Create app.py

app.py

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+import streamlit as st
+import cv2
+import imutils
+from imutils.video import VideoStream
+import matplotlib.pyplot as plt
+
+# Load Haar Cascade
+detector = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
+
+# Function to display images in Streamlit
+def plt_imshow(title, image):
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    st.image(image, caption=title, use_column_width=True)
+
+# File upload for video input
+st.title("Real-time Face Detection")
+st.write("Upload a video to perform face detection.")
+
+video_file = st.file_uploader("Choose a video...", type=["mp4", "mov", "avi"])
+
+if video_file is not None:
+    # Open the video file uploaded by the user
+    vs = cv2.VideoCapture(video_file)
+    writer = None
+
+    while True:
+        ret, frame = vs.read()
+        if not ret:
+            break
+
+        # Resize the frame
+        frame = imutils.resize(frame, width=500)
+        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+        # Detect faces
+        rects = detector.detectMultiScale(gray, scaleFactor=1.05, minNeighbors=5, minSize=(30, 30))
+
+        # Draw bounding boxes
+        for (x, y, w, h) in rects:
+            cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
+
+        # Display the frame in Streamlit
+        plt_imshow("Face Detection", frame)
+
+    vs.release()