CODEWITHSUNDEEP
pythonpython-3.xopencvimage-processing
NielsNL4
NielsNL4

Reputation: 640

How can i split up an image based on a horizontal line?

I need a way to split up the header and blueprint part of an image, but right now i'm out of ideas on how to do this. (cant use the original image so i tried to recreate it) document

I've tried to do this using opencv's houghlines but it detects a lot of different lines because of the blueprint so i cant find a clear spot to cut off the image.

(like this:) document with lines

What i need is 2 different images of the header and the blueprint, but right now i can't find a good way to do this so any help would be appreciated.

Upvotes: 3

Views: 3466

Answers (2)

Arkistarvh Kltzuonstev
Arkistarvh Kltzuonstev

Reputation: 6935

For your given input image :

input

If I use the following piece of code :

import cv2
import numpy as np
img = cv2.imread('test1.png')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray,50,150,apertureSize = 3)
lines = cv2.HoughLines(edges,1,np.pi/180,200)
for rho,theta in lines[0]:
    a = np.cos(theta)
    b = np.sin(theta)
    x0 = a*rho
    y0 = b*rho
    x1 = int(x0 + 1000*(-b))
    y1 = int(y0 + 1000*(a))
    x2 = int(x0 - 1000*(-b))
    y2 = int(y0 - 1000*(a))
    cv2.line(img,(x1,y1),(x2,y2),(0,0,255),2)
cv2.imwrite('houghlines3.jpg',img)

I get the following output :

output

See there is only one big straight line which differentiates the header and the blueprint part. You can get the starting and ending y co-ordinate of this line if you just print y1 and y2. For this case, they are y1 : 140, y2 : 141. Now what you need, is just crop the picture up to y pixel 141 value like this :

img = cv2.imread(path/to/original/image)
img_header = img[:141,:]
img_blueprint = img[141:, :]
cv2.imwrite("header.png", img_header)
cv2.imwrite("blueprint.png", img_blueprint)

Now, come to your problem. Here is a possible way. See the biggest straight line differentiating the header and the blueprint has got detected by three different red straight lines through the houghline transform. For these three lines, the starting y co-ordinates will be very much close like for example say 142, 145, 143. You need to append all ending y co-ordinates of the straight lines (y2) in a list and cluster all of them based on adjacency with a threshold value of 5-10 pixels, take the biggest cluster, take the largest ending y co-ordinate from the cluster and crop the original image accordingly.

Upvotes: 3

Mark Setchell
Mark Setchell

Reputation: 208003

If the line separating the header from the content is the longest black line across the width of the image, you can find it by summing the pixels across each row and then finding which row adds up to the least, i.e. has most black pixels:

# get sums across rows
rowSums = np.sum(image,axis=1)

# get index of blackest row
longestBlack = np.argmin(rowSums)

Upvotes: 1

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