</RunSynchronous>
|
||||
</component>
|
||||
<component name="Microsoft-Windows-TerminalServices-LocalSessionManager" processorArchitecture="amd64" publicKeyToken="31bf3856ad364e35" language="neutral" versionScope="nonSxS">
|
||||
<fDenyTSConnections>false</fDenyTSConnections>
|
||||
</component>
|
||||
<component name="Microsoft-Windows-TerminalServices-RDP-WinStationExtensions" processorArchitecture="amd64" publicKeyToken="31bf3856ad364e35" language="neutral" versionScope="nonSxS">
|
||||
<UserAuthentication>0</UserAuthentication>
|
||||
</component>
|
||||
<component name="Networking-MPSSVC-Svc" processorArchitecture="amd64" publicKeyToken="31bf3856ad364e35" language="neutral" versionScope="nonSxS">
|
||||
<FirewallGroups>
|
||||
<FirewallGroup wcm:action="add" wcm:keyValue="RemoteDesktop">
|
||||
<Active>true</Active>
|
||||
<Profile>all</Profile>
|
||||
<Group>@FirewallAPI.dll,-28752</Group>
|
||||
</FirewallGroup>
|
||||
</FirewallGroups>
|
||||
</component>
|
||||
</settings>
|
||||
<settings pass="auditSystem" />
|
||||
<settings pass="auditUser" />
|
||||
<settings pass="oobeSystem">
|
||||
<component name="Microsoft-Windows-SecureStartup-FilterDriver" processorArchitecture="amd64" publicKeyToken="31bf3856ad364e35" language="neutral" versionScope="nonSxS">
|
||||
<PreventDeviceEncryption>true</PreventDeviceEncryption>
|
||||
</component>
|
||||
<component name="Microsoft-Windows-EnhancedStorage-Adm" processorArchitecture="amd64" publicKeyToken="31bf3856ad364e35" language="neutral" versionScope="nonSxS">
|
||||
<TCGSecurityActivationDisabled>1</TCGSecurityActivationDisabled>
|
||||
</component>
|
||||
<component name="Microsoft-Windows-Shell-Setup" processorArchitecture="amd64" publicKeyToken="31bf3856ad364e35" language="neutral" versionScope="nonSxS">
|
||||
<UserAccounts>
|
||||
<LocalAccounts>
|
||||
<LocalAccount wcm:action="add">
|
||||
<Name>Docker</Name>
|
||||
<Group>Administrators</Group>
|
||||
<Password>
|
||||
<Value />
|
||||
<PlainText>true</PlainText>
|
||||
</Password>
|
||||
</LocalAccount>
|
||||
</LocalAccounts>
|
||||
<AdministratorPassword>
|
||||
<Value>password</Value>
|
||||
<PlainText>true</PlainText>
|
||||
</AdministratorPassword>
|
||||
</UserAccounts>
|
||||
<AutoLogon>
|
||||
<Username>Docker</Username>
|
||||
<Enabled>true</Enabled>
|
||||
<LogonCount>65432</LogonCount>
|
||||
<Password>
|
||||
<Value />
|
||||
<PlainText>true</PlainText>
|
||||
</Password>
|
||||
</AutoLogon>
|
||||
<Display>
|
||||
<ColorDepth>32</ColorDepth>
|
||||
<HorizontalResolution>1920</HorizontalResolution>
|
||||
<VerticalResolution>1080</VerticalResolution>
|
||||
</Display>
|
||||
<OOBE>
|
||||
<HideEULAPage>true</HideEULAPage>
|
||||
<HideLocalAccountScreen>true</HideLocalAccountScreen>
|
||||
<HideOEMRegistrationScreen>true</HideOEMRegistrationScreen>
|
||||
<HideOnlineAccountScreens>true</HideOnlineAccountScreens>
|
||||
<HideWirelessSetupInOOBE>true</HideWirelessSetupInOOBE>
|
||||
<NetworkLocation>Home</NetworkLocation>
|
||||
<ProtectYourPC>3</ProtectYourPC>
|
||||
<SkipUserOOBE>true</SkipUserOOBE>
|
||||
<SkipMachineOOBE>true</SkipMachineOOBE>
|
||||
</OOBE>
|
||||
<RegisteredOrganization>Dockur</RegisteredOrganization>
|
||||
<RegisteredOwner>Windows for Docker</RegisteredOwner>
|
||||
<FirstLogonCommands>
|
||||
<SynchronousCommand wcm:action="add">
|
||||
<Order>1</Order>
|
||||
<CommandLine>reg.exe add "HKLM\SYSTEM\CurrentControlSet\Services\LanmanWorkstation\Parameters" /v "AllowInsecureGuestAuth" /t REG_DWORD /d 1 /f</CommandLine>
|
||||
<Description>Allow guest access to network shares</Description>
|
||||
</SynchronousCommand>
|
||||
<SynchronousCommand wcm:action="add">
|
||||
<Order>2</Order>
|
||||
<CommandLine>reg.exe add "HKLM\SYSTEM\CurrentControlSet\Services\LanmanWorkstation\Parameters" /v "RequireSecuritySignature" /t REG_DWORD /d 0 /f</CommandLine>
|
||||
<Description>Disable SMB signing requirement</Description>
|
||||
</SynchronousCommand>
|
||||
<SynchronousCommand wcm:action="add">
|
||||
<Order>3</Order>
|
||||
<CommandLine>reg.exe add "HKLM\SYSTEM\CurrentControlSet\Control\Lsa" /v LimitBlankPasswordUse /t REG_DWORD /d 0 /f</CommandLine>
|
||||
<Description>Allow RDP login with blank password</Description>
|
||||
</SynchronousCommand>
|
||||
<SynchronousCommand wcm:action="add">
|
||||
<Order>4</Order>
|
||||
<CommandLine>reg.exe add "HKLM\SOFTWARE\Microsoft\Windows NT\CurrentVersion\PasswordLess\Device" /v "DevicePasswordLessBuildVersion" /t REG_DWORD /d 0 /f</CommandLine>
|
||||
<Description>Enable option for passwordless sign-in</Description>
|
||||
</SynchronousCommand>
|
||||
<SynchronousCommand wcm:action="add">
|
||||
<Order>5</Order>
|
||||
<CommandLine>cmd /C wmic useraccount where name="Docker" set PasswordExpires=false</CommandLine>
|
||||
<Description>Password Never Expires</Description>
|
||||
</SynchronousCommand>
|
||||
<SynchronousCommand wcm:action="add">
|
||||
<Order>6</Order>
|
||||
<CommandLine>cmd /C POWERCFG -H OFF</CommandLine>
|
||||
<Description>Disable Hibernation</Description>
|
||||
</SynchronousCommand>
|
||||
<SynchronousCommand wcm:action="add">
|
||||
<Order>7</Order>
|
||||
<CommandLine>cmd /C POWERCFG -X -monitor-timeout-ac 0</CommandLine>
|
||||
<Description>Disable monitor blanking</Description>
|
||||
</SynchronousCommand>
|
||||
<SynchronousCommand wcm:action="add">
|
||||
<Order>8</Order>
|
||||
<CommandLine>reg.exe add "HKLM\SOFTWARE\Policies\Microsoft\Edge" /v "HideFirstRunExperience" /t REG_DWORD /d 1 /f</CommandLine>
|
||||
<Description>Disable first-run experience in Edge</Description>
|
||||
</SynchronousCommand>
|
||||
<SynchronousCommand wcm:action="add">
|
||||
<Order>9</Order>
|
||||
<CommandLine>reg.exe add "HKCU\SOFTWARE\Microsoft\Windows\CurrentVersion\Explorer\Advanced" /v "HideFileExt" /t REG_DWORD /d 0 /f</CommandLine>
|
||||
<Description>Show file extensions in Explorer</Description>
|
||||
</SynchronousCommand>
|
||||
<SynchronousCommand wcm:action="add">
|
||||
<Order>10</Order>
|
||||
<CommandLine>reg.exe add "HKLM\SYSTEM\CurrentControlSet\Control\Power" /v "HibernateFileSizePercent" /t REG_DWORD /d 0 /f</CommandLine>
|
||||
<Description>Zero Hibernation File</Description>
|
||||
</SynchronousCommand>
|
||||
<SynchronousCommand wcm:action="add">
|
||||
<Order>11</Order>
|
||||
<CommandLine>reg.exe add "HKLM\SYSTEM\CurrentControlSet\Control\Power" /v "HibernateEnabled" /t REG_DWORD /d 0 /f</CommandLine>
|
||||
<Description>Disable Hibernation</Description>
|
||||
</SynchronousCommand>
|
||||
<SynchronousCommand wcm:action="add">
|
||||
<Order>12</Order>
|
||||
<CommandLine>cmd /C POWERCFG -X -standby-timeout-ac 0</CommandLine>
|
||||
<Description>Disable Sleep</Description>
|
||||
</SynchronousCommand>
|
||||
<SynchronousCommand wcm:action="add">
|
||||
<Order>13</Order>
|
||||
<CommandLine>reg.exe add "HKLM\SOFTWARE\Policies\Microsoft\Windows NT\Terminal Services" /v "fAllowUnlistedRemotePrograms" /t REG_DWORD /d 1 /f</CommandLine>
|
||||
<Description>Enable RemoteAPP to launch unlisted programs</Description>
|
||||
</SynchronousCommand>
|
||||
<SynchronousCommand wcm:action="add">
|
||||
<Order>14</Order>
|
||||
<CommandLine>reg.exe add "HKCU\SOFTWARE\Microsoft\Windows\CurrentVersion\Explorer\Advanced" /v "ShowTaskViewButton" /t REG_DWORD /d 0 /f</CommandLine>
|
||||
<Description>Remove Task View from the Taskbar</Description>
|
||||
</SynchronousCommand>
|
||||
<SynchronousCommand wcm:action="add">
|
||||
<Order>15</Order>
|
||||
<CommandLine>reg.exe add "HKCU\SOFTWARE\Microsoft\Windows\CurrentVersion\Explorer\Advanced" /v "TaskbarDa" /t REG_DWORD /d 0 /f</CommandLine>
|
||||
<Description>Remove Widgets from the Taskbar</Description>
|
||||
</SynchronousCommand>
|
||||
<SynchronousCommand wcm:action="add">
|
||||
<Order>16</Order>
|
||||
<CommandLine>reg.exe add "HKCU\SOFTWARE\Microsoft\Windows\CurrentVersion\Explorer\Advanced" /v "TaskbarMn" /t REG_DWORD /d 0 /f</CommandLine>
|
||||
<Description>Remove Chat from the Taskbar</Description>
|
||||
</SynchronousCommand>
|
||||
<SynchronousCommand wcm:action="add">
|
||||
<Order>17</Order>
|
||||
<CommandLine>reg.exe add "HKLM\SOFTWARE\Policies\Microsoft\Windows\WindowsUpdate\AU" /v "NoAutoUpdate" /t REG_DWORD /d 1 /f</CommandLine>
|
||||
<Description>Turn off Windows Update auto download</Description>
|
||||
</SynchronousCommand>
|
||||
<SynchronousCommand wcm:action="add">
|
||||
<Order>18</Order>
|
||||
<CommandLine>netsh advfirewall firewall set rule group="@FirewallAPI.dll,-32752" new enable=Yes</CommandLine>
|
||||
<Description>Enable Network Discovery</Description>
|
||||
</SynchronousCommand>
|
||||
<SynchronousCommand wcm:action="add">
|
||||
<Order>19</Order>
|
||||
<CommandLine>netsh advfirewall firewall set rule group="@FirewallAPI.dll,-28502" new enable=Yes</CommandLine>
|
||||
<Description>Enable File Sharing</Description>
|
||||
</SynchronousCommand>
|
||||
<SynchronousCommand wcm:action="add">
|
||||
<Order>20</Order>
|
||||
<CommandLine>reg.exe add "HKCU\Control Panel\UnsupportedHardwareNotificationCache" /v SV1 /d 0 /t REG_DWORD /f</CommandLine>
|
||||
<Description>Disable unsupported hardware notifications</Description>
|
||||
</SynchronousCommand>
|
||||
<SynchronousCommand wcm:action="add">
|
||||
<Order>21</Order>
|
||||
<CommandLine>reg.exe add "HKCU\Control Panel\UnsupportedHardwareNotificationCache" /v SV2 /d 0 /t REG_DWORD /f</CommandLine>
|
||||
<Description>Disable unsupported hardware notifications</Description>
|
||||
</SynchronousCommand>
|
||||
<SynchronousCommand wcm:action="add">
|
||||
<Order>22</Order>
|
||||
<CommandLine>pnputil -i -a C:\Windows\Drivers\viogpudo\viogpudo.inf</CommandLine>
|
||||
<Description>Install VirtIO display driver</Description>
|
||||
</SynchronousCommand>
|
||||
<SynchronousCommand wcm:action="add">
|
||||
<Order>23</Order>
|
||||
<CommandLine>cmd /C rd /q C:\Windows.old</CommandLine>
|
||||
<Description>Remove empty Windows.old folder</Description>
|
||||
</SynchronousCommand>
|
||||
<SynchronousCommand wcm:action="add">
|
||||
<Order>24</Order>
|
||||
<CommandLine>cmd /C if exist "C:\OEM\install.bat" start "Install" "cmd /C C:\OEM\install.bat"</CommandLine>
|
||||
<Description>Execute custom script from the OEM folder if exists</Description>
|
||||
</SynchronousCommand>
|
||||
</FirstLogonCommands>
|
||||
</component>
|
||||
</settings>
|
||||
</unattend>
|
||||
1
omnitool/omnibox/vm/win11iso/README.md
Normal file
1
omnitool/omnibox/vm/win11iso/README.md
Normal file
@@ -0,0 +1 @@
|
||||
Add your Win11E setup.iso to this folder
|
||||
31
omnitool/omnibox/vm/win11setup/firstboot/install.bat
Normal file
31
omnitool/omnibox/vm/win11setup/firstboot/install.bat
Normal file
@@ -0,0 +1,31 @@
|
||||
@echo off
|
||||
|
||||
SET ScriptFolder=\\host.lan\Data
|
||||
SET LogFile=%ScriptFolder%\firstboot_log.txt
|
||||
|
||||
echo Running PowerShell script... > %LogFile%
|
||||
|
||||
:: Check for PowerShell availability
|
||||
where powershell >> %LogFile% 2>&1
|
||||
if %ERRORLEVEL% neq 0 (
|
||||
echo PowerShell is not available! >> %LogFile%
|
||||
echo PowerShell is not available!
