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import gradio as gr
import os
import cv2
def inference(file, mask, model, alpha_influence, segmentation_strength):
im = cv2.imread(file, cv2.IMREAD_COLOR)
cv2.imwrite(os.path.join("input.png"), im)
from rembg import new_session, 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,
session=new_session(model),
only_mask=(True if mask == "Mask only" else False),
alpha=alpha_influence, # Control de influencia del canal alfa
bg_color=(0, 0, 0, segmentation_strength) # Control de fuerza de segmentación
)
o.write(output)
return os.path.join("output.png")
title = "RemBG"
description = "Gradio demo for RemBG. To use it, simply upload your image and adjust the alpha influence and segmentation strength."
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.inputs.Radio(
[
"Default",
"Mask only"
],
type="value",
default="Default",
label="Choices"
),
gr.inputs.Dropdown([
"u2net",
"u2netp",
"u2net_human_seg",
"u2net_cloth_seg",
"silueta",
"isnet-general-use",
"isnet-anime",
"sam",
],
type="value",
default="isnet-general-use",
label="Models"
),
gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.5, label="Alpha Influence"),
gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.5, label="Segmentation Strength"),
],
gr.outputs.Image(type="filepath", label="Output"),
title=title,
description=description,
article=article,
examples=[["lion.png", "Default", "u2net", 0.5, 0.5], ["girl.jpg", "Default", "u2net", 0.5, 0.5], ["anime-girl.jpg", "Default", "isnet-anime", 0.5, 0.5]],
enable_queue=True
).launch()
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