Create ref_seg.py

tasks/ref_seg.py

@@ -0,0 +1,46 @@
+# --------------------------------------------------------
+# 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 ([email protected])
+# --------------------------------------------------------
+
+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