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README.md

@@ -17,7 +17,7 @@ colorFrom: red
 colorTo: red
 sdk: gradio
 sdk_version: 4.29.0
-app_file: app.py
+app_file: hf_space.py
 pinned: false
 ---
 

hf_space.py

@@ -0,0 +1,88 @@
+import spaces
+import numpy as np
+import torch
+import torch.nn.functional as F
+import gradio as gr
+from ormbg.models.ormbg import ORMBG
+from PIL import Image
+
+model_path = "models/ormbg.pth"
+
+# Load the model globally but don't send to device yet
+net = ORMBG()
+net.load_state_dict(torch.load(model_path, map_location="cpu"))
+net.eval()
+
+
+def resize_image(image):
+    image = image.convert("RGB")
+    model_input_size = (1024, 1024)
+    image = image.resize(model_input_size, Image.BILINEAR)
+    return image
+
+
+@spaces.GPU
+@torch.inference_mode()
+def inference(image):
+    # Check for CUDA and set the device inside inference
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    net.to(device)
+
+    # Prepare input
+    orig_image = Image.fromarray(image)
+    w, h = orig_image.size
+    image = resize_image(orig_image)
+    im_np = np.array(image)
+    im_tensor = torch.tensor(im_np, dtype=torch.float32).permute(2, 0, 1)
+    im_tensor = torch.unsqueeze(im_tensor, 0)
+    im_tensor = torch.divide(im_tensor, 255.0)
+
+    if torch.cuda.is_available():
+        im_tensor = im_tensor.to(device)
+
+    # Inference
+    result = net(im_tensor)
+    # Post process
+    result = torch.squeeze(F.interpolate(result[0][0], size=(h, w), mode="bilinear"), 0)
+    ma = torch.max(result)
+    mi = torch.min(result)
+    result = (result - mi) / (ma - mi)
+    # Image to PIL
+    im_array = (result * 255).cpu().data.numpy().astype(np.uint8)
+    pil_im = Image.fromarray(np.squeeze(im_array))
+    # Paste the mask on the original image
+    new_im = Image.new("RGBA", pil_im.size, (0, 0, 0, 0))
+    new_im.paste(orig_image, mask=pil_im)
+
+    return new_im
+
+
+# Gradio interface setup
+title = "Open Remove Background Model (ormbg)"
+description = r"""
+This model is a <strong>fully open-source background remover</strong> optimized for images with humans. It is based on [Highly Accurate Dichotomous Image Segmentation research](https://github.com/xuebinqin/DIS). The model was trained with the synthetic <a href="https://huggingface.co/datasets/schirrmacher/humans">Human Segmentation Dataset</a>, <a href="https://paperswithcode.com/dataset/p3m-10k">P3M-10k</a> and <a href="https://paperswithcode.com/dataset/aim-500">AIM-500</a>.
+
+If you identify cases where the model fails, <a href='https://huggingface.co/schirrmacher/ormbg/discussions' target='_blank'>upload your examples</a>!
+
+- <a href='https://huggingface.co/schirrmacher/ormbg' target='_blank'>Model card</a>: find inference code, training information, tutorials
+- <a href='https://huggingface.co/schirrmacher/ormbg' target='_blank'>Dataset</a>: see training images, segmentation data, backgrounds
+- <a href='https://huggingface.co/schirrmacher/ormbg\#research' target='_blank'>Research</a>: see current approach for improvements
+"""
+
+examples = [
+    "./examples/image/example1.jpeg",
+    "./examples/image/example2.jpeg",
+    "./examples/image/example3.jpeg",
+]
+
+demo = gr.Interface(
+    fn=inference,
+    inputs="image",
+    outputs="image",
+    examples=examples,
+    title=title,
+    description=description,
+)
+
+if __name__ == "__main__":
+    demo.launch(share=False, allowed_paths=["ormbg", "models", "examples"])