如何使用 OpenCV 检测大量白色像素的区域? [英] How to detect region of large # of white pixels using OpenCV?

问题描述

我想检测图像中的徽标以将其删除.我有一个想法，即寻找具有大量像素的对象，然后将其删除.另一个想法是遍历所有白色像素(我已经反转了我的图像)并寻找形成大区域的像素，然后删除该区域.有没有比这个更好的算法.还有OpenCV中的哪些方法可以帮助我检测大像素数的对象.

I want to detect a logo inside an image in order to remove it. I have an idea which is to look for objects which have the big number of pixels then remove. Another idea is to loop through all the white pixels (I have inverted my image) and look for pixels which forms a large region and then remove this region. Is there any algorithm better that this one. Also which methods in OpenCV will help me to detect object of large pixels number.

推荐答案

我有一个方法可以做到这一点.我不知道这个方法是否适用于所有人，但在这里效果很好.

I have a method to do this. I don't know whether this method applicable to all, but it works good here.

以下是代码(在 Python 中):

Below is code ( in Python ):

首先将图像转换为灰度图像，调整图像大小，应用阈值，然后制作与调整后的灰度图像大小和类型相同的蒙版图像.(面具图像只是一个黑色图像)

First convert image to grayscale, resize image, apply threshold, and make a mask image of same size and type of that of resized grayscale image. (Mask image is just a black image)

import cv2
import numpy as np

img = cv2.imread('bus.png')
img = cv2.resize(img,(400,500))
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret,gray = cv2.threshold(gray,127,255,0)
gray2 = gray.copy()
mask = np.zeros(gray.shape,np.uint8)

现在在阈值图像中找到轮廓.过滤 500 到 5000 之间区域的轮廓.它很可能是一个大的白色斑点，显然不是字母.(请记住，这个区域是专门针对此图像的.我不知道您的其他图像.您必须自己找到).现在在填充为白色的蒙版图像上绘制此轮廓.

Now find contours in the threshold image. Filter the contour for area between 500 to 5000. It will be most probably a large white blob, obviously not letters. (Remember, this area is particular for this image. I dont know about your other images. You will have to find it yourself). Now draw this contour on the mask image filled with white color.

contours, hier = cv2.findContours(gray,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
for cnt in contours:
    if 200<cv2.contourArea(cnt)<5000:
        cv2.drawContours(img,[cnt],0,(0,255,0),2)
        cv2.drawContours(mask,[cnt],0,255,-1)

以下是检测到的轮廓图:

接下来是遮罩图片:

现在您使用 cv2.bitwise_not 函数反转图像.在那里您可以选择在我们提供蒙版图像的地方提供蒙版，以便该函数仅对输入图像中蒙版图像中存在白色的区域进行操作.

Now you invert image using cv2.bitwise_not function. There you have option for giving mask where we give our mask image so that function operates only on the area in input image where there is white in mask image.

cv2.bitwise_not(gray2,gray2,mask)

最后显示图像:

cv2.imshow('IMG',gray2)
cv2.waitKey(0)
cv2.destroyAllWindows()

结果如下:

注意:

上述方法是为了保留白方块中的ORANGE".这就是为什么存在一些文物的原因.如果你不想要那个橙色，它可以更准确.

Above method is done to preserve "ORANGE" in white square. That is why some artifacts are there. If you don't want that orange also, it can be more accurate.

只需找到区域过滤轮廓的边界矩形并绘制填充黑色的矩形即可.

Just find the bounding rectangle for area-filtered contours and draw rectangle filled with black color.

代码:

import cv2
import numpy as np

img = cv2.imread('bus.png')
img = cv2.resize(img,(400,500))
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret,gray = cv2.threshold(gray,127,255,0)
gray2 = gray.copy()

contours, hier = cv2.findContours(gray,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
for cnt in contours:
    if 200<cv2.contourArea(cnt)<5000:
        (x,y,w,h) = cv2.boundingRect(cnt)
        cv2.rectangle(gray2,(x,y),(x+w,y+h),0,-1)

cv2.imshow('IMG',gray2)
cv2.waitKey(0)
cv2.destroyAllWindows()

结果:

检测到的边界矩形:

然后用黑色填充这些矩形:

Then fillout those rectangles with black:

它比以前更好，当然如果你不想要橙色")

It is better than previous , of course if you don't want "ORANGE")

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