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app.py

@@ -1,35 +1,78 @@
 import streamlit as st
-from ultralytics import YOLO
-import numpy as np
-import cv2
 from PIL import Image
+import cv2
+import numpy as np
+from ultralytics import YOLO
+import os
+
+# Model Initialization
+model = YOLO('/content/yolo11l.pt')  # Adjust the path to your YOLOv11 model
+
+# Function to detect actions in images
+def detect_action(image_path):
+    results = model.predict(source=image_path, conf=0.25, save=False)
+    result = results[0]
+    detections = [
+        (model.names[int(box.cls[0])], float(box.conf[0])) for box in result.boxes
+    ]
+    
+    # Classify action based on detections
+    action_scores = classify_action(detections)
+    
+    return result.plot(), action_scores
 
-st.title("🔍 Suspicious Activity Detection with YOLOv11")
+def classify_action(detections):
+    detected_objects = [d[0] for d in detections]
+    
+    action_scores = {
+        'Stealing': 0.0,
+        'Sneaking': 0.0,
+        'Peaking': 0.0,
+        'Normal': 0.0
+    }
 
-# Load the model
-@st.cache_resource
-def load_model():
-    return YOLO("yolo11l.pt")
+    if 'person' in detected_objects:
+        if any(obj in detected_objects for obj in ['backpack', 'handbag', 'suitcase']):
+            action_scores['Stealing'] += 0.4
+        if 'refrigerator' in detected_objects:
+            action_scores['Stealing'] += 0.3
+        if [conf for obj, conf in detections if obj == 'person'][0] < 0.6:
+            action_scores['Sneaking'] += 0.5
+        if len(detected_objects) <= 2:
+            action_scores['Peaking'] += 0.5
 
-model = load_model()
+    if not any(score > 0.3 for score in action_scores.values()):
+        action_scores['Normal'] = 0.4
 
-uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+    return action_scores
 
-if uploaded_file:
+# Streamlit UI
+st.title('Suspicious Activity Detection')
+st.write('Upload an image to detect suspicious activities.')
+
+# File uploader
+uploaded_file = st.file_uploader("Choose an image...", type="jpg")
+if uploaded_file is not None:
+    # Read the image
     image = Image.open(uploaded_file)
-    st.image(image, caption="Uploaded Image", use_column_width=True)
-
-    if st.button("Detect Activity"):
-        img_array = np.array(image.convert("RGB"))[..., ::-1]  # Convert to BGR
-        results = model.predict(img_array)
-
-        for r in results:
-            plotted = r.plot()
-            st.image(plotted, caption="Detections", use_column_width=True)
-
-            st.subheader("Detected Objects:")
-            for box in r.boxes:
-                conf = float(box.conf[0])
-                cls = int(box.cls[0])
-                cls_name = model.names[cls]
-                st.write(f"- {cls_name} (Confidence: {conf:.2f})")
+    st.image(image, caption='Uploaded Image', use_column_width=True)
+    
+    # Save the uploaded file for processing
+    img_path = "/tmp/uploaded_image.jpg"
+    image.save(img_path)
+    
+    # Predict and display results
+    st.write("Detecting action...")
+    detected_image, action_scores = detect_action(img_path)
+    
+    st.image(detected_image, caption='Detected Image', use_column_width=True)
+    
+    # Display action scores
+    st.write("Action Probability Scores:")
+    for action, score in action_scores.items():
+        st.write(f"{action}: {score:.2%}")
+    
+    # Predict and display the most likely action
+    predicted_action = max(action_scores.items(), key=lambda x: x[1])
+    st.write(f"Predicted Action: {predicted_action[0]} ({predicted_action[1]:.2%} confidence)")
+