Update app.py

app.py

@@ -4,6 +4,7 @@ import numpy as np
 from PIL import Image
 import time
 import cvlib as cv
+from cvlib.object_detection import draw_bbox
 
 # Set page config
 st.set_page_config(page_title="Face Mask Detection", layout="wide")
@@ -18,7 +19,7 @@ if "camera_key" not in st.session_state:
 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"])
 
-# Dummy mask classifier (for now)
+# Dummy classifier (replace this with actual ML model if needed)
 def classify_face_dummy(face_img):
     mean = np.mean(face_img)
     if mean % 2 < 1:
@@ -27,12 +28,22 @@ def classify_face_dummy(face_img):
         return "No Mask", 0.60
 
 def detect_and_classify_faces(img_np):
-    faces, confidences = cv.detect_face(img_np)
+    # Ensure color format is BGR
+    if img_np.shape[2] == 3:  # 3 channels
+        img_bgr = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
+    else:
+        img_bgr = img_np
+
+    faces, confidences = cv.detect_face(img_bgr)
     results = []
+
+    height, width = img_np.shape[:2]
+
     for i, face in enumerate(faces):
-        (startX, startY) = face[0], face[1]
-        (endX, endY) = face[2], face[3]
-        face_crop = img_np[startY:endY, startX:endX]
+        startX, startY = max(face[0], 0), max(face[1], 0)
+        endX, endY = min(face[2], width - 1), min(face[3], height - 1)
+
+        face_crop = img_bgr[startY:endY, startX:endX]
 
         if face_crop.size == 0:
             continue
@@ -69,11 +80,12 @@ if input_method == "Camera Capture":
     if st.session_state.image_captured:
         with col2:
             st.markdown("### 🧠 Detection Result")
-            image = Image.open(st.session_state.image_captured)
+            image = Image.open(st.session_state.image_captured).convert("RGB")
             img_np = np.array(image)
 
             results = detect_and_classify_faces(img_np)
 
+            # Annotate image
             for res in results:
                 x, y, w, h = res["box"]
                 label = res["label"]
@@ -84,7 +96,7 @@ if input_method == "Camera Capture":
                 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", channels="BGR", use_container_width=True)
+            st.image(img_np, caption="Result Image", channels="RGB", use_container_width=True)
 
             for i, face in enumerate(results, 1):
                 label_color = "green" if face["label"] == "Mask" else "red"
@@ -103,13 +115,13 @@ elif input_method == "Upload Image":
         st.markdown("### 📁 Upload Image")
         uploaded_img = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
         if uploaded_img:
-            image = Image.open(uploaded_img)
+            image = Image.open(uploaded_img).convert("RGB")
             st.image(image, caption="Uploaded Image", use_container_width=True)
 
     if uploaded_img:
         with col2:
             st.markdown("### 🧠 Detection Result")
-            img_np = np.array(Image.open(uploaded_img))
+            img_np = np.array(image)
             results = detect_and_classify_faces(img_np)
 
             for res in results:
@@ -122,7 +134,7 @@ elif input_method == "Upload Image":
                 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", channels="BGR", use_container_width=True)
+            st.image(img_np, caption="Result Image", channels="RGB", use_container_width=True)
 
             for i, face in enumerate(results, 1):
                 label_color = "green" if face["label"] == "Mask" else "red"