Apply MatplotLib or custom colormap to OpenCV image

For Python >= 2.7, cmapy packages this functionality in a convenient way. Install it with:

Python 2.7:

pip install cmapy

Python 3.x:

pip3 install cmapy

Or, for Anaconda (from conda-forge):

conda install -c conda-forge cmapy 

And use it like this:

import cv2
import matplotlib.pyplot as plt
import cmapy

# Read image.
img = cv2.imread('imgs/woman.png')

# Colorize.
img_colorized = cv2.applyColorMap(img, cmapy.cmap('viridis'))

# Display
plt.imshow(img_colorized)
plt.show()

Different colormaps give something like this:

See all the available colormaps in action here.

Disclaimer: I wrote cmapy (because I needed this functionality for another project), and internally, it does pretty much the same as the other answers.

Answering my own question because I did not find an easy solution on StackOverflow:

def apply_custom_colormap(image_gray, cmap=plt.get_cmap('seismic')):

    assert image_gray.dtype == np.uint8, 'must be np.uint8 image'
    if image_gray.ndim == 3: image_gray = image_gray.squeeze(-1)

    # Initialize the matplotlib color map
    sm = plt.cm.ScalarMappable(cmap=cmap)

    # Obtain linear color range
    color_range = sm.to_rgba(np.linspace(0, 1, 256))[:,0:3]    # color range RGBA => RGB
    color_range = (color_range*255.0).astype(np.uint8)         # [0,1] => [0,255]
    color_range = np.squeeze(np.dstack([color_range[:,2], color_range[:,1], color_range[:,0]]), 0)  # RGB => BGR

    # Apply colormap for each channel individually
    channels = [cv2.LUT(image_gray, color_range[:,i]) for i in range(3)]
    return np.dstack(channels)


image_gray = cv2.imread('./lena.jpg', cv2.IMREAD_GRAYSCALE)
image_bgr = apply_custom_colormap(image_gray, cmap=plt.get_cmap('bwr'))

cv2.imshow('image with colormap', image_bgr)
cv2.waitKey(0)

Produces the image:

In recent versions of OpenCV (starting with 3.3), there's an overload of applyColorMap, which allows you to provide a custom colormap (either 1 or 3 channel). I've modified verified.human's code to simply generate a colormap suitable to use with this function.

I've taken few more opportunities to simplify the code:

• ScalarMappable.to_rgba can return bytes (in range 0-255) directly when you set bytes argument to True.
• We can use array indexing with negative step size to remove the alpha channels as well as switch from RGB to BGR in one step

Code:

import cv2
import numpy as np
from matplotlib import pyplot as plt


def get_mpl_colormap(cmap_name):
    cmap = plt.get_cmap(cmap_name)

    # Initialize the matplotlib color map
    sm = plt.cm.ScalarMappable(cmap=cmap)

    # Obtain linear color range
    color_range = sm.to_rgba(np.linspace(0, 1, 256), bytes=True)[:,2::-1]

    return color_range.reshape(256, 1, 3)


image_gray = cv2.imread('cage.png', cv2.IMREAD_GRAYSCALE)
image_bgr = cv2.applyColorMap(image_gray, get_mpl_colormap('bwr'))

cv2.imshow('image with colormap', image_bgr)
cv2.waitKey()