OpenCV（二）--阈值与平滑处理

# 阈值
def thresholdImg(img, mode):
    # mode:
    # cv2.THRESH_BINARY 超过阈值取最大值，未超过取0
    # cv2.THRESH_BINARY_INV 超过阈值取0，未超过取最大值
    # cv2.THRESH_TRUNC 超过阈值取阈值，未超过不变
    # cv2.THRESH_TOZERO 超过阈值不变，未超过取0
    # cv2.THRESH_TOZERO_INV 超过阈值取0，未超过不变
    img = changeGBRImgToGray(img)
    ret, newImg = cv2.threshold(img, 127, 255, mode)
    return newImg


# 给图片加噪声
def addImgNoise(img, level):
    rows, cols, chn = img.shape
    for i in range(level * 10000):
        x = np.random.randint(0, rows)
        y = np.random.randint(0, cols)
        img[x, y, :] = 255
    return img


# 添加滤波
global BLUR  # 取平均数
global GAUSSIAN  # 按照高斯分布加权计算平均数
global MEDIAN  # 取中间数
BLUR = 0
GAUSSIAN = 1
MEDIAN = 2


# 添加滤波
def addFilter(img, square, mode):
    if mode == 0:
        return cv2.blur(img, (square, square))
    if mode == 1:
        return cv2.GaussianBlur(img, (square, square), 1)
    if mode == 2:
        return cv2.medianBlur(img, square)


# 在一个窗口中展示多张图像
def showRowsOfImgs(title, *imgs):
    res = np.hstack(imgs)
    showImg(title, res)