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import gradio as gr
import numpy as np
from PIL import Image
import torch
from transformers import AutoProcessor, CLIPSegForImageSegmentation
# Load the CLIPSeg model and processor
processor = AutoProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined")
def segment_everything(image):
inputs = processor(text=["object"], images=[image], padding="max_length", return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
preds = outputs.logits.squeeze().sigmoid()
segmentation = (preds.numpy() * 255).astype(np.uint8)
return Image.fromarray(segmentation)
def segment_box(image, box):
if box is None:
return image
x1, y1, x2, y2 = box
cropped_image = image[y1:y2, x1:x2]
inputs = processor(text=["object"], images=[cropped_image], padding="max_length", return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
preds = outputs.logits.squeeze().sigmoid()
segmentation = np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8)
segmentation[y1:y2, x1:x2] = (preds.numpy() * 255).astype(np.uint8)
return Image.fromarray(segmentation)
def update_image(image, segmentation):
if segmentation is None:
return image
image_pil = Image.fromarray((image * 255).astype(np.uint8))
seg_pil = Image.fromarray(segmentation).convert('RGBA')
blended = Image.blend(image_pil.convert('RGBA'), seg_pil, 0.5)
return np.array(blended)
with gr.Blocks() as demo:
gr.Markdown("# Segment Anything-like Demo")
with gr.Row():
with gr.Column(scale=1):
input_image = gr.Image(label="Input Image", tool="select")
with gr.Row():
everything_btn = gr.Button("Everything")
box_btn = gr.Button("Box")
with gr.Column(scale=1):
output_image = gr.Image(label="Segmentation Result")
everything_btn.click(
fn=segment_everything,
inputs=[input_image],
outputs=[output_image]
)
box_btn.click(
fn=segment_box,
inputs=[input_image, input_image],
outputs=[output_image]
)
output_image.change(
fn=update_image,
inputs=[input_image, output_image],
outputs=[output_image]
)
demo.launch() |