Superimpose heatmap on a base image OpenCV Python

Question

Please look at this github page. I want to generate heat maps in this way using Python PIL,open cv or matplotlib library. Can somebody help me figure it out?

I could create a heat map for my network at the same size as the input, but I am not able superimpose them. The heatmap shape is (800,800) and the base image shape is (800,800,3)

Answer 1

Updated Answer -- 29th April, 2022.

After the repeated comments I have decided to update this post with a better visualization.

Consider the following image:

img = cv2.imread('image_path')

I obtained a binary image after performing binary threshold on the a-channel of the LAB converted image:

lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
a_component = lab[:,:,1]
th = cv2.threshold(a_component,140,255,cv2.THRESH_BINARY)[1]

Applying Gaussian blur:

blur = cv2.GaussianBlur(th,(13,13), 11)

The resulting heatmap:

heatmap_img = cv2.applyColorMap(blur, cv2.COLORMAP_JET)

Finally, superimposing the heatmap over the original image:

super_imposed_img = cv2.addWeighted(heatmap_img, 0.5, img, 0.5, 0)

Note: You can vary the weight parameters in the function cv2.addWeighted and observe the differences.

Answer 2

My code starts from a heatmap matrix (224,224) called cam, which is applied to the original image called frame, via opencv;

and it seems to work pretty well:

import numpy as np
from cv2 import cv2
from skimage import exposure 
...

capture = cv2.VideoCapture(...)
while True:
    ret, frame = capture.read()

    if ret:
        #resize original frame
        frame = cv2.resize(frame, (224, 224)) 

        #get color map
        cam = getMap(frame)
        map_img = exposure.rescale_intensity(cam, out_range=(0, 255))
        map_img = np.uint8(map_img)
        heatmap_img = cv2.applyColorMap(map_img, cv2.COLORMAP_JET)

        #merge map and frame
        fin = cv2.addWeighted(heatmap_img, 0.5, frame, 0.5, 0)

        #show result
        cv2.imshow('frame', fin)

the getMap() function gets the headmap given the frame;

I found some interesting free videos about this topic:

https://www.youtube.com/watch?v=vTY58-51XZA&t=14s

https://www.youtube.com/watch?v=4v9usdvGU50&t=208s