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import os
import gradio as gr
import cv2
from insightface.app import FaceAnalysis
from hsemotion_onnx.facial_emotions import HSEmotionRecognizer
def facial_emotion_recognition(img):
faces = face_detector.get(img)
if len(faces) > 0:
highest_score_box = (0, 0, 0, 0) # x, y, w, h
highest_score = 0
for face in faces:
if face['det_score'] > highest_score:
highest_score = face['det_score']
x1, y1, x2, y2 = face['bbox'].astype(int)
x_margin = int((x2 - x1) * face_margin)
y_margin = int((y2 - y1) * face_margin)
x = max(0, x1 - x_margin)
y = max(0, y1 - y_margin)
w = min(x2 + x_margin, img.shape[1]) - x
h = min(y2 + y_margin, img.shape[0]) - y
highest_score_box = (x, y, w, h)
x, y, w, h = highest_score_box
emotion, _ = hse_emo_model.predict_emotions(img[y:y+h, x:x+w], logits=True)
cv2.rectangle(img, (x, y), (x+w, y+h), (0, 0, 255), 2)
cv2.putText(img, emotion, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2, cv2.LINE_AA)
return img
face_margin = 0.1
model_name = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'buffalo_sc')
face_detector = FaceAnalysis(name=model_name, allowed_modules=['detection'], providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
face_detector.prepare(ctx_id=0, det_size=(640, 640))
hse_emo_model = HSEmotionRecognizer(model_name='enet_b0_8_best_vgaf')
webcam = gr.Image(image_mode='RGB', type='numpy', source='webcam', label='Input Image')
output = gr.Image(image_mode='RGB', type='numpy', label='Output Image')
app = gr.Interface(facial_emotion_recognition, inputs=webcam, outputs=output)
app.launch()
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