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import os
import warnings
import gradio as gr
import numpy as np
from PIL import Image
from lang_efficient_sam.LangEfficientSAM import LangEfficientSAM
from lang_efficient_sam.utils.draw_image import draw_image
warnings.filterwarnings("ignore")
model = LangEfficientSAM()
def predict(box_threshold, text_threshold, image_path, text_prompt):
print("Predicting... ", box_threshold, text_threshold, image_path, text_prompt)
image_pil = Image.open(image_path).convert("RGB")
masks, boxes, phrases, logits = model.predict(image_pil, text_prompt, box_threshold, text_threshold)
labels = [f"{phrase} {logit:.2f}" for phrase, logit in zip(phrases, logits)]
image_array = np.asarray(image_pil)
image = draw_image(image_array, masks, boxes, labels)
image = Image.fromarray(np.uint8(image)).convert("RGB")
return image
title = "LangEfficientSAM"
inputs = [
gr.Slider(0, 1, value=0.3, label="Box threshold"),
gr.Slider(0, 1, value=0.25, label="Text threshold"),
gr.Image(type="filepath", label='Image'),
gr.Textbox(lines=1, label="Text Prompt"),
]
outputs = [gr.Image(type="pil", label="Output Image")]
examples = [
[
0.20,
0.20,
os.path.join(os.path.dirname(__file__), "images", "living.jpg"),
"fabric",
],
[
0.36,
0.25,
os.path.join(os.path.dirname(__file__), "images", "fruits.jpg"),
"apple",
],
[
0.20,
0.20,
os.path.join(os.path.dirname(__file__), "images", "street.jpg"),
"car",
]
]
demo = gr.Interface(fn=predict,
inputs=inputs,
outputs=outputs,
examples=examples,
title=title)
demo.launch(debug=False, share=False)
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