Create app.py

app.py

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+import cv2
+import streamlit as st
+import numpy as np
+from PIL import Image
+
+# Load the pre-trained Haar Cascade face detector
+face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+
+def detect_faces(frame):
+    """
+    Detect faces in the frame.
+    Returns the frame with bounding boxes drawn around detected faces.
+    """
+    # Convert the frame to grayscale (Haar Cascade works on grayscale images)
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+    # Detect faces in the image
+    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
+
+    # Draw rectangles around the faces
+    for (x, y, w, h) in faces:
+        cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
+
+    return frame
+
+# Streamlit UI for the app
+st.title("Real-Time Face Detection")
+
+# Capture the video from the webcam
+camera = st.camera_input("Capture a photo")
+
+# Process the webcam image if available
+if camera:
+    # Convert the camera image into a numpy array
+    img = Image.open(camera)
+    img_array = np.array(img)
+    
+    # Convert the image to a format OpenCV can process (BGR)
+    img_bgr = cv2.cvtColor(img_array, cv2.COLOR_RGB2BGR)
+
+    # Detect faces in the image
+    result_frame = detect_faces(img_bgr)
+
+    # Convert result frame back to RGB (for displaying in Streamlit)
+    result_frame_rgb = cv2.cvtColor(result_frame, cv2.COLOR_BGR2RGB)
+
+    # Display the result in Streamlit
+    st.image(result_frame_rgb, caption="Detected Faces", use_column_width=True)