import os import numpy as np import random import segmentation_models as sm from matplotlib import pyplot as plt 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 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('/content/saved_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)