Alternatively, cv2.merge() can be used to turn a single channel binary mask layer into a three channel color image by merging the same layer together as the blue, green, and red layers of the new image. We pass in a list of the three color channel layers - all the same in this case - and the function returns a single image with those color channels. This effectively transforms a grayscale image of shape (height, width, 1) into (height, width, 3)

To address your problem

I did some thresholding on an image and want to label the contours in green, but they aren't showing up in green because my image is in black and white.

This is because you're trying to display three channels on a single channel image. To fix this, you can simply merge the three single channels

image = cv2.imread('image.png')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray_three = cv2.merge([gray,gray,gray])

Example

We create a color image with dimensions (200,200,3)

image = (np.random.standard_normal([200,200,3]) * 255).astype(np.uint8)

Next we convert it to grayscale and create another image using cv2.merge() with three gray channels

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray_three = cv2.merge([gray,gray,gray])

We now draw a filled contour onto the single channel grayscale image (left) with shape (200,200,1) and the three channel grayscale image with shape (200,200,3) (right). The left image showcases the problem you're experiencing since you're trying to display three channels on a single channel image. After merging the grayscale image into three channels, we can now apply color onto the image

contour = np.array([[10,10], [190, 10], [190, 80], [10, 80]])
cv2.fillPoly(gray, [contour], [36,255,12])
cv2.fillPoly(gray_three, [contour], [36,255,12])

Full code

import cv2
import numpy as np

# Create random color image
image = (np.random.standard_normal([200,200,3]) * 255).astype(np.uint8)

# Convert to grayscale (1 channel)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Merge channels to create color image (3 channels)
gray_three = cv2.merge([gray,gray,gray])

# Fill a contour on both the single channel and three channel image
contour = np.array([[10,10], [190, 10], [190, 80], [10, 80]])
cv2.fillPoly(gray, [contour], [36,255,12])
cv2.fillPoly(gray_three, [contour], [36,255,12])

cv2.imshow('image', image)
cv2.imshow('gray', gray)
cv2.imshow('gray_three', gray_three)
cv2.waitKey()

You can also use scikit-image, which provides some functions to convert an image in ndarray, like rgb2gray.

from skimage import color
from skimage import io

img = color.rgb2gray(io.imread('image.png'))

Notes: The weights used in this conversion are calibrated for contemporary CRT phosphors: Y = 0.2125 R + 0.7154 G + 0.0721 B

Alternatively, you can read image in grayscale by:

from skimage import io
img = io.imread('image.png', as_gray=True)