Create app.py

app.py

@@ -0,0 +1,91 @@
+import cv2
+import streamlit as st
+import imutils
+import numpy as np
+import matplotlib.pyplot as plt
+from PIL import Image
+
+# Streamlit page configuration
+st.set_page_config(page_title="Real-time Face Detection", layout="wide")
+
+# 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)
+    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)
+
+    # 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)
+
+    # Display the result in Streamlit
+    st.image(image, caption="Face Detected", channels="BGR", use_column_width=True)
+
+# Upload video file
+uploaded_video = st.file_uploader("Upload a Video", type=["mp4", "avi"])
+
+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 and convert the frame to grayscale
+        frame = imutils.resize(frame, width=500)
+        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+        # Perform face detection
+        rects = detector.detectMultiScale(gray, scaleFactor=1.05, minNeighbors=5, minSize=(30, 30), flags=cv2.CASCADE_SCALE_IMAGE)
+
+        # 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)
+
+        # Write the processed frame to the output video
+        writer.write(frame)
+
+    # Release the video capture and writer objects
+    video_capture.release()
+    writer.release()
+
+    # Show the output video in Streamlit
+    st.video(output_file)