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