|
||||
exit /b 1
|
||||
)
|
||||
|
||||
:: Add a 30-second delay
|
||||
echo Waiting for 30 seconds before continuing... >> %LogFile%
|
||||
timeout /t 30 /nobreak >> %LogFile% 2>&1
|
||||
|
||||
:: Run PowerShell script with ExecutionPolicy Bypass and log errors
|
||||
echo Running setup.ps1... >> %LogFile%
|
||||
|
||||
powershell -ExecutionPolicy Bypass -File "%ScriptFolder%\setup.ps1" >> %LogFile% 2>&1
|
||||
|
||||
if %ERRORLEVEL% neq 0 (
|
||||
echo An error occurred. See %LogFile% for details.
|
||||
) else (
|
||||
echo PowerShell script has completed successfully.
|
||||
)
|
||||
|
||||
echo PowerShell script has completed.
|
||||
7
omnitool/omnibox/vm/win11setup/setupscripts/on-logon.ps1
Normal file
7
omnitool/omnibox/vm/win11setup/setupscripts/on-logon.ps1
Normal file
@@ -0,0 +1,7 @@
|
||||
$scriptFolder = "\\host.lan\Data"
|
||||
$pythonScriptFile = "$scriptFolder\server\main.py"
|
||||
$pythonServerPort = 5000
|
||||
|
||||
# Start the flask computer use server
|
||||
Write-Host "Running the server on port $pythonServerPort"
|
||||
python $pythonScriptFile --port $pythonServerPort
|
||||
BIN
omnitool/omnibox/vm/win11setup/setupscripts/server/cursor.png
Normal file
BIN
omnitool/omnibox/vm/win11setup/setupscripts/server/cursor.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 3.1 KiB |
81
omnitool/omnibox/vm/win11setup/setupscripts/server/main.py
Normal file
81
omnitool/omnibox/vm/win11setup/setupscripts/server/main.py
Normal file
@@ -0,0 +1,81 @@
|
||||
import os
|
||||
import logging
|
||||
import argparse
|
||||
import shlex
|
||||
import subprocess
|
||||
from flask import Flask, request, jsonify, send_file
|
||||
import threading
|
||||
import traceback
|
||||
import pyautogui
|
||||
from PIL import Image
|
||||
from io import BytesIO
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--log_file", help="log file path", type=str,
|
||||
default=os.path.join(os.path.dirname(__file__), "server.log"))
|
||||
parser.add_argument("--port", help="port", type=int, default=5000)
|
||||
args = parser.parse_args()
|
||||
|
||||
logging.basicConfig(filename=args.log_file,level=logging.DEBUG, filemode='w' )
|
||||
logger = logging.getLogger('werkzeug')
|
||||
|
||||
app = Flask(__name__)
|
||||
|
||||
computer_control_lock = threading.Lock()
|
||||
|
||||
@app.route('/probe', methods=['GET'])
|
||||
def probe_endpoint():
|
||||
return jsonify({"status": "Probe successful", "message": "Service is operational"}), 200
|
||||
|
||||
@app.route('/execute', methods=['POST'])
|
||||
def execute_command():
|
||||
# Only execute one command at a time
|
||||
with computer_control_lock:
|
||||
data = request.json
|
||||
# The 'command' key in the JSON request should contain the command to be executed.
|
||||
shell = data.get('shell', False)
|
||||
command = data.get('command', "" if shell else [])
|
||||
|
||||
if isinstance(command, str) and not shell:
|
||||
command = shlex.split(command)
|
||||
|
||||
# Expand user directory
|
||||
for i, arg in enumerate(command):
|
||||
if arg.startswith("~/"):
|
||||
command[i] = os.path.expanduser(arg)
|
||||
|
||||
# Execute the command without any safety checks.
|
||||
try:
|
||||
result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=shell, text=True, timeout=120)
|
||||
return jsonify({
|
||||
'status': 'success',
|
||||
'output': result.stdout,
|
||||
'error': result.stderr,
|
||||
'returncode': result.returncode
|
||||
})
|
||||
except Exception as e:
|
||||
logger.error("\n" + traceback.format_exc() + "\n")
|
||||
return jsonify({
|
||||
'status': 'error',
|
||||
'message': str(e)
|
||||
}), 500
|
||||
|
||||
@app.route('/screenshot', methods=['GET'])
|
||||
def capture_screen_with_cursor():
|
||||
cursor_path = os.path.join(os.path.dirname(__file__), "cursor.png")
|
||||
screenshot = pyautogui.screenshot()
|
||||
cursor_x, cursor_y = pyautogui.position()
|
||||
cursor = Image.open(cursor_path)
|
||||
# make the cursor smaller
|
||||
cursor = cursor.resize((int(cursor.width / 1.5), int(cursor.height / 1.5)))
|
||||
screenshot.paste(cursor, (cursor_x, cursor_y), cursor)
|
||||
|
||||
|
||||
# Convert PIL Image to bytes and send
|
||||
img_io = BytesIO()
|
||||
screenshot.save(img_io, 'PNG')
|
||||
img_io.seek(0)
|
||||
return send_file(img_io, mimetype='image/png')
|
||||
|
||||
if __name__ == '__main__':
|
||||
app.run(debug=True, host="0.0.0.0", port=args.port)
|
||||
@@ -0,0 +1,2 @@
|
||||
flask
|
||||
PyAutoGUI
|
||||
197
omnitool/omnibox/vm/win11setup/setupscripts/setup-tools.psm1
Normal file
197
omnitool/omnibox/vm/win11setup/setupscripts/setup-tools.psm1
Normal file
@@ -0,0 +1,197 @@
|
||||
function Get-Tools {
|
||||
param(
|
||||
[string]$toolsConfigJson
|
||||
)
|
||||
|
||||
# Convert the JSON string to a PowerShell object
|
||||
$toolsList = $toolsConfigJson | ConvertFrom-Json
|
||||
|
||||
return $toolsList
|
||||
}
|
||||
|
||||
function Get-ToolDetails {
|
||||
param(
|
||||
$toolsList,
|
||||
[string]$toolName
|
||||
)
|
||||
|
||||
# Check if the program exists in the JSON data
|
||||
if ($toolsList.PSObject.Properties.Name -contains $toolName) {
|
||||
# Return the program details as a PowerShell object
|
||||
return $toolsList.$toolName
|
||||
} else {
|
||||
# Handle the case where the program is not found
|
||||
Write-Host "Program '$toolName' not found in the list."
|
||||
return $null
|
||||
}
|
||||
}
|
||||
|
||||
function Invoke-DownloadFileFromAvailableMirrors {
|
||||
param (
|
||||
[string[]]$mirrorUrls,
|
||||
[string]$outfile
|
||||
)
|
||||
foreach ($url in $mirrorUrls) {
|
||||
try {
|
||||
$result = Invoke-DownloadFile -url $url -outfile $outfile
|
||||
if ($result -eq $true) {
|
||||
Write-Host "Downloaded using $url"
|
||||
return $true
|
||||
}
|
||||
} catch {
|
||||
Write-Host "Error downloading from $url. Please check and update the mirrors."
|
||||
}
|
||||
}
|
||||
|
||||
Write-Host "Downloading from the provided mirrors failed. Please check and update the mirrors."
|
||||
return $false
|
||||
}
|
||||
|
||||
function Invoke-DownloadFile {
|
||||
param (
|
||||
[string]$url,
|
||||
[string]$outfile
|
||||
)
|
||||
# Makes download faster by disabling progress bar
|
||||
$ProgressPreference = "SilentlyContinue"
|
||||
|
||||
$retryCount = 0
|
||||
$maxRetries = 3
|
||||
$sleepSeconds = 2
|
||||
$maxSleepSeconds = 10
|
||||
$userAgent = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36"
|
||||
|
||||
# Ensure directory exists
|
||||
$directory = Split-Path -Path $outfile -Parent
|
||||
if (-Not (Test-Path -Path $directory)) {
|
||||
Write-Host "Creating directory $directory..."
|
||||
New-Item -Path $directory -ItemType Directory -Force | Out-Null
|
||||
}
|
||||
|
||||
while ($retryCount -lt $maxRetries) {
|
||||
try {
|
||||
Invoke-RestMethod -Uri $url -OutFile $outfile -Headers @{"User-Agent" = $userAgent}
|
||||
Write-Host "Download successful, file saved to: $outfile"
|
||||
break
|
||||
} catch {
|
||||
$retryCount++
|
||||
Write-Host "Attempt $retryCount of $maxRetries failed. Error: $($_.Exception.Message)"
|
||||
Start-Sleep -Seconds $sleepSeconds
|
||||
$sleepSeconds = [Math]::Min($sleepSeconds * 2, $maxSleepSeconds) # Exponential backoff with a cap
|
||||
}
|
||||
}
|
||||
|
||||
if ($retryCount -eq $maxRetries) {
|
||||
Write-Host "Failed to download the file after $maxRetries attempts."
|
||||
return $false
|
||||
}
|
||||
|
||||
return $true
|
||||
}
|
||||
|
||||
function Add-ToEnvPath {
|
||||
param (
|
||||
[string]$NewPath
|
||||
)
|
||||
|
||||
# Get the current PATH environment variable
|
||||
$envPath = [Environment]::GetEnvironmentVariable("PATH", "Machine")
|
||||
|
||||
# Append the new path to the existing PATH
|
||||
$newPath = "$envPath;$NewPath"
|
||||
|
||||
# Set the updated PATH environment variable
|
||||
[Environment]::SetEnvironmentVariable("PATH", $newPath, "Machine")
|
||||
|
||||
# Fetch updates from the shell
|
||||
$env:PATH += ";${newPath}"
|
||||
}
|
||||
|
||||
function Register-LogonTask {
|
||||
param(
|
||||
|
||||
[parameter(Mandatory = $true, ValueFromPipelineByPropertyName = $true, HelpMessage = "Name of the scheduled task")]
|
||||
[string]
|
||||
$TaskName,
|
||||
|
||||
[parameter(Mandatory = $true, ValueFromPipelineByPropertyName = $true, HelpMessage = "Path to the .py script")]
|
||||
[string]
|
||||
$ScriptPath,
|
||||
|
||||
[parameter(Mandatory = $false, ValueFromPipelineByPropertyName = $true, HelpMessage = "Arguments to the .py script")]
|
||||
[string]
|
||||
$Arguments = "",
|
||||
|
||||
[parameter(Mandatory = $false, ValueFromPipelineByPropertyName = $true, HelpMessage = "Local Account username")]
|
||||
[string]
|
||||
$LocalUser,
|
||||
|
||||
[parameter(Mandatory = $false, ValueFromPipelineByPropertyName = $true, HelpMessage = "Local Account password")]
|
||||
[string]
|
||||
$LocalPassword,
|
||||
|
||||
[parameter(Mandatory = $false, ValueFromPipelineByPropertyName = $true, HelpMessage = "Whether to execute the command as SYSTEM")]
|
||||
[switch]
|
||||
$AsSystem = $false,
|
||||
|
||||
[parameter(Mandatory = $false, ValueFromPipelineByPropertyName = $true, HelpMessage = "logging file")]
|
||||
[string]
|
||||
$LogFilePath
|
||||
)
|
||||
|
||||
$scriptDirectory = Split-Path $ScriptPath
|
||||
|
||||
$taskActionArgument = "-ExecutionPolicy Bypass -windowstyle hidden -Command `"try { . '$ScriptPath' $Arguments } catch { Write `$_.Exception.Message | Out-File $($TaskName)_Log.txt } finally { } `""
|
||||
$taskAction = New-ScheduledTaskAction -Execute "$PSHome\powershell.exe" -Argument $taskActionArgument -WorkingDirectory $scriptDirectory
|
||||
|
||||
$params = @{
|
||||
Force = $True
|
||||
Action = $taskAction
|
||||
RunLevel = "Highest"
|
||||
TaskName = $TaskName
|
||||
}
|
||||
|
||||
$taskTrigger = New-ScheduledTaskTrigger -AtLogOn
|
||||
$params.Add("Trigger", $taskTrigger)
|
||||
|
||||
if ($AsSystem) {
|
||||
$params.Add("User", "NT AUTHORITY\SYSTEM")
|
||||
}
|
||||
else {
|
||||
$params.Add("User", $LocalUser)
|
||||
if ($LocalPassword) {
|
||||
$params.Add("Password", $LocalPassword)
|
||||
}
|
||||
}
|
||||
|
||||
Write-Host "Registering scheduled task '$TaskName' to run 'powershell.exe $taskActionArgument'..."
