Landmark Detection from images

Landmark Detection/Web app/README.md

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+# Landmark-Detection
+Landmark Detection detects popular natural and human-made structures within an image.

Landmark Detection/Web app/app.py

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+import streamlit as st
+from PIL import Image
+
+import numpy as np
+import pandas as pd
+import tensorflow as tf
+import tensorflow_hub as hub # type: ignore
+
+# Load the model
+TF_MODEL_URL = 'https://tfhub.dev/google/on_device_vision/classifier/landmarks_classifier_asia_V1/1'
+IMAGE_SHAPE = (321, 321)
+
+classifier = tf.keras.Sequential([
+    tf.keras.layers.InputLayer(shape=IMAGE_SHAPE+(3,)),
+    tf.keras.layers.Lambda(lambda x: hub.KerasLayer(TF_MODEL_URL, output_key='predictions:logits')(x))
+])
+
+# Load the label map
+LABEL_MAP_URL = 'https://www.gstatic.com/aihub/tfhub/labelmaps/landmarks_classifier_asia_V1_label_map.csv'
+df = pd.read_csv(LABEL_MAP_URL)
+label_map = dict(zip(df.id, df.name))
+
+# Define the prediction function
+def classify_image(image):
+    img = np.array(image)/255.0
+    img = img[np.newaxis, ...]
+    prediction = classifier.predict(img)
+    return label_map[np.argmax(prediction)]
+
+# Streamlit app
+st.title("Landmark Detection Web App")
+
+uploaded_file = st.file_uploader("Choose an image...", type="jpeg")
+
+if uploaded_file is not None:
+    st.success("Image uploaded successfully!")
+    image = Image.open(uploaded_file).resize(IMAGE_SHAPE)
+    st.image(image, caption='Uploaded Image.', use_column_width=True)
+
+    if st.button("Classify Image"):
+        with st.spinner('Classifying...'):
+            label = classify_image(image)
+        st.success(f"Prediction: {label}")

Landmark Detection/requirements.txt

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+streamlit
+Pillow
+numpy
+pandas
+tensorflow
+tensorflow-hub