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| import os | |
| os.system('pip install -v -e .') | |
| 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) |