|
||||
Register-ScheduledTask @params
|
||||
}
|
||||
|
||||
# Function to attempt pip install and handle failures
|
||||
function Install-PythonPackages {
|
||||
param (
|
||||
[string]$Package = "",
|
||||
[string]$Arguments = "",
|
||||
[string]$RequirementsPath = ""
|
||||
)
|
||||
$RetryCount = 3
|
||||
$currentAttempt = 0
|
||||
while ($currentAttempt -lt $RetryCount) {
|
||||
if (-not [string]::IsNullOrWhiteSpace($RequirementsPath)) {
|
||||
& python -m pip install --no-cache-dir -r $RequirementsPath $Arguments
|
||||
} else {
|
||||
& python -m pip install --no-cache-dir $Package $Arguments
|
||||
}
|
||||
if ($LASTEXITCODE -eq 0) {
|
||||
Write-Host "Installation successful."
|
||||
return
|
||||
} else {
|
||||
Write-Host "Attempt $($currentAttempt + 1) failed. Retrying..."
|
||||
Start-Sleep -Seconds 10
|
||||
$currentAttempt++
|
||||
}
|
||||
}
|
||||
Write-Error "Failed to install after $RetryCount attempts."
|
||||
exit
|
||||
}
|
||||
392
omnitool/omnibox/vm/win11setup/setupscripts/setup.ps1
Normal file
392
omnitool/omnibox/vm/win11setup/setupscripts/setup.ps1
Normal file
@@ -0,0 +1,392 @@
|
||||
$ErrorActionPreference = "Continue" # until downloading from mirrors is more stable
|
||||
|
||||
# Section - General Setup
|
||||
$scriptFolder = "\\host.lan\Data"
|
||||
$toolsFolder = "C:\Users\$env:USERNAME\Tools"
|
||||
|
||||
# Load the shared setup-tools module
|
||||
Import-Module (Join-Path $scriptFolder -ChildPath "setup-tools.psm1")
|
||||
|
||||
# Check if profile exists
|
||||
if (-not (Test-Path $PROFILE)) {
|
||||
New-Item -ItemType File -Path $PROFILE -Force
|
||||
}
|
||||
|
||||
# Create a folder where we store all the standalone executables
|
||||
if (-not (Test-Path $toolsFolder)) {
|
||||
New-Item -ItemType Directory -Path $toolsFolder -Force
|
||||
$envPath = [Environment]::GetEnvironmentVariable("PATH", "Machine")
|
||||
$newPath = "$envPath;$toolsFolder"
|
||||
[Environment]::SetEnvironmentVariable("PATH", $newPath, "Machine")
|
||||
}
|
||||
|
||||
# Section - Tools Installation
|
||||
|
||||
# Set TLS version to 1.2 or higher
|
||||
[Net.ServicePointManager]::SecurityProtocol = [Net.SecurityProtocolType]::Tls12 -bor [Net.SecurityProtocolType]::Tls13
|
||||
|
||||
# Load the tools config json listing mirrors and aliases used for installing tools
|
||||
$toolsConfigJsonPath = Join-Path $scriptFolder -ChildPath "tools_config.json"
|
||||
$toolsConfigJson = Get-Content -Path $toolsConfigJsonPath -Raw
|
||||
$toolsList = Get-Tools -toolsConfigJson $toolsConfigJson
|
||||
|
||||
## - Python
|
||||
$pythonToolName = "Python"
|
||||
$userPythonPath = "$env:LOCALAPPDATA\Programs\Python"
|
||||
$pythonDetails = Get-ToolDetails -toolsList $toolsList -toolName $pythonToolName
|
||||
$pythonAlias = $pythonDetails.alias
|
||||
|
||||
# Check for Python installation
|
||||
$pythonExecutablePath = Get-ChildItem -Path $userPythonPath -Filter python.exe -Recurse -ErrorAction SilentlyContinue | Select-Object -First 1 -ExpandProperty FullName
|
||||
|
||||
# Force to install Python 3.10 as the pre-installed version on Windows may not work sometimes
|
||||
Write-Host "Downloading Python $pythonVersion..."
|
||||
$pythonInstallerFilePath = "$env:TEMP\python_installer.exe"
|
||||
$downloadResult = Invoke-DownloadFileFromAvailableMirrors -mirrorUrls $pythonDetails.mirrors -outfile $pythonInstallerFilePath
|
||||
if (-not $downloadResult) {
|
||||
Write-Host "Failed to download Python. Please try again later or install manually."
|
||||
} else {
|
||||
Write-Host "Installing Python for current user..."
|
||||
Start-Process -FilePath $pythonInstallerFilePath -Args "/quiet InstallAllUsers=0 PrependPath=0" -NoNewWindow -Wait
|
||||
$pythonExecutablePath = "$userPythonPath\Python310\python.exe"
|
||||
$setAliasExpression = "Set-Alias -Name $pythonAlias -Value `"$pythonExecutablePath`""
|
||||
Add-Content -Path $PROFILE -Value $setAliasExpression
|
||||
Invoke-Expression $setAliasExpression
|
||||
}
|
||||
|
||||
## - Git
|
||||
$gitToolName = "git"
|
||||
$gitToolDetails = Get-ToolDetails -toolsList $toolsList -toolName $gitToolName
|
||||
|
||||
# Check for Git installation
|
||||
try {
|
||||
git --version | Out-Null
|
||||
Write-Host "Git is already installed."
|
||||
} catch {
|
||||
Write-Host "Git is not installed. Downloading and installing Git..."
|
||||
$gitInstallerFilePath = "$env:TEMP\git_installer.exe"
|
||||
$downloadResult = Invoke-DownloadFileFromAvailableMirrors -mirrorUrls $gitToolDetails.mirrors -outfile $gitInstallerFilePath
|
||||
if (-not $downloadResult) {
|
||||
Write-Host "Failed to download Git. Please try again later or install manually."
|
||||
} else {
|
||||
Start-Process -FilePath $gitInstallerFilePath -Args "/VERYSILENT /NORESTART /NOCANCEL /SP-" -Wait
|
||||
Add-ToEnvPath -NewPath "C:\Program Files\Git\bin"
|
||||
|
||||
Write-Host "Git has been installed."
|
||||
}
|
||||
}
|
||||
|
||||
# - 7zip
|
||||
$7ZipToolName = "7zip"
|
||||
$7ZipToolDetails = Get-ToolDetails -toolsList $toolsList -toolName $7ZipToolName
|
||||
Write-Host "$7ZipToolDetails"
|
||||
|
||||
if (Get-Command 7z -ErrorAction SilentlyContinue) {
|
||||
Write-Host "7-Zip is already installed."
|
||||
}
|
||||
else {
|
||||
Write-Host "Installing 7-Zip..."
|
||||
|
||||
$7ZipInstallerFilePath = "$env:TEMP\7_zip.exe"
|
||||
Write-Host "$($7ZipToolDetails.mirrors)"
|
||||
$downloadResult = Invoke-DownloadFileFromAvailableMirrors -mirrorUrls $7ZipToolDetails.mirrors -outfile $7ZipInstallerFilePath
|
||||
if (-not $downloadResult) {
|
||||
Write-Host "Failed to download 7-Zip. Please try again later or install manually."
|
||||
} else {
|
||||
Start-Process -FilePath $7ZipInstallerFilePath -Args "/S" -Verb RunAs -Wait
|
||||
Remove-Item $7ZipInstallerFilePath
|
||||
|
||||
# add 7z to PATH
|
||||
Add-ToEnvPath -NewPath "${env:ProgramFiles}\7-Zip"
|
||||
}
|
||||
}
|
||||
|
||||
# - ffpmeg
|
||||
$ffpmegToolName = "ffmpeg"
|
||||
$ffpmegToolDetails = Get-ToolDetails -toolsList $toolsList -toolName $ffpmegToolName
|
||||
|
||||
if (Get-Command ffmpeg -ErrorAction SilentlyContinue) {
|
||||
Write-Host "ffmpeg is already installed."
|
||||
} else {
|
||||
Write-Host "ffmpeg is not installed. Installing it."
|
||||
$ffpmegInstallerFilePath = "C:\ffmpeg.7z"
|
||||
$downloadResult = Invoke-DownloadFileFromAvailableMirrors -mirrorUrls $ffpmegToolDetails.mirrors -outfile $ffpmegInstallerFilePath
|
||||
if (-not $downloadResult) {
|
||||
Write-Host "Failed to download ffmpeg. Please try again later or install manually."
|
||||
} else {
|
||||
Write-Host "Extracting $ffpmegInstallerFilePath..."
|
||||
7z x -y -o"C:\" "C:\ffmpeg.7z"
|
||||
|
||||
$ffmpegFolder = Get-ChildItem -Path "C:\" -Filter "ffmpeg-*" -Directory
|
||||
$ffmpegFolder = -join ("C:\", $ffmpegFolder)
|
||||
#remove ffmpeg folder if exists
|
||||
if (Test-Path "C:\ffmpeg") {
|
||||
Remove-Item -Path "C:\ffmpeg" -Recurse -Force
|
||||
}
|
||||
Rename-Item -Path "$ffmpegFolder" -NewName "ffmpeg"
|
||||
|
||||
Write-Host "Adding ffmpeg to PATH..."
|
||||
Add-ToEnvPath -NewPath "C:\ffmpeg\bin"
|
||||
|
||||
Write-Host "ffmpeg is installed"
|
||||
}
|
||||
}
|
||||
|
||||
# Disable Edge Auto Updates
|
||||
Stop-Process -Name "MicrosoftEdgeUpdate" -Force -ErrorAction SilentlyContinue
|
||||
$edgeUpdatePath = "${env:ProgramFiles(x86)}\Microsoft\EdgeUpdate"
|
||||
Remove-Item -Path $edgeUpdatePath -Recurse -Force -ErrorAction SilentlyContinue
|
||||
Write-Host "Edge Update processes terminated and directory removed."
|
||||
|
||||
# - Google Chrome
|
||||
$chromeToolName = "Google Chrome"
|
||||
$chromeToolDetails = Get-ToolDetails -toolsList $toolsList -toolName $chromeToolName
|
||||
$chromeExePath = "C:\Program Files\Google\Chrome\Application\chrome.exe"
|
||||
$chromeAlias = $chromeToolDetails.alias
|
||||
|
||||
# Check if Google Chrome is already installed by its alias
|
||||
if (Get-Command $chromeAlias -ErrorAction SilentlyContinue) {
|
||||
Write-Host "Google Chrome is already installed."
|
||||
} else {
|
||||
# Download the installer to the Temp directory
|
||||
$chromeInstallerFilePath = "$env:TEMP\chrome_installer.exe"
|
||||
$downloadResult = Invoke-DownloadFileFromAvailableMirrors -mirrorUrls $chromeToolDetails.mirrors -outfile $chromeInstallerFilePath
|
||||
if (-not $downloadResult) {
|
||||
Write-Host "Failed to download Google Chrome. Please try again later or install manually."
|
||||
} else {
|
||||
# Execute the installer silently with elevated permissions
|
||||
Start-Process -FilePath $chromeInstallerFilePath -ArgumentList "/silent", "/install" -Verb RunAs -Wait
|
||||
|
||||
# Remove the installer file after installation
|
||||
Remove-Item -Path $chromeInstallerFilePath
|
||||
|
||||
# Set alias
|
||||
$setAliasExpression = "Set-Alias -Name $chromeAlias -Value `"$chromeExePath`""
|
||||
Add-Content -Path $PROFILE -Value $setAliasExpression
|
||||
Invoke-Expression $setAliasExpression
|
||||
|
||||
# Add Chrome to the system PATH environment variable
|
||||
Add-ToEnvPath -NewPath "${env:ProgramFiles}\Google\Chrome\Application"
|
||||
|
||||
# Disable Google Chrome Auto Updates
|
||||
$chromeRegPath = "HKLM:\SOFTWARE\Policies\Google\Update"
|
||||
if (-not (Test-Path $chromeRegPath)) {
|
||||
New-Item -Path $chromeRegPath -Force
|
||||
}
|
||||
Set-ItemProperty -Path $chromeRegPath -Name "AutoUpdateCheckPeriodMinutes" -Value 0
|
||||
Set-ItemProperty -Path $chromeRegPath -Name "UpdateDefault" -Value 0
|
||||
}
|
||||
}
|
||||
|
||||
# - LibreOffice
|
||||
$libreOfficeToolName = "LibreOffice"
|
||||
$libreOfficeToolDetails = Get-ToolDetails -toolsList $toolsList -toolName $libreOfficeToolName
|
||||
|
||||
# Check for LibreOffice installation
|
||||
$installedVersion = (Get-WmiObject -Query "SELECT * FROM Win32_Product WHERE Name like 'LibreOffice%'").Version
|
||||
if (-not [string]::IsNullOrWhiteSpace($installedVersion)) {
|
||||
Write-Host "LibreOffice $version is already installed."
|
||||
} else {
|
||||
Write-Host "LibreOffice is not installed. Downloading and installing LibreOffice..."
|
||||
$libreOfficeInstallerFilePath = "$env:TEMP\libreOffice_installer.exe"
|
||||
|
||||
$downloadResult = Invoke-DownloadFileFromAvailableMirrors -mirrorUrls $libreOfficeToolDetails.mirrors -outfile $libreOfficeInstallerFilePath
|
||||
if (-not $downloadResult) {
|
||||
Write-Host "Failed to download LibreOffice. Please try again later or install manually."
|
||||
} else {
|
||||
Start-Process "msiexec.exe" -ArgumentList "/i `"$libreOfficeInstallerFilePath`" /quiet" -Wait -NoNewWindow
|
||||
Write-Host "LibreOffice has been installed."
