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import os
import cv2
from PIL import Image
import numpy as np
from sklearn.preprocessing import MinMaxScaler, StandardScaler
import segmentation_models as sm
from matplotlib import pyplot as plt
import random
from keras import backend as K
from keras.models import load_model
import gradio as gr
from google.colab import drive
drive.mount('/content/drive')
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_value
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 + (1 * focal_loss)
satellite_model = load_model('model/satellite-segmentation-full.h5',
custom_objects=({'dice_loss_plus_1focal_loss': total_loss,
'jaccard_coef': jaccard_coef}))
def process_input_image(image_source):
image = np.expand_dims(image_source, 0)
prediction = satellite_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)
my_app.close() |