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| import streamlit as st | |
| import cv2 | |
| import numpy as np | |
| import urllib.request | |
| # Download the Haar cascade XML file | |
| cascade_file_url = "https://raw.githubusercontent.com/opencv/opencv/master/data/haarcascades/haarcascade_frontalface_default.xml" | |
| cascade_file_path = "haarcascade_frontalface_default.xml" | |
| urllib.request.urlretrieve(cascade_file_url, cascade_file_path) | |
| # Load the pre-trained object detection model | |
| model = cv2.CascadeClassifier(cascade_file_path) | |
| # Function to perform object recognition | |
| def detect_objects(image): | |
| gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) | |
| objects = model.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5) | |
| for (x, y, w, h) in objects: | |
| cv2.rectangle(image, (x, y), (x + w, y + h), (255, 0, 0), 2) | |
| return image | |
| # Streamlit app | |
| st.title('Face Detection') | |
| uploaded_file = st.file_uploader('Upload an image', type=['jpg', 'jpeg', 'png']) | |
| if uploaded_file is not None: | |
| # Read the uploaded image | |
| image = cv2.imdecode(np.fromstring(uploaded_file.read(), np.uint8), 1) | |
| if image is None: | |
| st.write('Error: Unable to read the image. Please make sure the uploaded file is a valid image.') | |
| else: | |
| # Perform object recognition | |
| result = detect_objects(image) | |
| if result is None: | |
| st.write('Error: Face detection failed.') | |
| else: | |
| # Display the result | |
| st.image(result, channels='BGR') | |