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()

cv2.imread(path, 0) (or rather cv2.imread(path, cv2.IMREAD_GRAYSCALE)) asks the image file reading code to load the image as grayscale. For most file types, some 3rd party library is used to read them. If this library supports grayscale conversion, we’ll be using that library’s conversion routine.

Otherwise, we’re using OpenCV’s implementation of the conversion to grayscale.

There’s no reason to assume one implementation is better than the other, the differences observed are likely due to a different computation order, or to a different assumption about the whitepoint. But note that if the file has a color profile embedded, the 3rd party library might be able to use it to do the conversion, and so will have whitepoint information available. This color profile does not get loaded into OpenCV, so OpenCV will always make an assumption about the whitepoint.

Reading the image directly as grayscale is possibly a bit more efficient. If the 3rd party library converts each pixel as it’s read, we won’t need a temporary memory space to store the full color image (which takes up 3x as much memory as the grayscale image). For a format such as JPEG, which stores intensity and color information separately, reading as grayscale also avoids a lot of computation (we’re directly outputting the intensity value, rather than computing the RGB values and then converting those back to intensity).

Reading directly as grayscale has the possibility of giving different results if the image file gets converted to a different format, as then a different grayscale conversion will be used. Say, you convert your JPEG file to PNG, then using IMREAD_GRAYSCALE will use different libraries to do the grayscale conversion, using OpenCV’s conversion code will ensure both files read identically.

The cvtImage routine will simply copy your gray element to each of the three elements R, G, and B for each pixel. In other words if the pixel gray value is 26, then the new image will have R = 26, G = 26, B = 26.

The image presented will still LOOK grayscale even though it contains all 3 color components, all you have essentially done is to triple the space necessary to store the same image.

If indeed you want color to appear in the image (when you view it), this is truly impossible to go from grayscale back to the ORIGINAL colors. There are however means of pseudo-coloring or false coloring the image.

http://en.wikipedia.org/wiki/False_color

http://blog.martinperis.com/2011/09/opencv-pseudocolor-and-chroma-depth.html

http://podeplace.blogspot.com/2012/11/opencv-pseudocolors.html