Created app.py

app.py

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+import streamlit as st
+import cv2
+import supervision as sv
+from ultralytics import YOLO
+import numpy as np
+from PIL import Image
+import io
+import torch
+
+# Load the YOLO model
+@st.cache_resource
+def load_model():
+    model = YOLO("mosaic_medium_100_tiny_object.pt")  
+    model.to('cpu')  
+    return model
+
+model = load_model()
+
+def process_image(image):
+    # Convert PIL Image to numpy array
+    image_np = np.array(image)
+    
+    # Convert RGB to BGR (OpenCV uses BGR)
+    image_cv2 = cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR)
+
+    def callback(image_slice: np.ndarray) -> sv.Detections:
+        result = model(image_slice)[0]
+        return sv.Detections.from_ultralytics(result)
+
+    slicer = sv.InferenceSlicer(callback=callback, slice_wh=(256, 256), iou_threshold=0.8)
+
+    detections = slicer(image_cv2)
+
+    # Filter detections for building class (assuming class_id 2 is for buildings)
+    building_detections = detections[detections.class_id == 2]
+
+    label_annotator = sv.LabelAnnotator()
+    box_annotator = sv.BoxAnnotator()
+
+    annotated_image = box_annotator.annotate(scene=image_cv2.copy(), detections=building_detections)
+    annotated_image = label_annotator.annotate(scene=annotated_image, detections=building_detections)
+
+    # Convert BGR back to RGB for displaying in Streamlit
+    return cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB)
+
+def main():
+    st.title("Building Detection App")
+
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file)
+        st.image(image, caption="Uploaded Image", use_column_width=True)
+
+        if st.button("Detect Buildings"):
+            with st.spinner("Processing..."):
+                result_image = process_image(image)
+                st.image(result_image, caption="Processed Image", use_column_width=True)
+
+if __name__ == "__main__":
+    main()