| import numpy as np | |
| import cv2 | |
| import gradio as gr | |
| from PIL import Image | |
| def detect_faces(image): | |
| image_np = np.array(image) | |
| gray_image = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY) | |
| face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml") | |
| faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30)) | |
| for (x, y, w, h) in faces: | |
| cv2.rectangle(image_np, (x, y), (x+w, y+h), (0, 255, 0), 2) | |
| return image_np | |
| interface = gr.Interface( | |
| fn=detect_faces, | |
| inputs="image", | |
| outputs="image", | |
| title="Face Detection with Haar Cascade", | |
| description="Upload an image, and the model will detect faces and draw bounding boxes around them.", | |
| ) | |
| interface.launch() |