app.py

import os
import cv2
from PIL import Image 
import numpy as np 
from patchify import patchify
import segmentation_models as sm
from sklearn.preprocessing import MinMaxScaler, StandardScaler
from matplotlib import pyplot as plt
import random
from keras import backend as K
from 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_value


weights=[0.166,0.166,0.166,0.166,0.166,0.166]
dice_loss=sm.losses.DiceLoss(class_weights=weights)
focal_loss=sm.losses.CategoricalFocalLoss()
total_loss=dice_loss+(1*focal_loss)



saved1_model = load_model('model/DsN_model_segmentation.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 = saved1_model.predict(image)
  predicted_image = np.argmax(prediction, axis=3)

  predicted_image = predicted_image[0,:,:]
  predicted_image = predicted_image * 50
  print(predicted_image)
  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)