OpenCV-Python removes watermarks for images

Preface

The process of removing watermarks is contrary to adding watermarks, which involves techniques such as image repair, color matching and area filling. OpenCV-Python provides a variety of ways to handle different types of watermarks, including but not limited to solid color watermarks, translucent watermarks, and watermarks on complex backgrounds. The following will introduce several common watermarking strategies in detail and give specific implementation steps.

1. Use the inpaint method for image repair

()Functions are one of the powerful tools for image repair in OpenCV, which can effectively remove small area defects or unnecessary elements in pictures, such as watermarks. This method is based on the fast travel algorithm (FMM) proposed by Telea in 2004, and progresses gradually inward from the edge of the area to be repaired until all pixel points are repaired. In order to use this function, you need to prepare a mask with the location information of the watermark, where the watermark part is represented in white and the rest of the background is black. The code is as follows:

import cv2
import numpy as np

# Read original image and watermark masksrc = ('image_with_watermark.jpg')
mask = ('watermark_mask.png', cv2.IMREAD_GRAYSCALE)

# Perform image repairdst = (src, mask, 3, cv2.INPAINT_TELEA)

# Show results('Original Image', src)
('Watermark Mask', mask)
('Restored Image', dst)
(0)
()

2. Filter watermarks based on color range

When the color of the watermark is significantly different from the background, you can directly delete pixels within a specific color range by setting a threshold. For example, if the watermark is rendered in light gray (R=242, G=242, B=244), you can traverse the entire image, find all pixels close to that color and set them to the background color. This method is simple but has limited effect and is suitable for scenes with relatively simple backgrounds.

import cv2
import numpy as np

img = ('image_with_watermark.jpg')
h, w, l = 

for j in range(h):
    for k in range(w):
        # Remove light gray watermark        if (img[j][k][0] &gt; 240 and img[j][k][1] &gt; 240 and img[j][k][2] &gt; 240):
            img[j][k] = [255, 255, 255]  # Set to white
("Image Without Watermark", img)
(0)
()

3. Utilize deep learning models

For more complex watermark situations, such as translucent or multi-layered watermarks, traditional image processing methods may be difficult to achieve the desired effect. At this time, you can consider using deep learning methods to perform more refined repairs. Lama Cleaner is an open source project that uses convolutional neural networks (CNNs) to automatically detect and remove watermarks and other distractors from images. Such methods usually require support from training datasets, but are very effective choices for high-quality watermark removal tasks.

4. Pixel-level inverse color neutralization technology

Another more advanced technique is the pixel-based inverted neutralization method, which mimics the function of removing watermarks in Photoshop. By creating a reverse-color watermark image on white background and combining it with the original image, the original watermark influence can be effectively offset. The specific method is to calculate the difference between the original pixel value and the inverted color value on each channel, and then adjust the final output color value according to a certain formula.

import cv2
import numpy as np

src = ('image_with_watermark.jpg')
mask = ('white_background_watermark.png')

save = (, np.uint8)  # Create an empty image for saving
for row in range([0]):
    for col in range([1]):
        for channel in range([2]):
            if mask[row, col, channel] != 0:
                reverse_val = 255 - src[row, col, channel]
                val = 255 - int(reverse_val * 256 / mask[row, col, channel])
                if val &lt; 0:
                    val = 0
                save[row, col, channel] = val

('Restored Image', save)
(0)
()

5. Comprehensive application of various technologies

In actual operation, it is often not possible to get the best results by using a certain method alone, but to combine several different technologies. For example, after initially removing the watermark, you can also useinpaintThe function further optimizes the image quality; or first reduces most of the watermark impact through color filtering, and then uses inverse color neutralization technology to process the remaining part. In addition, you can also try to combine morphological operations, frequency domain filtering and other image processing techniques to deal with more difficult problems.

In short, removing watermarks is a challenging task, especially when facing watermarks of complex backgrounds or irregular shapes. However, with the help of the various techniques and tools mentioned above, we can greatly improve the success rate of watermark removal and the quality of the final image. In practice, it is recommended to flexibly choose appropriate methods according to the specific situation and constantly adjust the parameters until satisfactory results are achieved.

Summarize

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