import cv2
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
mpl.rcParams['font.sans-serif'] = ["SimHei"]
mpl.rcParams["axes.unicode_minus"] = False

def gasuss_noise(image, mean=0, var=0.001):
    '''
        添加高斯噪声
        image:原始图像
        mean : 均值
        var : 方差,越大，噪声越大
    '''
    image = np.array(image/255, dtype=float)#将原始图像的像素值进行归一化，除以255使得像素值在0-1之间
    noise = np.random.normal(mean, var ** 0.5, image.shape)#创建一个均值为mean，方差为var呈高斯分布的图像矩阵
    out = image + noise#将噪声和原始图像进行相加得到加噪后的图像
    if out.min() < 0:
        low_clip = -1.
    else:
        low_clip = 0.
    out = np.clip(out, low_clip, 1.0)#clip函数将元素的大小限制在了low_clip和1之间了，小于的用low_clip代替，大于1的用1代替
    out = np.uint8(out*255)#解除归一化，乘以255将加噪后的图像的像素值恢复
    #cv.imshow("gasuss", out)
    noise = noise*255
    return [noise,out]
def BGR2HSI(bgr_img):
    '''
    BGR转换HSI颜色模型
    :param bgr_img:
    :return:
    '''
    bgr=bgr_img.copy()
    B,G,R=cv2.split(bgr/255.0)
    hsi_img=bgr.copy()/255.0
    H,S,I=cv2.split(hsi_img)
    h,w=B.shape
    for i in range(h):
        for j in range(w):
            bgr_min=min(B[i,j],G[i,j],R[i,j])
            bgr_sum=B[i,j]+G[i,j]+R[i,j]
            I[i,j]=bgr_sum/3
            S[i,j]=1-3*bgr_min/bgr_sum
            cov=(R[i,j]-G[i,j])+(R[i,j]-B[i,j])
            var=2*np.sqrt((R[i,j]-G[i,j])**2+(R[i,j]-B[i,j])*(G[i,j]-B[i,j])**2)
            theta=np.arccos(cov/var)
            if G[i,j]>=B[i,j]:
                H[i,j]=theta/(2*np.pi)
            else:
                H[i,j]=(2*np.pi-theta)/(2*np.pi)
    hsi_img[:,:,0]=H
    hsi_img[:,:,1]=S
    hsi_img[:,:,2]=I
    return hsi_img
def HSI2BGR(hsi_img):
    '''
    HSI转换BGR颜色模型
    :param hsi_img:
    :return:
    '''
    hsi=hsi_img.copy()
    H,S,I=cv2.split(hsi)
    bgr_img=hsi_img.copy()
    B,G,R=cv2.split(bgr_img)
    h,w=B.shape
    for i in range(h):
        for j in range(w):
            if S[i,j]<1e-6:
                R[i,j]=I[i,j]
                G[i,j]=I[i,j]
                B[i,j]=I[i,j]
            else:
                H[i,j]*=360
                if H[i,j]>0 and H[i,j]<=120:
                    B[i,j]=(1-S[i,j])*I[i,j]
                    sigma=(H[i,j]-60)*np.pi/180
                    temp=np.tan(sigma)/np.sqrt(3)
                    G[i,j]=(1.5+1.5*temp)*I[i,j]-(0.5+1.5*temp)*B[i,j]
                    R[i,j]=3*I[i,j]-G[i,j]-B[i,j]
                elif H[i,j]>120 and H[i,j]<=240:
                    R[i,j]=(1-S[i,j])*I[i,j]
                    sigma=(H[i,j]-180)*np.pi/180
                    temp=np.tan(sigma)/np.sqrt(3)
                    B[i,j]=(1.5+1.5*temp)*I[i,j]-(0.5+1.5*temp)*R[i,j]
                    G[i,j]=3*I[i,j]-R[i,j]-B[i,j]
                elif H[i,j]>240 and H[i,j]<=360:
                    G[i,j]=(1-S[i,j])*I[i,j]
                    sigma=(H[i,j]-300)*np.pi/180
                    temp=np.tan(sigma)/np.sqrt(3)
                    R[i,j]=(1.5+1.5*temp)*I[i,j]-(0.5+1.5*temp)*G[i,j]
                    B[i,j]=3*I[i,j]-G[i,j]-R[i,j]
    bgr_img[:,:,0]=B
    bgr_img[:,:,1]=G
    bgr_img[:,:,2]=R
    return bgr_img
if __name__ == '__main__':
    #读取图片，添加高斯噪声，转换HSI
    src=cv2.imread('./cat/cat1.png')
    [noise,img]=gasuss_noise(src)
    hsi_img=BGR2HSI(img)
    #拆分通道
    B = img[:, :, 0]
    G = img[:, :, 1]
    R = img[:, :, 2]
    H = hsi_img[:, :, 0]
    S = hsi_img[:, :, 1]
    I = hsi_img[:, :, 2]
    plt.figure(1)
    plt.subplot(231), plt.imshow(B), plt.title('B')
    plt.subplot(232), plt.imshow(G), plt.title('G')
    plt.subplot(233), plt.imshow(R), plt.title('R')
    plt.subplot(234), plt.imshow(H), plt.title('H')
    plt.subplot(235), plt.imshow(S), plt.title('S')
    plt.subplot(236), plt.imshow(I), plt.title('I')
    B_blur = cv2.blur(B, (3, 3))
    G_blur = cv2.blur(G, (3, 3))
    R_blur = cv2.blur(R, (3, 3))
    img_new = cv2.merge([B_blur, G_blur, R_blur])  # 通道合并
    I_blur = cv2.blur(I, (3, 3))
    hsi_new = cv2.merge([H, S, I_blur])# 通道合并后转换位BGR显示
    hsi_new_img = HSI2BGR(hsi_new)
    cf = img_new - hsi_new_img

    plt.figure(2)
    plt.subplot(231), plt.imshow(img_new), plt.title('RGB模型下均值滤波'), plt.ylabel('k=3')
    plt.subplot(232), plt.imshow(hsi_new_img), plt.title('HSI模型下均值滤波')
    plt.subplot(233), plt.imshow(cf), plt.title('作差')
    # 均值滤波核变大后的效果
    B_blur = cv2.blur(B, (12, 12))
    G_blur = cv2.blur(G, (12, 12))
    R_blur = cv2.blur(R, (12, 12))
    img_new = cv2.merge([B_blur, G_blur, R_blur])  # 通道合并
    I_blur = cv2.blur(I, (12, 12))
    hsi_new = cv2.merge([H, S, I_blur])  # 通道合并后转换位BGR显示
    hsi_new_img = HSI2BGR(hsi_new)
    cf = img_new - hsi_new_img

    plt.subplot(234), plt.imshow(img_new), plt.ylabel('k=12')
    plt.subplot(235), plt.imshow(hsi_new_img)
    plt.subplot(236), plt.imshow(cf)
    plt.show()