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import cv2
import gradio as gr
import numpy as np
from fer import FER
from moviepy.editor import VideoFileClip
# Initialize the pre-trained detector once so you don't reinitialize on every function call.
detector = FER(mtcnn=True) # Optionally, you can set mtcnn to False to use a faster (but less accurate) cascade.
def emotion_recognition(image):
"""
Process the input image, detect emotions on faces,
and annotate the image with bounding boxes and emotion labels.
Parameters:
image (numpy.ndarray): Input image (RGB).
Returns:
numpy.ndarray: Annotated image with emotion labels.
"""
# fer works with RGB images which is what Gradio provides by default.
results = detector.detect_emotions(image)
annotated_image = image.copy()
# Loop through each detected face
for face in results:
(x, y, w, h) = face["box"]
# Get the dominant emotion for the detected face
dominant_emotion = max(face["emotions"].items(), key=lambda item: item[1])[0]
# Draw bounding box around face
cv2.rectangle(annotated_image, (x, y), (x + w, y + h), (0, 255, 0), 2)
# Put the emotion label above the bounding box
cv2.putText(annotated_image, dominant_emotion, (x, y - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.9, (36, 255, 12), 2)
return annotated_image
# Create a Gradio Interface
interface = gr.Interface(
fn=emotion_recognition,
inputs=gr.Image(type="numpy", label="Input Image"),
outputs=gr.Image(type="numpy", label="Annotated Image"),
title="Facial Emotion Recognition",
description="Upload an image and let the app detect and annotate facial emotions."
)
# Run the app locally
if __name__ == "__main__":
interface.launch()
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