first working space

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.mat filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,64 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# pyenv
+.python-version
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# vscode
+.vscode/

README.md

@@ -1,6 +1,6 @@
 ---
-title: Hyperspectral Oilspill Detection
-emoji: 😻
+title: Hyperspectral Oil Spill Detection
+emoji: 🛢️
 colorFrom: purple
 colorTo: blue
 sdk: streamlit
@@ -8,6 +8,7 @@ sdk_version: 1.43.2
 app_file: app.py
 pinned: false
 license: mit
+description: Model for Detecting Oil Spills Using Hyperspectral Images
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,53 @@
+import streamlit as st
+from scipy.io import loadmat
+import imageio.v2 as imageio
+import numpy as np
+
+MAT_FILE = "data/GM18.mat"
+PRED_FILE = "data/GM18_pred.png"
+
+@st.cache_data
+def load_data():
+    input_data = loadmat(MAT_FILE)
+    input_image = input_data["img"]
+    gt_image = input_data["map"]
+    pred_image = imageio.imread(PRED_FILE)
+    # normalize the images between max and min
+    gt_image = (gt_image - gt_image.min()) / (gt_image.max() - gt_image.min())
+    pred_image = (pred_image - pred_image.min()) / \
+        (pred_image.max() - pred_image.min())
+    # normalize between 5 and 95 percentile
+    input_image = (input_image - np.percentile(input_image, 5, axis=(0, 1))) / \
+        (np.percentile(input_image, 95, axis=(0, 1)) -
+         np.percentile(input_image, 5, axis=(0, 1)))
+    input_image = np.clip(input_image, 0, 1)
+    return input_image, gt_image, pred_image
+
+def main():
+    # add a title
+    st.title("Hyperspectral Sea Oil Spill Detection")
+    # add description 
+    st.write("This app displays an example of hyperspectral image of a sea oil spill, ground truth, and predicted image.")
+    input_image, gt_image, pred_image = load_data()
+    # Add a slider to select the channel
+    channel = st.slider("Hypersepctral images have a large number of channels, select a channel to display:", min_value=1, max_value=224, value=1)
+    # Create columns
+    col1, col2, col3 = st.columns(3)
+    # Display the images in columns
+    with col1:
+        st.image(input_image[:, :, channel-1], caption=f"Channel {channel}", use_container_width=True)
+    with col2:
+        st.image(gt_image, caption="Ground Truth", use_container_width=True)
+    with col3:
+        st.image(pred_image, caption="Predicted", use_container_width=True)
+    # print a table with the metrics
+    st.write("## Results")
+    st.write("The following table shows the metrics of the predicted image compared to the ground truth. The metrics are accuracy and intersection over union. The values are between 0 and 1, where 1 is the best value.")
+    # add a table with the results
+    st.table({
+        "Metric": ["Accuracy, sea", "Accuracy, oil", "Accuracy, average", "IOU, sea", "IOU, oil", "IOU, average"],
+        "Result": [0.962, 0.934, 0.948, 0.954, 0.719, 0.837]
+    })
+
+if __name__ == "__main__":
+    main()

data/GM18.mat

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cc2d55311834acf32912969cfb9ae8bfce37be06e402d04bb3183f4afe4f1e7a
+size 100464295

requirements.txt

@@ -0,0 +1,3 @@
+scipy==1.15.2
+imageio==2.37.0
+numpy==2.2.4