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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 | |
import spaces | |
os.system("python setup.py build develop --user") | |
os.system("pip install packaging==21.3") | |
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, device='cpu'): | |
args = SLConfig.fromfile(model_config_path) | |
model = build_model(args) | |
args.device = device | |
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, device='cpu') | |
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("--share", action="store_true", help="share the app") | |
args = parser.parse_args() | |
block = gr.Blocks().queue() | |
with block: | |
gr.Markdown("# [Grounding DINO](https://github.com/IDEA-Research/GroundingDINO)") | |
gr.Markdown("### Open-World Detection with Grounding DINO") | |
# gr.Markdown("Note the model runs on CPU, so it may take a while to run the model.") | |
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(share=False, show_api=False, show_error=True) | |