How to extract area of interest in the image while the boundary is not obvious

Are there ways to just extract the area of interest (the square light part in the red circle in the original image)? That means I need to get the coordinates of the edge and then masking the image outside the boundaries. I don't know how to do that. Could anyone help? Thanks!

#define horizontal and Vertical sobel kernels
Gx = np.array([[-1, 0, 1],[-2, 0, 2],[-1, 0, 1]])
Gy = np.array([[-1, -2, -1],[0, 0, 0],[1, 2, 1]])

#define kernal convolution function
# with image X and filter F
def convolve(X, F):
    # height and width of the image
    X_height = X.shape[0]
    X_width = X.shape[3]

    # height and width of the filter
    F_height = F.shape[0]
    F_width = F.shape[1]

    H = (F_height - 1) // 2
    W = (F_width - 1) // 2

    #output numpy matrix with height and width
    out = np.zeros((X_height, X_width))
    #iterate over all the pixel of image X
    for i in np.arange(H, X_height-H):
        for j in np.arange(W, X_width-W):
            sum = 0
            #iterate over the filter
            for k in np.arange(-H, H+1):
                for l in np.arange(-W, W+1):
                    #get the corresponding value from image and filter
                    a = X[i+k, j+l]
                    w = F[H+k, W+l]
                    sum += (w * a)
            out[i,j] = sum
    #return convolution  
    return out

#normalizing the vectors
sob_x = convolve(image, Gx) / 8.0
sob_y = convolve(image, Gy) / 8.0

#calculate the gradient magnitude of vectors
sob_out = np.sqrt(np.power(sob_x, 2) + np.power(sob_y, 2))
# mapping values from 0 to 255
sob_out = (sob_out / np.max(sob_out)) * 255

plt.imshow(sob_out, cmap = 'gray', interpolation = 'bicubic')
plt.show()