Change range of colors in an image using python

This is one way to approach the problem in Python/OpenCV. But for red, it is very hard to do because red spans 0 hue, which also is the hue for gray and white and black, which you have in your image. The other issue you have is that skin tones has red shades, so you cannot pick too large of ranges for your colors. Also when dealing with red ranges, you need two sets, one for hues up to 180 and another for hues above 0.

Input:

import cv2
import numpy as np

# load image
img = cv2.imread('red_clothes.jpg')

# convert to HSV
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
h,s,v = cv2.split(hsv)

blue_hue = 120
red_hue = 0

# diff hue (blue_hue - red_hue)
diff_hue = blue_hue - red_hue

# create mask for red color in hsv
lower1 = (150,150,150)
upper1 = (180,255,255)
mask1 = cv2.inRange(hsv, lower1, upper1)
lower2 = (0,150,150)
upper2 = (30,255,255)
mask2 = cv2.inRange(hsv, lower2, upper2)
mask = cv2.add(mask1,mask2)
mask = cv2.merge([mask,mask,mask])

# apply morphology to clean mask
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (9,9))
mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)

# modify hue channel by adding difference and modulo 180 
hnew = np.mod(h + diff_hue, 180).astype(np.uint8)

# recombine channels
hsv_new = cv2.merge([hnew,s,v])

# convert back to bgr
bgr_new = cv2.cvtColor(hsv_new, cv2.COLOR_HSV2BGR)

# blend with original using mask
result = np.where(mask==(255, 255, 255), bgr_new, img)

# save output
cv2.imwrite('red_clothes_mask.png', mask)
cv2.imwrite('red_clothes_hue_shift.png', bgr_new)
cv2.imwrite('red_clothes_red2blue.png', result)

# Display various images to see the steps
cv2.imshow('mask1',mask1)
cv2.imshow('mask2',mask2)
cv2.imshow('mask',mask)
cv2.imshow('bgr_new',bgr_new)
cv2.imshow('result',result)
cv2.waitKey(0)
cv2.destroyAllWindows()

Mask:

Hue Shifted Image:

Blend between Input and Hue Shifted Image using Mask to blend:

So the result is speckled because of the black mixed with the red and from limited ranges due to skin color.

You can start with red, but the trick is to invert the image so red is now at hue 90 in OpenCV range and for example blue is at hue 30. So in Python/OpenCV, you can do the following:

Input:

import cv2
import numpy as np

# load image
img = cv2.imread('red_clothes.jpg')

# invert image
imginv = 255 - img

# convert to HSV
hsv = cv2.cvtColor(imginv, cv2.COLOR_BGR2HSV)
h,s,v = cv2.split(hsv)

blueinv_hue = 30  #(=120+180/2=210-180=30)
redinv_hue = 90  #(=0+180/2=90)

# diff hue (blue_hue - red_hue)
diff_hue = blueinv_hue - redinv_hue

# create mask for redinv color in hsv
lower = (80,150,150)
upper = (100,255,255)
mask = cv2.inRange(hsv, lower, upper)
mask = cv2.merge([mask,mask,mask])

# apply morphology to clean mask
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (9,9))
mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)

# modify hue channel by adding difference and modulo 180 
hnew = np.mod(h + diff_hue, 180).astype(np.uint8)

# recombine channels
hsv_new = cv2.merge([hnew,s,v])

# convert back to bgr
bgrinv_new = cv2.cvtColor(hsv_new, cv2.COLOR_HSV2BGR)

# invert
bgr_new = 255 -bgrinv_new

# blend with original using mask
result = np.where(mask==(255, 255, 255), bgr_new, img)

# save output
cv2.imwrite('red_clothes_mask.png', mask)
cv2.imwrite('red_clothes_hue_shift.png', bgr_new)
cv2.imwrite('red_clothes_red2blue.png', result)

# Display various images to see the steps
cv2.imshow('mask',mask)
cv2.imshow('bgr_new',bgr_new)
cv2.imshow('result',result)
cv2.waitKey(0)
cv2.destroyAllWindows()

Mask:

Red to Blue before masking:

Red to Blue after masking:

However, one is still limited by the fact that red is close to skin tones, so the range for red is limited.

Starting with a blue image rather than red allows one to use an expanded range for inRange() and do a better job in Python/OpenCV. Here is a change from blue to red.

Input:

import cv2
import numpy as np

# load image
img = cv2.imread('blue_clothes.jpg')

# convert to HSV
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
h,s,v = cv2.split(hsv)

red_hue = 0
blue_hue = 120

# diff hue (red_hue - blue_hue)
diff_hue = red_hue - blue_hue

# create mask for blue color in hsv
lower = (100,90,90)
upper = (140,255,255)
mask = cv2.inRange(hsv, lower, upper)
mask = cv2.merge([mask,mask,mask])

# apply morphology to clean mask
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (9,9))
mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)

# modify hue channel by adding difference and modulo 180 
hnew = np.mod(h + diff_hue, 180).astype(np.uint8)

# recombine channels
hsv_new = cv2.merge([hnew,s,v])

# convert back to bgr
bgr_new = cv2.cvtColor(hsv_new, cv2.COLOR_HSV2BGR)

# blend with original using mask
result = np.where(mask==(255, 255, 255), bgr_new, img)

# save output
cv2.imwrite('blue_clothes_mask.png', mask)
cv2.imwrite('blue_clothes_hue_shift.png', bgr_new)
cv2.imwrite('blue_clothes_blue2red.png', result)

# Display various images to see the steps
cv2.imshow('mask',mask)
cv2.imshow('bgr_new',bgr_new)
cv2.imshow('result',result)
cv2.waitKey(0)
cv2.destroyAllWindows()

Mask:

Blue to Red before masking:

Blue to Red after masking: