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import numpy as np
import cv2
import gradio as gr
# Load Haar Cascade classifier
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
# Face Detection Function
def detect_faces(image_np,slider):
# Convert image to grayscale
gray_image = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
# Detect faces
faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
# Draw rectangles around faces
for (x, y, w, h) in faces:
cv2.rectangle(image_np, (x, y), (x + w, y + h), (0, 255, 0), 2)
return image_np,len(faces)
# slider = gr.Slider(minimum=1, maximum=2, step=.1, label="Adjust the ScaleFactor")
# Create Gradio Interface
iface = gr.Interface(
fn=detect_faces,
inputs=["image",gr.Slider(minimum=1, maximum=2, step=.1, label="Adjust the ScaleFactor")],
outputs=["image",gr.Label("faces count")],
title="Face Detection",
description="Upload an image, and the model will detect faces and draw bounding boxes around them."
)
iface.launch()
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