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import PIL
import numpy as np
import gradio as gr
from algorithm.pipeline import Pipeline
class App:
def __init__(self,
nmf='L1NormRegularizedNMF',
dataset='YaleB',
reduce=3,
noise_type='salt_and_pepper',
noise_level=0.10,
random_state=99,
scaler='MinMax'):
self.pipeline = Pipeline(nmf=nmf,
dataset=dataset,
reduce=reduce,
noise_type=noise_type,
noise_level=noise_level,
random_state=random_state,
scaler=scaler)
def align_reduce(self, dataset_name):
return 1 if dataset_name == 'ORL' else 3
def reset_pipeline(self, nmf, dataset, reduce, noise_type, noise_level, random_state, scaler):
noise_type, noise_level = self.convert_level_to_number(noise_type, noise_level)
self.pipeline = Pipeline(nmf=nmf,
dataset=dataset,
reduce=reduce,
noise_type=noise_type,
noise_level=noise_level,
random_state=random_state,
scaler=scaler)
def convert_level_to_number(self, type, level):
map_dict = {"Uniform": {"Low": 0.1, "High": 0.3},
"Gaussian": {"Low": 0.05, "High": 0.08},
"Laplacian": {"Low": 0.04, "High": 0.06},
"Salt & Pepper": {"Low": 0.02, "High": 0.1},
"Block": {"Low": 10, "High": 15}}
type_name = type.lower() if type != "Salt & pepper" else "salt_and_pepper"
return type_name, map_dict[type][level]
def execute(self, max_iter=500, idx=9):
self.pipeline.execute(max_iter=max_iter)
return *self.visualize(idx), *self.metrics()
def visualize(self, idx=9):
image_raw, image_noise, image_recon = self.pipeline.visualization(idx=idx)
return self.array2image(image_raw), self.array2image(image_noise), self.array2image(image_recon)
def metrics(self):
return self.pipeline.metrics
def array2image(self, array):
image_size = self.pipeline.img_size
return PIL.Image.fromarray(self.scale_pixel(array)).resize((image_size))
def scale_pixel(self, image):
return ((image - image.min()) / (image.max() - image.min()) * 255).astype(np.uint8)
def clear_params(self):
self.pipeline = Pipeline()
return 'L1NormRegularizedNMF', 'YaleB', 3, 'Salt & Pepper', 'Low', 99, 'MinMax'
app = App()
image_size = app.pipeline.img_size
with gr.Blocks() as demo:
gr.Markdown("# NMF Image Reconstruction")
with gr.Row():
with gr.Group():
with gr.Row():
nmf = gr.Dropdown(
label="NMF Algorithm",
choices=['L1NormRegularizedNMF', 'L2NormNMF', 'KLDivergenceNMF',
'ISDivergenceNMF', 'L21NormNMF', 'HSCostNMF',
'CappedNormNMF', 'CauchyNMF'],
value='L1NormRegularizedNMF',
info="Choose the NMF algorithm.")
dataset = gr.Dropdown(
label="Dataset",
choices=['ORL', 'YaleB'],
value='YaleB',
info="Choose the dataset.")
reduce = gr.Number(
value=3,
label="Reduce",
info="Choose the reduce.")
with gr.Row():
noise_type = gr.Dropdown(
label="Noise Type",
choices=['Uniform', 'Gaussian', 'Laplacian', 'Salt & Pepper', 'Block'],
value='Salt & Pepper',
info="Choose the noise type.")
noise_level = gr.Radio(
choices=['Low', 'High'],
value='Low',
label="Noise Level",
info="Choose the noise level."
)
with gr.Row():
random_state = gr.Number(
value=99,
label="Random State",
info="Choose the random state.",)
scaler = gr.Dropdown(
label="Scaler",
choices=['MinMax', 'Standard'],
value='MinMax',
info="Choose the scaler.")
with gr.Row():
max_iter= gr.Number(
value=500,
label="Max Iteration",
info="Choose the max iteration.")
idx = gr.Number(
value=9,
label="Image Index",
info="Choose the image index.")
with gr.Row():
execute_bt = gr.Button(value="Execute Algorithm",)
clear_params_bt = gr.Button(
value="Clear Parameters")
with gr.Group():
with gr.Row():
output_image_raw = gr.Image(
height=image_size[1],
width=image_size[0],
image_mode="L",
label="Original Image",
show_download_button=True,
show_share_button=True,)
output_image_noise = gr.Image(
height=image_size[1],
width=image_size[0],
label="Noisy Image",
image_mode="L",
show_download_button=True,
show_share_button=True,)
output_image_recon = gr.Image(
height=image_size[1],
width=image_size[0],
label="Reconstructed Image",
image_mode="L",
show_download_button=True,
show_share_button=True,)
with gr.Row():
rmse = gr.Number(
label="RMSE",
info="Average root mean square error",
precision=4,)
acc = gr.Number(
label="Acc",
info="Accuracy",
precision=4,)
nmi = gr.Number(
label="NMI",
info="Normalized mutual information",
precision=4,)
clear_output_bt = gr.ClearButton(
value="Clear Output",
components=[output_image_raw, output_image_noise, output_image_recon, rmse, acc, nmi],)
nmf.input(app.reset_pipeline, inputs=[nmf, dataset, reduce, noise_type, noise_level, random_state, scaler])
dataset.input(app.reset_pipeline, inputs=[nmf, dataset, reduce, noise_type, noise_level, random_state, scaler])
dataset.input(app.align_reduce, inputs=[dataset], outputs=[reduce])
reduce.input(app.reset_pipeline, inputs=[nmf, dataset, reduce, noise_type, noise_level, random_state, scaler])
noise_type.input(app.reset_pipeline, inputs=[nmf, dataset, reduce, noise_type, noise_level, random_state, scaler])
noise_level.input(app.reset_pipeline, inputs=[nmf, dataset, reduce, noise_type, noise_level, random_state, scaler])
random_state.input(app.reset_pipeline, inputs=[nmf, dataset, reduce, noise_type, noise_level, random_state, scaler])
scaler.input(app.reset_pipeline, inputs=[nmf, dataset, reduce, noise_type, noise_level, random_state, scaler])
idx.input(app.visualize, inputs=[idx], outputs=[output_image_raw, output_image_noise, output_image_recon])
execute_bt.click(app.execute, inputs=[max_iter, idx], outputs=[output_image_raw, output_image_noise, output_image_recon, rmse, acc, nmi])
clear_params_bt.click(app.clear_params, outputs=[nmf, dataset, reduce, noise_type, noise_level, random_state, scaler])
if __name__ == '__main__':
demo.queue()
demo.launch(inbrowser=True,
share=True,
server_name="0.0.0.0",
server_port=8080) |