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import streamlit as st
from PIL import Image
import cv2
import numpy as np
from ultralytics import YOLO
import os
# Model Initialization
model = YOLO(https://colab.research.google.com/drive/1IAyIjPN1J_9s5MhJ0uCsccxfcA0WfXl2?authuser=1#scrollTo=bA29p0kfmhlP)
# 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
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
}
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
if not any(score > 0.3 for score in action_scores.values()):
action_scores['Normal'] = 0.4
return action_scores
# 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)
# 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)")
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