| |
| |
|
|
| import gradio as gr |
| import os |
| import cv2 |
|
|
| def inference(file, af, mask): |
| im = cv2.imread(file, cv2.IMREAD_COLOR) |
| cv2.imwrite(os.path.join("input.png"), im) |
|
|
| from rembg import remove |
|
|
| input_path = 'input.png' |
| output_path = 'output.png' |
|
|
| with open(input_path, 'rb') as i: |
| with open(output_path, 'wb') as o: |
| input = i.read() |
| output = remove(input, alpha_matting_erode_size = af, only_mask = (True if mask == "Mask only" else False)) |
| o.write(output) |
| return os.path.join("output.png") |
| |
| title = "RemBG" |
| description = "Gradio demo for RemBG. To use it, simply upload your image and wait. Read more at the link below." |
| article = "<p style='text-align: center;'><a href='https://github.com/danielgatis/rembg' target='_blank'>Github Repo</a></p>" |
|
|
|
|
| gr.Interface( |
| inference, |
| [gr.inputs.Image(type="filepath", label="Input"), gr.Slider(10, 25, value=10, label="Alpha matting"), gr.Radio(choices = ["Alpha matting", "Mask only"], value = "Alpha matting")], |
| gr.outputs.Image(type="file", label="Output"), |
| title=title, |
| description=description, |
| article=article, |
| examples=[['lion.png']], |
| enable_queue=True |
| ).launch() |
