Update app.py

app.py

@@ -1,91 +1,47 @@
-import cv2
 import streamlit as st
-import imutils
+import cv2
 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")
+# Load the Haar Cascade face detector
+cascade_path = "haarcascade_frontalface_default.xml"
+detector = cv2.CascadeClassifier(cascade_path)
 
-# Title for the Streamlit app
-st.title("Real-time Face Detection App")
+# Streamlit app title
+st.title("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)
+# Sidebar instructions
+st.sidebar.title("Upload an Image")
+st.sidebar.write("Upload an image to detect faces.")
 
-# Upload the image
-uploaded_file = st.file_uploader("Upload an Image", type=["jpg", "png", "jpeg"])
+# Upload file option
+uploaded_file = st.sidebar.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
 
 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)
+    # Convert uploaded file to OpenCV format
+    file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
+    image = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
 
-# 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)
+    # Check if the image was loaded successfully
+    if image is None:
+        st.error("Error: Could not load the image. Please try again.")
+    else:
+        # Convert 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)
+        rects = detector.detectMultiScale(
+            gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30)
+        )
+        
+        st.write(f"Detected {len(rects)} face(s).")
 
         # 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)
+            cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
 
-    # Release the video capture and writer objects
-    video_capture.release()
-    writer.release()
+        # Convert the image back to RGB for display in Streamlit
+        image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
 
-    # Show the output video in Streamlit
-    st.video(output_file)
+        # Display the image with detected faces
+        st.image(image_rgb, caption="Detected Faces", use_column_width=True)