import base64, os import json import torch import gradio as gr import argparse # 新增：导入argparse from typing import Optional from PIL import Image, ImageDraw import numpy as np import matplotlib.pyplot as plt from qwen_vl_utils import process_vision_info from datasets import load_dataset from transformers import AutoProcessor from gui_actor.constants import chat_template from gui_actor.modeling_qwen25vl import Qwen2_5_VLForConditionalGenerationWithPointer from gui_actor.inference import inference MAX_PIXELS = 3200 * 1800 def resize_image(image, resize_to_pixels=MAX_PIXELS): image_width, image_height = image.size if (resize_to_pixels is not None) and ((image_width * image_height) != resize_to_pixels): resize_ratio = (resize_to_pixels / (image_width * image_height)) ** 0.5 image_width_resized, image_height_resized = int(image_width * resize_ratio), int(image_height * resize_ratio) image = image.resize((image_width_resized, image_height_resized)) return image @torch.inference_mode() def draw_point(image: Image.Image, point: list, radius=8, color=(255, 0, 0, 128)): overlay = Image.new('RGBA', image.size, (255, 255, 255, 0)) overlay_draw = ImageDraw.Draw(overlay) x, y = point overlay_draw.ellipse( [(x - radius, y - radius), (x + radius, y + radius)], outline=color, width=5 ) image = image.convert('RGBA') combined = Image.alpha_composite(image, overlay) combined = combined.convert('RGB') return combined @torch.inference_mode() def get_attn_map(image, attn_scores, n_width, n_height): w, h = image.size scores = np.array(attn_scores[0]).reshape(n_height, n_width) scores_norm = (scores - scores.min()) / (scores.max() - scores.min()) score_map = Image.fromarray((scores_norm * 255).astype(np.uint8)).resize((w, h), resample=Image.NEAREST) colormap = plt.get_cmap('jet') colored_score_map = colormap(np.array(score_map) / 255.0) colored_score_map = (colored_score_map[:, :, :3] * 255).astype(np.uint8) colored_overlay = Image.fromarray(colored_score_map) blended = Image.blend(image, colored_overlay, alpha=0.3) return blended # 加载模型 if torch.cuda.is_available(): model_name_or_path = "microsoft/GUI-Actor-7B-Qwen2.5-VL" data_processor = AutoProcessor.from_pretrained(model_name_or_path) tokenizer = data_processor.tokenizer model = Qwen2_5_VLForConditionalGenerationWithPointer.from_pretrained( model_name_or_path, torch_dtype=torch.bfloat16, device_map="cuda:0", attn_implementation="flash_attention_2" ).eval() else: model_name_or_path = "microsoft/GUI-Actor-3B-Qwen2.5-VL" data_processor = AutoProcessor.from_pretrained(model_name_or_path) tokenizer = data_processor.tokenizer model = Qwen2_5_VLForConditionalGenerationWithPointer.from_pretrained( model_name_or_path, torch_dtype=torch.bfloat16, device_map="cpu" ).eval() title = "GUI-Actor" header = """

🎯 GUI-Actor: Coordinate-Free Visual Grounding for GUI Agents

Qianhui Wu*, Kanzhi Cheng*, Rui Yang*, Chaoyun Zhang, Jianwei Yang, Huiqiang Jiang, Jian Mu, Baolin Peng, Bo Qiao, Reuben Tan, Si Qin, Lars Liden
Qingwei Lin, Huan Zhang, Tong Zhang, Jianbing Zhang, Dongmei Zhang, Jianfeng Gao
🌐 Project Page | 📄 arXiv Paper | 💻 Github Repo
""" theme = "soft" css = """#anno-img .mask {opacity: 0.5; transition: all 0.2s ease-in-out;} #anno-img .mask.active {opacity: 0.7}""" @torch.inference_mode() def process(image, instruction): # 调整图像大小 w, h = image.size if w * h > MAX_PIXELS: image = resize_image(image) conversation = [ { "role": "system", "content": [ { "type": "text", "text": "You are a GUI agent. Given a screenshot of the current GUI and a human instruction, your task is to locate the screen element that corresponds to the instruction. You should output a PyAutoGUI action that performs a click on the correct position. To indicate the click location, we will use some special tokens, which is used to refer to a visual patch later. For example, you can output: pyautogui.click().", } ] }, { "role": "user", "content": [ { "type": "image", "image": image, }, { "type": "text", "text": instruction, }, ], }, ] try: pred = inference(conversation, model, tokenizer, data_processor, use_placeholder=True, topk=3) except Exception as e: print(e) return image, f"Error: {e}", None px, py = pred["topk_points"][0] output_coord = f"({px:.4f}, {py:.4f})" img_with_point = draw_point(image, (px * w, py * h)) n_width, n_height = pred["n_width"], pred["n_height"] attn_scores = pred["attn_scores"] att_map = get_attn_map(image, attn_scores, n_width, n_height) return img_with_point, output_coord, att_map def main(): # 新增：主函数，使用argparse解析参数 parser = argparse.ArgumentParser(description="GUI-Actor 服务") parser.add_argument("--port", type=int, default=9876, help="服务端口（默认：9876）") parser.add_argument("--host", default="localhost", help="服务主机（默认：localhost）") args = parser.parse_args() # 创建Gradio界面 with gr.Blocks(title=title, css=css) as demo: gr.Markdown(header) with gr.Row(): with gr.Column(): input_image = gr.Image( type='pil', label='Upload image') input_instruction = gr.Textbox(label='Instruction', placeholder='Text your (low-level) instruction here') submit_button = gr.Button( value='Submit', variant='primary') with gr.Column(): image_with_point = gr.Image(type='pil', label='Image with Point (red circle)') with gr.Accordion('Detailed prediction'): pred_xy = gr.Textbox(label='Predicted Coordinates', placeholder='(x, y)') att_map = gr.Image(type='pil', label='Attention Map') submit_button.click( fn=process, inputs=[ input_image, input_instruction ], outputs=[image_with_point, pred_xy, att_map] ) # 启动服务（使用解析的参数） print(f"🚀 GUI-Actor 服务启动中...") print(f"🌐 访问地址: http://{args.host}:{args.port}") demo.queue().launch( server_name=args.host, server_port=args.port, share=True ) if __name__ == "__main__": # 新增：程序入口 main()