Reputation: 344
I have scanned images of various products packagings as following
These are in different sizes. I need to remove white background on right and under. I have tried several solutions but only following code is giving me the best results.
import cv2
import numpy as np
# Load image, convert to grayscale, Gaussian blur, Otsu's threshold
image = cv2.imread("img.jpg")
original = image.copy()
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (3,3), 0)
thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
# Obtain bounding rectangle and extract ROI
x,y,w,h = cv2.boundingRect(thresh)
cv2.rectangle(image, (x, y), (x + w, y + h), (36,255,12), 2)
ROI = original[y:y+h, x:x+w]
# Add alpha channel
b,g,r = cv2.split(ROI)
alpha = np.ones(b.shape, dtype=b.dtype) * 50
ROI = cv2.merge([b,g,r,alpha])
cv2.imwrite('thresh.jpg', thresh)
cv2.imwrite('image.jpg', image)
cv2.imwrite('ROI.jpg', ROI)
But still it is not cropping out whole white background as it can be seen in following image it only selects a part of it and then crops it. Please can some help me with how can I properly remove white background? Thank you.
Upvotes: 0
Views: 78
Reputation: 1424
I didn't use the "opencv" library, I have used "skimage". After removing the white background, the resolution of the picture has been affected, I don't know if you can accept it. Here is the code:
from skimage import io
def corp_margin(img, threshold=600):
img2=img.sum(axis=2)
(row,col)=img2.shape
row_top=0
raw_down=0
col_top=0
col_down=0
for r in range(0,row):
if img2.sum(axis=1)[r]<threshold*col:
row_top=r
break
for r in range(row-1,0,-1):
if img2.sum(axis=1)[r]<threshold*col:
raw_down=r
break
for c in range(0,col):
if img2.sum(axis=0)[c]<threshold*row:
col_top=c
break
for c in range(col-1,0,-1):
if img2.sum(axis=0)[c]<threshold*row:
col_down=c
break
new_img=img[row_top:raw_down+1,col_top:col_down+1,0:3]
return new_img
image = io.imread("img.jpg")
img_re = corp_margin(image)
io.imshow(img_re)
io.imsave('new_image.jpg', img_re)
Upvotes: 1