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app.py

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+import streamlit as st
+from ultralytics import YOLO
+import numpy as np
+import cv2
+
+# Load models
+model = YOLO("best-3.pt")  # load a custom model for segmentation (protection zone)
+model2 = YOLO('yolo11s.pt')  # load a second model for object detection
+
+# Streamlit app title
+st.title("Protection Zone and Object Detection")
+
+# Upload image
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Read the image
+    image = uploaded_file.read()
+    image_np = np.frombuffer(image, np.uint8)
+    image_cv = cv2.imdecode(image_np, cv2.IMREAD_COLOR)
+    
+    # Predict protection zone with the first model
+    segment_results = model(image_cv)  # predict segments
+    protection_mask = np.zeros(image_cv.shape[:2], dtype=np.uint8)  # create an empty mask
+    
+    for result in segment_results:
+        if result.masks is not None:
+            for segment in result.masks.data:
+                # Convert segment to numpy array and ensure it's the right shape and type
+                segment_array = segment.cpu().numpy().astype(np.uint8)
+                segment_array = cv2.resize(segment_array, (image_cv.shape[1], image_cv.shape[0]))
+                protection_mask = cv2.bitwise_or(protection_mask, segment_array * 255)
+    
+    # Create a copy of the original image to draw on
+    output_image = image_cv.copy()
+    
+    # Overlay the protection zone mask on the output image
+    protection_overlay = cv2.applyColorMap(protection_mask, cv2.COLORMAP_COOL)
+    output_image = cv2.addWeighted(output_image, 0.7, protection_overlay, 0.3, 0)
+    
+    # Predict objects with the second model
+    object_results = model2(image_cv)  # predict objects using model2
+    
+    for result in object_results:
+        boxes = result.boxes.xyxy.cpu().numpy().astype(int)
+        for box in boxes:
+            x1, y1, x2, y2 = box
+            # Check if the object is within the protection zone
+            object_mask = np.zeros(image_cv.shape[:2], dtype=np.uint8)
+            object_mask[y1:y2, x1:x2] = 1  # create a mask for the object
+            # Check overlap
+            overlap = cv2.bitwise_and(protection_mask, object_mask)
+            color = (0, 0, 255) if np.sum(overlap) > 0 else (0, 255, 0)  # red if in zone, green if outside
+            # Draw bounding box around the object
+            cv2.rectangle(output_image, (x1, y1), (x2, y2), color, 2)
+    
+    # Display the final image
+    st.image(output_image, caption="Protection Zone and Detected Objects", channels="BGR")
+else:
+    st.write("Please upload an image to process.")

best-3.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:df8f4b98a7ee8cafb7f56513fa604f75a7f693efa4a11cc07d65f9234e52d666
+size 61130925

yolo11s.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:85a76fe86dd8afe384648546b56a7a78580c7cb7b404fc595f97969322d502d5
+size 19313732