从阈值图像 opencv python 中去除噪声

Remove noise from threshold image opencv python

提问by muazfaiz

I am trying to get the corners of the box in image. Following are example images, their threshold results and on the right after the arrow are the results that I need. You might have seen these images before too on slack because I am using these images for my example questions on slack.

我试图在图像中获取盒子的角。以下是示例图像，它们的阈值结果和箭头后面的右侧是我需要的结果。您之前可能也曾在 slack 上看过这些图像，因为我将这些图像用于有关 slack 的示例问题。

Following is the code that allows me reach till the middle image.

以下是允许我到达中间图像的代码。

import cv2
import numpy as np

img_file = 'C:/Users/box.jpg'
img = cv2.imread(img_file, cv2.IMREAD_COLOR)
img = cv2.blur(img, (5, 5))

hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
h, s, v = cv2.split(hsv)

thresh0 = cv2.adaptiveThreshold(s, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2)
thresh1 = cv2.adaptiveThreshold(v, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2)
thresh2 = cv2.adaptiveThreshold(v, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2)
thresh = cv2.bitwise_or(thresh0, thresh1)

cv2.imshow('Image-thresh0', thresh0)
cv2.waitKey(0)
cv2.imshow('Image-thresh1', thresh1)
cv2.waitKey(0)
cv2.imshow('Image-thresh2', thresh2)
cv2.waitKey(0)

Is there any method in opencv that can do it for me. I tried dilation cv2.dilate()and erosion cv2.erode()but it doesn't work in my cases.Or if not then what could be alternative ways of doing it ? Thanks

opencv 中是否有任何方法可以为我做到这一点。我尝试了扩张cv2.dilate()和侵蚀，cv2.erode()但它在我的情况下不起作用。或者如果没有，那么有什么替代方法呢？谢谢

Canny version of the image ... On the left with low threshold and on the right with high threshold

图像的精明版本......左边是低阈值，右边是高阈值

回答by Shreesha N

Below is a python implementation of @dhanushka's approach

下面是@dhanushka 方法的 python 实现

import cv2
import numpy as np

# load color image
im = cv2.imread('input.jpg')

# smooth the image with alternative closing and opening
# with an enlarging kernel
morph = im.copy()

kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 1))
morph = cv2.morphologyEx(morph, cv2.MORPH_CLOSE, kernel)
morph = cv2.morphologyEx(morph, cv2.MORPH_OPEN, kernel)

kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2))

# take morphological gradient
gradient_image = cv2.morphologyEx(morph, cv2.MORPH_GRADIENT, kernel)

# split the gradient image into channels
image_channels = np.split(np.asarray(gradient_image), 3, axis=2)

channel_height, channel_width, _ = image_channels[0].shape

# apply Otsu threshold to each channel
for i in range(0, 3):
    _, image_channels[i] = cv2.threshold(~image_channels[i], 0, 255, cv2.THRESH_OTSU | cv2.THRESH_BINARY)
    image_channels[i] = np.reshape(image_channels[i], newshape=(channel_height, channel_width, 1))

# merge the channels
image_channels = np.concatenate((image_channels[0], image_channels[1], image_channels[2]), axis=2)

# save the denoised image
cv2.imwrite('output.jpg', image_channels)

The above code doesn't give good results if the image you are dealing are invoices(or has large amount of text on a white background). In order to get good results on such images, remove

如果您处理的图像是发票（或在白色背景上有大量文本），则上述代码不会给出好的结果。为了在此类图像上获得良好的结果，请删除

gradient_image = cv2.morphologyEx(morph, cv2.MORPH_GRADIENT, kernel)

and pass morphobj to the split function and remove the ~symbol inside for loop

并将morphobj传递给 split 函数并删除~for 循环内的符号

回答by dhanushka

You can smooth the image to some degree by applying alternative morphological closing and opening operations with an enlarging structuring element.Here are the original and smoothed versions.