|
||||
|
||||
# Add LibreOffice to the system PATH environment variable
|
||||
Add-ToEnvPath -NewPath "C:\Program Files\LibreOffice\program"
|
||||
}
|
||||
}
|
||||
|
||||
# - VLC
|
||||
$vlcToolName = "VLC"
|
||||
$vlcToolDetails = Get-ToolDetails -toolsList $toolsList -toolName $vlcToolName
|
||||
$vlcAlias = $vlcToolDetails.alias
|
||||
$vlcExecutableFilePath = "C:\Program Files\VideoLAN\VLC\vlc.exe"
|
||||
|
||||
# Check if VLC is already installed by checking the VLC command
|
||||
if (Test-Path $vlcExecutableFilePath) {
|
||||
Write-Host "VLC is already installed."
|
||||
} else {
|
||||
# Download the installer to the Temp directory
|
||||
$vlcInstallerFilePath = "$env:TEMP\vlc_installer.exe"
|
||||
$downloadResult = Invoke-DownloadFileFromAvailableMirrors -mirrorUrls $vlcToolDetails.mirrors -outfile $vlcInstallerFilePath
|
||||
if (-not $downloadResult) {
|
||||
Write-Host "Failed to download VLC. Please try again later or install manually."
|
||||
} else {
|
||||
# Execute the installer silently with elevated permissions
|
||||
Start-Process -FilePath $vlcInstallerFilePath -ArgumentList "/S" -Verb RunAs -Wait
|
||||
|
||||
# Remove the installer file after installation
|
||||
Remove-Item -Path $vlcInstallerFilePath
|
||||
|
||||
# Set alias
|
||||
$setAliasExpression = "Set-Alias -Name $vlcAlias -Value `"$vlcExecutableFilePath`""
|
||||
Add-Content -Path $PROFILE -Value $setAliasExpression
|
||||
Invoke-Expression $setAliasExpression
|
||||
|
||||
# Add VLC to the system PATH environment variable
|
||||
Add-ToEnvPath -NewPath "C:\Program Files\VideoLAN\VLC"
|
||||
}
|
||||
}
|
||||
|
||||
# - GIMP
|
||||
$gimpToolName = "GIMP"
|
||||
$gimpToolDetails = Get-ToolDetails -toolsList $toolsList -toolName $gimpToolName
|
||||
$gimpAlias = $gimpToolDetails.alias
|
||||
$gimpExecutablePath = "C:\Program Files\GIMP 2\bin\gimp-2.10.exe"
|
||||
|
||||
# Check if GIMP is already installed by checking the GIMP executable path
|
||||
if (Test-Path $gimpExecutablePath) {
|
||||
Write-Host "GIMP is already installed."
|
||||
} else {
|
||||
# Download the installer to the Temp directory
|
||||
$gimpInstallerFilePath = "$env:TEMP\gimp_installer.exe"
|
||||
$downloadResult = Invoke-DownloadFileFromAvailableMirrors -mirrorUrls $gimpToolDetails.mirrors -outfile $gimpInstallerFilePath
|
||||
if (-not $downloadResult) {
|
||||
Write-Host "Failed to download GIMP. Please try again later or install manually."
|
||||
} else {
|
||||
# Execute the installer silently with elevated permissions
|
||||
Start-Process -FilePath $gimpInstallerFilePath -ArgumentList "/VERYSILENT /ALLUSERS" -Verb RunAs -Wait
|
||||
|
||||
# Remove the installer file after installation
|
||||
Remove-Item -Path $gimpInstallerFilePath
|
||||
|
||||
# Set alias
|
||||
$setAliasExpression = "Set-Alias -Name $gimpAlias -Value `"$gimpExecutablePath`""
|
||||
Add-Content -Path $PROFILE -Value $setAliasExpression
|
||||
Invoke-Expression $setAliasExpression
|
||||
|
||||
# Add GIMP to the system PATH environment variable
|
||||
Add-ToEnvPath -NewPath "C:\Program Files\GIMP 2\bin"
|
||||
}
|
||||
}
|
||||
|
||||
# - VS Code
|
||||
$vsCodeToolName = "VS Code"
|
||||
$vsCodeToolDetails = Get-ToolDetails -toolsList $toolsList -toolName $vsCodeToolName
|
||||
$vsCodeAlias = $gimpToolDetails.alias
|
||||
$vsCodeExecutablePath = "C:\Users\$env:USERNAME\AppData\Local\Programs\Microsoft VS Code\Code.exe"
|
||||
|
||||
# Check if VS Code is already installed by checking the VS Code executable path
|
||||
if (Test-Path $vsCodeExecutablePath) {
|
||||
Write-Host "VS Code is already installed."
|
||||
} else {
|
||||
# Download the installer to the Temp directory
|
||||
$vsCodeInstallerFilePath = "$env:TEMP\VSCodeSetup.exe"
|
||||
$downloadResult = Invoke-DownloadFileFromAvailableMirrors -mirrorUrls $vsCodeToolDetails.mirrors -outfile $vsCodeInstallerFilePath
|
||||
if (-not $downloadResult) {
|
||||
Write-Host "Failed to download VS Code. Please try again later or install manually."
|
||||
} else {
|
||||
# Execute the installer silently with elevated permissions
|
||||
Start-Process -FilePath $vsCodeInstallerFilePath -ArgumentList "/VERYSILENT", "/mergetasks=!runcode" -Verb RunAs -Wait
|
||||
|
||||
# Remove the installer file after installation
|
||||
Remove-Item -Path $vsCodeInstallerFilePath
|
||||
|
||||
# Set alias
|
||||
$setAliasExpression = "Set-Alias -Name $vsCodeAlias -Value `"$vsCodeExecutablePath`""
|
||||
Add-Content -Path $PROFILE -Value $setAliasExpression
|
||||
Invoke-Expression $setAliasExpression
|
||||
|
||||
# Add VS Code to the system PATH environment variable
|
||||
Add-ToEnvPath -NewPath "C:\Users\$env:USERNAME\AppData\Local\Programs\Microsoft VS Code\bin"
|
||||
|
||||
# Disable Visual Studio Code Auto Updates
|
||||
$vsCodeSettingsPath = "${env:APPDATA}\Code\User\settings.json"
|
||||
if (-not (Test-Path $vsCodeSettingsPath)) {
|
||||
# Create the directory if it doesn't exist
|
||||
$dirPath = Split-Path -Path $vsCodeSettingsPath -Parent
|
||||
if (-not (Test-Path $dirPath)) {
|
||||
New-Item -ItemType Directory -Path $dirPath -Force
|
||||
}
|
||||
# Initialize an empty hashtable to act as the JSON object
|
||||
$settingsObj = @{}
|
||||
$settingsObj["update.mode"] = "none" # Set update mode to none
|
||||
$settingsObj | ConvertTo-Json | Set-Content $vsCodeSettingsPath
|
||||
} else {
|
||||
# If the file exists, modify it
|
||||
$settingsObj = Get-Content $vsCodeSettingsPath | ConvertFrom-Json
|
||||
$settingsObj["update.mode"] = "none"
|
||||
$settingsObj | ConvertTo-Json | Set-Content $vsCodeSettingsPath
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
# - Thunderbird
|
||||
$thunderbirdToolName = "Thunderbird"
|
||||
$thunderbirdToolDetails = Get-ToolDetails -toolsList $toolsList -toolName $thunderbirdToolName
|
||||
$thunderbirdAlias = $thunderbirdToolDetails.alias
|
||||
$thunderbirdExecutablePath = "C:\Program Files\Mozilla Thunderbird\thunderbird.exe"
|
||||
|
||||
# Check if Thunderbird is already installed by checking the Thunderbird executable path
|
||||
if (Test-Path $thunderbirdExecutablePath) {
|
||||
Write-Host "Thunderbird is already installed."
|
||||
} else {
|
||||
# Download the installer to the Temp directory
|
||||
$thunderbirdInstallerFilePath = "$env:TEMP\ThunderbirdSetup.exe"
|
||||
$downloadResult = Invoke-DownloadFileFromAvailableMirrors -mirrorUrls $thunderbirdToolDetails.mirrors -outfile $thunderbirdInstallerFilePath
|
||||
if (-not $downloadResult) {
|
||||
Write-Host "Failed to download Thunderbird. Please try again later or install manually."
|
||||
} else {
|
||||
# Execute the installer silently with elevated permissions
|
||||
Start-Process -FilePath $thunderbirdInstallerFilePath -ArgumentList "/S" -Verb RunAs -Wait
|
||||
|
||||
# Remove the installer file after installation
|
||||
Remove-Item -Path $thunderbirdInstallerFilePath
|
||||
|
||||
# Set alias
|
||||
$setAliasExpression = "Set-Alias -Name $thunderbirdAlias -Value `"$thunderbirdExecutablePath`""
|
||||
Add-Content -Path $PROFILE -Value $setAliasExpression
|
||||
Invoke-Expression $setAliasExpression
|
||||
|
||||
# Add Thunderbird to the system PATH environment variable
|
||||
Add-ToEnvPath -NewPath "C:\Program Files\Mozilla Thunderbird"
|
||||
}
|
||||
}
|
||||
|
||||
# - Server Setup
|
||||
|
||||
$pythonServerPort = 5000
|
||||
$onLogonTaskName = "Server_OnLogon"
|
||||
$requirementsFile = "$scriptFolder\server\requirements.txt"
|
||||
|
||||
# Ensure pip is updated to the latest version
|
||||
Install-PythonPackages -Package "pip" -Arguments "--upgrade"
|
||||
|
||||
Install-PythonPackages -Package "wheel"
|
||||
Install-PythonPackages -Package "pywinauto"
|
||||
|
||||
# Install Python packages from requirements.txt using Python's pip module
|
||||
if (Test-Path $requirementsFile) {
|
||||
Write-Host "Installing required Python packages using pip from requirements file..."
|
||||
Install-PythonPackages -RequirementsPath $requirementsFile
|
||||
} else {
|
||||
Write-Error "Requirements file not found: $requirementsFile"
|
||||
exit
|
||||
}
|
||||
|
||||
# Add a firewall rule to allow incoming connections on the specified port for the Python executable
|
||||
$pythonServerRuleName = "PythonHTTPServer-$pythonServerPort"
|
||||
if (-not (Get-NetFirewallRule -Name $pythonServerRuleName -ErrorAction SilentlyContinue)) {
|
||||
New-NetFirewallRule -DisplayName $pythonServerRuleName -Direction Inbound -Program $pythonExecutablePath -Protocol TCP -LocalPort $pythonServerPort -Action Allow -Profile Any
|
||||
Write-Host "Firewall rule added to allow traffic on port $pythonServerPort for Python"
|
||||
} else {
|
||||
Write-Host "Firewall rule already exists. $pythonServerRuleName "
|
||||
}
|
||||
|
||||
$onLogonScriptPath = "$scriptFolder\on-logon.ps1"
|
||||
# Check if the scheduled task exists before unregistering it
|
||||
if (Get-ScheduledTask -TaskName $onLogonTaskName -ErrorAction SilentlyContinue) {
|
||||
Write-Host "Scheduled task $onLogonTaskName already exists."
|
||||
} else {
|
||||
Write-Host "Registering new task $onLogonTaskName..."
