Duplicate from Paushigaa/semantic-segmentation-of-aerial-imagery

Co-authored-by: Paushigaa S <[email protected]>

.gitattributes

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README.md

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+---
+title: Semantic Segmentation Of Aerial Imagery
+emoji: 🌖
+colorFrom: yellow
+colorTo: yellow
+sdk: gradio
+sdk_version: 3.36.1
+app_file: app.py
+pinned: false
+license: mit
+duplicated_from: Paushigaa/semantic-segmentation-of-aerial-imagery
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import os
+os.environ["SM_FRAMEWORK"] = "tf.keras"
+from tensorflow import keras
+import cv2
+from PIL import Image
+import numpy as np
+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
+
+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()

model/model.txt

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+This project detects various segmentations of satellite images using the U-Net model. 

model/satellite-segmentation-full.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ea13be9dd68c5edb9072c85815e9be3db29650f5f327c9b4a31c6ae973511254
+size 23503352

requirements.txt

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+segmentation-models
+gradio
+opencv-python
+keras
+tensorflow