Increasing Intensity of Certain Image Areas in OpenCV

Question

I currently have the following image and the salience map below which reflects the attention areas of the first image:

Both of them are the same size. What I am trying to do is amplify the region of areas that are very white in the salient region. For example, the eyes, collar and hair would be a bit more highlighted. I have the following code which I have tried to split the image into h, s, v and then multiply through but the output is black and white. Any help is greatly appreciated:

hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
h, s, v = cv2.split(hsv_image)

dimensions = (384, 384)  
saliencyMap = cv2.resize(saliencyMap, dimensions)
s1 = s * saliencyMap.astype(s.dtype)


hsv_image = cv2.merge([h, s1, v])
out = cv2.cvtColor(hsv_image, cv2.COLOR_HSV2BGR)

cv2.imshow('example', out)
cv2.waitKey()

Answer 1

Here is how to do that in Python/OpenCV. Add the two images (from your other post). Modify the mask to have values near a mean of mid-gray. Separate the image into H,S,V channels. Apply the mask to the Saturation channel doing hard light composition. Combine the new saturation with the old hue and value channels and convert back to BGR.

Input 1:

Input 2:

Mask:

import cv2
import numpy as np

# read image 1
img1 = cv2.imread('img1.png')
hh, ww = img1.shape[:2]

# read image 2 and resize to same size as img1
img2 = cv2.imread('img2.png')
img2 = cv2.resize(img2, (ww,hh))

# read saliency mask as grayscale and resize to same size as img1
mask = cv2.imread('mask.png')
mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
mask = cv2.resize(mask, (ww,hh))

# add img1 and img2
img12 = cv2.add(img1, img2)

# get mean of mask and shift mean to mid-gray
# desirable for hard light compositing
# add bias as needed
mean = np.mean(mask)
bias = -32
shift = 128 - mean + bias
mask = cv2.add(mask, shift)

# threshold mask at mid gray and convert to 3 channels
# (needed to use as src < 0.5 "if" condition in hard light)
thresh = cv2.threshold(mask, 128, 255, cv2.THRESH_BINARY)[1]

# convert img12 to hsv
hsv = cv2.cvtColor(img12, cv2.COLOR_BGR2HSV)

# separate channels
hue,sat,val = cv2.split(hsv)

# do hard light composite of saturation and mask
# see CSS specs at https://www.w3.org/TR/compositing-1/#blendinghardlight
satf = sat.astype(np.uint8)/255
maskf =  mask.astype(np.uint8)/255
threshf =  thresh.astype(np.uint8)/255
threshf_inv = 1 - threshf
low = 2.0 * satf * maskf
high = 1 - 2.0 * (1-satf) * (1-maskf)
new_sat = ( 255 * (low * threshf_inv + high * threshf) ).clip(0, 255).astype(np.uint8)

# combine new_sat with old hue and val
result = cv2.merge([hue,new_sat,val])

# save results
cv2.imwrite('img12_sat_hardlight.png', result)

# show results
cv2.imshow('img12', img12)
cv2.imshow('mask', mask)
cv2.imshow('thresh', thresh)
cv2.imshow('result', result)
cv2.waitKey(0)
cv2.destroyAllWindows()

Result: