|
from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation |
|
import gradio as gr |
|
from PIL import Image |
|
import torch |
|
import matplotlib.pyplot as plt |
|
import cv2 |
|
|
|
processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined") |
|
model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined") |
|
|
|
def process_image(image, prompts): |
|
prompts = list(prompts) |
|
inputs = processor(text=prompts, images=[image] * len(prompts), padding="max_length", return_tensors="pt") |
|
|
|
|
|
with torch.no_grad(): |
|
outputs = model(**inputs) |
|
preds = outputs.logits.unsqueeze(1) |
|
|
|
filename = f"mask.png" |
|
plt.imsave(filename,torch.sigmoid(preds[0][0])) |
|
|
|
img2 = cv2.imread(filename) |
|
gray_image = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY) |
|
|
|
(thresh, bw_image) = cv2.threshold(gray_image, 100, 255, cv2.THRESH_BINARY) |
|
|
|
|
|
cv2.cvtColor(bw_image, cv2.COLOR_BGR2RGB) |
|
|
|
return Image.fromarray(bw_image) |
|
|
|
title = "Interactive demo: zero-shot image segmentation with CLIPSeg" |
|
description = "Demo for using CLIPSeg, a CLIP-based model for zero- and one-shot image segmentation. To use it, simply upload an image and add a text to mask (identify in the image), or use one of the examples below and click 'submit'. Results will show up in a few seconds." |
|
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2112.10003'>CLIPSeg: Image Segmentation Using Text and Image Prompts</a> | <a href='https://huggingface.co/docs/transformers/main/en/model_doc/clipseg'>HuggingFace docs</a></p>" |
|
|
|
examples = [["example_image.png", "wood"]] |
|
|
|
interface = gr.Interface(fn=process_image, |
|
inputs=[gr.Image(type="pil"), gr.Textbox(label="What do you want to identify (separated by comma)?")], |
|
outputs=gr.Image(type="pil"), |
|
title=title, |
|
description=description, |
|
article=article, |
|
examples=examples) |
|
|
|
interface.launch(debug=True) |