如何使用opencv丢弃图像的边缘? [英] How to discard the edges of an image using opencv?

问题描述

我正在预处理一些图像，以便从我感兴趣的区域中删除背景.但是，由于相机的聚焦，我长凳上的图像边缘呈圆形.如何丢弃这些倒圆的边缘，并且只能从图像中删除我感兴趣的对象?下面的代码可以删除图像的背景，但是由于周围的边缘而无法正常工作.

I'm pre-processing some images in order to remove the background from my area of ​​interest. However, the images on my bench have rounded edges due to the focus of the camera. How do I discard these rounded edges and be able to remove only my object of interest from the image? The code below I can remove the background of the image, but it does not work right due to the edges around.

import numpy as np
import cv2

#Read the image and perform threshold and get its height and weight
img = cv2.imread('IMD408.bmp')
h, w = img.shape[:2]

# Transform to gray colorspace and blur the image.
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray,(5,5),0)

# Make a fake rectangle arround the image that will seperate the main contour.
cv2.rectangle(blur, (0,0), (w,h), (255,255,255), 10)

# Perform Otsu threshold.
_,thresh = cv2.threshold(blur,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)

# Create a mask for bitwise operation
mask = np.zeros((h, w), np.uint8)

# Search for contours and iterate over contours. Make threshold for size to
# eliminate others.
contours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)

for i in contours:
    cnt = cv2.contourArea(i)
    if 1000000 >cnt > 100000:
        cv2.drawContours(mask, [i],-1, 255, -1)


# Perform the bitwise operation.
res = cv2.bitwise_and(img, img, mask=mask)

# Display the result.
cv2.imwrite('IMD408.png', res)
cv2.imshow('img', res)
cv2.waitKey(0)
cv2.destroyAllWindows()

输入图像:

退出:

错误:

推荐答案

由于您提到所有图像都具有相同的色相，因此这对于它们来说应该很好用.步骤是做一些白平衡，这会增加对比度.

Since you mentioned that all the images have the same hue, then this should work well for them. Steps is to do some white balancing which will increase the contrast a bit.

获取灰度.

阈值灰度图像.小于127的值设置为255(白色).这将为您提供一个二进制图像，它将成为原始图像的遮罩.

Threshold the grayscale image. Values less than 127 are set to 255 (white). This will give you a binary image, which will become a mask for the original image.

涂上口罩

如果要获得更好的结果，可能必须尝试使用​​阈值，这里是

You might have to play around with the thresholding if you want better results, here is the link for that. But this should get you started. I'm using a different OpenCV version compared to you might have to tweak the code a bit.

import cv2

def equaliseWhiteBalance(image):
    ''' Return equilised WB of an image '''
    wb = cv2.xphoto.createSimpleWB()                        #Create WB Object
    imgWB = wb.balanceWhite(img)                            #Balance White on image
    r,g,b = cv2.split(imgWB)                                #Get individual r,g,b channels
    r_equ  = cv2.equalizeHist(r)                            #Equalise RED channel
    g_equ  = cv2.equalizeHist(g)                            #Equalise GREEN channel
    b_equ  = cv2.equalizeHist(b)                            #Equalise BLUE channel
    img_equ_WB = cv2.merge([r_equ,g_equ,b_equ])             #Merge equalised channels
    return imgWB

#Read the image
img = cv2.imread('IMD408.bmp')
result = img.copy()

#Get whiteBalance of image
imgWB = equaliseWhiteBalance(img)

cv2.imshow('img', imgWB)
cv2.waitKey(0)

# Get gray image
gray = cv2.cvtColor(imgWB,cv2.COLOR_RGB2GRAY)
cv2.imshow('img', gray)
cv2.waitKey(0)

# Perform threshold
_, thresh = cv2.threshold(gray,127,255,cv2.THRESH_BINARY)
cv2.imshow('img', thresh)
cv2.waitKey(0)

# Apply mask
result[thresh!=0] = (255,255,255)

cv2.imshow('img', result)
cv2.waitKey(0)

如果每个图像的所有暗角小插图的大小都不同，则建议在二进制(蒙版)图像上寻找轮廓的质心.距图像任何一个角短"距离的质心将成为暗角，因此可以将其值从黑色更改为白色.

If all the dark corner vignettes have different sizes per image, then I suggest looking for centroid of contours on the binary (mask) image. Centroids with a 'short' distance to any corner of your image will be the dark vignettes, so their value can be changed from black to white.

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