File size: 3,114 Bytes
bdf1b61 3c424f5 bdf1b61 9147ea0 bdf1b61 f8e626c bdf1b61 3c424f5 848d7db 3c424f5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 |
import streamlit as st
from PIL import Image
from ultralytics import YOLO
# Set up Streamlit page
st.set_page_config(page_title="Suspicious Activity Detection", layout="centered")
# Load YOLOv11 model
@st.cache_resource
def load_model():
return YOLO("yolo11l .pt") # Ensure model filename matches
model = load_model()
# ------------------ Improved Action Classification Logic ------------------
def classify_action(detections):
"""
Classify activity based on object types, count, and confidence.
"""
action_scores = {'Stealing': 0, 'Sneaking': 0, 'Peaking': 0, 'Normal': 0}
objects = [d[0] for d in detections]
confidences = [d[1] for d in detections]
has_person = 'person' in objects
has_bag = any(obj in objects for obj in ['handbag', 'backpack'])
few_objects = len(set(objects)) <= 2
mostly_person = objects.count('person') >= len(objects) * 0.6 if objects else False
max_conf = max(confidences) if confidences else 0.0
# Decision tree for classification
if has_person:
if has_bag and len(objects) >= 3:
action_scores['Stealing'] += 1.0
elif max_conf < 0.55 and few_objects:
action_scores['Sneaking'] += 1.0
elif mostly_person and few_objects and max_conf >= 0.55:
action_scores['Peaking'] += 1.0
else:
action_scores['Normal'] += 1.0
else:
action_scores['Normal'] += 1.0
# Normalize scores
total = sum(action_scores.values())
if total > 0:
for k in action_scores:
action_scores[k] /= total
return action_scores
# ------------------ Detection Function ------------------
def detect_action(image_path):
results = model.predict(source=image_path, conf=0.35, iou=0.5, save=False, verbose=False)
result = results[0]
detections = [
(model.names[int(cls)], float(conf))
for cls, conf in zip(result.boxes.cls, result.boxes.conf)
]
annotated_image = result.plot()
action_scores = classify_action(detections)
return annotated_image, action_scores
# ------------------ Streamlit UI ------------------
st.title("🛡️ Suspicious Activity Detection")
st.markdown("Upload an image to detect if someone is **Stealing**, **Sneaking**, **Peaking**, or acting **Normal**.")
uploaded_file = st.file_uploader("📤 Upload an image", type=["jpg", "jpeg", "png"])
if uploaded_file:
image = Image.open(uploaded_file).convert("RGB")
st.image(image, caption="Uploaded Image", use_column_width=True)
temp_path = "/tmp/uploaded.jpg"
image.save(temp_path)
with st.spinner("🔍 Detecting suspicious activity..."):
detected_image, action_scores = detect_action(temp_path)
st.image(detected_image, caption="🔍 Detection Results", use_column_width=True)
st.subheader("📊 Action Confidence Scores")
for action, score in action_scores.items():
st.write(f"**{action}**: {score:.2%}")
top_action = max(action_scores.items(), key=lambda x: x[1])
st.success(f"🎯 **Predicted Action:** {top_action[0]} ({top_action[1]:.2%} confidence)") |