|
||||
Register-LogonTask -TaskName $onLogonTaskName -ScriptPath $onLogonScriptPath -LocalUser "Docker"
|
||||
}
|
||||
|
||||
Start-Sleep -Seconds 10
|
||||
Start-ScheduledTask -TaskName $onLogonTaskName
|
||||
@@ -0,0 +1,71 @@
|
||||
{
|
||||
"Python": {
|
||||
"mirrors": [
|
||||
"https://www.python.org/ftp/python/3.10.0/python-3.10.0-amd64.exe"
|
||||
],
|
||||
"alias": "python"
|
||||
},
|
||||
"git": {
|
||||
"mirrors": [
|
||||
"https://github.com/git-for-windows/git/releases/download/v2.37.1.windows.1/Git-2.37.1-64-bit.exe"
|
||||
]
|
||||
},
|
||||
"7zip": {
|
||||
"mirrors": [
|
||||
"https://www.7-zip.org/a/7z2407-x64.exe"
|
||||
]
|
||||
},
|
||||
"ffmpeg": {
|
||||
"mirrors": [
|
||||
"https://www.gyan.dev/ffmpeg/builds/ffmpeg-release-essentials.7z"
|
||||
]
|
||||
},
|
||||
"Google Chrome": {
|
||||
"mirrors": [
|
||||
"https://dl.google.com/chrome/install/latest/chrome_installer.exe"
|
||||
],
|
||||
"alias": "google-chrome"
|
||||
},
|
||||
"LibreOffice": {
|
||||
"mirrors": [
|
||||
"https://mirror.raiolanetworks.com/tdf/libreoffice/stable/24.8.4/win/x86_64/LibreOffice_24.8.4_Win_x86-64.msi",
|
||||
"https://mirrors.iu13.net/tdf/libreoffice/stable/24.8.4/win/x86_64/LibreOffice_24.8.4_Win_x86-64.msi",
|
||||
"https://download.documentfoundation.org/libreoffice/stable/24.8.4/win/x86_64/LibreOffice_24.8.4_Win_x86-64.msi"
|
||||
]
|
||||
},
|
||||
"VLC": {
|
||||
"mirrors": [
|
||||
"https://ftp.free.org/mirrors/videolan/vlc/3.0.21/win64/vlc-3.0.21-win64.exe",
|
||||
"https://mirror.fcix.net/videolan-ftp/vlc/3.0.21/win64/vlc-3.0.21-win64.exe",
|
||||
"https://mirror.raiolanetworks.com/videolan/vlc/3.0.21/win64/vlc-3.0.21-win64.exe"
|
||||
],
|
||||
"alias": "vlc"
|
||||
},
|
||||
"GIMP": {
|
||||
"mirrors": [
|
||||
"https://www-ftp.lip6.fr/pub/gimp/gimp/v2.10/windows/gimp-2.10.38-setup.exe",
|
||||
"https://download.gimp.org/gimp/v2.10/windows/gimp-2.10.38-setup.exe",
|
||||
"https://www-ftp.lip6.fr/pub/gimp/gimp/v2.10/windows/gimp-2.10.0-setup.exe"
|
||||
],
|
||||
"alias": "gimp"
|
||||
},
|
||||
"VS Code": {
|
||||
"mirrors": [
|
||||
"https://update.code.visualstudio.com/latest/win32-x64-user/stable"
|
||||
],
|
||||
"alias": "code"
|
||||
},
|
||||
"Thunderbird": {
|
||||
"mirrors": [
|
||||
"https://download-installer.cdn.mozilla.net/pub/thunderbird/releases/115.12.1/win64/en-US/Thunderbird%20Setup%20115.12.1.exe",
|
||||
"https://archive.mozilla.org/pub/thunderbird/releases/115.12.1/win64/en-US/Thunderbird%20Setup%20115.12.1.exe"
|
||||
],
|
||||
"alias": "thunderbird"
|
||||
},
|
||||
"Caddy Proxy": {
|
||||
"mirrors": [
|
||||
"https://caddyserver.com/api/download?os=windows&arch=amd64"
|
||||
],
|
||||
"alias": "caddy"
|
||||
}
|
||||
}
|
||||
51
omnitool/omniparserserver/omniparserserver.py
Normal file
51
omnitool/omniparserserver/omniparserserver.py
Normal file
@@ -0,0 +1,51 @@
|
||||
'''
|
||||
python -m omniparserserver --som_model_path ../../weights/icon_detect/model.pt --caption_model_name florence2 --caption_model_path ../../weights/icon_caption_florence --device cuda --BOX_TRESHOLD 0.05
|
||||
'''
|
||||
|
||||
import sys
|
||||
import os
|
||||
import time
|
||||
from fastapi import FastAPI
|
||||
from pydantic import BaseModel
|
||||
import argparse
|
||||
import uvicorn
|
||||
root_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
||||
sys.path.append(root_dir)
|
||||
from util.omniparser import Omniparser
|
||||
|
||||
def parse_arguments():
|
||||
parser = argparse.ArgumentParser(description='Omniparser API')
|
||||
parser.add_argument('--som_model_path', type=str, default='../../weights/icon_detect/model.pt', help='Path to the som model')
|
||||
parser.add_argument('--caption_model_name', type=str, default='florence2', help='Name of the caption model')
|
||||
parser.add_argument('--caption_model_path', type=str, default='../../weights/icon_caption_florence', help='Path to the caption model')
|
||||
parser.add_argument('--device', type=str, default='cpu', help='Device to run the model')
|
||||
parser.add_argument('--BOX_TRESHOLD', type=float, default=0.05, help='Threshold for box detection')
|
||||
parser.add_argument('--host', type=str, default='0.0.0.0', help='Host for the API')
|
||||
parser.add_argument('--port', type=int, default=8000, help='Port for the API')
|
||||
args = parser.parse_args()
|
||||
return args
|
||||
|
||||
args = parse_arguments()
|
||||
config = vars(args)
|
||||
|
||||
app = FastAPI()
|
||||
omniparser = Omniparser(config)
|
||||
|
||||
class ParseRequest(BaseModel):
|
||||
base64_image: str
|
||||
|
||||
@app.post("/parse/")
|
||||
async def parse(parse_request: ParseRequest):
|
||||
print('start parsing...')
|
||||
start = time.time()
|
||||
dino_labled_img, parsed_content_list = omniparser.parse(parse_request.base64_image)
|
||||
latency = time.time() - start
|
||||
print('time:', latency)
|
||||
return {"som_image_base64": dino_labled_img, "parsed_content_list": parsed_content_list, 'latency': latency}
|
||||
|
||||
@app.get("/probe/")
|
||||
async def root():
|
||||
return {"message": "Omniparser API ready"}
|
||||
|
||||
if __name__ == "__main__":
|
||||
uvicorn.run("omniparserserver:app", host=args.host, port=args.port, reload=True)
|
||||
98
omnitool/readme.md
Normal file
98
omnitool/readme.md
Normal file
@@ -0,0 +1,98 @@
|
||||
<img src="../imgs/header_bar.png" alt="OmniTool Header" width="100%">
|
||||
|
||||
# OmniTool
|
||||
|
||||
Control a Windows 11 VM with OmniParser + your vision model of choice.
|
||||
|
||||
## Highlights:
|
||||
|
||||
1. **OmniParser V2** is 60% faster than V1 and now understands a wide variety of OS, app and inside app icons!
|
||||
2. **OmniBox** uses 50% less disk space than other Windows VMs for agent testing, whilst providing the same computer use API
|
||||
3. **OmniTool** supports out of the box the following vision models - OpenAI (4o/o1/o3-mini), DeepSeek (R1), Qwen (2.5VL) or Anthropic Computer Use
|
||||
|
||||
## Overview
|
||||
|
||||
There are three components:
|
||||
|
||||
<table style="border-collapse: collapse; border: none;">
|
||||
<tr>
|
||||
<td style="border: none;"><img src="../imgs/omniparsericon.png" width="50"></td>
|
||||
<td style="border: none;"><strong>omniparserserver</strong></td>
|
||||
<td style="border: none;">FastAPI server running OmniParser V2.</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td style="border: none;"><img src="../imgs/omniboxicon.png" width="50"></td>
|
||||
<td style="border: none;"><strong>omnibox</strong></td>
|
||||
<td style="border: none;">A Windows 11 VM running in a Docker container.</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td style="border: none;"><img src="../imgs/gradioicon.png" width="50"></td>
|
||||
<td style="border: none;"><strong>gradio</strong></td>
|
||||
<td style="border: none;">UI to provide commands and watch reasoning + execution on OmniBox</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
## Notes:
|
||||
|
||||
1. Though **OmniParser V2** can run on a CPU, we have separated this out if you want to run it fast on a GPU machine
|
||||
2. The **OmniBox** Windows 11 VM docker is dependent on KVM so can only run quickly on Windows and Linux. This can run on a CPU machine (doesn't need GPU).
|
||||
3. The Gradio UI can also run on a CPU machine. We suggest running **omnibox** and **gradio** on the same CPU machine and **omniparserserver** on a GPU server.
|
||||
|
||||
## Setup
|
||||
|
||||
1. **omniparserserver**:
|
||||
|
||||
a. If you already have a conda environment for OmniParser, you can use that. Else follow the following steps to create one
|
||||
|
||||
b. Ensure conda is installed with `conda --version` or install from the [Anaconda website](https://www.anaconda.com/download/success)
|
||||
|
||||
c. Navigate to the root of the repo with `cd OmniParser`
|
||||
|
||||
d. Create a conda python environment with `conda create -n "omni" python==3.12`
|
||||
|
||||
e. Set the python environment to be used with `conda activate omni`
|
||||
|
||||
f. Install the dependencies with `pip install -r requirements.txt`
|
||||
|
||||
g. Continue from here if you already had the conda environment.
|
||||
|
||||
h. Ensure you have the V2 weights downloaded in weights folder (**ensure caption weights folder is called icon_caption_florence**). If not download them with:
|
||||
```
|
||||
rm -rf weights/icon_detect weights/icon_caption weights/icon_caption_florence
|
||||
for f in icon_detect/{train_args.yaml,model.pt,model.yaml} icon_caption/{config.json,generation_config.json,model.safetensors}; do huggingface-cli download microsoft/OmniParser-v2.0 "$f" --local-dir weights; done
|
||||
mv weights/icon_caption weights/icon_caption_florence
|
||||
```
|
||||
|
||||
h. Navigate to the server directory with `cd OmniParser/omnitool/omniparserserver`
|
||||
|
||||
i. Start the server with `python -m omniparserserver`
|
||||
|
||||
2. **omnibox**:
|
||||
|
||||
a. Install Docker Desktop
|
||||
|
||||
b. Visit [Microsoft Evaluation Center](https://info.microsoft.com/ww-landing-windows-11-enterprise.html), accept the Terms of Service, and download a **Windows 11 Enterprise Evaluation (90-day trial, English, United States)** ISO file [~6GB]. Rename the file to `custom.iso` and copy it to the directory `OmniParser/omnitool/omnibox/vm/win11iso`
|
||||
|
||||
c. Navigate to vm management script directory with`cd OmniParser/omnitool/omnibox/scripts`
|
||||
|
||||
d. Build the docker container [400MB] and install the ISO to a storage folder [20GB] with `./manage_vm.sh create`
|
||||
|
||||
e. After creating the first time it will store a save of the VM state in `vm/win11storage`. You can then manage the VM with `./manage_vm.sh start` and `./manage_vm.sh stop`. To delete the VM, use `./manage_vm.sh delete` and delete the `OmniParser/omnitool/omnibox/vm/win11storage` directory.
|
||||
|
||||
3. **gradio**:
|
||||
|
||||
a. Navigate to the gradio directory with `cd OmniParser/omnitool/gradio`
|
||||
|
||||
b. Ensure you have activated the conda python environment with `conda activate omni`
|
||||
|
||||
c. Start the server with `python app.py --windows_host_url localhost:8006 --omniparser_server_url localhost:8000`
|
||||
|
||||
d. Open the URL in the terminal output, set your API Key and start playing with the AI agent!
|
||||
|
||||
## Risks and Mitigations
|
||||
To align with the Microsoft AI principles and Responsible AI practices, we conduct risk mitigation by training the icon caption model with Responsible AI data, which helps the model avoid inferring sensitive attributes (e.g.race, religion etc.) of the individuals which happen to be in icon images as much as possible. At the same time, we encourage user to apply OmniParser only for screenshot that does not contain harmful/violent content. For the OmniTool, we conduct threat model analysis using Microsoft Threat Modeling Tool. We advise human to stay in the loop in order to minimize risk.
|
||||
|
||||
|
||||
## Acknowledgment
|
||||
Kudos to the amazing resources that are invaluable in the development of our code: [Claude Computer Use](https://github.com/anthropics/anthropic-quickstarts/blob/main/computer-use-demo/README.md), [OS World](https://github.com/xlang-ai/OSWorld), [Windows Agent Arena](https://github.com/microsoft/WindowsAgentArena), and [computer_use_ootb](https://github.com/showlab/computer_use_ootb).
|
||||
We are grateful for helpful suggestions and feedbacks provided by Francesco Bonacci, Jianwei Yang, Dillon DuPont, Yue Wu, Anh Nguyen.
|
||||
@@ -4,7 +4,7 @@ torchvision
|
||||
supervision==0.18.0
|
||||
openai==1.3.5
|
||||
transformers
|
||||
ultralytics==8.1.24
|
||||
ultralytics==8.3.70
|
||||
azure-identity
|
||||
numpy
|
||||
opencv-python
|
||||
@@ -16,3 +16,17 @@ timm
|
||||
einops==0.8.0
|
||||
paddlepaddle
|
||||
paddleocr
|
||||
ruff==0.6.7
|
||||
pre-commit==3.8.0
|
||||
pytest==8.3.3
|
||||
pytest-asyncio==0.23.6
|
||||
pyautogui==0.9.54
|
||||
streamlit>=1.38.0
|
||||
anthropic[bedrock,vertex]>=0.37.1
|
||||
jsonschema==4.22.0
|
||||
boto3>=1.28.57
|
||||
google-auth<3,>=2
|
||||
screeninfo
|
||||
uiautomation
|
||||
dashscope
|
||||
groq
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
@@ -1,425 +0,0 @@
|
||||
'''
|
||||
Adapted from https://github.com/google-research/google-research/tree/master/android_in_the_wild
|
||||
'''
|
||||
|
||||
import jax
|
||||
import jax.numpy as jnp
|
||||
import numpy as np
|
||||
|
||||
# import action_type as action_type_lib
|
||||
import enum
|
||||
|
||||
class ActionType(enum.IntEnum):
|
||||
# Placeholders for unused enum values
|
||||
UNUSED_0 = 0
|
||||
UNUSED_1 = 1
|
||||
UNUSED_2 = 2
|
||||
UNUSED_8 = 8
|
||||
UNUSED_9 = 9
|
||||
|
||||
########### Agent actions ###########
|
||||
|
||||
# A type action that sends text to the emulator. Note that this simply sends
|
||||
# text and does not perform any clicks for element focus or enter presses for
|
||||
# submitting text.
|
||||
TYPE = 3
|
||||
|
||||
# The dual point action used to represent all gestures.
|
||||
DUAL_POINT = 4
|
||||
|
||||
# These actions differentiate pressing the home and back button from touches.
|
||||
# They represent explicit presses of back and home performed using ADB.
|
||||
PRESS_BACK = 5
|
||||
PRESS_HOME = 6
|
||||
|
||||
# An action representing that ADB command for hitting enter was performed.
|
||||
PRESS_ENTER = 7
|
||||
|
||||
########### Episode status actions ###########
|
||||
|
||||
# An action used to indicate the desired task has been completed and resets
|
||||
# the environment. This action should also be used in the case that the task
|
||||
# has already been completed and there is nothing to do.
|
||||
# e.g. The task is to turn on the Wi-Fi when it is already on
|
||||
STATUS_TASK_COMPLETE = 10
|
||||
|
||||
# An action used to indicate that desired task is impossible to complete and
|
||||
# resets the environment. This can be a result of many different things
|
||||
# including UI changes, Android version differences, etc.
|
||||
STATUS_TASK_IMPOSSIBLE = 11
|
||||
|
||||
|
||||
_TAP_DISTANCE_THRESHOLD = 0.14 # Fraction of the screen
|
||||
ANNOTATION_WIDTH_AUGMENT_FRACTION = 1.4
|
||||
ANNOTATION_HEIGHT_AUGMENT_FRACTION = 1.4
|
||||
|
||||
# Interval determining if an action is a tap or a swipe.
|
||||
_SWIPE_DISTANCE_THRESHOLD = 0.04
|
||||
|
||||
|
||||
def _yx_in_bounding_boxes(
|
||||
yx, bounding_boxes
|
||||
):
|
||||
"""Check if the (y,x) point is contained in each bounding box.
