[python] How does one convert a grayscale image to RGB in OpenCV (Python)?

I'm learning image processing using OpenCV for a realtime application. 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.

Early in the program I used gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) to convert from RGB to grayscale, but to go back I'm confused, and the function backtorgb = cv2.cvtColor(gray,cv2.CV_GRAY2RGB) is giving:

AttributeError: 'module' object has no attribute 'CV_GRAY2RGB'.

The code below does not appear to be drawing contours in green. Is this because it's a grayscale image? If so, can I convert the grayscale image back to RGB to visualize the contours in green?

import numpy as np
import cv2
import time

cap = cv2.VideoCapture(0)
while(cap.isOpened()):

    ret, frame = cap.read()

    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    ret, gb = cv2.threshold(gray,128,255,cv2.THRESH_BINARY)

    gb = cv2.bitwise_not(gb)

    contour,hier = cv2.findContours(gb,cv2.RETR_CCOMP,cv2.CHAIN_APPROX_SIMPLE)

    for cnt in contour:
        cv2.drawContours(gb,[cnt],0,255,-1)
    gray = cv2.bitwise_not(gb)

    cv2.drawContours(gray,contour,-1,(0,255,0),3)

    cv2.imshow('test', gray)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

One you convert your image to gray-scale you cannot got back. You have gone from three channel to one, when you try to go back all three numbers will be the same. So the short answer is no you cannot go back. The reason your backtorgb function this throwing that error is because it needs to be in the format:

CvtColor(input, output, CV_GRAY2BGR)

OpenCV use BGR not RGB, so if you fix the ordering it should work, though your image will still be gray.

Try this:

import cv2
import cv

color_img = cv2.cvtColor(gray_img, cv.CV_GRAY2RGB)

I discovered, while using opencv, that some of the constants are defined in the cv2 module, and other in the cv module.

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()