Python OpenCV实现图像增强操作详解

# 变暗
def Darker(image,percetage=0.9):
    image_copy = image.copy()
    w = image.shape[1]
    h = image.shape[0]
    #get darker
    for xi in range(0,w):
        for xj in range(0,h):
            image_copy[xj,xi,0] = int(image[xj,xi,0]*percetage)
            image_copy[xj,xi,1] = int(image[xj,xi,1]*percetage)
            image_copy[xj,xi,2] = int(image[xj,xi,2]*percetage)
    return image_copy

# 明亮
def Brighter(image, percetage=1.1):
    image_copy = image.copy()
    w = image.shape[1]
    h = image.shape[0]
    #get brighter
    for xi in range(0,w):
        for xj in range(0,h):
            image_copy[xj,xi,0] = np.clip(int(image[xj,xi,0]*percetage),a_max=255,a_min=0)
            image_copy[xj,xi,1] = np.clip(int(image[xj,xi,1]*percetage),a_max=255,a_min=0)
            image_copy[xj,xi,2] = np.clip(int(image[xj,xi,2]*percetage),a_max=255,a_min=0)
    return image_copy

# 旋转，R可控制图片放大缩小
def Rotate(image, angle=15, scale=1):
    w = image.shape[1]
    h = image.shape[0]
    #rotate matrix
    M = cv2.getRotationMatrix2D((w/2,h/2), angle, scale)
    #rotate
    image = cv2.warpAffine(image,M,(w,h))
    return image

# 水平翻转
def Horizontal(image):
    return cv2.flip(image,1,dst=None)
 
# 垂直翻转
def Vertical(image):
    return cv2.flip(image,0,dst=None)

def gaussian_noise(image, mean=0, var=0.02):
    # 添加高斯噪声
    # mean : 均值
    # var : 方差
    image = np.array(image / 255, dtype=float)
    noise = np.random.normal(mean, var ** 0.5, image.shape)
    out = image + noise
    if out.min() < 0:
        low_clip = -1.
    else:
        low_clip = 0.
    out = np.clip(out, low_clip, 1.0)
    out = np.uint8(out * 255)
    return out

# 放大缩小
def Scale(image, scale):
    return cv2.resize(image,None,fx=scale,fy=scale,interpolation=cv2.INTER_LINEAR)
# 平移
def Move(img,x,y):
    img_info=img.shape
    height=img_info[0]
    width=img_info[1]
 
    mat_translation=np.float32([[2,0,x],[0,2,y]])  #变换矩阵：设置平移变换所需的计算矩阵：2行3列
    #[[1,0,20],[0,1,50]]   表示平移变换：其中x表示水平方向上的平移距离，y表示竖直方向上的平移距离。
    dst=cv2.warpAffine(img,mat_translation,(width,height))  #变换函数
# 椒盐噪声
def SaltAndPepper(src,percetage=0.05):
    SP_NoiseImg=src.copy()
    SP_NoiseNum=int(percetage*src.shape[0]*src.shape[1])
    for i in range(SP_NoiseNum):
        randR=np.random.randint(0,src.shape[0]-1)
        randG=np.random.randint(0,src.shape[1]-1)
        randB=np.random.randint(0,3)
        if np.random.randint(0,1)==0:
            SP_NoiseImg[randR,randG,randB]=0
        else:
            SP_NoiseImg[randR,randG,randB]=255
    return SP_NoiseImg
#模糊
def Blur(img):
    blur = cv2.GaussianBlur(img, (7, 7), 1.5)
    # #      cv2.GaussianBlur(图像，卷积核，标准差）
    return blur