HSV过滤灰色水印

过滤颜色

RGB过滤红色

代码参考https://blog.csdn.net/sinat_29957455/article/details/109998166

def remove_red_color(image):
    """
    去除红色
    Otsu’s非常适合于图像灰度直方图具有双峰的情况，他会在双峰之间找到一个值作为阈值，对于非双峰图像，可能并不是很好用。

    """

    # 获得红色通道
    blue_c, green_c, red_c = cv2.split(image)

    # 多传入一个参数cv2.THRESH_OTSU，并且把阈值thresh设为0，算法会找到最优阈值
    #
    thresh, ret = cv2.threshold(red_c, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    # 实测调整为95%效果好一些
    filter_condition = int(thresh * 0.95)

    _, red_thresh = cv2.threshold(red_c, filter_condition, 255, cv2.THRESH_BINARY)

    # 把图片转回 3 通道
    result_img = np.expand_dims(red_thresh, axis=2)
    result_img = np.concatenate((result_img, result_img, result_img), axis=-1)

    return result_img
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HSV过滤灰色水印

参考：https://blog.csdn.net/qq_27524749/article/details/103124817

 
1

选择HSV阈值方法，参考：https://www.coder.work/article/2086510

import cv2
import sys
import numpy as np

def nothing(x):
    pass

# Create a window
cv2.namedWindow('image')

# create trackbars for color change
cv2.createTrackbar('HMin','image',0,179,nothing) # Hue is from 0-179 for Opencv
cv2.createTrackbar('SMin','image',0,255,nothing)
cv2.createTrackbar('VMin','image',0,255,nothing)
cv2.createTrackbar('HMax','image',0,179,nothing)
cv2.createTrackbar('SMax','image',0,255,nothing)
cv2.createTrackbar('VMax','image',0,255,nothing)

# Set default value for MAX HSV trackbars.
cv2.setTrackbarPos('HMax', 'image', 179)
cv2.setTrackbarPos('SMax', 'image', 255)
cv2.setTrackbarPos('VMax', 'image', 255)

# Initialize to check if HSV min/max value changes
hMin = sMin = vMin = hMax = sMax = vMax = 0
phMin = psMin = pvMin = phMax = psMax = pvMax = 0

img = cv2.imread('1.png')
output = img
waitTime = 33

while(1):

    # get current positions of all trackbars
    hMin = cv2.getTrackbarPos('HMin','image')
    sMin = cv2.getTrackbarPos('SMin','image')
    vMin = cv2.getTrackbarPos('VMin','image')

    hMax = cv2.getTrackbarPos('HMax','image')
    sMax = cv2.getTrackbarPos('SMax','image')
    vMax = cv2.getTrackbarPos('VMax','image')

    # Set minimum and max HSV values to display
    lower = np.array([hMin, sMin, vMin])
    upper = np.array([hMax, sMax, vMax])

    # Create HSV Image and threshold into a range.
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    mask = cv2.inRange(hsv, lower, upper)
    output = cv2.bitwise_and(img,img, mask= mask)

    # Print if there is a change in HSV value
    if( (phMin != hMin) | (psMin != sMin) | (pvMin != vMin) | (phMax != hMax) | (psMax != sMax) | (pvMax != vMax) ):
        print("(hMin = %d , sMin = %d, vMin = %d), (hMax = %d , sMax = %d, vMax = %d)" % (hMin , sMin , vMin, hMax, sMax , vMax))
        phMin = hMin
        psMin = sMin
        pvMin = vMin
        phMax = hMax
        psMax = sMax
        pvMax = vMax

    # Display output image
    cv2.imshow('image',output)

    # Wait longer to prevent freeze for videos.
    if cv2.waitKey(waitTime) & 0xFF == ord('q'):
        break

cv2.destroyAllWindows()
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效果图：

由于水印是非连续灰色点，使用中值滤波效果更好

去水印思路

1. 像素值相加大于阈值为白色
   代码和图片参考：https://bbs.huaweicloud.com/blogs/317237

像素值相加大概600（217+217+217）以上的像素点，都改成[255,255,255]就

from itertools import product
from PIL import Image

img = Image.open('test.png')
width, height = img.size
for pos in product(range(width), range(height)):
    if sum(img.getpixel(pos)[:3]) > 600:
        img.putpixel(pos, (255,255,255))
img.save('removed_1.png')
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2. 水印与原始图片相加
   https://blog.51cto.com/u_15162069/2820338
   手动取得水印所在区域ROI（感兴趣区域），并为水印创建蒙层（mask），然后借助蒙层对图片进行修补以消除水印。

opencv inpant 原理 https://www.cnblogs.com/lfri/p/10618417.html

3.如果水印没有连续，可以通过通过面积筛选轮廓，再用白色画轮廓

img = cv2.imread(r'xxx.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray[gray>205] = 255
canny = cv2.Canny(gray, 0, 255)  # 第一个阈值和第二个阈值
 
_, contours, HIERARCHY = cv2.findContours(canny, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
test_img = img.copy()

test_img_res = cv2.drawContours(test_img, candidate_table, -1, (255, 255, 255), 2)  
                                 
cv2.imwrite('test_img_res.jpg', test_img_res)
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