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import argparse
from functools import partial
import cv2
import requests
import os
from io import BytesIO
from PIL import Image
import numpy as np
from pathlib import Path
import gradio as gr
import warnings
import torch
os.system("python setup.py build develop --user")
warnings.filterwarnings("ignore")
from groundingdino.models import build_model
from groundingdino.util.slconfig import SLConfig
from groundingdino.util.utils import clean_state_dict
from groundingdino.util.inference import annotate, load_image, predict
import groundingdino.datasets.transforms as T
from huggingface_hub import hf_hub_download
# Use this command for evaluate the GLIP-T model
config_file = "groundingdino/config/GroundingDINO_SwinT_OGC.py"
ckpt_repo_id = "ShilongLiu/GroundingDINO"
ckpt_filenmae = "groundingdino_swint_ogc.pth"
def load_model_hf(model_config_path, repo_id, filename):
args = SLConfig.fromfile(model_config_path)
args.device = 'cuda'
model = build_model(args)
cache_file = hf_hub_download(repo_id=repo_id, filename=filename)
checkpoint = torch.load(cache_file, map_location='cpu')
log = model.load_state_dict(clean_state_dict(checkpoint['model']), strict=False)
print("Model loaded from {} \n => {}".format(cache_file, log))
_ = model.eval()
return model
def image_transform_grounding(init_image):
transform = T.Compose([
T.RandomResize([800], max_size=1333),
T.ToTensor(),
T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
image, _ = transform(init_image, None) # 3, h, w
return init_image, image
def image_transform_grounding_for_vis(init_image):
transform = T.Compose([
T.RandomResize([800], max_size=1333),
])
image, _ = transform(init_image, None) # 3, h, w
return image
model = load_model_hf(config_file, ckpt_repo_id, ckpt_filenmae)
def run_grounding(input_image, grounding_caption, box_threshold, text_threshold):
init_image = input_image.convert("RGB")
original_size = init_image.size
_, image_tensor = image_transform_grounding(init_image)
image_pil: Image = image_transform_grounding_for_vis(init_image)
# run grounidng
boxes, logits, phrases = predict(model, image_tensor, grounding_caption, box_threshold, text_threshold)
annotated_frame = annotate(image_source=np.asarray(image_pil), boxes=boxes, logits=logits, phrases=phrases)
image_with_box = Image.fromarray(cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB))
return image_with_box
if __name__ == "__main__":
parser = argparse.ArgumentParser("Grounding DINO demo", add_help=True)
parser.add_argument("--debug", action="store_true", help="using debug mode")
parser.add_argument("--non-share", action="store_true", help="not share the app")
args = parser.parse_args()
args.share = (not args.non_share)
block = gr.Blocks().queue()
with block:
gr.Markdown("# Grounding DINO")
gr.Markdown("### Open-World Detection with Grounding DINO")
with gr.Row():
with gr.Column():
input_image = gr.Image(source='upload', type="pil")
grounding_caption = gr.Textbox(label="Detection Prompt")
run_button = gr.Button(label="Run")
with gr.Accordion("Advanced options", open=False):
box_threshold = gr.Slider(
label="Box Threshold", minimum=0.0, maximum=1.0, value=0.25, step=0.001
)
text_threshold = gr.Slider(
label="Text Threshold", minimum=0.0, maximum=1.0, value=0.25, step=0.001
)
with gr.Column():
gallery = gr.outputs.Image(
type="pil",
# label="grounding results"
).style(full_width=True, full_height=True)
# gallery = gr.Gallery(label="Generated images", show_label=False).style(
# grid=[1], height="auto", container=True, full_width=True, full_height=True)
run_button.click(fn=run_grounding, inputs=[
input_image, grounding_caption, box_threshold, text_threshold], outputs=[gallery])
block.launch(server_name='0.0.0.0', server_port=7579, debug=args.debug, share=args.share)
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