import cv2
import numpy as np


def test():
    raw = cv2.imread("./data/003.png")

    # 设定颜色HSV范围，假定为红色
    redLower = np.array([30, 0, 0])
    redUpper = np.array([255, 193, 255])

    img = raw

    # 将图像转化为HSV格式
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

    # 去除颜色范围外的其余颜色
    mask = cv2.inRange(hsv, redLower, redUpper)
    cv2.imshow("mask", mask)

    # 二值化操作
    ret, binary = cv2.threshold(mask, 0, 255, cv2.THRESH_BINARY)

    # 膨胀操作，因为是对线条进行提取定位，所以腐蚀可能会造成更大间隔的断点，将线条切断，因此仅做膨胀操作
    kernel = np.ones((5, 5), np.uint8)
    dilation = cv2.dilate(binary, kernel, iterations=1)
    #
    # img2 = cv2.bitwise_and(img, img, mask=mask)
    # cv2.imshow('image', mask)
    # cv2.waitKey(0)
    # cv2.destroyAllWindows()

    # 获取图像轮廓坐标，其中contours为坐标值，此处只检测外形轮廓
    contours, hierarchy = cv2.findContours(dilation, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    aim_box = (0, 0, 0, 0)
    if len(contours) > 0:
        print('Run!')
        # cv2.boundingRect()返回轮廓矩阵的坐标值，四个值为x, y, w, h， 其中x, y为左上角坐标，w,h为矩阵的宽和高
        boxes = [cv2.boundingRect(c) for c in contours]
        for box in boxes:
            x, y, w, h = box
            if box[2] * box[3] > aim_box[2] * aim_box[3]:
                aim_box = box
            # print(box)
    else:
        pass

    # x, y, w, h = aim_box
    # gray = cv2.GaussianBlur(raw, (5, 5), 0)
    # canny = cv2.Canny(gray, 70, 210)
    # contours, hierarchy = cv2.findContours(canny.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
    # contours = sorted(contours, key=cv2.contourArea, reverse=True)[:3]
    #
    # drawed = cv2.drawContours(raw, contours, -1, (0, 0, 255), 2)
    # print(f"轮廓数量：{len(contours)}")
    # cv2.imshow("raw", cv2.imread("./data/003.png"))
    # cv2.imshow("test1", drawed)
    # cv2.imshow("test", canny)
    cv2.waitKey(0)


# Press the green button in the gutter to run the script.
if __name__ == '__main__':
    test()

# See PyCharm help at https://www.jetbrains.com/help/pycharm/