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import gradio as gr
import io
from PIL import Image
import numpy as np
from models import make_image_controlnet, make_inpainting
from preprocessing import preprocess_seg_mask, get_image, get_mask
def image_to_byte_array(image: Image) -> bytes:
# BytesIO is a fake file stored in memory
imgByteArr = io.BytesIO()
# image.save expects a file as a argument, passing a bytes io ins
image.save(imgByteArr, format='png') # image.format
# Turn the BytesIO object back into a bytes object
imgByteArr = imgByteArr.getvalue()
return imgByteArr
def predict(input_img1,input_img2):
print("predict")
input_img1 = input_img1.resize((512, 512))
input_img2 = input_img2.resize((512, 512))
canvas_mask = np.array(input_img2)
mask = get_mask(canvas_mask)
print(input_img1, mask)
result_image = make_inpainting(positive_prompt='test1',
image=input_img1,
mask_image=mask,
negative_prompt="xxx",
)
return result_image
gradio_app = gr.Interface(
predict,
inputs=[gr.Image(label="img", sources=['upload', 'webcam'], type="pil"),
gr.Image(label="mask", sources=['upload', 'webcam'], type="pil")
],
outputs= gr.Image(label="resp"),
title="rem fur 1",
)
gradio_app.launch(share=True)
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