Create ref_in.py

tasks/ref_in.py

@@ -0,0 +1,77 @@
+# --------------------------------------------------------
+# X-Decoder -- Generalized Decoding for Pixel, Image, and Language
+# Copyright (c) 2022 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Jianwei Yang ([email protected]), Xueyan Zou ([email protected])
+# --------------------------------------------------------
+
+import torch
+import numpy as np
+from PIL import Image
+from utils.inpainting import pad_image
+from torchvision import transforms
+from utils.visualizer import Visualizer
+from diffusers import StableDiffusionInpaintPipeline
+from detectron2.utils.colormap import random_color
+from detectron2.data import MetadataCatalog
+from scipy import ndimage
+
+
+t = []
+t.append(transforms.Resize(512, interpolation=Image.BICUBIC))
+transform = transforms.Compose(t)
+metadata = MetadataCatalog.get('ade20k_panoptic_train')
+
+pipe = StableDiffusionInpaintPipeline.from_pretrained(
+    # "stabilityai/stable-diffusion-2-inpainting",
+    "runwayml/stable-diffusion-inpainting",
+    revision="fp16", 
+    torch_dtype=torch.float16,
+).to("cuda")
+
+def crop_image(input_image):
+    crop_w, crop_h = np.floor(np.array(input_image.size) / 64).astype(int) * 64
+    im_cropped = Image.fromarray(np.array(input_image)[:crop_h, :crop_w])
+    return im_cropped
+
+def referring_inpainting(model, image, texts, inpainting_text, *args, **kwargs):
+    model.model.metadata = metadata
+    texts = [[texts if texts.strip().endswith('.') else (texts.strip() + '.')]]
+    image_ori = crop_image(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()
+    
+    if inpainting_text not in ['no', '']:
+        # if we want to do inpainting
+        image_crop = image_ori
+        struct2 = ndimage.generate_binary_structure(2, 2)
+        mask_dilated = ndimage.binary_dilation(grd_mask[0], structure=struct2, iterations=3).astype(grd_mask[0].dtype)
+        mask = Image.fromarray(mask_dilated * 255).convert('RGB')
+        image_and_mask = {
+            "image": image_crop,
+            "mask": mask,
+        }
+        width = image_crop.size[0]; height = image_crop.size[1]
+        images_inpainting = pipe(prompt = inpainting_text.strip(), image=image_and_mask['image'], mask_image=image_and_mask['mask'], height=height, width=width).images[0]
+        # put images_inpainting back to original image
+        # image_ori.paste(images_inpainting)        
+        torch.cuda.empty_cache()
+        return Image.fromarray(res) ,'' , images_inpainting
+    else:
+        torch.cuda.empty_cache()
+        return image_ori, 'text', Image.fromarray(res)