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import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
from skimage.transform import resize
import tensorflow as tf
from tensorflow.keras.models import load_model
from huggingface_hub import snapshot_download
import gradio as gr
import os
import io
REPO_ID = "amosfang/segmentation_u_net"
def pil_image_as_numpy_array(pilimg):
img_array = tf.keras.utils.img_to_array(pilimg)
return img_array
def resize_image(image, input_shape=(224, 224, 3)):
# Convert to NumPy array and normalize
image_array = pil_image_as_numpy_array(image)
image = image_array.astype(np.float32) / 255.
# Resize the image to 224x224
image_resized = resize(image, input_shape, anti_aliasing=True)
return image_resized
def load_model_file(filename):
model_dir = snapshot_download(REPO_ID)
saved_model_filepath = os.path.join(model_dir, filename)
unet_model = load_model(saved_model_filepath)
return unet_model
def ensemble_predict(X_array):
#
# Call the predict methods of the unet_model and the vgg16_unet_model
# to retrieve their predictions.
#
# Sum the two predictions together and return their results.
# You can also consider multiplying a different weight on
# one or both of the models to improve performance
X_array = np.expand_dims(X_array, axis=0)
unet_model = load_model_file('base_u_net.0098-acc-0.75-val_acc-0.74-loss-0.79.h5')
vgg16_model = load_model_file('vgg16_u_net.0092-acc-0.74-val_acc-0.74-loss-0.82.h5')
resnet50_model = load_model_file('resnet50_u_net.0095-acc-0.79-val_acc-0.76-loss-0.72.h5')
pred_y_unet = unet_model.predict(X_array)
pred_y_vgg16 = vgg16_model.predict(X_array)
pred_y_resnet50 = resnet50_model.predict(X_array)
return (pred_y_unet + pred_y_vgg16 + pred_y_resnet50) / 3
def get_predictions(y_prediction_encoded):
# Convert predictions to categorical indices
predicted_label_indices = np.argmax(y_prediction_encoded, axis=-1) + 1
return predicted_label_indices
def predict(image):
sample_image_resized = resize_image(image)
y_pred = ensemble_predict(sample_image_resized)
y_pred = get_predictions(y_pred).squeeze()
# Create a figure without saving it to a file
fig, ax = plt.subplots()
cax = ax.imshow(y_pred, cmap='viridis', vmin=1, vmax=7)
# Convert the figure to a PIL Image
image_buffer = io.BytesIO()
plt.savefig(image_buffer, format='png')
image_buffer.seek(0)
image_pil = Image.open(image_buffer)
# Close the figure to release resources
plt.close(fig)
return image_pil
# Specify paths to example images
sample_images = [['989953_sat.jpg'], ['999380_sat.jpg'], ['988205_sat.jpg']]
# Launch Gradio Interface
gr.Interface(
predict,
title='Land Cover Segmentation',
inputs=[gr.Image()],
outputs=[gr.Image()],
examples=sample_images
).launch(debug=True, share=True)
# Launch the interface
iface.launch(share=True) |