Spaces:
Sleeping
Sleeping
File size: 2,451 Bytes
dfdcd97 a3ee867 dfdcd97 a3ee867 7a7f5c3 a3ee867 54db35c a3ee867 54db35c a3ee867 54db35c a3ee867 7a7f5c3 54db35c a3ee867 54db35c dfdcd97 a3ee867 54db35c a3ee867 54db35c a3ee867 54db35c a3ee867 dfdcd97 a3ee867 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 |
import gradio as gr
import numpy as np
from PIL import Image, ImageDraw
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 = map(int, box)
mask = Image.new('L', (image.shape[1], image.shape[0]), 0)
draw = ImageDraw.Draw(mask)
draw.rectangle([x1, y1, x2, y2], fill=255)
inputs = processor(text=["object in box"], images=[image], mask_pixels=np.array(mask), 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 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")
box_input = gr.Box(label="Select Box")
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, box_input],
outputs=[output_image]
)
output_image.change(
fn=update_image,
inputs=[input_image, output_image],
outputs=[output_image]
)
demo.launch() |