| import onnxruntime | |
| from torchvision import transforms | |
| import torch | |
| import torch.nn.functional as F | |
| import gradio as gr | |
| sess_options = onnxruntime.SessionOptions() | |
| sess_options.enable_profiling = True | |
| sess_options.add_session_config_entry('session.load_model_format', 'ONNX') | |
| ort_sess = onnxruntime.InferenceSession("RFNet.onnx", sess_options=sess_options) | |
| preprocess_img = transforms.Compose([ | |
| transforms.Resize((352,352)), | |
| transforms.ToTensor(), | |
| transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])]) | |
| preprocess_depth = transforms.Compose([ | |
| transforms.Resize((352,352)), | |
| transforms.ToTensor()]) | |
| def inference(img,depth,GT): | |
| h,w = img.size | |
| img = preprocess_img(img).unsqueeze(0) | |
| depth = preprocess_depth(depth.convert('L')).unsqueeze(0) | |
| ort_inputs = {ort_sess.get_inputs()[0].name: img.numpy(), ort_sess.get_inputs()[1].name: depth.numpy()} | |
| ort_outs = ort_sess.run(None, ort_inputs) | |
| output_image = torch.tensor(ort_outs[0]) | |
| res = F.interpolate(output_image, size=(w,h), mode='bilinear', align_corners=False) | |
| res = torch.sigmoid(res) | |
| res = res.data.cpu().numpy().squeeze() | |
| res = (res - res.min()) / (res.max() - res.min() + 1e-8) | |
| return res | |
| title = "Robust RGB-D Fusion for Saliency Detection" | |
| description = """ Deployment of the paper: | |
| [Robust RGB-D Fusion for Saliency Detection](https://arxiv.org/pdf/2208.01762.pdf) | |
| published at the International Conference on 3D Vision 2022 (3DV 2022). | |
| Paper Code can be found at [Zongwei97/RFNet](https://github.com/Zongwei97/RFnet). | |
| Deployed Code can be found at [shriarul5273/Robust_RGB-D_Saliency_Detection](https://github.com/shriarul5273/Robust_RGB-D_Saliency_Detection). | |
| Use example Image and corresponding Depth Map (from NJU2K dataset) or upload your own Image and Depth Map. | |
| """ | |
| article = """ # Citation | |
| If you find this repo useful, please consider citing: | |
| ``` | |
| @article{wu2022robust, | |
| title={Robust RGB-D Fusion for Saliency Detection}, | |
| author={Wu, Zongwei and Gobichettipalayam, Shriarulmozhivarman and Tamadazte, Brahim and Allibert, Guillaume and Paudel, Danda Pani and Demonceaux, Cedric}, | |
| journal={3DV}, | |
| year={2022} | |
| } | |
| ``` | |
| """ | |
| examples = [['images/image_1.jpg','images/depth_1.png','images/gt_1.png'], | |
| ['images/image_2.jpg','images/depth_2.png','images/gt_2.png'], | |
| ['images/image_3.jpg','images/depth_3.png','images/gt_3.png'], | |
| ['images/image_4.jpg','images/depth_4.png','images/gt_4.png'], | |
| ['images/image_5.jpg','images/depth_5.png','images/gt_5.png']] | |
| input_1 = gr.Image(type='pil', label="RGB Image", source="upload") | |
| input_2 = gr.Image(type='pil', label="Depth Image", source="upload") | |
| input_3 = gr.Image(type='pil', label="Ground Truth", source="upload") | |
| outputs = gr.Image(type="pil", label="Saliency Map") | |
| gr.Interface(inference, inputs=[input_1,input_2,input_3], outputs=outputs, | |
| title=title,examples=examples, | |
| description=description, | |
| article=article,cache_examples=False).launch(server_name="0.0.0.0") | |