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# prompt: save the gradio app into a file
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
# Note: 'keras<3.x' or 'tf_keras' must be installed (legacy)
# See https://github.com/keras-team/tf-keras for more details.
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:
target_shape = (320, 320)
elif best_mode == landscape_mode:
target_shape = (240, 320)
else:
target_shape = (320, 240)
return target_shape
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)
output_array = output_tensor.numpy().squeeze()
output_array = plt.cm.inferno(output_array)[..., :3]
return output_array
def predict_saliency(image):
input_image = np.array(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_map = postprocess_output(output_tensor, vertical_padding, horizontal_padding, original_shape)
alpha = 0.65
blended_image = alpha * saliency_map + (1 - alpha) * input_image / 255
return blended_image
iface = gr.Interface(
fn=predict_saliency,
inputs=gr.Image(type="pil"),
outputs=gr.Image(type="numpy"),
title="MSI-Net Saliency Map",
description="Upload an image to generate its saliency map.",
)
iface.launch(share=True) |