import cv2
import numpy as np
import math


# RGB色彩空间
def Rgb(path):
    img = cv2.imread(path, 1)
    b = img[:, :, 0]
    g = img[:, :, 1]
    r = img[:, :, 2]
    cv2.imwrite('./media/RGB/b.jpg', b)
    cv2.imwrite('./media/RGB/g.jpg', g)
    cv2.imwrite('./media/RGB/r.jpg', r)


# HSV色彩空间
def Hsv(path):
    img = cv2.imread(path, 1)
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    h = hsv[:, :, 0]
    s = hsv[:, :, 1]
    v = hsv[:, :, 2]
    cv2.imwrite("./media/HSV/h.jpg", h)
    cv2.imwrite("./media/HSV/s.jpg", s)
    cv2.imwrite("./media/HSV/v.jpg", v)


def Un(path):
    img = cv2.imread(path, 0)
    result = ~img
    cv2.imwrite("./media/UN/un.jpg", result)


def Add(paths):
    # 只做了两个图像叠加，多个怎么做
    img1 = cv2.imread(paths[0], 1)
    img2 = cv2.imread(paths[1], 1)
    img = cv2.add(img1, img2)
    cv2.imwrite("./media/ADD/add.jpg", img)


def Subtract(paths):
    img1 = cv2.imread(paths[0], 1)
    img2 = cv2.imread(paths[1], 1)
    img = cv2.subtract(img1, img2)
    cv2.imwrite("./media/SUBTRACT/subtract.jpg", img)


# 形态学操作
def OpenAndClose(path):
    # 二值转换
    img = cv2.imread(path, 1)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    retval, src = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
    # 定义十字形结构元素
    kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (10, 10), (-1, -1))
    # 对二值图进行开运算和闭运算操作
    im_op = cv2.morphologyEx(src, cv2.MORPH_OPEN, kernel)
    im_cl = cv2.morphologyEx(src, cv2.MORPH_CLOSE, kernel)
    cv2.imwrite('./media/OPANDCL/op.png', im_op)
    cv2.imwrite('./media/OPANDCL/cl.png', im_cl)


# 边缘检测和图像增强
def Sharp(path):
    CRH = cv2.imread(path, 1)
    gradient = np.zeros_like(CRH)
    CRH = CRH.astype('float')
    h, w = CRH.shape[:2]
    for x in range(h - 1):
        for y in range(w - 1):
            gx = abs(CRH[x + 1, y] - CRH[x, y])
            gy = abs(CRH[x, y + 1] - CRH[x, y])
            gradient[x, y] = gx + gy
    sharp = CRH + gradient

    sharp = np.where(sharp > 255, 255, sharp)
    sharp = np.where(sharp < 0, 0, sharp)
    gradient = gradient.astype('uint8')
    sharp = sharp.astype('uint8')
    cv2.imwrite('./media/SHARP/gradient.png', gradient)
    cv2.imwrite('./media/SHARP/sharp.png', sharp)


# 镜像
def Turn(path):
    img = cv2.imread(path)
    # 水平镜像
    horizontal = cv2.flip(img, 1, dst=None)
    # 垂直镜像
    vertical = cv2.flip(img, 0, dst=None)
    # 对角镜像
    cross = cv2.flip(img, -1, dst=None)
    cv2.imwrite("./media/TURN/horizontal.jpg", horizontal)
    cv2.imwrite("./media/TURN/vertical.jpg", vertical)
    cv2.imwrite("./media/TURN/cross.jpg", cross)


# 腐蚀
def Erosion(path):
    src = cv2.imread(path, cv2.IMREAD_UNCHANGED)
    # 10x10的交叉型结构元
    kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (10, 10), (-1, -1))
    erosion = cv2.erode(src, kernel)
    cv2.imwrite("./media/EROSION/erosion.jpg", erosion)


# 膨胀
def Dilation(path):
    src = cv2.imread(path, cv2.IMREAD_UNCHANGED)
    # 10x10的交叉结构元
    kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (10, 10), (-1, -1))
    dilation = cv2.dilate(src, kernel)
    cv2.imwrite("./media/DILATION/dilation.png", dilation)


# 噪声及滤波
def Noise(path):
    image = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
    output = np.zeros(image.shape, np.uint8)
    output2 = np.zeros(image.shape, np.uint8)
    # 叠加噪声
    for i in range(image.shape[0]):
        for j in range(image.shape[1]):
            if image[i][j] < 40:
                # 添加食盐噪声
                output[i][j] = 255
            elif image[i][j] > 200:
                # 添加胡椒噪声
                output[i][j] = 0
                # 不添加噪声
            else:
                output[i][j] = image[i][j]

    # 均值滤波
    for i in range(output.shape[0]):
        for j in range(output.shape[1]):
            ji = 1.0
            for n in range(-1, 2):
                if 0 <= i < output.shape[0] and 0 <= j + n < output.shape[1]:
                    ji = ji * output[i][j + n]
            output2[i][j] = int(ji ** (1 / 3))

    cv2.imwrite("./media/NOISE/noise.jpg", output)
    cv2.imwrite("./media/NOISE/meanFilter.jpg", output2)

# # 扩展平移缩放
# def moveandchange(path):
#     img = cv2.imread(path, 1)
#     l, w, h = img.shape
#     # 扩展，使用双线性插值法
#     img = cv2.resize(img, (0, 0), fx=2, fy=2, interpolation=cv2.INTER_LINEAR)
#     height, width, channel = img.shape
#     # 移动
#     M = np.float32([[1, 0, 30], [0, 1, 60]])
#     img = cv2.warpAffine(img, M, (width, height))
#     # 旋转
#     rows, cols, depth = img.shape
#     M = cv2.getRotationMatrix2D((cols / 2, rows / 2), 45, 1)
#     dst = cv2.warpAffine(img, M, (width, height))
#
#     cv2.imwrite('./media/CHANGE/out.png', dst)


# # 仿射变换
# def affine(path):
#     img = cv2.imread(path)
#     rows, cols = img.shape[: 2]
#     # 设置图像仿射变化矩阵
#     post1 = np.float32([[50, 50], [200, 50], [50, 200]])
#     post2 = np.float32([[10, 100], [200, 50], [100, 250]])
#     M = cv2.getAffineTransform(post1, post2)
#     # 图像仿射变换
#     result = cv2.warpAffine(img, M, (rows, cols))
#     cv2.imwrite("./media/AFFINE/affine.jpg", result)