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# --------------------------------------------------------
# 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 |