change to onnx

.gitignore

@@ -157,8 +157,10 @@ cython_debug/
 #  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-#.idea/
+.idea/
 
 *.jpeg
 *.png
-.DS_Store 
+.DS_Store
+
+saved_models/*

README.md

@@ -5,7 +5,7 @@ colorFrom: red
 colorTo: red
 sdk: gradio
 sdk_version: 5.49.0
-app_file: app.py
+app_file: app-onnx.py
 pinned: false
 license: openrail
 short_description: Removes background using DIS

app-onnx.py

@@ -0,0 +1,118 @@
+#######################################################################################
+#
+# MIT License
+#
+# Copyright (c) [2025] [leonelhs@gmail.com]
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+#
+#######################################################################################
+
+# This file implements an API endpoint for DIS background image removal system.
+# [Self space] - [https://huggingface.co/spaces/leonelhs/removebg]
+#
+# Source code is based on or inspired by several projects.
+# For more details and proper attribution, please refer to the following resources:
+#
+# - [DIS] - [https://github.com/xuebinqin/DIS]
+# - [removebg] - [https://huggingface.co/spaces/gaviego/removebg]
+# https://github.com/gaurav0651/dis-bg-remover
+
+from itertools import islice
+
+import cv2
+import gradio as gr
+import numpy as np
+import onnxruntime as ort
+from PIL import Image
+from huggingface_hub import hf_hub_download
+
+REPO_ID = "leonelhs/removators"
+
+# Load the ONNX model
+model_path = hf_hub_download(repo_id=REPO_ID, filename='isnet.onnx')
+
+session = ort.InferenceSession(model_path)
+
+def normalize(image, mean, std):
+    """Normalize a numpy image with mean and standard deviation."""
+    return (image / 255.0 - mean) / std
+
+def predict(image_path):
+    input_size = (1024, 1024)
+
+    img = cv2.imread(image_path, cv2.IMREAD_COLOR)
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # Convert from BGR to RGB if using OpenCV
+
+    # If image is grayscale, convert to RGB
+    if len(img.shape) == 2:
+        img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
+
+    # Normalize the image using NumPy
+    img = img.astype(np.float32)  # Convert to float
+    im_normalized = normalize(img, mean=[0.5, 0.5, 0.5], std=[1.0, 1.0, 1.0])
+
+    # Resize the image
+    img_resized = cv2.resize(im_normalized, input_size, interpolation=cv2.INTER_LINEAR)
+    img_resized = np.transpose(img_resized, (2, 0, 1))  # CHW format
+    img_resized = np.expand_dims(img_resized, axis=0)  # Add batch dimension
+
+    # Run inference
+    img_resized = img_resized.astype(np.float32)
+    ort_inputs = {session.get_inputs()[0].name: img_resized}
+    prediction = session.run(None, ort_inputs)
+
+    # Process the model output
+    result = prediction[0][0]  # Assuming single output and single batch
+    result = np.clip(result, 0, 1)  # Assuming you want to clip the result to [0, 1]
+    result = (result * 255).astype(np.uint8)  # Rescale to [0, 255]
+    result = np.transpose(result, (1, 2, 0))  # HWC format
+    # Resize to original shape
+    original_shape = img.shape[:2]
+    return cv2.resize(result, (original_shape[1], original_shape[0]), interpolation=cv2.INTER_LINEAR)
+
+
+def cuts(image):
+    mask = predict(image)
+    mask = Image.fromarray(mask).convert('L')
+    cutted = Image.open(image).convert("RGB")
+    cutted.putalpha(mask)
+    return [image, cutted], mask
+
+with gr.Blocks(title="DIS") as app:
+    navbar = gr.Navbar(visible=True, main_page_name="Workspace")
+    gr.Markdown("## Dichotomous Image Segmentation")
+    with gr.Row():
+        with gr.Column(scale=1):
+            inp_image = gr.Image(type="filepath", label="Upload Image")
+            btn_predict = gr.Button(variant="primary", value="Remove background")
+        with gr.Column(scale=2):
+            with gr.Row():
+                preview = gr.ImageSlider(type="filepath", label="Comparer")
+
+    btn_predict.click(cuts, inputs=[inp_image], outputs=[preview, inp_image])
+
+with app.route("Readme", "/readme"):
+    with open("README.md") as f:
+        for line in islice(f, 12, None):
+            gr.Markdown(line.strip())
+
+
+app.launch(share=False, debug=True, show_error=True, mcp_server=True, pwa=True)
+app.queue()

export_onnx.py

@@ -0,0 +1,32 @@
+import torch
+from huggingface_hub import hf_hub_download
+from models.isnet import ISNetDIS
+
+REPO_ID = "leonelhs/removators"
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+net = ISNetDIS()
+
+model_path = hf_hub_download(repo_id=REPO_ID, filename='isnet.pth')
+net.load_state_dict(torch.load(model_path, map_location=device))
+net.to(device)
+net.eval()
+
+dummy_input = torch.ones(1, 3, 1024, 1024)
+
+# Export the model
+torch.onnx.export(
+    net,                          # model
+    dummy_input,                # example input
+    "linear_model.onnx",        # output file
+    input_names=["input"],      # name inputs
+    output_names=["output"],    # name outputs
+    dynamic_axes={              # allow variable batch size
+        "input": {0: "batch_size"},
+        "output": {0: "batch_size"}
+    },
+    opset_version=17            # ONNX version
+)
+
+print("Model exported to linear_model.onnx")

requirements.txt

@@ -1,2 +1,10 @@
-torch>=2.8.0
-torchvision>=0.23.0
+# Enable only for pythorch app.py
+# torch>=2.8.0
+# torchvision>=0.23.0
+
+# Requirements for app-onnx.py
+numpy==2.2.6
+onnxruntime==1.22.1
+opencv-python-headless==4.12.0.88
+pillow==11.3.0
+