import math

import cv2
import dlib

from imutils import face_utils


detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor("G:\\test\\shape_predictor_68_face_landmarks.dat")

cap = cv2.VideoCapture(0)
ret, image = cap.read()

while ret:

    # sta = time.time()
    ret, image = cap.read()

    # image = cv2.resize(image, (640,384))
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    rects = detector(gray, 1)

    # enumerate()方法用于将一个可遍历的数据对象(列表、元组、字典)组合
    # 为一个索引序列，同时列出 数据下标 和 数据 ，一般用在for循环中
    check_smoke = False
    if rects is None:
        continue
    for (i, rect) in enumerate(rects):
        shape = predictor(gray, rect)  # 标记人脸中的68个landmark点
        shape = face_utils.shape_to_np(shape)  # shape转换成68个坐标点矩阵
        (mStart, mEnd) = face_utils.FACIAL_LANDMARKS_68_IDXS["mouth"]

        (x_face, y_face, w_face, h_face) = face_utils.rect_to_bb(rect)  # 返回人脸框的左上角坐标和矩形框的尺寸
        # cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)

        # cv2.putText(image, "Face #{}".format(i + 1), (x - 10, y - 10),
        #             cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

        # landmarksNum = 0;
        m_49 = None
        m_55 = None
        m_52 = None
        m_58 = None

        for id, (x, y) in enumerate(shape):

            if id == 49:
                m_49 = (x, y)
                # cv2.circle(image, (x, y), 2, (0, 0, 255), -1)

            elif id == 52:
                m_52 = (x, y)
                # cv2.circle(image, (x, y), 2, (0, 0, 255), -1)
            elif id == 55:
                m_55 = (x, y)
                # cv2.circle(image, (x, y), 2, (0, 0, 255), -1)

            elif id == 58:
                m_58 = (x, y)
                # cv2.circle(image, (x, y), 2, (0, 0, 255), -1)
        mouse_center = (int((m_49[0] + m_55[0]) / 2), int((m_49[1] + m_55[1]) / 2))
        mouse_w = abs(m_55[0] - m_49[0])
        mouse_h = abs(m_58[1] - m_52[1])

        # mouse_h=m_55[0]-m_49[0]
        mouse_left = (mouse_center[0] - 2 * mouse_w, mouse_center[1] - 2 * mouse_h)
        mouse_right = (mouse_center[0] + 2 * mouse_w, mouse_center[1] + 2 * mouse_h)

        cv2.rectangle(image, mouse_left, mouse_right, (0, 0, 255), 2)

        """
        crop image
        """
        crop_smoke = image[mouse_left[1]:mouse_right[1], mouse_left[0]:mouse_right[0], :]

        gray = cv2.cvtColor(crop_smoke, cv2.COLOR_RGB2GRAY)
        # 将灰度图转换为二值图像
        ret, thresh = cv2.threshold(gray, 180, 255, cv2.THRESH_BINARY)

        # contours, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
        #                                        cv2.CHAIN_APPROX_SIMPLE)  # 该函数计算一幅图像中目标的轮廓

        # cua = cv2.Canny(crop_smoke, 600, 384)
        # r = cv2.bilateralFilter(crop_smoke, 9, 75, 75)
        contours, cnt = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        # area = cv2.contourArea(contours[0])
        # print('contourArea', area)
        d_limit_x=0.0
        d_limit_y=0.0
        for c in contours:

            if cv2.contourArea(c) < 10 or cv2.contourArea(
                    c) > 30:  # 对于矩形区域，只显示大于给定阈值的轮廓，所以一些微小的变化不会显示。对于光照不变和噪声低的摄像头可不设定轮廓最小尺寸的阈值
                continue

            res = cv2.minAreaRect(c)
            cen_ = res[0]

            #
            len = math.sqrt(((mouse_center[0] - cen_[0] - mouse_left[0]) ** 2) + (
                    (mouse_center[1] - cen_[1] - mouse_left[1]) ** 2))
            print(len)
            x = cen_[0] + mouse_center[0]
            y = cen_[1] + mouse_center[1]

            d_limit_x = x_face + w_face + mouse_w * 2
            d_limit_y = y_face + h_face + mouse_w * 2

            if len < mouse_w * 2:

                cv2.circle(image, (int(mouse_center[0]), int(mouse_center[1])), 2, (0, 0, 255), -1)
                cv2.circle(image, (int(cen_[0]), int(cen_[1])), 2, (0, 0, 255), -1)

                (x, y, w, h) = cv2.boundingRect(c)  # 该函数计算矩形的边界框

                x = mouse_left[0] + x
                y = mouse_left[1] + y
                if x < d_limit_x and y < d_limit_y:
                    cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
                check_smoke = True
        cv2.putText(image, "x:{}".format(x), (0, 10), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
        cv2.putText(image, "y:{}".format(y), (0, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
        cv2.putText(image, "d_limit_x:{}".format(d_limit_x), (0, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
        cv2.putText(image, "d_limit_y:{}".format(d_limit_y), (0, 100), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255),2)
    cv2.imshow("test",image)

    if check_smoke: cv2.putText(image,"smoke!!!",(10, 60),cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cv2.destroyAllWindows()
cap.release()