app.py

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)