How to convert a grayscale image to heatmap image with Python OpenCV

Question

I have a (540, 960, 1) shaped image with values ranging from [0..255] which is black and white. I need to convert it to a "heatmap" representation. As an example, pixels with 255 should be of most heat and pixels with 0 should be with least heat. Others in-between. I also need to return the heat maps as Numpy arrays so I can later merge them to a video. Is there a way to achieve this?

Solution

Here are two methods, one using Matplotlib and one using only OpenCV

Method #1: OpenCV + matplotlib.pyplot.get_cmap

To implement a grayscale (1-channel) -> heatmap (3-channel) conversion, we first load in the image as grayscale. By default, OpenCV reads in an image as 3-channel, 8-bit BGR. We can directly load in an image as grayscale using cv2.imread() with the cv2.IMREAD_GRAYSCALE parameter or use cv2.cvtColor() to convert a BGR image to grayscale with the cv2.COLOR_BGR2GRAY parameter. Once we load in the image, we throw this grayscale image into Matplotlib to obtain our heatmap image. Matplotlib returns a RGB format so we must convert back to Numpy format and switch to BGR colorspace for use with OpenCV. Here's a example using a scientific infrared camera image as input with the inferno colormap. See choosing color maps in Matplotlib for available built-in colormaps depending on your desired use case.

Input image:

Output heatmap image:

Code

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

image = cv2.imread('frame.png', 0)
colormap = plt.get_cmap('inferno')
heatmap = (colormap(image) * 2**16).astype(np.uint16)[:,:,:3]
heatmap = cv2.cvtColor(heatmap, cv2.COLOR_RGB2BGR)

cv2.imshow('image', image)
cv2.imshow('heatmap', heatmap)
cv2.waitKey()

Method #2: cv2.applyColorMap()

We can use OpenCV's built in heatmap function. Here's the result using the cv2.COLORMAP_HOT heatmap

Code

import cv2

image = cv2.imread('frame.png', 0)
heatmap = cv2.applyColorMap(image, cv2.COLORMAP_HOT)

cv2.imshow('heatmap', heatmap)
cv2.waitKey()

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Note: Although OpenCV's built-in implementation is short and quick, I recommend using Method #1 since there is a larger colormap selection. Matplotlib has hundreds of various colormaps and allows you to create your own custom color maps while OpenCV only has 12 to choose from. Here's the built in OpenCV colormap selection:

Comments

[harinsamaranayake]

So how to convert it to an heat map?

[jhill515]

The 2D array is itself a heatmap. Think of it as I(u,v) is the "heat" (intensity) at coordinates (u,v).

[Mark Ransom]

OpenCV allows you to use custom colormaps too, it's just not as easy or straight forward as using the built-in ones.