Remove everything of a specific color (with a color variation tolerance) from an image with Python-CodePudding

I have some text in blue #00a2e8, and some text in black on a PNG image (white background).

How to remove everything in blue (including text in blue) on an image with Python PIL or OpenCV, with a certain tolerance for the variations of color?

Indeed, every pixel of the text is not perfectly of the same color, there are variations, shades of blue.

Here is what I was thinking:

convert from RGB to HSV
find the Hue h0 for the blue
do a Numpy mask for Hue in the interval [h0-10, h0 10]
set these pixels to white

Before coding this, is there a more standard way to do this with PIL or OpenCV Python?

Example

CodePudding user response：

Your image has some issues. Firstly, it has a completely superfluous alpha channel which can be ignored. Secondly, the colours around your blues are quite a long way from blue!

I used your planned approach and found the removal was pretty poor:

#!/usr/bin/env python3

import cv2
import numpy as np

# Load image
im = cv2.imread('nwP8M.png')

# Define lower and upper limits of our blue
BlueMin = np.array([90,  200, 200],np.uint8)
BlueMax = np.array([100, 255, 255],np.uint8)

# Go to HSV colourspace and get mask of blue pixels
HSV  = cv2.cvtColor(im,cv2.COLOR_BGR2HSV)
mask = cv2.inRange(HSV, BlueMin, BlueMax)

# Make all pixels in mask white
im[mask>0] = [255,255,255]
cv2.imwrite('DEBUG-plainMask.png', im)

That gives this:

If you broaden the range, to get the rough edges, you start to affect the green letters, so instead I dilated the mask so that pixels spatially near the blues are made white as well as pixels chromatically near the blues:

# Try dilating (enlarging) mask with 3x3 structuring element
SE   = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3,3))
mask = cv2.dilate(mask, kernel, iterations=1)

# Make all pixels in mask white
im[mask>0] = [255,255,255]
cv2.imwrite('result.png', im)

That gets you this:

You may wish to diddle with the actual values for your other images, but the principle is the same.

CodePudding user response：

I think you are looking for the function inRange:

thresh = 5
bgr = [255 - thresh, thresh , thresh ]
minBGR = np.array([bgr[0] - thresh, bgr[1] - thresh, bgr[2] - thresh])
maxBGR = np.array([bgr[0]   thresh, bgr[1]   thresh, bgr[2]   thresh])
maskBGR = cv2.inRange(image, minBGR, maxBGR)
resultBGR = cv2.bitwise_or(image, maskBGR)