Initial commit: Adding corrosion detection app

app.py

@@ -0,0 +1,41 @@
+import gradio as gr
+from ultralytics import YOLO
+import torch
+
+# Set device to CPU
+device = "cpu"
+
+# Load your trained YOLOv8 model
+model = YOLO('best.pt')
+model.to(device)
+
+def detect_corrosion(input_image):
+    """
+    Performs corrosion detection on the input image and returns the image with bounding boxes.
+    """
+    # Run inference on the image
+    results = model(input_image)
+
+    # The 'plot' method returns a NumPy array with the bounding boxes and labels drawn
+    plotted_image = results[0].plot() # This returns a BGR numpy array
+    
+    # Convert BGR to RGB for web display
+    plotted_image_rgb = plotted_image[..., ::-1]
+
+    return plotted_image_rgb
+
+# --- Gradio Interface ---
+# Create the interface without the examples
+iface = gr.Interface(
+    fn=detect_corrosion,
+    inputs=gr.Image(type="numpy", label="Upload Ship Hull Image"),
+    outputs=gr.Image(type="numpy", label="Detection Result"),
+    title="🚢 Corrosion Detection in Ship Hulls",
+    description="An AI-powered tool to detect corrosion patches on ship hulls. Upload an image, and the YOLOv8 model will highlight any detected corrosion areas.",
+    article="Model: YOLOv8 | Developed for maritime maintenance.",
+    allow_flagging="never"
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()

best.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1d1fe42a82fa16ae60b080a7f97b466cc0d7c5c1b060fcd8840466f4a5979cdf
+size 87652083

requirements.txt

@@ -0,0 +1,4 @@
+gradio
+ultralytics
+torch --index-url https://download.pytorch.org/whl/cpu
+torchvision --index-url https://download.pytorch.org/whl/cpu