Spaces:
Runtime error
Runtime error
| import torch | |
| import random | |
| import gradio as gr | |
| import numpy as np | |
| from transformers import MaskFormerFeatureExtractor, MaskFormerForInstanceSegmentation | |
| # Use GPU if available | |
| if torch.cuda.is_available(): | |
| device = torch.device("cuda") | |
| else: | |
| device = torch.device("cpu") | |
| model = MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-tiny-ade").to(device) | |
| model.eval() | |
| preprocessor = MaskFormerFeatureExtractor.from_pretrained("facebook/maskformer-swin-tiny-ade") | |
| def visualize_instance_seg_mask(mask): | |
| # Initialize image | |
| image = np.zeros((mask.shape[0], mask.shape[1], 3)) | |
| labels = np.unique(mask) | |
| label2color = {label: (random.randint(0, 1), random.randint(0, 255), random.randint(0, 255)) for label in labels} | |
| for i in range(image.shape[0]): | |
| for j in range(image.shape[1]): | |
| image[i, j, :] = label2color[mask[i, j]] | |
| image = image / 255 | |
| return image | |
| def query_image(img): | |
| target_size = (img.shape[0], img.shape[1]) | |
| inputs = preprocessor(images=img, return_tensors="pt") | |
| with torch.no_grad(): | |
| outputs = model(**inputs) | |
| outputs.class_queries_logits = outputs.class_queries_logits.cpu() | |
| outputs.masks_queries_logits = outputs.masks_queries_logits.cpu() | |
| results = preprocessor.post_process_segmentation(outputs=outputs, target_size=target_size)[0].cpu().detach() | |
| results = torch.argmax(results, dim=0).numpy() | |
| results = visualize_instance_seg_mask(results) | |
| return results | |
| description = """ | |
| Gradio demo for <a href="https://huggingface.co/docs/transformers/main/en/model_doc/maskformer">MaskFormer</a>, | |
| introduced in <a href="https://arxiv.org/abs/2107.06278">Per-Pixel Classification is Not All You Need for Semantic Segmentation | |
| </a>. | |
| \n\n"Mask2Former is a unified framework architecture based on MaskFormer meta-architecture that achieves SOTA on panoptic, | |
| instance and semantic segmentation across four popular datasets (ADE20K, Cityscapes, COCO, Mapillary Vistas). You can use | |
| MaskFormer for semantic, instance (illustrated in the demo) and panoptic segmentation. | |
| """ | |
| demo = gr.Interface( | |
| query_image, | |
| inputs=[gr.Image()], | |
| outputs="image", | |
| title="MaskFormer Demo", | |
| description=description, | |
| examples=["assets/test_image_35.png", "assets/test_image_82.png"] | |
| ) | |
| demo.launch() |