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from PIL import Image
import requests
import torch
import matplotlib.pyplot as plt
import numpy as np
import gradio as gr
from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined")
def visualize_segmentation(image, prompts, preds):
_, ax = plt.subplots(1, len(prompts) + 1, figsize=(3*(len(prompts) + 1), 4))
[a.axis('off') for a in ax.flatten()]
ax[0].imshow(image)
[ax[i+1].imshow(torch.sigmoid(preds[i][0])) for i in range(len(prompts))];
[ax[i+1].text(0, -15, prompt) for i, prompt in enumerate(prompts)];
def segment(img, clases):
prompts = clases.split(',')
inputs = processor(text=prompts, images=[image] * len(img), padding="max_length", return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
preds = outputs.logits.unsqueeze(1)
return "Hello " + prompts + "!!"
demo = gr.Interface(fn=greet, inputs=["image","text"], outputs="text")
demo.launch()
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