您可以通过应用具有放大结构元素的替代形态关闭和打开操作来在一定程度上平滑图像。这是原始版本和平滑版本。

Then take the morphological gradient of the image.

然后取图像的形态梯度。

Then apply Otsu threshold to each of the channels, and merge those channels.

然后将 Otsu 阈值应用于每个通道，并合并这些通道。

If your image sizes are different (larger), you might want to either change some of the parameters of the code or resize the images roughly to the sizes used here. The code is in c++but it won't be difficult to port it to python.

如果您的图像尺寸不同（更大），您可能需要更改代码的某些参数或将图像大致调整为此处使用的尺寸。代码在，c++但移植到python.

/* load color image */
Mat im = imread(INPUT_FOLDER_PATH + string("2.jpg"));
/* 
smooth the image with alternative closing and opening
with an enlarging kernel
*/
Mat morph = im.clone();
for (int r = 1; r < 4; r++)
{
    Mat kernel = getStructuringElement(MORPH_ELLIPSE, Size(2*r+1, 2*r+1));
    morphologyEx(morph, morph, CV_MOP_CLOSE, kernel);
    morphologyEx(morph, morph, CV_MOP_OPEN, kernel);
}
/* take morphological gradient */
Mat mgrad;
Mat kernel = getStructuringElement(MORPH_ELLIPSE, Size(3, 3));
morphologyEx(morph, mgrad, CV_MOP_GRADIENT, kernel);

Mat ch[3], merged;
/* split the gradient image into channels */
split(mgrad, ch);
/* apply Otsu threshold to each channel */
threshold(ch[0], ch[0], 0, 255, CV_THRESH_BINARY | CV_THRESH_OTSU);
threshold(ch[1], ch[1], 0, 255, CV_THRESH_BINARY | CV_THRESH_OTSU);
threshold(ch[2], ch[2], 0, 255, CV_THRESH_BINARY | CV_THRESH_OTSU);
/* merge the channels */
merge(ch, 3, merged);

回答by m3h0w

Not sure about how robust that solution will be but the idea is pretty simple. The edges of the box should be more pronounced than all the other high frequencies on those images. Thus using some basic preprocessing should allow to emphasize them.

不确定该解决方案的稳健性，但这个想法非常简单。盒子的边缘应该比这些图像上的所有其他高频更明显。因此，使用一些基本的预处理应该可以强调它们。

I used your code to make a prototype but the contour finding doesn't have to be the right path. Also sorry for the iterative unsharp masking - didn't have time to adjust the parameters.

我使用您的代码制作了原型，但轮廓查找不一定是正确的路径。也很抱歉迭代非锐化遮罩 - 没有时间调整参数。

import cv2
import numpy as np

def unsharp_mask(img, blur_size = (9,9), imgWeight = 1.5, gaussianWeight = -0.5):
    gaussian = cv2.GaussianBlur(img, (5,5), 0)
    return cv2.addWeighted(img, imgWeight, gaussian, gaussianWeight, 0)

img_file = 'box.png'
img = cv2.imread(img_file, cv2.IMREAD_COLOR)
img = cv2.blur(img, (5, 5))
img = unsharp_mask(img)
img = unsharp_mask(img)
img = unsharp_mask(img)

hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
h, s, v = cv2.split(hsv)

thresh = cv2.adaptiveThreshold(s, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2)
_, contours, heirarchy = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cnts = sorted(contours, key = cv2.contourArea, reverse = True)
#for cnt in cnts:
canvas_for_contours = thresh.copy()
cv2.drawContours(thresh, cnts[:-1], 0, (0,255,0), 3)
cv2.drawContours(canvas_for_contours, contours, 0, (0,255,0), 3)
cv2.imshow('Result', canvas_for_contours - thresh)
cv2.imwrite("result.jpg", canvas_for_contours - thresh)
cv2.waitKey(0)