Update app.py

app.py

@@ -3,145 +3,129 @@ import cv2
 import numpy as np
 from PIL import Image
 import time
+import cvlib as cv
+from cvlib.object_detection import draw_bbox
 
-# Set app layout
-st.set_page_config(page_title="Face Mask Detection", layout="wide")
-st.markdown("<h1 style='text-align: center;'>😷 Face Mask Detection</h1>", unsafe_allow_html=True)
-
-# Input method selection
-input_method = st.selectbox("Choose Input Method", ["Camera Capture", "Upload Image"])
-
-# ---------- Simulated Face Detection Function (Replace with real model) ----------
-def detect_faces(img):
-    # Dummy example — replace this with real detection logic
-    height, width = img.shape[:2]
-    return [
-        {
-            "box": [int(width * 0.3), int(height * 0.3), 150, 150],
-            "label": "No Mask",
-            "confidence": 0.65
-        },
-        {
-            "box": [int(width * 0.65), int(height * 0.25), 130, 130],
-            "label": "Mask",
-            "confidence": 0.88
-        }
-    ]
-# ----------------------------------------------------------------------------------
-
-# Session state variables
+# Session state handling
 if "camera_image" not in st.session_state:
     st.session_state.camera_image = None
 if "camera_key" not in st.session_state:
     st.session_state.camera_key = str(time.time())
 
-# Layout columns
+# App setup
+st.set_page_config(page_title="Face Mask Detection", layout="wide")
+st.markdown("<h1 style='text-align: center;'>😷 Face Mask Detection</h1>", unsafe_allow_html=True)
+input_method = st.selectbox("Choose Input Method", ["Camera Capture", "Upload Image"])
+
+# Face detection with dummy classifier
+def detect_and_classify_faces(img_np):
+    faces, confidences = cv.detect_face(img_np)
+    results = []
+
+    for face in faces:
+        (startX, startY) = face[0], face[1]
+        (endX, endY) = face[2], face[3]
+
+        # Simulated mask classification (replace with real model)
+        face_img = img_np[startY:endY, startX:endX]
+        mean_pixel = np.mean(face_img)
+        if mean_pixel % 2 < 1:
+            label = "Mask"
+            confidence = 0.90
+        else:
+            label = "No Mask"
+            confidence = 0.65
+
+        results.append({
+            "box": [startX, startY, endX - startX, endY - startY],
+            "label": label,
+            "confidence": confidence
+        })
+
+    return results
+
+# Columns
 col1, col2 = st.columns(2)
 
-# Camera Capture
+# --------------- CAMERA INPUT MODE ---------------
 if input_method == "Camera Capture":
     with col1:
-        st.markdown("### 🖼️ Capturing Image")
+        st.markdown("### 📸 Capturing Image")
 
-        # Show webcam input
         camera_img = st.camera_input("Take a photo", key=st.session_state.camera_key)
 
         if camera_img:
             st.session_state.camera_image = camera_img
+            img = Image.open(st.session_state.camera_image)
+            st.image(img, caption="Captured Image")
 
-            # Display captured image
-            st.image(camera_img, caption="Captured Image", use_column_width=True)
-
-            # Clear button
             if st.button("❌ Clear photo"):
-                # Reset session state and re-trigger camera
                 st.session_state.camera_image = None
                 st.session_state.camera_key = str(time.time())
                 st.experimental_rerun()
 
-    # Process detection if image exists
     if st.session_state.camera_image:
         with col2:
             st.markdown("### 🧠 Detection Result")
 
             img = Image.open(st.session_state.camera_image)
             img_np = np.array(img)
-
-            faces = detect_faces(img_np)
+            faces = detect_and_classify_faces(img_np)
 
             for face in faces:
                 x, y, w, h = face["box"]
                 label = face["label"]
-                confidence = face["confidence"]
+                conf = face["confidence"]
+                color = (0, 255, 0) if label == "Mask" else (0, 0, 255)
 
