first

app.py

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+import os
+import cv2
+import numpy as np
+import streamlit as st
+import tensorflow as tf
+
+
+def process_image(uploaded_file):
+    file_content = uploaded_file.read()
+    nparr = np.frombuffer(file_content, np.uint8)
+    image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+    return image
+
+
+model = tf.saved_model.load("./model")
+
+
+def perform_segmentation(image):
+    resized_image = cv2.resize(image, (128, 128))
+    resized_image = cv2.cvtColor(resized_image, cv2.COLOR_BGR2RGB)
+    normalized_image = resized_image / 255.0
+
+    input_array = np.expand_dims(normalized_image, axis=0).astype(np.float32)
+    segmented_mask = model(tf.constant(input_array))[0]
+
+    threshold = 0.5
+    binary_mask = (segmented_mask > threshold).numpy().astype(np.uint8)
+
+    # Resize the binary mask to match the size of the input image
+    binary_mask_resized = cv2.resize(binary_mask, (image.shape[1], image.shape[0]))
+
+    segmented_image = cv2.bitwise_and(image, image, mask=binary_mask_resized)
+
+    return segmented_image
+
+
+def main():
+    st.title("Solution Challenge")
+
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+    if uploaded_file is not None:
+        image = process_image(uploaded_file)
+        segmented_image = perform_segmentation(image)
+
+        # Create two columns to display images side by side
+        col1, col2 = st.columns(2)
+
+        # Display original image in the first column
+        col1.image(image, caption="Original Image", use_column_width=True)
+
+        # Display segmented image in the second column
+        col2.image(
+            segmented_image, caption="Segmentation Results", use_column_width=True
+        )
+
+
+if __name__ == "__main__":
+    main()

model/fingerprint.pb

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8bef387a304bfd676326f7d2ec4811ccae1f3bb1f4746953f3df145213f4fa67
+size 55

model/saved_model.pb

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+version https://git-lfs.github.com/spec/v1
+oid sha256:732f4eba03c3087cc644415be96778eee346cb6a4799f6c19d9134aa7c7defda
+size 1605495

requirements.txt

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+streamlit
+tensorflow
+opencv-python