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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+project_face_mask_detection.keras filter=lfs diff=lfs merge=lfs -text

app.py

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+# import streamlit as st
+# import numpy as np
+# import cv2
+# from keras.models import load_model
+# from keras.preprocessing.image import img_to_array
+# from PIL import Image
+
+# # Set page config
+# st.set_page_config(page_title="Face Mask Detection", layout="centered")
+
+# # Load model once
+# @st.cache_resource
+# def load_model_cached():
+#     return load_model("project_face_mask_detection.keras")  # Make sure this is trained on cropped face images
+
+# model = load_model_cached()
+
+# # Load Haar Cascade for face detection
+# face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
+
+# # Function to detect face and predict
+# def detect_and_predict(image_input):
+#     image_np = np.array(image_input.convert("RGB"))
+#     gray = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
+
+#     faces = face_cascade.detectMultiScale(gray, 1.1, 4)
+
+#     if len(faces) == 0:
+#         return image_input, None, "No face detected"
+
+#     x, y, w, h = faces[0]  # Just take the first detected face
+#     face_roi = image_np[y:y+h, x:x+w]
+#     face_pil = Image.fromarray(face_roi).resize((200, 200))
+#     img_array = img_to_array(face_pil) / 255.0
+#     img_array = np.expand_dims(img_array, axis=0)
+
+#     prediction = model.predict(img_array)[0][0]
+#     confidence = (1 - prediction) if prediction < 0.5 else prediction
+#     label = "✅ Mask Detected" if prediction < 0.5 else "🚫 No Mask Detected"
+
+#     # Draw rectangle and label
+#     color = (0, 255, 0) if prediction < 0.5 else (255, 0, 0)
+#     cv2.rectangle(image_np, (x, y), (x + w, y + h), color, 2)
+#     cv2.putText(image_np, f"{label} ({confidence*100:.2f}%)",(x, y - 10),
+#                 cv2.FONT_HERSHEY_SIMPLEX, 0.4, color, 2)
+
+#     return Image.fromarray(image_np), confidence, label
+
+# # App UI
+# st.title("😷 Intelligent Mask Detection Platform")
+# st.markdown("Upload a face image or use your webcam to check if a mask is being worn.")
+
+# # Tabs
+# tab1, tab2 = st.tabs(["📤 Upload Image", "📷 Use Webcam"])
+
+# with tab1:
+#     uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+#     if uploaded_file:
+#         try:
+#             image_input = Image.open(uploaded_file)
+#             st.image(image_input, caption="Uploaded Image", use_container_width=True)
+
+#             with st.spinner("Analyzing..."):
+#                 result_img, confidence, label = detect_and_predict(image_input)
+
+#             st.image(result_img, caption="Detection Result", use_container_width=True)
+#             if confidence is not None:
+#                 st.metric("Confidence", f"{confidence*100:.2f}%")
+#                 if "Mask" in label:
+#                     st.success(label)
+#                 else:
+#                     st.error(label)
+#             else:
+#                 st.warning(label)
+
+#         except Exception as e:
+#             st.error(f"❌ Error: {str(e)}")
+
+# with tab2:
+#     camera_image = st.camera_input("Take a picture")
+#     if camera_image:
+#         try:
+#             image_input = Image.open(camera_image)
+#             st.image(image_input, caption="Webcam Snapshot", use_container_width=True)
+
+#             with st.spinner("Analyzing..."):
+#                 result_img, confidence, label = detect_and_predict(image_input)
+
+#             st.image(result_img, caption="Detection Result", use_container_width=True)
+#             if confidence is not None:
+#                 st.metric("Confidence", f"{confidence*100:.2f}%")
+#                 if "Mask" in label:
+#                     st.success(label)
+#                 else:
+#                     st.error(label)
+#             else:
+#                 st.warning(label)
+
+#         except Exception as e:
+#             st.error(f"❌ Error: {str(e)}")
+
+
+import streamlit as st
+import numpy as np
+import cv2
+from keras.models import load_model
+from keras.preprocessing.image import img_to_array
+from PIL import Image
+
+# Page config with improved UI layout
+st.set_page_config(