|
||||
|
||||
Args:
|
||||
yx: The (y, x) coordinate in pixels of the point.
|
||||
bounding_boxes: A 2D int array of shape (num_bboxes, 4), where each row
|
||||
represents a bounding box: (y_top_left, x_top_left, box_height,
|
||||
box_width). Note: containment is inclusive of the bounding box edges.
|
||||
|
||||
Returns:
|
||||
is_inside: A 1D bool array where each element specifies if the point is
|
||||
contained within the respective box.
|
||||
"""
|
||||
y, x = yx
|
||||
|
||||
# `bounding_boxes` has shape (n_elements, 4); we extract each array along the
|
||||
# last axis into shape (n_elements, 1), then squeeze unneeded dimension.
|
||||
top, left, height, width = [
|
||||
jnp.squeeze(v, axis=-1) for v in jnp.split(bounding_boxes, 4, axis=-1)
|
||||
]
|
||||
|
||||
# The y-axis is inverted for AndroidEnv, so bottom = top + height.
|
||||
bottom, right = top + height, left + width
|
||||
|
||||
return jnp.logical_and(y >= top, y <= bottom) & jnp.logical_and(
|
||||
x >= left, x <= right)
|
||||
|
||||
|
||||
def _resize_annotation_bounding_boxes(
|
||||
annotation_positions, annotation_width_augment_fraction,
|
||||
annotation_height_augment_fraction):
|
||||
"""Resize the bounding boxes by the given fractions.
|
||||
|
||||
Args:
|
||||
annotation_positions: Array of shape (N, 4), where each row represents the
|
||||
(y, x, height, width) of the bounding boxes.
|
||||
annotation_width_augment_fraction: The fraction to augment the box widths,
|
||||
E.g., 1.4 == 240% total increase.
|
||||
annotation_height_augment_fraction: Same as described for width, but for box
|
||||
height.
|
||||
|
||||
Returns:
|
||||
Resized bounding box.
|
||||
|
||||
"""
|
||||
height_change = (
|
||||
annotation_height_augment_fraction * annotation_positions[:, 2])
|
||||
width_change = (
|
||||
annotation_width_augment_fraction * annotation_positions[:, 3])
|
||||
|
||||
# Limit bounding box positions to the screen.
|
||||
resized_annotations = jnp.stack([
|
||||
jnp.maximum(0, annotation_positions[:, 0] - (height_change / 2)),
|
||||
jnp.maximum(0, annotation_positions[:, 1] - (width_change / 2)),
|
||||
jnp.minimum(1, annotation_positions[:, 2] + height_change),
|
||||
jnp.minimum(1, annotation_positions[:, 3] + width_change),
|
||||
],
|
||||
axis=1)
|
||||
return resized_annotations
|
||||
|
||||
|
||||
def is_tap_action(normalized_start_yx,
|
||||
normalized_end_yx):
|
||||
distance = jnp.linalg.norm(
|
||||
jnp.array(normalized_start_yx) - jnp.array(normalized_end_yx))
|
||||
return distance <= _SWIPE_DISTANCE_THRESHOLD
|
||||
|
||||
|
||||
def _is_non_dual_point_action(action_type):
|
||||
return jnp.not_equal(action_type, ActionType.DUAL_POINT)
|
||||
|
||||
|
||||
def _check_tap_actions_match(
|
||||
tap_1_yx,
|
||||
tap_2_yx,
|
||||
annotation_positions,
|
||||
matching_tap_distance_threshold_screen_percentage,
|
||||
annotation_width_augment_fraction,
|
||||
annotation_height_augment_fraction,
|
||||
):
|
||||
"""Determines if two tap actions are the same."""
|
||||
resized_annotation_positions = _resize_annotation_bounding_boxes(
|
||||
annotation_positions,
|
||||
annotation_width_augment_fraction,
|
||||
annotation_height_augment_fraction,
|
||||
)
|
||||
|
||||
# Check if the ground truth tap action falls in an annotation's bounding box.
|
||||
tap1_in_box = _yx_in_bounding_boxes(tap_1_yx, resized_annotation_positions)
|
||||
tap2_in_box = _yx_in_bounding_boxes(tap_2_yx, resized_annotation_positions)
|
||||
both_in_box = jnp.max(tap1_in_box & tap2_in_box)
|
||||
|
||||
# If the ground-truth tap action falls outside any of the annotation
|
||||
# bounding boxes or one of the actions is inside a bounding box and the other
|
||||
# is outside bounding box or vice versa, compare the points using Euclidean
|
||||
# distance.
|
||||
within_threshold = (
|
||||
jnp.linalg.norm(jnp.array(tap_1_yx) - jnp.array(tap_2_yx))
|
||||
<= matching_tap_distance_threshold_screen_percentage
|
||||
)
|
||||
return jnp.logical_or(both_in_box, within_threshold)
|
||||
|
||||
|
||||
def _check_drag_actions_match(
|
||||
drag_1_touch_yx,
|
||||
drag_1_lift_yx,
|
||||
drag_2_touch_yx,
|
||||
drag_2_lift_yx,
|
||||
):
|
||||
"""Determines if two drag actions are the same."""
|
||||
# Store drag deltas (the change in the y and x coordinates from touch to
|
||||
# lift), magnitudes, and the index of the main axis, which is the axis with
|
||||
# the greatest change in coordinate value (e.g. a drag starting at (0, 0) and
|
||||
# ending at (0.3, 0.5) has a main axis index of 1).
|
||||
drag_1_deltas = drag_1_lift_yx - drag_1_touch_yx
|
||||
drag_1_magnitudes = jnp.abs(drag_1_deltas)
|
||||
drag_1_main_axis = np.argmax(drag_1_magnitudes)
|
||||
drag_2_deltas = drag_2_lift_yx - drag_2_touch_yx
|
||||
drag_2_magnitudes = jnp.abs(drag_2_deltas)
|
||||
drag_2_main_axis = np.argmax(drag_2_magnitudes)
|
||||
|
||||
return jnp.equal(drag_1_main_axis, drag_2_main_axis)
|
||||
|
||||
|
||||
def check_actions_match(
|
||||
action_1_touch_yx,
|
||||
action_1_lift_yx,
|
||||
action_1_action_type,
|
||||
action_2_touch_yx,
|
||||
action_2_lift_yx,
|
||||
action_2_action_type,
|
||||
annotation_positions,
|
||||
tap_distance_threshold = _TAP_DISTANCE_THRESHOLD,
|
||||
annotation_width_augment_fraction = ANNOTATION_WIDTH_AUGMENT_FRACTION,
|
||||
annotation_height_augment_fraction = ANNOTATION_HEIGHT_AUGMENT_FRACTION,
|
||||
):
|
||||
"""Determines if two actions are considered to be the same.
|
||||
|
||||
Two actions being "the same" is defined here as two actions that would result
|
||||
in a similar screen state.
|
||||
|
||||
Args:
|
||||
action_1_touch_yx: The (y, x) coordinates of the first action's touch.
|
||||
action_1_lift_yx: The (y, x) coordinates of the first action's lift.
|
||||
action_1_action_type: The action type of the first action.
|
||||
action_2_touch_yx: The (y, x) coordinates of the second action's touch.
|
||||
action_2_lift_yx: The (y, x) coordinates of the second action's lift.
|
||||
action_2_action_type: The action type of the second action.
|
||||
annotation_positions: The positions of the UI annotations for the screen. It
|
||||
is A 2D int array of shape (num_bboxes, 4), where each row represents a
|
||||
bounding box: (y_top_left, x_top_left, box_height, box_width). Note that
|
||||
containment is inclusive of the bounding box edges.
|
||||
tap_distance_threshold: The threshold that determines if two taps result in
|
||||
a matching screen state if they don't fall the same bounding boxes.
|
||||
annotation_width_augment_fraction: The fraction to increase the width of the
|
||||
bounding box by.
|
||||
annotation_height_augment_fraction: The fraction to increase the height of
|
||||
of the bounding box by.
|
||||
|
||||
Returns:
|
||||
A boolean representing whether the two given actions are the same or not.
|
||||
"""
|
||||
action_1_touch_yx = jnp.asarray(action_1_touch_yx)
|
||||
action_1_lift_yx = jnp.asarray(action_1_lift_yx)
|
||||
action_2_touch_yx = jnp.asarray(action_2_touch_yx)
|
||||
action_2_lift_yx = jnp.asarray(action_2_lift_yx)
|
||||
|
||||
# Checks if at least one of the actions is global (i.e. not DUAL_POINT),
|
||||
# because if that is the case, only the actions' types need to be compared.
|
||||
has_non_dual_point_action = jnp.logical_or(
|
||||
_is_non_dual_point_action(action_1_action_type),
|
||||
_is_non_dual_point_action(action_2_action_type),
|
||||
)
|
||||
#print("non dual point: "+str(has_non_dual_point_action))
|
||||
|
||||
different_dual_point_types = jnp.logical_xor(
|
||||
is_tap_action(action_1_touch_yx, action_1_lift_yx),
|
||||
is_tap_action(action_2_touch_yx, action_2_lift_yx),
|
||||
)
|
||||
#print("different dual type: "+str(different_dual_point_types))
|
||||
|
||||
is_tap = jnp.logical_and(
|
||||
is_tap_action(action_1_touch_yx, action_1_lift_yx),
|
||||
is_tap_action(action_2_touch_yx, action_2_lift_yx),
|
||||
)
|
||||
#print("is tap: "+str(is_tap))
|
||||
|
||||
taps_match = _check_tap_actions_match(
|
||||
action_1_touch_yx,
|
||||
action_2_touch_yx,
|
||||
annotation_positions,
|
||||
tap_distance_threshold,
|
||||
annotation_width_augment_fraction,
|
||||
annotation_height_augment_fraction,
|
||||
)
|
||||
#print("tap match: "+str(taps_match))
|
||||
|
||||
taps_match = jnp.logical_and(is_tap, taps_match)
|
||||
#print("tap match: "+str(taps_match))
|
||||
|
||||
drags_match = _check_drag_actions_match(
|
||||
action_1_touch_yx, action_1_lift_yx, action_2_touch_yx, action_2_lift_yx
|
||||
)
|
||||
drags_match = jnp.where(is_tap, False, drags_match)
|
||||
#print("drag match: "+str(drags_match))
|
||||
|
||||
return jnp.where(
|
||||
has_non_dual_point_action,
|
||||
jnp.equal(action_1_action_type, action_2_action_type),
|
||||
jnp.where(
|
||||
different_dual_point_types,
|
||||
False,
|
||||
jnp.logical_or(taps_match, drags_match),
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
def action_2_format(step_data):
|
||||
# 把test数据集中的动作格式转换为计算matching score的格式
|
||||
action_type = step_data["action_type_id"]
|
||||
|
||||
if action_type == 4:
|
||||
if step_data["action_type_text"] == 'click': # 点击
|
||||
touch_point = step_data["touch"]
|
||||
lift_point = step_data["lift"]
|
||||
else: # 上下左右滑动
|
||||
if step_data["action_type_text"] == 'scroll down':
|
||||
touch_point = [0.5, 0.8]
|
||||
lift_point = [0.5, 0.2]
|
||||
elif step_data["action_type_text"] == 'scroll up':
|
||||
touch_point = [0.5, 0.2]
|
||||
lift_point = [0.5, 0.8]
|
||||
elif step_data["action_type_text"] == 'scroll left':
|
||||
touch_point = [0.2, 0.5]
|
||||
lift_point = [0.8, 0.5]
|
||||
elif step_data["action_type_text"] == 'scroll right':
|
||||
touch_point = [0.8, 0.5]
|
||||
lift_point = [0.2, 0.5]
|
||||
else:
|
||||
touch_point = [-1.0, -1.0]
|
||||
lift_point = [-1.0, -1.0]
|
||||
|
||||
if action_type == 3:
|
||||
typed_text = step_data["type_text"]
|
||||
else:
|
||||
typed_text = ""
|
||||
|
||||
action = {"action_type": action_type, "touch_point": touch_point, "lift_point": lift_point,
|
||||
"typed_text": typed_text}
|
||||
|
||||
action["touch_point"] = [action["touch_point"][1], action["touch_point"][0]]
|
||||
action["lift_point"] = [action["lift_point"][1], action["lift_point"][0]]
|
||||
action["typed_text"] = action["typed_text"].lower()
|
||||
|
||||
return action
|
||||
|
||||
|
||||
def pred_2_format(step_data):
|
||||
# 把模型输出的内容转换为计算action_matching的格式
|
||||
action_type = step_data["action_type"]
|
||||
|
||||
if action_type == 4: # 点击
|
||||
action_type_new = 4
|
||||
touch_point = step_data["click_point"]
|
||||
lift_point = step_data["click_point"]
|
||||
typed_text = ""
|
||||
elif action_type == 0:
|
||||
action_type_new = 4
|
||||
touch_point = [0.5, 0.8]
|
||||
lift_point = [0.5, 0.2]
|
||||
typed_text = ""
|
||||
elif action_type == 1:
|
||||
action_type_new = 4
|
||||
touch_point = [0.5, 0.2]
|
||||
lift_point = [0.5, 0.8]
|
||||
typed_text = ""
|
||||
elif action_type == 8:
|
||||
action_type_new = 4
|
||||
touch_point = [0.2, 0.5]
|
||||
lift_point = [0.8, 0.5]
|
||||
typed_text = ""
|
