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import cv2 as cv
import numpy as np
import gradio as gr
import datetime
from huggingface_hub import hf_hub_download
from facial_fer_model import FacialExpressionRecog
from yunet import YuNet
# Download ONNX model from Hugging Face
FD_MODEL_PATH = hf_hub_download(repo_id="opencv/face_detection_yunet", filename="face_detection_yunet_2023mar.onnx")
FER_MODEL_PATH = hf_hub_download(repo_id="opencv/facial_expression_recognition", filename="facial_expression_recognition_mobilefacenet_2022july.onnx")
backend_id = cv.dnn.DNN_BACKEND_OPENCV
target_id = cv.dnn.DNN_TARGET_CPU
fer_model = FacialExpressionRecog(modelPath=FER_MODEL_PATH, backendId=backend_id, targetId=target_id)
detect_model = YuNet(modelPath=FD_MODEL_PATH)
def visualize(image, det_res, fer_res):
output = image.copy()
landmark_color = [(255, 0, 0), (0, 0, 255), (0, 255, 0), (255, 0, 255), (0, 255, 255)]
for det, fer_type in zip(det_res, fer_res):
bbox = det[0:4].astype(np.int32)
fer_type_str = FacialExpressionRecog.getDesc(fer_type)
cv.rectangle(output, (bbox[0], bbox[1]), (bbox[0]+bbox[2], bbox[1]+bbox[3]), (0, 255, 0), 2)
cv.putText(output, fer_type_str, (bbox[0], bbox[1] - 10), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1)
landmarks = det[4:14].astype(np.int32).reshape((5, 2))
for idx, landmark in enumerate(landmarks):
cv.circle(output, landmark, 2, landmark_color[idx], 2)
return output
def detect_expression(input_image):
image = cv.cvtColor(input_image, cv.COLOR_RGB2BGR)
h, w, _ = image.shape
detect_model.setInputSize([w, h])
dets = detect_model.infer(image)
if dets is None:
return cv.cvtColor(image, cv.COLOR_BGR2RGB)
fer_res = []
for face_points in dets:
result = fer_model.infer(image, face_points[:-1])
fer_res.append(result[0])
output = visualize(image, dets, fer_res)
return cv.cvtColor(output, cv.COLOR_BGR2RGB)
# Gradio Interface
with gr.Blocks(css='''.example * {
font-style: italic;
font-size: 18px !important;
color: #0ea5e9 !important;
}''') as demo:
gr.Markdown("### Facial Expression Recognition (FER) with OpenCV DNN")
gr.Markdown("Detects faces and recognizes facial expressions using YuNet + MobileFaceNet ONNX models.")
with gr.Row():
input_image = gr.Image(type="numpy", label="Upload Image")
output_image = gr.Image(type="numpy", label="Facial Expression Result")
# Clear output when new image is uploaded
input_image.change(fn=lambda: (None), outputs=output_image)
with gr.Row():
submit_btn = gr.Button("Submit", variant="primary")
clear_btn = gr.Button("Clear")
submit_btn.click(fn=detect_expression, inputs=input_image, outputs=output_image)
clear_btn.click(fn=lambda:(None, None), outputs=[input_image, output_image])
gr.Markdown("Click on any example to try it.", elem_classes=["example"])
gr.Examples(
examples=[
["examples/lena.jpg"],
["examples/gray_face.png"]
],
inputs=input_image
)
if __name__ == "__main__":
demo.launch()
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