增加图像亮度不溢出 [英] Increase image brightness without overflow

问题描述

我在尝试增加图像亮度时遇到问题.

I got a problem when trying to increase image brightness.

这是原始图像:

我想要得到的图像是这样的:

The image I wanted to get is like this:

现在使用以下代码增加亮度:

Now to increase the brightness with the following code:

    image = cv2.imread("/home/wni/vbshare/tmp/a4_index2.png",0)

    if sum(image[0])/len(image[0])<200:
        new = np.where((255-image)<image,255,image*2)
    else:
        new = image
    return new

而且，我得到了以下图像:

And, I got the following image:

所以，似乎有些点的亮度溢出了.

So, seems brightness of some points overflowed.

我尝试将阈值从 200 更改为其他数字，例如125, 100, 140 等但是，图像亮度几乎保持不变或溢出.

And I tried to change the threshold from 200 to some other number, e.g. 125, 100, 140 .etc However, the image brightness stays either almost same dark or overflow.

环境:

Python:2.7.10

Python: 2.7.10

Opencv:3.2.0

Opencv: 3.2.0

对此的任何建议表示赞赏.

Any suggestion for this is appreciated.

谢谢.

推荐答案

这是我对清理特定图像的简单算法的尝试.随意使用它并进一步调整以获得所需的结果.

Here's my shot at a simple algorithm for cleaning up that particular image. Feel free to play with it and tweak it further to get the desired result.

注意:显示的代码应该适用于 OpenCV 的 2.4.x 和 3.x 分支.

NB: The code shown should work both with the 2.4.x and 3.x branches of OpenCV.

将输入图像加载为灰度.

Load the input image as grayscale.

img = cv2.imread('paper.jpg',0)

步骤 1

放大图像，以去除文本.这一步在一定程度上有助于保留条码.

Step 1

Dilate the image, in order to get rid of the text. This step somewhat helps to preserve the bar code.

dilated_img = cv2.dilate(img, np.ones((7,7), np.uint8)) 

使用合适大小的内核对结果进行中值模糊以进一步抑制任何文本.

Median blur the result with a decent sized kernel to further suppress any text.

这应该会为我们提供一个相当不错的背景图像，其中包含所有阴影和/或变色.

This should get us a fairly good background image that contains all the shadows and/or discoloration.

bg_img = cv2.medianBlur(dilated_img, 21)

计算我们刚刚得到的原始和背景之间的差异.相同的位为黑色(接近 0 差异)，文本为白色(差异大).

Calculate the difference between the original and the background we just obtained. The bits that are identical will be black (close to 0 difference), the text will be white (large difference).

因为我们想要白底黑字，所以我们反转结果.

Since we want black on white, we invert the result.

diff_img = 255 - cv2.absdiff(img, bg_img)

标准化图像，以便我们使用完整的动态范围.

Normalize the image, so that we use the full dynamic range.

norm_img = diff_img.copy() # Needed for 3.x compatibility
cv2.normalize(diff_img, norm_img, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8UC1)

在这一点上，我们的纸张仍然有些灰色.我们可以将其截断，然后重新规范化图像.

At this point we still have the paper somewhat gray. We can truncate that away, and re-normalize the image.

_, thr_img = cv2.threshold(norm_img, 230, 0, cv2.THRESH_TRUNC)
cv2.normalize(thr_img, thr_img, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8UC1)

完成...

嗯，至少对我来说是这样；) 你可能想要裁剪它，然后做任何你想做的其他后处理.

Well, at least for me ;) You will probably want to crop it, and do whatever other post-processing you desire.

注意:在获得差异图像后，可能值得切换到更高的精度(16+ 位整数或浮点数)，以最大程度地减少重复归一化中累积的舍入误差.

Note: It might be worth switching to higher precision (16+ bit int or float) after you get the difference image, in order to minimize accumulating rounding errors in the repeated normalizations.

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