how to change the text layout color to white and text to black using python?

You may convert the image to HSV, apply threshold to find colored regions, and copy the result of cv2.threshold with cv2.THRESH_BINARY_INV only to the colored regions.

Main stages of the suggested solution:

• Convert from BGR to HSV color space, and get the saturation color channel.
  all black and white are zero, and colored pixels are above zero.
• Apply threshold to the saturation channel.
• Find contours on the binarized saturation channel.
• Draw the contours as white (255 values) on black background to form a mask.
• Apply morphological closing for closing some black gaps.
• Get only the area inside the mask from img_binary_inv (result of your code using cv2.THRESH_BINARY_INV).
• Copy masked img_binary_inv to img_grey only in pixels that mask is white.

Complete code sample:

import numpy as np
import cv2

img_bgr = cv2.imread('img.jpg')  # Read image as BGR

img_grey = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY)  # Convert from BGR to grayscale.

thresh = 170
img_binary_inv = cv2.threshold(img_grey, thresh, 255, cv2.THRESH_BINARY_INV)[1]  # Apply threshold and invert black/white

# Convert from BGR to HSV color space.
hsv = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2HSV)

# Get the saturation color channel - all black and white are zero, and colored pixels are above zero.
s = hsv[:, :, 1]

thresh = 100
s_binary = cv2.threshold(s, thresh, 255, cv2.THRESH_BINARY)[1]  # Apply threshold to the saturation channel.

# Find contours on s_binary
cnts = cv2.findContours(s_binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)[-2]  # Use index [-2] to be compatible to OpenCV 3 and 4

# Draw the contours as white (255 values) on black background.
mask = np.zeros_like(s_binary)
cv2.drawContours(mask, cnts, -1, 255, -1)

# Apply morphological closing for closing some black gaps.
mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, np.ones((3, 3)))

# Get only the area inside the mask from img_binary_inv
masked_binary_inv = cv2.bitwise_or(img_binary_inv, img_binary_inv, mask=mask)

# Copy masked_binary_inv to img_grey only in pixels that mask is white.
cv2.copyTo(masked_binary_inv, mask, img_grey)

cv2.imwrite('img_grey.png', img_grey)  # Save result (as PNG and not JPEG for better quality).

# Show images
cv2.imshow('img_bgr', img_bgr)
cv2.imshow('s_binary', s_binary)
cv2.imshow('mask', mask)
cv2.imshow('masked_binary_inv', masked_binary_inv)
cv2.imshow('img_grey', img_grey)
cv2.waitKey()
cv2.destroyAllWindows()

Result (img_grey):

The result doesn't look so good, due to the relatively low quality of the input image.

Intermediate results:

s_binary:

mask:

masked_binary_inv: