How Can I only keep text with specific color from image via opencv and python?

Question

I have some invoice image with some text overlapping, which make some trouble for later processing, and what I only is the text in black. some I want to remove the text which is in other colors.

is there any way to achieve this?

the image is attached as example.

I have tried to solve it with opencv, but i still can't solve this:

import numpy as np import cv2
img = cv2.imread('11.png')

lower = np.array([150,150,150]) 

upper = np.array([200,200,200])

mask = cv2.inRange(img, lower, upper) 
res = cv2.bitwise_and(img, img, mask=mask) 
cv2.imwrite('22.png',res)

[image with multiple color][1]

[1]: https://i.stack.imgur.com/nWQrV.pngstrong text

Answer 1

The text is darker and less saturated. And as suggested as @J.D. the HSV color space is good. But his range is wrong.

In OpenCV, the H ranges in [0, 180], while the S/V ranges in [0, 255]

Here is a colormap I made in the last year, I think it's helpful.

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(1) Use cv2.inRange

(2) Just threshold the V(HSV) channel:

th, threshed = cv2.threshold(v, 150, 255, cv2.THRESH_BINARY_INV)

(3) Just threshold the S(HSV) channel:

th, threshed2 = cv2.threshold(s, 30, 255, cv2.THRESH_BINARY_INV)

The result:

---

The demo code:

# 2018/12/30 22:21 
# 2018/12/30 23:25 

import cv2 

img = cv2.imread("test.png")
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
h,s,v = cv2.split(hsv)

mask = cv2.inRange(hsv, (0,0,0), (180, 50, 130))
dst1 = cv2.bitwise_and(img, img, mask=mask)

th, threshed = cv2.threshold(v, 150, 255, cv2.THRESH_BINARY_INV)
dst2 = cv2.bitwise_and(img, img, mask=threshed)

th, threshed2 = cv2.threshold(s, 30, 255, cv2.THRESH_BINARY_INV)
dst3 = cv2.bitwise_and(img, img, mask=threshed2)

cv2.imwrite("dst1.png", dst1)
cv2.imwrite("dst2.png", dst2)
cv2.imwrite("dst3.png", dst3)

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1. How to detect colored patches in an image using OpenCV?

2. How to define a threshold value to detect only green colour objects in an image :Opencv

Answer 2

Converting to the HSV colorspace makes selecting colors easier.

The code below does what you want. Result:

import numpy as np 
import cv2

kernel = np.ones((2,2),np.uint8)
# load image
img = cv2.imread("image.png")

# Convert BGR to HSV
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

# define range of black color in HSV
lower_val = np.array([0,0,0])
upper_val = np.array([179,100,130])

# Threshold the HSV image to get only black colors
mask = cv2.inRange(hsv, lower_val, upper_val)

# Bitwise-AND mask and original image
res = cv2.bitwise_and(img,img, mask= mask)
# invert the mask to get black letters on white background
res2 = cv2.bitwise_not(mask)

# display image
cv2.imshow("img", res)
cv2.imshow("img2", res2)
cv2.waitKey(0)
cv2.destroyAllWindows()

To change the level of black selected, tweak from the upper_val, the value currently set at 130. Higher = allow lighter shades (it's called the Value). Also the value currently at 100: lower = allow less color (actually: saturation). Read more about the HSV colorspace here.

I always find the image below very helpfull. The bottom 'disc' is all black. As you move up in Value, lighter pixels are also selected. The pixels with low saturation stay shades of gray until white (the center), the pixels with high saturation get colored(the edge).That's why you tweak those values.

Edit: As @Silencer pointed out, my range was off. Fixed it.