import os os.system('pip install -v -e .') os.system('pip install opencv-python') import argparse from typing import Dict, List from gdino import GroundingDINOAPIWrapper, visualize import gradio as gr import numpy as np import cv2 def arg_parse(): parser = argparse.ArgumentParser(description="Gradio Demo for T-Rex2") parser.add_argument( "--token", type=str, default='trex-huggingface-demo', help="This token is only for gradio space. Please do not take it away for your own purpose!", ) args = parser.parse_args() return args def resize_image_with_aspect_ratio(image: np.ndarray, min_size: int = 800, max_size: int = 1333) -> np.ndarray: h, w = image.shape[:2] aspect_ratio = w / h # Determine the scaling factor based on the constraints if h < w: new_height = min_size new_width = int(new_height * aspect_ratio) if new_width > max_size: new_width = max_size new_height = int(new_width / aspect_ratio) else: new_width = min_size new_height = int(new_width / aspect_ratio) if new_height > max_size: new_height = max_size new_width = int(new_height * aspect_ratio) # Resize the image resized_image = cv2.resize(image, (new_width, new_height), interpolation=cv2.INTER_AREA) return resized_image def inference(image, prompt: str, return_mask: bool = False, return_score: bool = False) -> gr.Image: # shrink image first to save computation if return_mask: image = resize_image_with_aspect_ratio(image, min_size=600, max_size=1000) prompts = dict(image=image, prompt=prompt) results = gdino.inference(prompts, return_mask=return_mask) image_pil = visualize(image, results, return_mask=return_mask, draw_score=return_score) return image_pil args = arg_parse() gdino = GroundingDINOAPIWrapper(args.token) if __name__ == "__main__": with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue")) as demo: with gr.Row(): with gr.Column(): input_image = gr.Image(label="Input Image") with gr.Column(): output_image = gr.Image(label="Output Image") with gr.Row(): return_mask = gr.Checkbox(label="Return Mask") return_score = gr.Checkbox(label="Return Score") prompt = gr.Textbox(label="Prompt", placeholder="e.g., person.pigeon.tree") run = gr.Button(value="Run") with gr.Row(): gr.Examples( examples=[ ['asset/demo.jpg', 'person . pigeon . tree'], ['asset/demo2.jpeg', 'wireless walkie-talkie . life jacket . atlantic cod . man . vehicle . accessory . cell phone .'], ['asset/demo3.jpeg', 'wine rack . bottle . basket'], ['asset/demo4.jpeg', 'Mosque. golden dome. smaller domes. minarets. arched windows. white facade. cars. electrical lines. streetlights. trees. pedestrians. blue sky. shadows'], ['asset/demo5.jpeg', 'stately building. columns. sculptures. Spanish flag. clouds. blue sky. street. taxis. van. city bus. traffic lights. street lamps. road markings. pedestrians. sidewalk. traffic sign. palm trees'] ], inputs=[input_image, prompt], ) run.click(inference, inputs=[input_image, prompt, return_mask, return_score], outputs=output_image) demo.launch(debug=True)