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update demo to 4.18.0 gradio version

pull/295/head
Skwarson96 2024-02-13 22:29:21 +01:00
parent 2b62f419c2
commit 66be972730
1 changed files with 13 additions and 22 deletions
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35
demo/gradio_app.py
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@ -1,22 +1,16 @@
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 warnings
import torch
# prepare the environment
os.system("python setup.py build develop --user")
os.system("pip install packaging==21.3")
os.system("pip install gradio")
# os.system("python setup.py build develop --user")
# os.system("pip install packaging==21.3")
# os.system("pip install gradio")
warnings.filterwarnings("ignore")
@ -26,13 +20,12 @@ import gradio as gr
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
from groundingdino.util.inference import annotate, load_image, predict, load_model
import groundingdino.datasets.transforms as T
from huggingface_hub import hf_hub_download
# Use this command for evaluate the Grounding DINO model
config_file = "groundingdino/config/GroundingDINO_SwinT_OGC.py"
ckpt_repo_id = "ShilongLiu/GroundingDINO"
@ -40,7 +33,7 @@ ckpt_filenmae = "groundingdino_swint_ogc.pth"
def load_model_hf(model_config_path, repo_id, filename, device='cpu'):
args = SLConfig.fromfile(model_config_path)
args = SLConfig.fromfile(model_config_path)
model = build_model(args)
args.device = device
@ -49,7 +42,7 @@ def load_model_hf(model_config_path, repo_id, filename, device='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
return model
def image_transform_grounding(init_image):
transform = T.Compose([
@ -71,13 +64,12 @@ 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')
boxes, logits, phrases = predict(model, image_tensor, grounding_caption, box_threshold, text_threshold, device='cuda')
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))
@ -98,9 +90,9 @@ if __name__ == "__main__":
with gr.Row():
with gr.Column():
input_image = gr.Image(source='upload', type="pil")
input_image = gr.Image(label='upload', type="pil")
grounding_caption = gr.Textbox(label="Detection Prompt")
run_button = gr.Button(label="Run")
run_button = gr.Button(value="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
@ -110,10 +102,10 @@ if __name__ == "__main__":
)
with gr.Column():
gallery = gr.outputs.Image(
type="pil",
# label="grounding results"
).style(full_width=True, full_height=True)
gallery = gr.components.Image(
label="grounding results",
type="pil"
)
# gallery = gr.Gallery(label="Generated images", show_label=False).style(
# grid=[1], height="auto", container=True, full_width=True, full_height=True)
@ -122,4 +114,3 @@ if __name__ == "__main__":
block.launch(server_name='0.0.0.0', server_port=7579, debug=args.debug, share=args.share)