-                color = (0, 255, 0) if "Mask" in label else (0, 0, 255)
                 cv2.rectangle(img_np, (x, y), (x + w, y + h), color, 2)
-                cv2.putText(
-                    img_np,
-                    f"{label} ({confidence*100:.2f}%)",
-                    (x, y - 10),
-                    cv2.FONT_HERSHEY_SIMPLEX,
-                    0.6,
-                    color,
-                    2,
-                )
-
-            st.image(img_np, caption="Result Image", use_column_width=True)
-
-            # Face summary
+                cv2.putText(img_np, f"{label} ({conf*100:.2f}%)", (x, y - 10),
+                            cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)
+
+            st.image(img_np, caption="Result Image", use_container_width=True)
+
+            # Summary
             for i, face in enumerate(faces, 1):
-                label = face["label"]
-                confidence = face["confidence"]
-                label_color = "green" if "Mask" in label else "red"
-                st.markdown(f"**Face {i}:** <span style='color:{label_color}'>{label}</span> ({confidence*100:.2f}%)", unsafe_allow_html=True)
+                label_color = "green" if face["label"] == "Mask" else "red"
+                st.markdown(f"**Face {i}:** <span style='color:{label_color}'>{face['label']}</span> ({face['confidence']*100:.2f}%)", unsafe_allow_html=True)
 
-            # Overall result
-            if any("No Mask" in face["label"] for face in faces):
+            if any(face["label"] == "No Mask" for face in faces):
                 st.error("⚠️ One or more people not wearing a mask!")
             else:
                 st.success("✅ All faces have masks.")
 
-# Upload Image
+# --------------- UPLOAD IMAGE MODE ---------------
 elif input_method == "Upload Image":
-    uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
-    if uploaded_file:
-        with col1:
-            st.markdown("### 🖼️ Captured Image")
+    with col1:
+        st.markdown("### 📷 Captured Image")
+        uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+        if uploaded_file:
             img = Image.open(uploaded_file)
-            st.image(img, caption="Uploaded Image", use_column_width=True)
+            st.image(img, caption="Uploaded Image")
 
+    if uploaded_file:
         with col2:
             st.markdown("### 🧠 Detection Result")
-            img_np = np.array(img)
-
-            faces = detect_faces(img_np)
+            img_np = np.array(Image.open(uploaded_file))
+            faces = detect_and_classify_faces(img_np)
 
             for face in faces:
                 x, y, w, h = face["box"]
                 label = face["label"]
-                confidence = face["confidence"]
+                conf = face["confidence"]
+                color = (0, 255, 0) if label == "Mask" else (0, 0, 255)
 
-                color = (0, 255, 0) if "Mask" in label else (0, 0, 255)
                 cv2.rectangle(img_np, (x, y), (x + w, y + h), color, 2)
-                cv2.putText(
-                    img_np,
-                    f"{label} ({confidence*100:.2f}%)",
-                    (x, y - 10),
-                    cv2.FONT_HERSHEY_SIMPLEX,
-                    0.6,
-                    color,
-                    2,
-                )
-
-            st.image(img_np, caption="Result Image", use_column_width=True)
+                cv2.putText(img_np, f"{label} ({conf*100:.2f}%)", (x, y - 10),
+                            cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)
+
+            st.image(img_np, caption="Result Image", use_container_width=True)
 
             for i, face in enumerate(faces, 1):
-                label = face["label"]
-                confidence = face["confidence"]
-                label_color = "green" if "Mask" in label else "red"
-                st.markdown(f"**Face {i}:** <span style='color:{label_color}'>{label}</span> ({confidence*100:.2f}%)", unsafe_allow_html=True)
+                label_color = "green" if face["label"] == "Mask" else "red"
+                st.markdown(f"**Face {i}:** <span style='color:{label_color}'>{face['label']}</span> ({face['confidence']*100:.2f}%)", unsafe_allow_html=True)
 
-            if any("No Mask" in face["label"] for face in faces):
+            if any(face["label"] == "No Mask" for face in faces):
                 st.error("⚠️ One or more people not wearing a mask!")
             else:
                 st.success("✅ All faces have masks.")