Init

best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9379f467a6e97a72bbda1ff81096e56f8f87831660b29436fc5783a043e0b94b
+size 23815362

main.py

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+import streamlit as st
+import numpy as np
+from ultralytics import YOLO
+from PIL import Image
+import supervision as sv
+
+model = YOLO('best.pt')
+mask_annotator = sv.MaskAnnotator()
+box_annontator = sv.BoxAnnotator()
+
+
+st.title("Yolov8 Solar Panel Segmentation")
+
+uploaded_file = st.file_uploader("Upload an image", type=["png", "jpg", "jpeg"], accept_multiple_files=False)
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file).convert('RGB')
+    image_arr = np.array(image)
+    
+    result = model.predict(image)[0] # Use `image` because of RGB to BGR conversion 
+    
+    detections = sv.Detections.from_ultralytics(result)
+    
+    labels = [
+        f"solar panel {confidence:0.2f}"
+        for _, _, confidence, _, _
+        in detections
+    ]
+    annotated_image = mask_annotator.annotate(image_arr, detections)
+    annotated_image = box_annontator.annotate(annotated_image, detections, labels=labels)
+    
+    
+    st.image(annotated_image, caption='Uploaded Image.', use_column_width=True)

requirements.txt

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+torch
+torchvision
+ultralytics
+streamlit
+supervision