import cv2

# 加载人脸识别器的模型
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

# 打开摄像头
cap = cv2.VideoCapture(0)

# 初始化人脸框位置
prev_faces = []

# 定义绘制带圆角矩形边框的函数
def draw_rounded_rectangle(img, x, y, w, h, radius, color, thickness):
    # 画上面的边框
    cv2.line(img, (x + radius, y), (x + w - radius, y), color, thickness)
    # 画下面的边框
    cv2.line(img, (x + radius, y + h), (x + w - radius, y + h), color, thickness)
    # 画左边的边框
    cv2.line(img, (x, y + radius), (x, y + h - radius), color, thickness)
    # 画右边的边框
    cv2.line(img, (x + w, y + radius), (x + w, y + h - radius), color, thickness)
    # 画左上角的圆弧
    cv2.ellipse(img, (x + radius, y + radius), (radius, radius), 180, 0, 90, color, thickness)
    # 画右上角的圆弧
    cv2.ellipse(img, (x + w - radius, y + radius), (radius, radius), 270, 0, 90, color, thickness)
    # 画右下角的圆弧
    cv2.ellipse(img, (x + w - radius, y + h - radius), (radius, radius), 0, 0, 90, color, thickness)
    # 画左下角的圆弧
    cv2.ellipse(img, (x + radius, y + h - radius), (radius, radius), 90, 0, 90, color, thickness)
# 定义平滑处理人脸位置的函数
def smooth_faces(faces, prev_faces=None, alpha=0):
    if prev_faces is None or len(prev_faces) == 0:
        return faces

    smoothed_faces = []
    for (x, y, w, h) in faces:
        found = False
        for (prev_x, prev_y, prev_w, prev_h) in prev_faces:
            if x - 30 <= prev_x <= x + 30 and y - 30 <= prev_y <= y + 30:
                smoothed_x = int(alpha * x + (1 - alpha) * prev_x)
                smoothed_y = int(alpha * y + (1 - alpha) * prev_y)
                smoothed_w = int(alpha * w + (1 - alpha) * prev_w)
                smoothed_h = int(alpha * h + (1 - alpha) * prev_h)
                smoothed_faces.append((smoothed_x, smoothed_y, smoothed_w, smoothed_h))
                found = True
                break

        if not found:
            smoothed_faces.append((x, y, w, h))

    return smoothed_faces

# 连续未检测到人脸的帧计数器
no_face_count = 0
max_no_face_count = 100  # 连续未检测到人脸的最大帧数

while True:
    # 读取摄像头图像
    ret, frame = cap.read()

    if not ret:
        # 如果无法读取摄像头图像，则退出循环
        break

    # 将图像转换为灰度图像
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # 检测人脸
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
    # 平滑处理人脸位置
    faces = list(faces)
    faces = smooth_faces(faces, prev_faces, alpha=0.3)
    prev_faces = faces.copy()

    # 在图像上绘制带圆角矩形边框
    if len(faces) > 0:
        for (x, y, w, h) in faces:
            # 绘制带圆角矩形边框
            radius = int(min(w, h) / 10)
            draw_rounded_rectangle(frame, x, y, w, h, radius, (18, 153, 255), 2)
            # 在人脸框上方显示文本信息
            text = "Face"
            font = cv2.FONT_HERSHEY_SIMPLEX
            font_scale = 0.8
            thickness = 2
            text_size, _ = cv2.getTextSize(text, font, font_scale, thickness)
            text_x = x + int((w - text_size[0]) / 2)
            text_y = y - 10
            cv2.putText(frame, text, (text_x, text_y), font, font_scale, (18, 153, 255), thickness, cv2.LINE_AA)
    # 显示图像
    cv2.imshow('Face Detection', frame)

    # 按下ESC键退出循环
    if cv2.waitKey(1) == 27:
        break

# 释放摄像头和关闭窗口
cap.release()
cv2.destroyAllWindows()