Update app.py

app.py

@@ -1,44 +1,91 @@
+import cv2
 import streamlit as st
 import imutils
-from imutils.video import VideoStream
+import numpy as np
 import matplotlib.pyplot as plt
+from PIL import Image
 
-# Load Haar Cascade
-detector = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
+# Streamlit page configuration
+st.set_page_config(page_title="Real-time Face Detection", layout="wide")
 
-# Function to display images in Streamlit
+# Title for the Streamlit app
+st.title("Real-time Face Detection App")
+
+# Function to display images using matplotlib
 def plt_imshow(title, image):
+    # Convert the image frame from BGR to RGB color space
     image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-    st.image(image, caption=title, use_column_width=True)
+    plt.imshow(image)
+    plt.title(title)
+    plt.axis('off')
+    st.pyplot(plt)
+
+# Upload the image
+uploaded_file = st.file_uploader("Upload an Image", type=["jpg", "png", "jpeg"])
+
+if uploaded_file is not None:
+    # Read the uploaded image
+    image = Image.open(uploaded_file)
+    image = np.array(image)
+    
+    # Load the Haar Cascade face detector
+    cascade_path = "haarcascade_frontalface_default.xml"
+    detector = cv2.CascadeClassifier(cascade_path)
+
+    # Convert the image to grayscale
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+    # Perform face detection
+    rects = detector.detectMultiScale(gray, scaleFactor=1.05, minNeighbors=5, minSize=(30, 30), flags=cv2.CASCADE_SCALE_IMAGE)
 
-# File upload for video input
-st.title("Real-time Face Detection")
-st.write("Upload a video to perform face detection.")
+    # Draw bounding boxes around detected faces
+    for (x, y, w, h) in rects:
+        cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
 
-video_file = st.file_uploader("Choose a video...", type=["mp4", "mov", "avi"])
+    # Display the result in Streamlit
+    st.image(image, caption="Face Detected", channels="BGR", use_column_width=True)
 
-if video_file is not None:
-    # Open the video file uploaded by the user
-    vs = cv2.VideoCapture(video_file)
-    writer = None
+# Upload video file
+uploaded_video = st.file_uploader("Upload a Video", type=["mp4", "avi"])
 
-    while True:
-        ret, frame = vs.read()
+if uploaded_video is not None:
+    # Open the uploaded video
+    video_file = uploaded_video.read()
+    
+    # Use OpenCV to read the video
+    video_capture = cv2.VideoCapture(video_file)
+
+    # Load the Haar Cascade face detector
+    cascade_path = "haarcascade_frontalface_default.xml"
+    detector = cv2.CascadeClassifier(cascade_path)
+
+    # Create a video writer to save the output video
+    fourcc = cv2.VideoWriter_fourcc(*"MJPG")
+    output_file = "output_video.avi"
+    writer = cv2.VideoWriter(output_file, fourcc, 20, (640, 480))
+
+    while video_capture.isOpened():
+        ret, frame = video_capture.read()
         if not ret:
             break
 
-        # Resize the frame
+        # Resize and convert the frame to grayscale
         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))
+        # Perform face detection
+        rects = detector.detectMultiScale(gray, scaleFactor=1.05, minNeighbors=5, minSize=(30, 30), flags=cv2.CASCADE_SCALE_IMAGE)
 
-        # Draw bounding boxes
+        # Draw bounding boxes around detected faces
         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)
+        # Write the processed frame to the output video
+        writer.write(frame)
+
+    # Release the video capture and writer objects
+    video_capture.release()
+    writer.release()
 
-    vs.release()
+    # Show the output video in Streamlit
+    st.video(output_file)