| from pathlib import Path |
| import pickle |
| import json |
| import zipfile |
|
|
| import keras |
| import numpy as np |
| from PIL import Image |
| import streamlit as st |
|
|
|
|
| ROOT = Path(__file__).resolve().parents[1] |
| SATELLITE_LABELS = [ |
| "Sea or Lake", "Highway", "River", "Pasture", "Industrial", |
| "Residential", "Permanent Crop", "Annual Crop", "Forest", |
| "Herbaceous Vegetation", |
| ] |
| st.set_page_config(page_title="Traffic & Satellite Vision Hub", page_icon="🛰️", layout="wide") |
|
|
|
|
| @st.cache_resource |
| def load_traffic_assets(): |
| model = keras.models.load_model(ROOT / "traffic_sign.h5", compile=False) |
| with open(ROOT / "labels.pkl", "rb") as file: |
| labels = pickle.load(file) |
| return model, labels |
|
|
|
|
| @st.cache_resource |
| def load_satellite_model(): |
| path = ROOT / "satellite_image_tf.keras" |
| try: |
| return keras.models.load_model(path, compile=False) |
| except ValueError: |
| |
| with zipfile.ZipFile(path) as archive: |
| config = json.loads(archive.read("config.json")) |
| layers = config["config"]["layers"] |
| raw_base = keras.saving.deserialize_keras_object( |
| layers[1], custom_objects={"Functional": keras.Model} |
| ) |
| base = keras.Model(raw_base.input, raw_base.output[0]) |
| tail = [keras.layers.deserialize(layer) for layer in layers[2:]] |
| model = keras.Sequential([keras.Input((224, 224, 3)), base, *tail]) |
| model.load_weights(path) |
| return model |
|
|
|
|
| def image_array(image, size): |
| resized = image.convert("RGB").resize(size, Image.Resampling.LANCZOS) |
| return resized, np.expand_dims(np.asarray(resized, dtype="float32") / 255.0, 0) |
|
|
|
|
| def show_ranked(prediction, labels, count=3): |
| indices = np.argsort(prediction)[-count:][::-1] |
| for rank, index in enumerate(indices, 1): |
| st.write(f"{rank}. **{labels[int(index)]}** — {float(prediction[index]):.1%}") |
|
|
|
|
| st.title("🛰️ Traffic & Satellite Vision Hub") |
| st.caption("Two end-to-end computer-vision projects with real TensorFlow model inference.") |
| traffic_tab, satellite_tab = st.tabs(["German Traffic Signs", "Satellite Land Cover"]) |
|
|
| with traffic_tab: |
| st.subheader("German traffic sign recognition") |
| st.write("Photograph or upload a close, centered traffic sign. The model recognizes 43 GTSRB classes.") |
| camera = st.camera_input("Take a traffic sign photo") |
| uploaded = st.file_uploader("Or upload an image", ["jpg", "jpeg", "png"], key="traffic") |
| traffic_file = camera if camera is not None else uploaded |
| if traffic_file: |
| image = Image.open(traffic_file) |
| st.image(image, width=340) |
| if st.button("Classify traffic sign", type="primary", use_container_width=True): |
| model, label_map = load_traffic_assets() |
| _, model_input = image_array(image, (30, 30)) |
| prediction = np.asarray(model.predict(model_input, verbose=0))[0] |
| best = int(np.argmax(prediction)) |
| st.metric("Prediction", label_map[best], f"{float(prediction[best]):.1%} confidence") |
| st.subheader("Top 3 predictions") |
| show_ranked(prediction, label_map) |
| if float(prediction[best]) < 0.65: |
| st.warning("Low confidence: crop the image so the sign is clear and fills most of the frame.") |
|
|
| with satellite_tab: |
| st.subheader("EuroSAT land-cover classification") |
| st.write("Upload a top-down satellite tile. The EfficientNet-based model distinguishes 10 land-cover types.") |
| satellite_file = st.file_uploader("Upload satellite image", ["jpg", "jpeg", "png"], key="satellite") |
| if satellite_file: |
| image = Image.open(satellite_file) |
| st.image(image, width=340) |
| if st.button("Classify land cover", type="primary", use_container_width=True): |
| model = load_satellite_model() |
| _, model_input = image_array(image, (224, 224)) |
| prediction = np.asarray(model.predict(model_input, verbose=0))[0] |
| best = int(np.argmax(prediction)) |
| st.metric("Predicted land cover", SATELLITE_LABELS[best], f"{float(prediction[best]):.1%} confidence") |
| st.subheader("Top 3 predictions") |
| show_ranked(prediction, SATELLITE_LABELS) |
|
|
| with st.expander("About these projects"): |
| st.write("The traffic model uses the 43-class German Traffic Sign Recognition Benchmark. The satellite model uses the 10-class EuroSAT Sentinel-2 land-cover benchmark. Predictions are portfolio demonstrations and should not be used for driving or operational mapping decisions.") |
|
|
