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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 open_panoseg(model, image, texts, inpainting_text, *args, **kwargs):
stuff_classes = [x.strip() for x in texts.split(';')[0].replace('stuff:','').split(',')]
thing_classes = [x.strip() for x in texts.split(';')[1].replace('thing:','').split(',')]
thing_colors = [random_color(rgb=True, maximum=255).astype(np.int32).tolist() for _ in range(len(thing_classes))]
stuff_colors = [random_color(rgb=True, maximum=255).astype(np.int32).tolist() for _ in range(len(stuff_classes))]
thing_dataset_id_to_contiguous_id = {x:x for x in range(len(thing_classes))}
stuff_dataset_id_to_contiguous_id = {x+len(thing_classes):x for x in range(len(stuff_classes))}
MetadataCatalog.get("demo").set(
thing_colors=thing_colors,
thing_classes=thing_classes,
thing_dataset_id_to_contiguous_id=thing_dataset_id_to_contiguous_id,
stuff_colors=stuff_colors,
stuff_classes=stuff_classes,
stuff_dataset_id_to_contiguous_id=stuff_dataset_id_to_contiguous_id,
)
model.model.sem_seg_head.predictor.lang_encoder.get_text_embeddings(thing_classes + stuff_classes + ["background"], is_eval=True)
metadata = MetadataCatalog.get('demo')
model.model.metadata = metadata
model.model.sem_seg_head.num_classes = len(thing_classes + stuff_classes)
with torch.no_grad():
image_ori = transform(image)
width = image_ori.size[0]
height = image_ori.size[1]
image = transform(image_ori)
image = np.asarray(image)
images = torch.from_numpy(image.copy()).permute(2,0,1).cuda()
batch_inputs = [{'image': images, 'height': height, 'width': width}]
outputs = model.forward(batch_inputs)
visual = Visualizer(image_ori, metadata=metadata)
pano_seg = outputs[-1]['panoptic_seg'][0]
pano_seg_info = outputs[-1]['panoptic_seg'][1]
for i in range(len(pano_seg_info)):
if pano_seg_info[i]['category_id'] in metadata.thing_dataset_id_to_contiguous_id.keys():
pano_seg_info[i]['category_id'] = metadata.thing_dataset_id_to_contiguous_id[pano_seg_info[i]['category_id']]
else:
pano_seg_info[i]['isthing'] = False
pano_seg_info[i]['category_id'] = metadata.stuff_dataset_id_to_contiguous_id[pano_seg_info[i]['category_id']]
demo = visual.draw_panoptic_seg(pano_seg.cpu(), pano_seg_info) # rgb Image
res = demo.get_image()
MetadataCatalog.remove('demo')
torch.cuda.empty_cache()
return Image.fromarray(res), '', None |