| import gradio as gr |
| import cv2 |
| import numpy as np |
| from keras.models import load_model |
|
|
| |
| model = load_model('model.keras') |
|
|
| |
| face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') |
|
|
| |
| labels_dict = { |
| 0: 'Angry', 1: 'Disgust', 2: 'Fear', 3: 'Happy', |
| 4: 'Neutral', 5: 'Sad', 6: 'Smirk', 7: 'Surprise' |
| } |
|
|
| |
| def detect_emotion(image): |
| frame = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) |
| gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) |
| faces = face_cascade.detectMultiScale(gray, 1.3, 5) |
|
|
| for (x, y, w, h) in faces: |
| face = gray[y:y+h, x:x+w] |
| resized = cv2.resize(face, (48, 48)) |
| normalized = resized / 255.0 |
| reshaped = np.reshape(normalized, (1, 48, 48, 1)) |
| result = model.predict(reshaped) |
| label = np.argmax(result, axis=1)[0] |
| emotion = labels_dict[label] |
|
|
| |
| cv2.rectangle(frame, (x, y), (x + w, y + h), (50, 50, 255), 2) |
| cv2.rectangle(frame, (x, y - 40), (x + w, y), (50, 50, 255), -1) |
| cv2.putText(frame, emotion, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, |
| 0.8, (255, 255, 255), 2) |
|
|
| return cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) |
|
|
| |
| iface = gr.Interface( |
| fn=detect_emotion, |
| inputs=gr.Image(source="webcam", type="numpy"), |
| outputs="image", |
| live=False |
| ) |
|
|
| iface.launch() |
|
|
