How to remove white borders/edges around people in an image using python?

Here are two ways:

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 of erosion is dilation, which enhances the image. Usually, the effect of dialing to "normalize" the shape manipulation is performed after corrosion. OpenCV combines these steps into one operation called morphological opening. Opening is just another name for eroding and then dilating, opening will usually give you smoother results than just eroding.

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

result

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