How to remove black part from the image?

mevatron's answer is one way where amount of black region is minimised while retaining full image.

Another option is removing complete black region where you also loose some part of image, but result will be a neat looking rectangular image. Below is the Python code.

Here, you find three main corners of the image as below:

I have marked those values. (1,x2), (x1,1), (x3,y3). It is based on the assumption that your image starts from (1,1).

Code :

First steps are same as mevatron's. Blur the image to remove noise, threshold the image, then find contours.

import cv2
import numpy as np

img = cv2.imread('office.jpg')
img = cv2.resize(img,(800,400))

gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
gray = cv2.medianBlur(gray,3)

ret,thresh = cv2.threshold(gray,1,255,0)
contours,hierarchy = cv2.findContours(thresh,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)

Now find the biggest contour which is your image. It is to avoid noise in case if any (Most probably there won't be any). Or you can use mevatron's method.

max_area = -1
best_cnt = None

for cnt in contours:
    
    area = cv2.contourArea(cnt)
    if area > max_area:
        max_area = area
        best_cnt = cnt

Now approximate the contour to remove unnecessary points in contour values found, but it preserve all corner values.

approx = cv2.approxPolyDP(best_cnt,0.01*cv2.arcLength(best_cnt,True),True)

Now we find the corners.

First, we find (x3,y3). It is farthest point. So x3*y3 will be very large. So we find products of all pair of points and select the pair with maximum product.

far = approx[np.product(approx,2).argmax()][0]

Next (1,x2). It is the point where first element is one,then