Add application file

Dockerfile

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+FROM ubuntu:20.04
+
+RUN apt-get update && apt-get install -y \
+    python3 \
+    python3-pip && pip3 install --upgrade pip
+RUN mkdir /app
+COPY . /app
+WORKDIR /app
+
+RUN pip3 install -r requirements.txt
+
+CMD ["python3", "app.py"]

RFNet.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2dabbbc813e55ce3d9ceb9d51986358d8324716fdde9893ae8f595de8bc8c68b
+size 365671553

app.py

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+import onnxruntime
+from torchvision import transforms
+import torch
+import torch.nn.functional as F
+import gradio as gr
+ort_sess = onnxruntime.InferenceSession("RFNet.onnx")
+
+
+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).launch()

requirements.txt

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+torch==1.12.0
+torchvision==0.13.0
+Pillow==9.2.0
+gradio==3.2.0
+onnxruntime==1.12.1