tasks/ref_seg.py

# --------------------------------------------------------
# X-Decoder -- Generalized Decoding for Pixel, Image, and Language
# Copyright (c) 2022 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Xueyan Zou (xueyan@cs.wisc.edu)
# --------------------------------------------------------

import torch
import numpy as np
from PIL import Image
from torchvision import transforms
from utils.visualizer import Visualizer
from detectron2.utils.colormap import random_color
from detectron2.data import MetadataCatalog


t = []
t.append(transforms.Resize(512, interpolation=Image.BICUBIC))
transform = transforms.Compose(t)
metadata = MetadataCatalog.get('ade20k_panoptic_train')

def referring_segmentation(model, image, texts, inpainting_text, *args, **kwargs):
    model.model.metadata = metadata
    texts = texts.strip()
    texts = [[text.strip() if text.endswith('.') else (text + '.')] for text in texts.split(',')]
    image_ori = transform(image)

    with torch.no_grad():
        width = image_ori.size[0]
        height = image_ori.size[1]
        image = np.asarray(image_ori)
        image_ori_np = np.asarray(image_ori)
        images = torch.from_numpy(image.copy()).permute(2,0,1).cuda()

        batch_inputs = [{'image': images, 'height': height, 'width': width, 'groundings': {'texts': texts}}]        
        outputs = model.model.evaluate_grounding(batch_inputs, None)
        visual = Visualizer(image_ori_np, metadata=metadata)

        grd_mask = (outputs[0]['grounding_mask'] > 0).float().cpu().numpy()
        for idx, mask in enumerate(grd_mask):
            color = random_color(rgb=True, maximum=1).astype(np.int32).tolist()
            demo = visual.draw_binary_mask(mask, color=color, text=texts[idx])
        res = demo.get_image()
    
    torch.cuda.empty_cache()
    return Image.fromarray(res), '', None