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-import gradio as gr -from gradio_imageslider import ImageSlider -from loadimg import load_img -import spaces -from transformers import AutoModelForImageSegmentation -import torch -from torchvision import transforms - -torch.set_float32_matmul_precision(["high", "highest"][0]) - -birefnet = AutoModelForImageSegmentation.from_pretrained( - "briaai/RMBG-2.0", trust_remote_code=True -) -birefnet.to("cuda") -transform_image = transforms.Compose( - [ - transforms.Resize((1024, 1024)), - transforms.ToTensor(), - transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), - ] -) - -output_folder = 'output_images' -if not os.path.exists(output_folder): - os.makedirs(output_folder) - -def fn(image): - im = load_img(image, output_type="pil") - im = im.convert("RGB") - origin = im.copy() - image = process(im) - image_path = os.path.join(output_folder, "no_bg_image.png") - image.save(image_path) - return (image, origin), image_path - -@spaces.GPU -def process(image): - image_size = image.size - input_images = transform_image(image).unsqueeze(0).to("cuda") - # Prediction - with torch.no_grad(): - preds = birefnet(input_images)[-1].sigmoid().cpu() - pred = preds[0].squeeze() - pred_pil = transforms.ToPILImage()(pred) - mask = pred_pil.resize(image_size) - image.putalpha(mask) - return image - -def process_file(f): - name_path = f.rsplit(".",1)[0]+".png" - im = load_img(f, output_type="pil") - im = im.convert("RGB") - transparent = process(im) - transparent.save(name_path) - return name_path - -slider1 = ImageSlider(label="RMBG-2.0", type="pil") -slider2 = ImageSlider(label="RMBG-2.0", type="pil") -image = gr.Image(label="Upload an image") -image2 = gr.Image(label="Upload an image",type="filepath") -text = gr.Textbox(label="Paste an image URL") -png_file = gr.File(label="output png file") - - -chameleon = load_img("giraffe.jpg", output_type="pil") - -url = "http://farm9.staticflickr.com/8488/8228323072_76eeddfea3_z.jpg" - -tab1 = gr.Interface( - fn, inputs=image, outputs=[slider1, gr.File(label="output png file")], examples=[chameleon], api_name="image" -) - -tab2 = gr.Interface(fn, inputs=text, outputs=[slider2, gr.File(label="output png file")], examples=[url], api_name="text") -tab3 = gr.Interface(process_file, inputs=image2, outputs=png_file, examples=["giraffe.jpg"], api_name="png") - - -demo = gr.TabbedInterface( - [tab1, tab2], ["input image", "input url"], title = ( - "RMBG-2.0 for background removal
" - "" - "Background removal model developed by " - "BRIA.AI, trained on a carefully selected dataset,
" - "and is available as an open-source model for non-commercial use.
" - " For testing upload your image and wait.
" - "Commercial use license | " - "Model card | " - "Blog" - "
" - "" - "API Endpoint available on: " - "Bria.ai, " - "fal.ai
" - "ComfyUI node is available here: " - "ComfyUI Node
" - "Purchase commercial weigths for commercial use: " - "here" - "" -) - - - -) - -if __name__ == "__main__": - demo.launch(show_error=True) \ No newline at end of file diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..e69de29