app.py

from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
import gradio as gr
from PIL import Image
import torch
import matplotlib.pyplot as plt
import torch
import numpy as np

processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined")


def process_image(image, prompt, threhsold):
    inputs = processor(
        text=prompt, images=image, padding="max_length", return_tensors="pt"
    )

    # predict
    with torch.no_grad():
        outputs = model(**inputs)
        preds = outputs.logits

    pred = torch.sigmoid(preds)
    mat = pred.cpu().numpy()
    mask = Image.fromarray(np.uint8(mat * 255), "L")
    mask = mask.convert("RGB")
    mask = mask.resize(image.size)
    mask = np.array(mask)[:, :, 0]

    # normalize the mask
    mask_min = mask.min()
    mask_max = mask.max()
    mask = (mask - mask_min) / (mask_max - mask_min)
    return mask


def get_masks(prompts, img, threhsold):
    prompts = prompts.split(",")
    masks = []
    for prompt in prompts:
        mask = process_image(img, prompt, threhsold)
        mask = mask > threhsold
    
        fig, ax = plt.subplots()
        ax.imshow(img)
        ax.imshow(mask, alpha=0.5, cmap="jet")
        ax.axis("off")
        plt.tight_layout()
        masks.append(mask)
    return masks


def extract_image(img, pos_prompts, neg_prompts, threshold):
    positive_masks = get_masks(pos_prompts, img, threshold)
    negative_masks = get_masks(neg_prompts, img, threshold)

    # combine masks into one masks, logic OR
    pos_mask = np.any(np.stack(positive_masks), axis=0)
    neg_mask = np.any(np.stack(negative_masks), axis=0)
    final_mask = pos_mask & ~neg_mask

    # extract the final image
    final_mask = Image.fromarray(final_mask.astype(np.uint8) * 255, "L")
    inverse_mask = np.invert(final_mask)
    output_image = Image.new("RGBA", img.size, (0, 0, 0, 0))
    output_image.paste(img, mask=final_mask)

    return output_image, final_mask, inverse_mask


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>"


with gr.Blocks() as demo:
    gr.Markdown("# CLIPSeg: Image Segmentation Using Text and Image Prompts")
    gr.Markdown(article)
    gr.Markdown(description)

    with gr.Row():
        with gr.Column():
            input_image = gr.Image(type="pil")
            positive_prompts = gr.Textbox(
                label="Please describe what you want to identify (comma separated)"
            )
            negative_prompts = gr.Textbox(
                label="Please describe what you want to ignore (comma separated)"
            )

            input_slider_T = gr.Slider(
                minimum=0, maximum=1, value=0.4, label="Threshold"
            )
            btn_process = gr.Button(label="Process")

        with gr.Column():
            output_image = gr.Image(label="Result")
            output_mask = gr.Image(label="Mask")
            inverse_mask = gr.Image(label="Inverse")

    btn_process.click(
        extract_image,
        inputs=[
            input_image,
            positive_prompts,
            negative_prompts,
            input_slider_T,
        ],
        outputs=[output_image, output_mask, inverse_mask],
        api_name="mask"
    )


demo.launch()