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app.py

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+import streamlit as st
+from PIL import Image
+import numpy as np
+import cv2
+from ultralytics import YOLO
+
+# Load your PPE detection model
+model = YOLO('ppe-kit.pt')  # Replace with your PPE detection model path
+
+# Set page configuration
+st.set_page_config(
+    page_title="PPE Kit Detection",
+    page_icon="🦺",
+    layout="wide",
+    initial_sidebar_state="expanded",
+)
+
+# Sidebar contents
+st.sidebar.title("🦺 PPE Kit Detection")
+st.sidebar.write("Upload an image to detect PPE kits.")
+
+# Upload image
+uploaded_file = st.sidebar.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+# Main content
+st.title("🦺 PPE Kit Detection")
+st.write(
+    """
+    This application uses a state-of-the-art YOLO model to detect PPE kits and safety measures in images.
+    It identifies items like helmets, masks, safety vests, and more. Upload an image to see the detection in action.
+    """
+)
+
+if uploaded_file is not None:
+    # Display uploaded image
+    image = Image.open(uploaded_file)
+    
+    # Create two columns for displaying the image
+    col1, col2 = st.columns(2)
+
+    with col1:
+        st.header("Uploaded Image")
+        st.image(image, caption='Uploaded Image', use_column_width=True)
+    
+    with col2:
+        st.header("Detected PPE and Safety Items")
+
+        # Convert the image to OpenCV format
+        image_cv = np.array(image)
+        image_cv = image_cv[:, :, ::-1].copy()  # Convert RGB to BGR
+
+        # Run inference
+        results = model(image_cv)
+
+        # Process the results
+        boxes = results[0].boxes
+        labels = {
+            0: 'Helmet', 1: 'Mask', 2: 'NO-Helmet', 3: 'NO-Mask', 4: 'NO-Safety Vest',
+            5: 'Person', 6: 'Safety Cone', 7: 'Safety Vest', 8: 'machinery', 9: 'vehicle'
+        }
+
+        detected_items = []  # List to store detected items and accuracy
+
+        # Define a set of class IDs to skip (machinery, vehicle, and person)
+        skip_classes = {5, 8, 9}  # Person, machinery, vehicle
+
+        # Draw bounding boxes and labels on the image
+        for box in boxes:
+            x1, y1, x2, y2 = map(int, box.xyxy[0])
+            confidence = box.conf[0]
+            class_id = int(box.cls[0])
+            label = labels.get(class_id, 'Unknown')
+
+            # Skip drawing and storing items if they belong to the skip_classes set
+            if class_id in skip_classes:
+                continue
+
+            # Store detected items with accuracy
+            detected_items.append(f"{label} ({confidence * 100:.2f}%)")
+
+            # Draw the box
+            cv2.rectangle(image_cv, (x1, y1), (x2, y2), (0, 255, 0), 2)
+            # Put the label
+            cv2.putText(image_cv, f"{label} {confidence:.2f}", (x1, y1 - 10),
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.9, (36, 255, 12), 2)
+
+        # Convert the image back to RGB
+        image_cv = image_cv[:, :, ::-1].copy()
+        st.image(image_cv, caption='Detected PPE and Safety Items', use_column_width=True)
+
+    # Centered detected items below the image
+    st.markdown("<h2 style='text-align: center;'>Detected Items with Accuracy:</h2>", unsafe_allow_html=True)
+    
+    # Display the detected items in a centered fashion
+    for item in detected_items:
+        st.markdown(f"<h4 style='text-align: center;'>{item}</h4>", unsafe_allow_html=True)
+
+    st.success("Detection complete!")
+
+else:
+    st.info("Please upload an image to start detection.")
+
+# Footer
+st.sidebar.markdown("### About")
+st.sidebar.info(
+    "This app uses a YOLO model to detect PPE kits like helmets, masks, safety vests, and other safety-related items. "
+    "Upload an image and the model will identify these items in the image."
+)

ppe-kit.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4d07bbd92ca30d5c12dd67ccf52b2f54f533c9ccfef534284124682ef9f56129
+size 6251955

requirements.txt

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+ultralytics==8.2.34
+streamlit