import cv2 as cv
import numpy as np


# 圖像梯度（由x,y方向上的偏導(dǎo)數(shù)和偏移構(gòu)成），有一階導(dǎo)數(shù)（sobel算子）和二階導(dǎo)數(shù)（Laplace算子）
# 用于求解圖像邊緣，一階的極大值，二階的零點(diǎn)
# 一階偏導(dǎo)在圖像中為一階差分，再變成算子（即權(quán)值）與圖像像素值乘積相加，二階同理
def sobel_demo(image):
  grad_x = cv.Sobel(image, cv.CV_32F, 1, 0) # 采用Scharr邊緣更突出
  grad_y = cv.Sobel(image, cv.CV_32F, 0, 1)

  gradx = cv.convertScaleAbs(grad_x) # 由于算完的圖像有正有負(fù)，所以對其取絕對值
  grady = cv.convertScaleAbs(grad_y)

  # 計(jì)算兩個圖像的權(quán)值和，dst = src1*alpha + src2*beta + gamma
  gradxy = cv.addWeighted(gradx, 0.5, grady, 0.5, 0)

  cv.imshow("gradx", gradx)
  cv.imshow("grady", grady)
  cv.imshow("gradient", gradxy)


def laplace_demo(image): # 二階導(dǎo)數(shù)，邊緣更細(xì)
  dst = cv.Laplacian(image,cv.CV_32F)
  lpls = cv.convertScaleAbs(dst)
  cv.imshow("laplace_demo", lpls)


def custom_laplace(image):
  # 以下算子與上面的Laplace_demo()是一樣的，增強(qiáng)采用np.array([[1, 1, 1], [1, -8, 1], [1, 1, 1]])
  kernel = np.array([[1, 1, 1], [1, -8, 1], [1, 1, 1]])
  dst = cv.filter2D(image, cv.CV_32F, kernel=kernel)
  lpls = cv.convertScaleAbs(dst)
  cv.imshow("custom_laplace", lpls)


def main():
  src = cv.imread("../images/lena.jpg")
  cv.imshow("lena",src)
  # sobel_demo(src)
  laplace_demo(src)
  custom_laplace(src)
  cv.waitKey(0) # 等有鍵輸入或者1000ms后自動將窗口消除，0表示只用鍵輸入結(jié)束窗口
  cv.destroyAllWindows() # 關(guān)閉所有窗口


if __name__ == '__main__':
  main()