+    page_title="😷 Smart Face Mask Detection",
+    layout="wide",
+    page_icon="😷"
+)
+
+# Load the model with caching
+@st.cache_resource
+def load_model_cached():
+    return load_model("project_face_mask_detection.keras")
+
+model = load_model_cached()
+
+# Haar Cascade for face detection
+face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
+
+# Sidebar for app description and tips
+with st.sidebar:
+    st.title("🧠 About This App")
+    st.markdown("""
+    This app uses deep learning to detect whether a person is wearing a face mask.
+    
+    - Upload or capture an image.
+    - Get instant feedback.
+    - Built with Streamlit & Keras.
+    """)
+    st.info("Tip: Use well-lit images with clear faces for best results.")
+    st.markdown("---")
+    st.caption("📍 Developed by YourName • 2025")
+
+# Core detection function
+def detect_and_predict(image_input):
+    image_np = np.array(image_input.convert("RGB"))
+    gray = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
+    faces = face_cascade.detectMultiScale(gray, 1.1, 4)
+
+    if len(faces) == 0:
+        return image_input, None, "⚠️ No face detected"
+
+    x, y, w, h = faces[0]
+    face_roi = image_np[y:y+h, x:x+w]
+    face_pil = Image.fromarray(face_roi).resize((200, 200))
+    img_array = img_to_array(face_pil) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+
+    prediction = model.predict(img_array)[0][0]
+    confidence = (1 - prediction) if prediction < 0.5 else prediction
+    label = "✅ Mask Detected" if prediction < 0.5 else "🚫 No Mask Detected"
+
+    # Drawing results
+    color = (0, 255, 0) if prediction < 0.5 else (255, 0, 0)
+    cv2.rectangle(image_np, (x, y), (x + w, y + h), color, 2)
+    cv2.putText(image_np, f"{label} ({confidence*100:.2f}%)", (x, y - 10),
+                cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)
+
+    return Image.fromarray(image_np), confidence, label
+
+# App Title
+st.markdown("<h1 style='text-align: center;'>😷 AI Face Mask Detection System</h1>", unsafe_allow_html=True)
+st.markdown("<p style='text-align: center;'>Upload or capture an image to analyze mask presence.</p>", unsafe_allow_html=True)
+
+# Tabs for Upload and Webcam
+tab1, tab2 = st.tabs(["📤 Upload Image", "📷 Use Webcam"])
+
+# Upload Image Tab
+with tab1:
+    uploaded_file = st.file_uploader("Choose an image file", type=["jpg", "jpeg", "png"])
+    if uploaded_file:
+        image_input = Image.open(uploaded_file)
+        st.image(image_input, caption="Uploaded Image", use_container_width=True)
+
+        with st.spinner("Analyzing with AI model..."):
+            result_img, confidence, label = detect_and_predict(image_input)
+
+        col1, col2 = st.columns(2)
+        with col1:
+            st.image(result_img, caption="Detection Output", use_container_width=True)
+        with col2:
+            if confidence is not None:
+                st.metric("Confidence Score", f"{confidence*100:.2f}%")
+                if "Mask" in label:
+                    st.success(label)
+                else:
+                    st.error(label)
+            else:
+                st.warning(label)
+
+# Webcam Tab
+with tab2:
+    camera_image = st.camera_input("Take a picture using webcam")
+    if camera_image:
+        image_input = Image.open(camera_image)
+        st.image(image_input, caption="Webcam Snapshot", use_container_width=True)
+
+        with st.spinner("Analyzing..."):
+            result_img, confidence, label = detect_and_predict(image_input)
+
+        col1, col2 = st.columns(2)
+        with col1:
+            st.image(result_img, caption="Detection Output", use_container_width=True)
+        with col2:
+            if confidence is not None:
+                st.metric("Confidence Score", f"{confidence*100:.2f}%")
+                if "Mask" in label:
+                    st.success(label)
+                else:
+                    st.error(label)
+            else:
+                st.warning(label)

project_face_mask_detection.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7d8ad036045b1f4ae1e0e7e7cd334bee94aad98743caf2d9d705b77681257e17
+size 1510661

requirements.txt

@@ -0,0 +1,6 @@
+opencv-python-headless
+streamlit
+tensorflow
+keras
+Pillow
+numpy