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import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from huggingface_hub import snapshot_download, from_pretrained_keras
import gradio as gr
# Load the model
model = from_pretrained_keras("alexanderkroner/MSI-Net")
hf_dir = snapshot_download(repo_id="alexanderkroner/MSI-Net")
def get_target_shape(original_shape):
original_aspect_ratio = original_shape[0] / original_shape[1]
square_mode = abs(original_aspect_ratio - 1.0)
landscape_mode = abs(original_aspect_ratio - 240 / 320)
portrait_mode = abs(original_aspect_ratio - 320 / 240)
best_mode = min(square_mode, landscape_mode, portrait_mode)
if best_mode == square_mode:
return (320, 320)
elif best_mode == landscape_mode:
return (240, 320)
else:
return (320, 240)
def preprocess_input(input_image, target_shape):
input_tensor = tf.expand_dims(input_image, axis=0)
input_tensor = tf.image.resize(input_tensor, target_shape, preserve_aspect_ratio=True)
vertical_padding = target_shape[0] - input_tensor.shape[1]
horizontal_padding = target_shape[1] - input_tensor.shape[2]
vertical_padding_1 = vertical_padding // 2
vertical_padding_2 = vertical_padding - vertical_padding_1
horizontal_padding_1 = horizontal_padding // 2
horizontal_padding_2 = horizontal_padding - horizontal_padding_1
input_tensor = tf.pad(
input_tensor,
[
[0, 0],
[vertical_padding_1, vertical_padding_2],
[horizontal_padding_1, horizontal_padding_2],
[0, 0],
],
)
return input_tensor, [vertical_padding_1, vertical_padding_2], [horizontal_padding_1, horizontal_padding_2]
def postprocess_output(output_tensor, vertical_padding, horizontal_padding, original_shape):
output_tensor = output_tensor[
:,
vertical_padding[0] : output_tensor.shape[1] - vertical_padding[1],
horizontal_padding[0] : output_tensor.shape[2] - horizontal_padding[1],
:,
]
output_tensor = tf.image.resize(output_tensor, original_shape)
return output_tensor.numpy().squeeze()
def process_image(input_image):
input_image = np.array(input_image, dtype=np.float32)
original_shape = input_image.shape[:2]
target_shape = get_target_shape(original_shape)
input_tensor, vertical_padding, horizontal_padding = preprocess_input(input_image, target_shape)
output_tensor = model(input_tensor)["output"]
saliency_gray = postprocess_output(output_tensor, vertical_padding, horizontal_padding, original_shape)
total_saliency = np.sum(saliency_gray)
saliency_rgb = plt.cm.inferno(saliency_gray)[..., :3]
alpha = 0.9
blended_image = alpha * saliency_rgb + (1 - alpha) * input_image / 255
return blended_image, f"Total grayscale saliency: {total_saliency:.2f}"
def predict_single(image):
return process_image(image)
def predict_dual(image1, image2):
result1_img, result1_val = process_image(image1)
result2_img, result2_val = process_image(image2)
return result1_img, result1_val, result2_img, result2_val
with gr.Blocks(title="MSI-Net Saliency App") as demo:
gr.Markdown("## MSI-Net Saliency Map Viewer")
with gr.Tabs():
with gr.Tab("Single Image"):
gr.Markdown("### Upload an image to see its saliency map and total grayscale saliency value.")
with gr.Row():
input_image_single = gr.Image(type="pil", label="Input Image")
with gr.Row():
output_image_single = gr.Image(type="numpy", label="Saliency Map")
output_text_single = gr.Textbox(label="Grayscale Sum")
submit_single = gr.Button("Generate Saliency")
submit_single.click(fn=predict_single, inputs=input_image_single, outputs=[output_image_single, output_text_single])
with gr.Tab("Compare Two Images"):
gr.Markdown("### Upload two images to compare their saliency maps and grayscale saliency values.")
with gr.Row():
input_image1 = gr.Image(type="pil", label="Image 1")
input_image2 = gr.Image(type="pil", label="Image 2")
with gr.Row():
output_image1 = gr.Image(type="numpy", label="Saliency Map 1")
output_text1 = gr.Textbox(label="Grayscale Sum 1")
output_image2 = gr.Image(type="numpy", label="Saliency Map 2")
output_text2 = gr.Textbox(label="Grayscale Sum 2")
submit_dual = gr.Button("Compare Saliency")
submit_dual.click(fn=predict_dual, inputs=[input_image1, input_image2], outputs=[output_image1, output_text1, output_image2, output_text2])
demo.launch(share=True)
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