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from keras.models import load_model
import cv2
import numpy as np
import gradio as gr
# Load the pre-trained model
model = load_model('keras_model.h5')
# Load the class labels
with open('labels.txt', 'r') as f:
class_names = [line.strip() for line in f.readlines()]
# Load the Haar cascade classifier for face detection
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
font = cv2.FONT_HERSHEY_SIMPLEX
# Define a function to detect faces in a given frame
def detect_faces(frame):
# Detect faces in the frame
faces = face_cascade.detectMultiScale(frame, scaleFactor=1.3, minNeighbors=5)
# You can process the frame here if needed
# e.g., apply filters, transformations, or object detection
for (x, y, w, h) in faces:
# Extract the face region
face_img = frame[y:y+h, x:x+w]
# Preprocess the face image
face_img = cv2.resize(face_img, (224, 224))
face_img = np.expand_dims(face_img, axis=0)
face_img = face_img / 255.0
# Predict the class probabilities
pred_probs = model.predict(face_img)[0]
class_idx = np.argmax(pred_probs)
class_prob = pred_probs[class_idx]
# Get the class name and display it on the image
class_name = class_names[class_idx]
if class_prob*100 < 70:
cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 0, 255), 2)
text = '{}: {:.2f}%'.format('Unknown', class_prob * 100)
cv2.putText(frame, text, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)
else:
cv2.putText(frame, class_name, (x, y - 10), font, 1, (0, 255, 0), 2)
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
text = '{}: {:.2f}%'.format(class_name, class_prob * 100)
cv2.putText(frame, text, (x, y + h + 30), font, 0.5, (0, 255, 0), 1)
return frame[:, :, ::-1]
# Create a Gradio interface for face detection with webcam input
face_detection = gr.Interface(fn=detect_faces,
inputs=gr.inputs.Video(type="webcam"),
outputs="image",
title="Face Detection with OpenCV",
live=True)
# Launch the interface
face_detection.launch()
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