3直方图与二值化，图像梯度

#直方图--增强对比度
def equalHist_demo(image):
    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
    dst = cv.equalizeHist(gray)
    cv.imshow("equalHist_demo", dst)


def clahe_demo(image):
    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
    clahe = cv.createCLAHE(clipLimit=5.0, tileGridSize=(8, 8))
    dst = clahe.apply(gray)
    cv.imshow("clahe_demo", dst)
#直方图投影--视频跟踪

#二值化
import cv2 as cv
import numpy as np


def threshold_demo(image):
    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
    ret, binary = cv.threshold(gray, 127, 255, cv.THRESH_BINARY|cv.THRESH_OTSU)#OTSU计算阈值
    print("threshold value %s"%ret)
    cv.imshow("binary", binary)


def local_threshold(image):
    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
    binary = cv.adaptiveThreshold(gray, 255, cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY, 25, 10)
    cv.imshow("binary", binary)


def custom_threshold(image):#局部二值化
    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
    h, w = gray.shape[:2]
    m = np.reshape(gray, [1, w*h])
    mean = m.sum() / (w*h)
    print("mean : ", mean)
    ret, binary = cv.threshold(gray, mean, 255, cv.THRESH_BINARY)
    cv.imshow("binary", binary)


print("--------- Python OpenCV Tutorial ---------")
src = cv.imread("D:/vcprojects/images/test.png")
cv.namedWindow("input image", cv.WINDOW_AUTOSIZE)
cv.imshow("input image", src)
custom_threshold(src)
cv.waitKey(0)

cv.destroyAllWindows()

#图像梯度
def lapalian_demo(image):#拉普拉斯算子
    #dst = cv.Laplacian(image, cv.CV_32F)
    #lpls = cv.convertScaleAbs(dst)
    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("lapalian_demo", lpls)


def sobel_demo(image):#soble算子
    grad_x = cv.Scharr(image, cv.CV_32F, 1, 0)
    grad_y = cv.Scharr(image, cv.CV_32F, 0, 1)
    gradx = cv.convertScaleAbs(grad_x)
    grady = cv.convertScaleAbs(grad_y)
    cv.imshow("gradient-x", gradx)
    cv.imshow("gradient-y", grady)

    gradxy = cv.addWeighted(gradx, 0.5, grady, 0.5, 0)
    cv.imshow("gradient", gradxy)