image_mean = np.array([127, 127, 127])
image_std = 128.0
iou_threshold = 0.3
center_variance = 0.1
size_variance = 0.2
min_boxes = [
    [10.0, 16.0, 24.0], 
    [32.0, 48.0], 
    [64.0, 96.0], 
    [128.0, 192.0, 256.0]
]
strides = [8.0, 16.0, 32.0, 64.0]
threshold = 0.5
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def define_img_size(image_size):
    shrinkage_list = []
    feature_map_w_h_list = []
    for size in image_size:
        feature_map = [int(ceil(size / stride)) for stride in strides]
        feature_map_w_h_list.append(feature_map)
 
    for i in range(0, len(image_size)):
        shrinkage_list.append(strides)
    priors = generate_priors(
        feature_map_w_h_list, shrinkage_list, image_size, min_boxes
    )
    return priors
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上述的 define_img_size 函数旨在为目标检测任务生成先验边界框（priors）。该函数以 image_size 参数作为输入，根据提供的图像尺寸和一组预定义的步长值计算特征图的尺寸。这些特征图的尺寸反映了卷积神经网络（CNN）层对不同输入图像尺度的期望输出尺寸。实质上，SSD中的先验边界框提供了一种高效的方法，可以在网络的单次前向传递中同时预测多个边界框及其关联的类别得分，从而实现实时目标检测。

def FER_live_cam():
    emotion_dict = {
        0: 'neutral', 
        1: 'happiness', 
        2: 'surprise', 
        3: 'sadness',
        4: 'anger', 
        5: 'disgust', 
        6: 'fear'
    }
 
    # cap = cv2.VideoCapture('video1.mp4')
    cap = cv2.VideoCapture(0)
 
    frame_width = int(cap.get(3))
    frame_height = int(cap.get(4))
    size = (frame_width, frame_height)
    result = cv2.VideoWriter('result.avi', 
                         cv2.VideoWriter_fourcc(*'MJPG'),
                         10, size)
 
    # Read ONNX model
    model = 'onnx_model.onnx'
    model = cv2.dnn.readNetFromONNX('emotion-ferplus-8.onnx')
     
    # Read the Caffe face detector.
    model_path = 'RFB-320/RFB-320.caffemodel'
    proto_path = 'RFB-320/RFB-320.prototxt'
    net = dnn.readNetFromCaffe(proto_path, model_path)
    input_size = [320, 240]
    width = input_size[0]
    height = input_size[1]
    priors = define_img_size(input_size)
 
    while cap.isOpened():
        ret, frame = cap.read()
        if ret:
            img_ori = frame
            #print("frame size: ", frame.shape)
            rect = cv2.resize(img_ori, (width, height))
            rect = cv2.cvtColor(rect, cv2.COLOR_BGR2RGB)
            net.setInput(dnn.blobFromImage(
                rect, 1 / image_std, (width, height), 127)
            )
            start_time = time.time()
            boxes, scores = net.forward(["boxes", "scores"])
            boxes = np.expand_dims(np.reshape(boxes, (-1, 4)), axis=0)
            scores = np.expand_dims(np.reshape(scores, (-1, 2)), axis=0)
            boxes = convert_locations_to_boxes(
                boxes, priors, center_variance, size_variance
            )
            boxes = center_form_to_corner_form(boxes)
            boxes, labels, probs = predict(
                img_ori.shape[1], 
                img_ori.shape[0], 
                scores, 
                boxes, 
                threshold
            )
            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
            for (x1, y1, x2, y2) in boxes:
                w = x2 - x1
                h = y2 - y1
                cv2.rectangle(frame, (x1,y1), (x2, y2), (255,0,0), 2)
                resize_frame = cv2.resize(
                    gray[y1:y1 + h, x1:x1 + w], (64, 64)
                )
                resize_frame = resize_frame.reshape(1, 1, 64, 64)
                model.setInput(resize_frame)
                output = model.forward()
                end_time = time.time()
                fps = 1 / (end_time - start_time)
                print(f"FPS: {fps:.1f}")
                pred = emotion_dict[list(output[0]).index(max(output[0]))]
                cv2.rectangle(
                    img_ori, 
                    (x1, y1), 
                    (x2, y2), 
                    (0, 255, 0), 
                    2,
                    lineType=cv2.LINE_AA
                )
                cv2.putText(
                    frame, 
                    pred, 
                    (x1, y1), 
                    cv2.FONT_HERSHEY_SIMPLEX, 
                    0.8, 
                    (0, 255, 0), 
                    2,
                    lineType=cv2.LINE_AA
                )
 
            result.write(frame)
         
            cv2.imshow('frame', frame)
            if cv2.waitKey(1) & 0xFF == ord('q'):
                break
        else:
            break
 
    cap.release()
    result.release()
    cv2.destroyAllWindows()
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