如何使用 python 去除图像中图形周围的白色边框/边缘？

Question

I want to remove the white border between the black mask and the body image

Image input examples:

Image output with thickness 1:

Image output with thickness 2:

I tried some games with Blur and thresholds that I found in here I also used this code to find and draw the contour

    thickness = 3
    image = cv2.imread('../finetune/22.png')
    blank_mask = np.zeros(image.shape, dtype=np.uint8)
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    cnts = cv2.findContours(gray, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    cnts = cnts[0] if len(cnts) == 2 else cnts[1]

    cv2.drawContours(image, cnts, -1, (255,0,0), thickness)
    cv2.imshow('image', image)
    cv2.imwrite('../finetune/22-'+str(thickness)+'r.png',image)
    cv2.waitKey()

However the contour I've found is the black mask edge and not the white line I played with the thickness and it works nice but on each image this contour is different, also the thickness is not equal throughout the figure

what is the best precise way to remove it?

Answer 1

Here are two methods:

Method #1: cv2.erode()

You can use erosion is to erode away the boundaries of the white foreground object. Essentially the idea is to perform 2D convolution with a kernel. A kernel can be created using cv2.getStructuingElement() where you can pass the shape and size of the desired kernel to create. Typical kernels are cv2.MORPH_RECT , cv2.MORPH_ELLIPSE , or cv2.MORPH_CROSS . The kernel slides through the image where a pixel is considered a 1 if all the pixels under the kernel is 1 otherwise it is eroded to 0 . The net effect is that all pixels on the boundaries will be discarded depending on the shape and size of the kernel. The thickness of the foreground decreases and is useful for removing small white noise or to detach objects. You can adjust the strength of the erosion with the number of iterations to perform.

kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3,3))
erode = cv2.erode(image, kernel, iterations=1)

Method #1: Opening with cv2.morphologyEx()

The opposite to erosion is dilation which enhances the image. Typically, dialtion is performed after erosion to "normalize" the effect of the morphological operation. OpenCV combines these steps into a single operation called morphological opening. Opening is just another name for erosion followed by dilation and will typically give you smoother results compared to only eroding.

kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3,3))
opening = cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel)

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Result

You can experiment with the kernel shape and the number of iterations. To remove more noise, increase the kernel size and the number of iterations while to remove less, decrease the kernel size and the number of iterations.

import cv2

image = cv2.imread('1.png')
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3,3))
opening = cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel, iterations=3)

cv2.imshow('opening', opening)
cv2.waitKey()

Answer 2

The answer below sent by nathancy show exactly the result I want to achieve but it does not helping me with my root problem

When I use drawContours I could draw the contours on my mask and improve it

So this is more information about my problem:

After I've got a mask using an image segmentation method I want to change the background (in the example I use black background) but I still have white contour between the new background and the figure

Original image:

https://drive.google.com/open?id=1P39VCEe2FTqkD6JbdM4ueMr_71C6nI42

Mask:

https://drive.google.com/open?id=1LTHaclsDOxRJCI9t5bLg3PeanseRR9bc

Output:

https://drive.google.com/open?id=1-uQx77-fmMf_9qFSgNMBvo77Q6Ag8qEZ

This is the code I use:

    image = cv2.imread('../finetune/1.png')
    mask = cv2.imread('../finetune/1mask.png')

    output = np.zeros(image.shape, dtype=np.uint8)

    output[np.where(mask == 255)] = image[np.where(mask == 255)]

    cv2.imshow("output",output)
    cv2.imwrite('../finetune/1.output.png',output)

helping the answer above I can take the contour again and create new mask coordinately but I'm sure there is better elegant way to do so

to clarify, I want to improve the mask in order to prevent the white border when I put it on a new background