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import os | |
import numpy as np | |
import random | |
import segmentation_models as sm | |
from matplotlib import pyplot as plt | |
import tensorflow as tf | |
from tensorflow.keras import backend as K | |
from tensorflow.keras.models import load_model | |
import gradio as gr | |
def jaccard_coef(y_true, y_pred): | |
y_true_flatten = K.flatten(y_true) | |
y_pred_flatten = K.flatten(y_pred) | |
intersection = K.sum(y_true_flatten * y_pred_flatten) | |
final_coef_value = (intersection + 1.0) / (K.sum(y_true_flatten) + K.sum(y_pred_flatten) - intersection + 1.0) | |
return final_coef | |
weights = [0.1666, 0.1666, 0.1666, 0.1666, 0.1666, 0.1666] | |
dice_loss = sm.losses.DiceLoss(class_weights = weights) | |
focal_loss = sm.losses.CategoricalFocalLoss() | |
total_loss = dice_loss + focal_loss | |
saved_model = load_model('satellite_segmentation_model.h5', | |
custom_objects=({'dice_loss_plus_focal_loss': total_loss, 'jaccard_coef': jaccard_coef})) | |
def process_input_image(image_source): | |
image = np.expand_dims(image_source, 0) | |
prediction = saved_model.predict(image) | |
predicted_image = np.argmax(prediction, axis=3) | |
predicted_image = predicted_image[0, :, :] | |
predicted_image = predicted_image * 50 | |
return 'Predicted Masked Image', predicted_image | |
my_app = gr.Blocks() | |
with my_app: | |
gr.Markdown("Statellite Image Segmentation Application UI with Gradio") | |
with gr.Tabs(): | |
with gr.TabItem("Select your image"): | |
with gr.Row(): | |
with gr.Column(): | |
img_source = gr.Image(label="Please select source Image", shape=(256, 256)) | |
source_image_loader = gr.Button("Load above Image") | |
with gr.Column(): | |
output_label = gr.Label(label="Image Info") | |
img_output = gr.Image(label="Image Output") | |
source_image_loader.click( | |
process_input_image, | |
[ | |
img_source | |
], | |
[ | |
output_label, | |
img_output | |
] | |
) | |
my_app.launch(debug = True) |