||||
elif action_type == 9:
|
||||
action_type_new = 4
|
||||
touch_point = [0.8, 0.5]
|
||||
lift_point = [0.2, 0.5]
|
||||
typed_text = ""
|
||||
else:
|
||||
action_type_new = action_type
|
||||
touch_point = [-1.0, -1.0]
|
||||
lift_point = [-1.0, -1.0]
|
||||
typed_text = ""
|
||||
if action_type_new == 3:
|
||||
typed_text = step_data["typed_text"]
|
||||
|
||||
action = {"action_type": action_type_new, "touch_point": touch_point, "lift_point": lift_point,
|
||||
"typed_text": typed_text}
|
||||
|
||||
action["touch_point"] = [action["touch_point"][1], action["touch_point"][0]]
|
||||
action["lift_point"] = [action["lift_point"][1], action["lift_point"][0]]
|
||||
action["typed_text"] = action["typed_text"].lower()
|
||||
|
||||
return action
|
||||
|
||||
|
||||
def pred_2_format_simplified(step_data):
|
||||
# 把模型输出的内容转换为计算action_matching的格式
|
||||
action_type = step_data["action_type"]
|
||||
|
||||
if action_type == 'click' : # 点击
|
||||
action_type_new = 4
|
||||
touch_point = step_data["click_point"]
|
||||
lift_point = step_data["click_point"]
|
||||
typed_text = ""
|
||||
elif action_type == 'scroll' and step_data["direction"] == 'down':
|
||||
action_type_new = 4
|
||||
touch_point = [0.5, 0.8]
|
||||
lift_point = [0.5, 0.2]
|
||||
typed_text = ""
|
||||
elif action_type == 'scroll' and step_data["direction"] == 'up':
|
||||
action_type_new = 4
|
||||
touch_point = [0.5, 0.2]
|
||||
lift_point = [0.5, 0.8]
|
||||
typed_text = ""
|
||||
elif action_type == 'scroll' and step_data["direction"] == 'left':
|
||||
action_type_new = 4
|
||||
touch_point = [0.2, 0.5]
|
||||
lift_point = [0.8, 0.5]
|
||||
typed_text = ""
|
||||
elif action_type == 'scroll' and step_data["direction"] == 'right':
|
||||
action_type_new = 4
|
||||
touch_point = [0.8, 0.5]
|
||||
lift_point = [0.2, 0.5]
|
||||
typed_text = ""
|
||||
elif action_type == 'type':
|
||||
action_type_new = 3
|
||||
touch_point = [-1.0, -1.0]
|
||||
lift_point = [-1.0, -1.0]
|
||||
typed_text = step_data["text"]
|
||||
elif action_type == 'navigate_back':
|
||||
action_type_new = 5
|
||||
touch_point = [-1.0, -1.0]
|
||||
lift_point = [-1.0, -1.0]
|
||||
typed_text = ""
|
||||
elif action_type == 'navigate_home':
|
||||
action_type_new = 6
|
||||
touch_point = [-1.0, -1.0]
|
||||
lift_point = [-1.0, -1.0]
|
||||
typed_text = ""
|
||||
else:
|
||||
action_type_new = action_type
|
||||
touch_point = [-1.0, -1.0]
|
||||
lift_point = [-1.0, -1.0]
|
||||
typed_text = ""
|
||||
# if action_type_new == 'type':
|
||||
# typed_text = step_data["text"]
|
||||
|
||||
action = {"action_type": action_type_new, "touch_point": touch_point, "lift_point": lift_point,
|
||||
"typed_text": typed_text}
|
||||
|
||||
action["touch_point"] = [action["touch_point"][1], action["touch_point"][0]]
|
||||
action["lift_point"] = [action["lift_point"][1], action["lift_point"][0]]
|
||||
action["typed_text"] = action["typed_text"].lower()
|
||||
|
||||
return action
|
||||
@@ -1,45 +0,0 @@
|
||||
'''
|
||||
Adapted from https://github.com/google-research/google-research/tree/master/android_in_the_wild
|
||||
'''
|
||||
|
||||
import enum
|
||||
|
||||
class ActionType(enum.IntEnum):
|
||||
|
||||
# Placeholders for unused enum values
|
||||
UNUSED_0 = 0
|
||||
UNUSED_1 = 1
|
||||
UNUSED_2 = 2
|
||||
UNUSED_8 = 8
|
||||
UNUSED_9 = 9
|
||||
|
||||
########### Agent actions ###########
|
||||
|
||||
# A type action that sends text to the emulator. Note that this simply sends
|
||||
# text and does not perform any clicks for element focus or enter presses for
|
||||
# submitting text.
|
||||
TYPE = 3
|
||||
|
||||
# The dual point action used to represent all gestures.
|
||||
DUAL_POINT = 4
|
||||
|
||||
# These actions differentiate pressing the home and back button from touches.
|
||||
# They represent explicit presses of back and home performed using ADB.
|
||||
PRESS_BACK = 5
|
||||
PRESS_HOME = 6
|
||||
|
||||
# An action representing that ADB command for hitting enter was performed.
|
||||
PRESS_ENTER = 7
|
||||
|
||||
########### Episode status actions ###########
|
||||
|
||||
# An action used to indicate the desired task has been completed and resets
|
||||
# the environment. This action should also be used in the case that the task
|
||||
# has already been completed and there is nothing to do.
|
||||
# e.g. The task is to turn on the Wi-Fi when it is already on
|
||||
STATUS_TASK_COMPLETE = 10
|
||||
|
||||
# An action used to indicate that desired task is impossible to complete and
|
||||
# resets the environment. This can be a result of many different things
|
||||
# including UI changes, Android version differences, etc.
|
||||
STATUS_TASK_IMPOSSIBLE = 11
|
||||
32
util/omniparser.py
Normal file
32
util/omniparser.py
Normal file
@@ -0,0 +1,32 @@
|
||||
from util.utils import get_som_labeled_img, get_caption_model_processor, get_yolo_model, check_ocr_box
|
||||
import torch
|
||||
from PIL import Image
|
||||
import io
|
||||
import base64
|
||||
from typing import Dict
|
||||
class Omniparser(object):
|
||||
def __init__(self, config: Dict):
|
||||
self.config = config
|
||||
device = 'cuda' if torch.cuda.is_available() else 'cpu'
|
||||
|
||||
self.som_model = get_yolo_model(model_path=config['som_model_path'])
|
||||
self.caption_model_processor = get_caption_model_processor(model_name=config['caption_model_name'], model_name_or_path=config['caption_model_path'], device=device)
|
||||
print('Omniparser initialized!!!')
|
||||
|
||||
def parse(self, image_base64: str):
|
||||
image_bytes = base64.b64decode(image_base64)
|
||||
image = Image.open(io.BytesIO(image_bytes))
|
||||
print('image size:', image.size)
|
||||
|
||||
box_overlay_ratio = max(image.size) / 3200
|
||||
draw_bbox_config = {
|
||||
'text_scale': 0.8 * box_overlay_ratio,
|
||||
'text_thickness': max(int(2 * box_overlay_ratio), 1),
|
||||
'text_padding': max(int(3 * box_overlay_ratio), 1),
|
||||
'thickness': max(int(3 * box_overlay_ratio), 1),
|
||||
}
|
||||
|
||||
(text, ocr_bbox), _ = check_ocr_box(image, display_img=False, output_bb_format='xyxy', easyocr_args={'text_threshold': 0.8}, use_paddleocr=False)
|
||||
dino_labled_img, label_coordinates, parsed_content_list = get_som_labeled_img(image, self.som_model, BOX_TRESHOLD = self.config['BOX_TRESHOLD'], output_coord_in_ratio=True, ocr_bbox=ocr_bbox,draw_bbox_config=draw_bbox_config, caption_model_processor=self.caption_model_processor, ocr_text=text,use_local_semantics=True, iou_threshold=0.7, scale_img=False, batch_size=128)
|
||||
|
||||
return dino_labled_img, parsed_content_list
|
||||
111
utils.py → util/utils.py
Executable file → Normal file
111
utils.py → util/utils.py
Executable file → Normal file
@@ -35,12 +35,13 @@ import base64
|
||||
import os
|
||||
import ast
|
||||
import torch
|
||||
from typing import Tuple, List
|
||||
from typing import Tuple, List, Union
|
||||
from torchvision.ops import box_convert
|
||||
import re
|
||||
from torchvision.transforms import ToPILImage
|
||||
import supervision as sv
|
||||
import torchvision.transforms as T
|
||||
from util.box_annotator import BoxAnnotator
|
||||
|
||||
|
||||
def get_caption_model_processor(model_name, model_name_or_path="Salesforce/blip2-opt-2.7b", device=None):
|
||||
@@ -75,9 +76,8 @@ def get_yolo_model(model_path):
|
||||
|
||||
|
||||
@torch.inference_mode()
|
||||
def get_parsed_content_icon(filtered_boxes, starting_idx, image_source, caption_model_processor, prompt=None, batch_size=32):
|
||||
def get_parsed_content_icon(filtered_boxes, starting_idx, image_source, caption_model_processor, prompt=None, batch_size=None):
|
||||
# Number of samples per batch, --> 256 roughly takes 23 GB of GPU memory for florence model
|
||||
|
||||
to_pil = ToPILImage()
|
||||
if starting_idx:
|
||||
non_ocr_boxes = filtered_boxes[starting_idx:]
|
||||
@@ -85,10 +85,14 @@ def get_parsed_content_icon(filtered_boxes, starting_idx, image_source, caption_
|
||||
non_ocr_boxes = filtered_boxes
|
||||
croped_pil_image = []
|
||||
for i, coord in enumerate(non_ocr_boxes):
|
||||
xmin, xmax = int(coord[0]*image_source.shape[1]), int(coord[2]*image_source.shape[1])
|
||||
ymin, ymax = int(coord[1]*image_source.shape[0]), int(coord[3]*image_source.shape[0])
|
||||
cropped_image = image_source[ymin:ymax, xmin:xmax, :]
|
||||
croped_pil_image.append(to_pil(cropped_image))
|
||||
try:
|
||||
xmin, xmax = int(coord[0]*image_source.shape[1]), int(coord[2]*image_source.shape[1])
|
||||
ymin, ymax = int(coord[1]*image_source.shape[0]), int(coord[3]*image_source.shape[0])
|
||||
cropped_image = image_source[ymin:ymax, xmin:xmax, :]
|
||||
cropped_image = cv2.resize(cropped_image, (64, 64))
|
||||
croped_pil_image.append(to_pil(cropped_image))
|
||||
except:
|
||||
continue
|
||||
|
||||
model, processor = caption_model_processor['model'], caption_model_processor['processor']
|
||||
if not prompt:
|
||||
@@ -99,15 +103,17 @@ def get_parsed_content_icon(filtered_boxes, starting_idx, image_source, caption_
|
||||
|
||||
generated_texts = []
|
||||
device = model.device
|
||||
# batch_size = 64
|
||||
for i in range(0, len(croped_pil_image), batch_size):
|
||||
start = time.time()
|
||||
batch = croped_pil_image[i:i+batch_size]
|
||||
t1 = time.time()
|
||||
if model.device.type == 'cuda':
|
||||
inputs = processor(images=batch, text=[prompt]*len(batch), return_tensors="pt").to(device=device, dtype=torch.float16)
|
||||
inputs = processor(images=batch, text=[prompt]*len(batch), return_tensors="pt", do_resize=False).to(device=device, dtype=torch.float16)
|
||||
else:
|
||||
inputs = processor(images=batch, text=[prompt]*len(batch), return_tensors="pt").to(device=device)
|
||||
if 'florence' in model.config.name_or_path:
|
||||
generated_ids = model.generate(input_ids=inputs["input_ids"],pixel_values=inputs["pixel_values"],max_new_tokens=100,num_beams=3, do_sample=False)
|
||||
generated_ids = model.generate(input_ids=inputs["input_ids"],pixel_values=inputs["pixel_values"],max_new_tokens=20,num_beams=1, do_sample=False)
|
||||
else:
|
||||
generated_ids = model.generate(**inputs, max_length=100, num_beams=5, no_repeat_ngram_size=2, early_stopping=True, num_return_sequences=1) # temperature=0.01, do_sample=True,
|
||||
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)
|
||||
@@ -272,10 +278,10 @@ def remove_overlap_new(boxes, iou_threshold, ocr_bbox=None):
|
||||
is_valid_box = False
|
||||
break
|
||||
if is_valid_box:
|
||||
# add the following 2 lines to include ocr bbox
|
||||
if ocr_bbox:
|
||||
# keep yolo boxes + prioritize ocr label
|
||||
box_added = False
|
||||
ocr_labels = ''
|
||||
for box3_elem in ocr_bbox:
|
||||
if not box_added:
|
||||
box3 = box3_elem['bbox']
|
||||
@@ -283,25 +289,22 @@ def remove_overlap_new(boxes, iou_threshold, ocr_bbox=None):
|
||||
# box_added = True
|
||||
# delete the box3_elem from ocr_bbox
|
||||
try:
|
||||
filtered_boxes.append({'type': 'text', 'bbox': box1_elem['bbox'], 'interactivity': True, 'content': box3_elem['content']})
|
||||
# gather all ocr labels
|
||||
