import cv2

from BPApi import BPModel


def main():
    cap = cv2.VideoCapture(0)  # 使用摄像头

    #设置视频宽高
    cap.set(3, 1920)
    cap.set(4, 1080)

    video_fs = cap.get(5)
    # print(video_fs)

    # 加载模型
    model = BPModel(model_path=r'final/best.pth', fps=video_fs)

    frames = []

    text = ["calculating..."]
    font = cv2.FONT_HERSHEY_SIMPLEX
    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

    while True:
        ret, frame = cap.read()

        # 检测人脸
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        faces = face_cascade.detectMultiScale(gray, 1.3, 5)

        if faces is not None and len(faces) > 0:
            # 将第一个人脸区域的图像截取
            x, y, w, h = faces[0]
            face = frame[y:y + h, x:x + w]

            frames.append(face)

            cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 255, 0), 2)
            print(len(frames))

        if len(frames) == 250:

            sbp_outputs, dbp_outputs = model.predict(frames)

            print(sbp_outputs, dbp_outputs)

            text.clear()
            text.append('SBP: {:.2f} mmHg'.format(sbp_outputs))
            text.append('DBP: {:.2f} mmHg'.format(dbp_outputs))

            frames = []
            # 去除列表最前面的100个元素
            # frames=frames[50:]

        for i, t in enumerate(text):
            cv2.putText(frame, t, (10, 60 + i * 20), font, 0.6, (0, 255, 0), 2)
        cv2.imshow('Blood Pressure Detection', frame)
        key = cv2.waitKey(1) & 0xFF
        if key == ord('q'):
            break

    cap.release()
    cv2.destroyAllWindows()


if __name__ == '__main__':
    main()