Pathovis/basic_preprocessing_steps

1from read_whole_sliding_images import *2from stain_normalization import *3import gradio as gr4wsi2png = gr.Interface(fn=wsi2png, inputs=gr.File(), outputs=gr.Image(type='numpy'),5                       examples=wsi2png_examples)6bound_png = gr.Interface(fn=read_bounds_of_image, inputs=gr.Image(type='numpy'),7                         outputs=gr.Image(type='numpy'), examples=read_bounds_examples)8 9 10inp1 = gr.File()11inp2 = gr.Slider(1, 10, 4, step=0.25, label="Resolution")12# mask_wsi = gr.Interface(fn=mask_image, inputs=[inp1, inp2], outputs=gr.Image(type='numpy'), examples=mask_wsi_examples)13 14normalize_wsi = gr.Interface(fn=normalize_stain, inputs=gr.File(), outputs=gr.Image(type='numpy'),15                               examples=stain_normalization_wsi_examples)16 17 18# demo = gr.TabbedInterface([wsi2png, bound_png, mask_wsi, normalize_wsi], ["WSI2PNG", "Bound Image", "Mask WSI", "Normalize Stain"])19demo = gr.TabbedInterface([wsi2png, bound_png, normalize_wsi], ["WSI2PNG", "Bound Image",20                                                                          "Normalize Stain"])21 22if __name__ == "__main__":23    demo.launch(show_api=True)