created app.py

app.py

@@ -0,0 +1,70 @@
+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()