ocr_labels += box3_elem['content'] + ' '
|
||||
filtered_boxes.remove(box3_elem)
|
||||
# print('remove ocr bbox:', box3_elem)
|
||||
except:
|
||||
continue
|
||||
# break
|
||||
elif is_inside(box1, box3): # icon inside ocr
|
||||
elif is_inside(box1, box3): # icon inside ocr, don't added this icon box, no need to check other ocr bbox bc no overlap between ocr bbox, icon can only be in one ocr box
|
||||
box_added = True
|
||||
# try:
|
||||
# filtered_boxes.append({'type': 'icon', 'bbox': box1_elem['bbox'], 'interactivity': True, 'content': None})
|
||||
# filtered_boxes.remove(box3_elem)
|
||||
# except:
|
||||
# continue
|
||||
break
|
||||
else:
|
||||
continue
|
||||
if not box_added:
|
||||
filtered_boxes.append({'type': 'icon', 'bbox': box1_elem['bbox'], 'interactivity': True, 'content': None})
|
||||
|
||||
if ocr_labels:
|
||||
filtered_boxes.append({'type': 'icon', 'bbox': box1_elem['bbox'], 'interactivity': True, 'content': ocr_labels, 'source':'box_yolo_content_ocr'})
|
||||
else:
|
||||
filtered_boxes.append({'type': 'icon', 'bbox': box1_elem['bbox'], 'interactivity': True, 'content': None, 'source':'box_yolo_content_yolo'})
|
||||
else:
|
||||
filtered_boxes.append(box1)
|
||||
return filtered_boxes # torch.tensor(filtered_boxes)
|
||||
@@ -344,7 +347,6 @@ def annotate(image_source: np.ndarray, boxes: torch.Tensor, logits: torch.Tensor
|
||||
|
||||
labels = [f"{phrase}" for phrase in range(boxes.shape[0])]
|
||||
|
||||
from util.box_annotator import BoxAnnotator
|
||||
box_annotator = BoxAnnotator(text_scale=text_scale, text_padding=text_padding,text_thickness=text_thickness,thickness=thickness) # 0.8 for mobile/web, 0.3 for desktop # 0.4 for mind2web
|
||||
annotated_frame = image_source.copy()
|
||||
annotated_frame = box_annotator.annotate(scene=annotated_frame, detections=detections, labels=labels, image_size=(w,h))
|
||||
@@ -374,20 +376,20 @@ def predict(model, image, caption, box_threshold, text_threshold):
|
||||
return boxes, logits, phrases
|
||||
|
||||
|
||||
def predict_yolo(model, image_path, box_threshold, imgsz, scale_img, iou_threshold=0.7):
|
||||
def predict_yolo(model, image, box_threshold, imgsz, scale_img, iou_threshold=0.7):
|
||||
""" Use huggingface model to replace the original model
|
||||
"""
|
||||
# model = model['model']
|
||||
if scale_img:
|
||||
result = model.predict(
|
||||
source=image_path,
|
||||
source=image,
|
||||
conf=box_threshold,
|
||||
imgsz=imgsz,
|
||||
iou=iou_threshold, # default 0.7
|
||||
)
|
||||
else:
|
||||
result = model.predict(
|
||||
source=image_path,
|
||||
source=image,
|
||||
conf=box_threshold,
|
||||
iou=iou_threshold, # default 0.7
|
||||
)
|
||||
@@ -397,34 +399,41 @@ def predict_yolo(model, image_path, box_threshold, imgsz, scale_img, iou_thresho
|
||||
|
||||
return boxes, conf, phrases
|
||||
|
||||
def int_box_area(box, w, h):
|
||||
x1, y1, x2, y2 = box
|
||||
int_box = [int(x1*w), int(y1*h), int(x2*w), int(y2*h)]
|
||||
area = (int_box[2] - int_box[0]) * (int_box[3] - int_box[1])
|
||||
return area
|
||||
|
||||
def get_som_labeled_img(img_path, model=None, BOX_TRESHOLD = 0.01, output_coord_in_ratio=False, ocr_bbox=None, text_scale=0.4, text_padding=5, draw_bbox_config=None, caption_model_processor=None, ocr_text=[], use_local_semantics=True, iou_threshold=0.9,prompt=None, scale_img=False, imgsz=None, batch_size=None):
|
||||
""" ocr_bbox: list of xyxy format bbox
|
||||
def get_som_labeled_img(image_source: Union[str, Image.Image], model=None, BOX_TRESHOLD=0.01, output_coord_in_ratio=False, ocr_bbox=None, text_scale=0.4, text_padding=5, draw_bbox_config=None, caption_model_processor=None, ocr_text=[], use_local_semantics=True, iou_threshold=0.9,prompt=None, scale_img=False, imgsz=None, batch_size=64):
|
||||
"""Process either an image path or Image object
|
||||
|
||||
Args:
|
||||
image_source: Either a file path (str) or PIL Image object
|
||||
...
|
||||
"""
|
||||
image_source = Image.open(img_path).convert("RGB")
|
||||
if isinstance(image_source, str):
|
||||
image_source = Image.open(image_source).convert("RGB")
|
||||
|
||||
w, h = image_source.size
|
||||
if not imgsz:
|
||||
imgsz = (h, w)
|
||||
# print('image size:', w, h)
|
||||
xyxy, logits, phrases = predict_yolo(model=model, image_path=img_path, box_threshold=BOX_TRESHOLD, imgsz=imgsz, scale_img=scale_img, iou_threshold=0.1)
|
||||
xyxy, logits, phrases = predict_yolo(model=model, image=image_source, box_threshold=BOX_TRESHOLD, imgsz=imgsz, scale_img=scale_img, iou_threshold=0.1)
|
||||
xyxy = xyxy / torch.Tensor([w, h, w, h]).to(xyxy.device)
|
||||
image_source = np.asarray(image_source)
|
||||
phrases = [str(i) for i in range(len(phrases))]
|
||||
|
||||
# annotate the image with labels
|
||||
h, w, _ = image_source.shape
|
||||
if ocr_bbox:
|
||||
ocr_bbox = torch.tensor(ocr_bbox) / torch.Tensor([w, h, w, h])
|
||||
ocr_bbox=ocr_bbox.tolist()
|
||||
else:
|
||||
print('no ocr bbox!!!')
|
||||
ocr_bbox = None
|
||||
# filtered_boxes = remove_overlap(boxes=xyxy, iou_threshold=iou_threshold, ocr_bbox=ocr_bbox)
|
||||
# starting_idx = len(ocr_bbox)
|
||||
# print('len(filtered_boxes):', len(filtered_boxes), starting_idx)
|
||||
|
||||
ocr_bbox_elem = [{'type': 'text', 'bbox':box, 'interactivity':False, 'content':txt} for box, txt in zip(ocr_bbox, ocr_text)]
|
||||
xyxy_elem = [{'type': 'icon', 'bbox':box, 'interactivity':True, 'content':None} for box in xyxy.tolist()]
|
||||
ocr_bbox_elem = [{'type': 'text', 'bbox':box, 'interactivity':False, 'content':txt, 'source': 'box_ocr_content_ocr'} for box, txt in zip(ocr_bbox, ocr_text) if int_box_area(box, w, h) > 0]
|
||||
xyxy_elem = [{'type': 'icon', 'bbox':box, 'interactivity':True, 'content':None} for box in xyxy.tolist() if int_box_area(box, w, h) > 0]
|
||||
filtered_boxes = remove_overlap_new(boxes=xyxy_elem, iou_threshold=iou_threshold, ocr_bbox=ocr_bbox_elem)
|
||||
|
||||
# sort the filtered_boxes so that the one with 'content': None is at the end, and get the index of the first 'content': None
|
||||
@@ -432,9 +441,10 @@ def get_som_labeled_img(img_path, model=None, BOX_TRESHOLD = 0.01, output_coord_
|
||||
# get the index of the first 'content': None
|
||||
starting_idx = next((i for i, box in enumerate(filtered_boxes_elem) if box['content'] is None), -1)
|
||||
filtered_boxes = torch.tensor([box['bbox'] for box in filtered_boxes_elem])
|
||||
|
||||
print('len(filtered_boxes):', len(filtered_boxes), starting_idx)
|
||||
|
||||
# get parsed icon local semantics
|
||||
time1 = time.time()
|
||||
if use_local_semantics:
|
||||
caption_model = caption_model_processor['model']
|
||||
if 'phi3_v' in caption_model.config.model_type:
|
||||
@@ -454,6 +464,7 @@ def get_som_labeled_img(img_path, model=None, BOX_TRESHOLD = 0.01, output_coord_
|
||||
else:
|
||||
ocr_text = [f"Text Box ID {i}: {txt}" for i, txt in enumerate(ocr_text)]
|
||||
parsed_content_merged = ocr_text
|
||||
print('time to get parsed content:', time.time()-time1)
|
||||
|
||||
filtered_boxes = box_convert(boxes=filtered_boxes, in_fmt="xyxy", out_fmt="cxcywh")
|
||||
|
||||
@@ -470,7 +481,6 @@ def get_som_labeled_img(img_path, model=None, BOX_TRESHOLD = 0.01, output_coord_
|
||||
pil_img.save(buffered, format="PNG")
|
||||
encoded_image = base64.b64encode(buffered.getvalue()).decode('ascii')
|
||||
if output_coord_in_ratio:
|
||||
# h, w, _ = image_source.shape
|
||||
label_coordinates = {k: [v[0]/w, v[1]/h, v[2]/w, v[3]/h] for k, v in label_coordinates.items()}
|
||||
assert w == annotated_frame.shape[1] and h == annotated_frame.shape[0]
|
||||
|
||||
@@ -492,45 +502,42 @@ def get_xywh_yolo(input):
|
||||
x, y, w, h = int(x), int(y), int(w), int(h)
|
||||
return x, y, w, h
|
||||
|
||||
|
||||
|
||||
def check_ocr_box(image_path, display_img = True, output_bb_format='xywh', goal_filtering=None, easyocr_args=None, use_paddleocr=False):
|
||||
def check_ocr_box(image_source: Union[str, Image.Image], display_img = True, output_bb_format='xywh', goal_filtering=None, easyocr_args=None, use_paddleocr=False):
|
||||
if isinstance(image_source, str):
|
||||
image_source = Image.open(image_source)
|
||||
if image_source.mode == 'RGBA':
|
||||
# Convert RGBA to RGB to avoid alpha channel issues
|
||||
image_source = image_source.convert('RGB')
|
||||
image_np = np.array(image_source)
|
||||
w, h = image_source.size
|
||||
if use_paddleocr:
|
||||
if easyocr_args is None:
|
||||
text_threshold = 0.5
|
||||
else:
|
||||
text_threshold = easyocr_args['text_threshold']
|
||||
result = paddle_ocr.ocr(image_path, cls=False)[0]
|
||||
conf = [item[1] for item in result]
|
||||
result = paddle_ocr.ocr(image_np, cls=False)[0]
|
||||
coord = [item[0] for item in result if item[1][1] > text_threshold]
|
||||
text = [item[1][0] for item in result if item[1][1] > text_threshold]
|
||||
else: # EasyOCR
|
||||
if easyocr_args is None:
|
||||
easyocr_args = {}
|
||||
result = reader.readtext(image_path, **easyocr_args)
|
||||
# print('goal filtering pred:', result[-5:])
|
||||
result = reader.readtext(image_np, **easyocr_args)
|
||||
coord = [item[0] for item in result]
|
||||
text = [item[1] for item in result]
|
||||
# read the image using cv2
|
||||
if display_img:
|
||||
opencv_img = cv2.imread(image_path)
|
||||
opencv_img = cv2.cvtColor(opencv_img, cv2.COLOR_RGB2BGR)
|
||||
opencv_img = cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR)
|
||||
bb = []
|
||||
for item in coord:
|
||||
x, y, a, b = get_xywh(item)
|
||||
# print(x, y, a, b)
|
||||
bb.append((x, y, a, b))
|
||||
cv2.rectangle(opencv_img, (x, y), (x+a, y+b), (0, 255, 0), 2)
|
||||
|
||||
# Display the image
|
||||
plt.imshow(opencv_img)
|
||||
# matplotlib expects RGB
|
||||
plt.imshow(cv2.cvtColor(opencv_img, cv2.COLOR_BGR2RGB))
|
||||
else:
|
||||
if output_bb_format == 'xywh':
|
||||
bb = [get_xywh(item) for item in coord]
|
||||
elif output_bb_format == 'xyxy':
|
||||
bb = [get_xyxy(item) for item in coord]
|
||||
# print('bounding box!!!', bb)
|
||||
return (text, bb), goal_filtering
|
||||
|
||||
|
||||
|
||||
@@ -1,23 +0,0 @@
|
||||
import torch
|
||||
from ultralytics.nn.tasks import DetectionModel
|
||||
from safetensors.torch import load_file
|
||||
import argparse
|
||||
import yaml
|
||||
import os
|
||||
|
||||
# accept args to specify v1
|
||||
parser = argparse.ArgumentParser(description='add weight directory')
|
||||
parser.add_argument('--weights_dir', type=str, required=True, help='Specify the path to the safetensor file', default='weights/icon_detect')
|
||||
args = parser.parse_args()
|
||||
|
||||
tensor_dict = load_file(os.path.join(args.weights_dir, "model.safetensors"))
|
||||
model = DetectionModel(os.path.join(args.weights_dir, "model.yaml"))
|
||||
|
||||
model.load_state_dict(tensor_dict)
|
||||
save_dict = {'model':model}
|
||||
|
||||
with open(os.path.join(args.weights_dir, "train_args.yaml"), 'r') as file:
|
||||
train_args = yaml.safe_load(file)
|
||||
save_dict.update(train_args)
|
||||
torch.save(save_dict, os.path.join(args.weights_dir, "best.pt"))
|
||||
|
||||
Reference in New Issue
Block a user