Python, OpenCV and rectangles on scanned image

Question

Hello! Mastering OpenCV, I encountered a problem: I can't find any of these boxes to them then cut. Tell me, please, what filters and logic to use?

#!/usr/bin/env python
import cv2
import os

img_path = os.path.join('img', '1.jpg')
image = cv2.imread(img_path)

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.bilateralFilter(gray, 11, 17, 17)
edged = cv2.Canny(gray, 30, 200)

cv2.imshow('gray', gray)
cv2.waitKey(0)

cv2.imshow('edged', edged)
cv2.waitKey(0)


(_, cnts, _) = cv2.findContours(edged.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

for c in cnts:
    peri = cv2.arcLength(c, True)
    approx = cv2.approxPolyDP(c, 0.02 * peri, True)

    if len(approx) == 4:
        cv2.drawContours(image, [approx], -1, (0, 255, 0), 3)


cv2.imshow('result', image)
cv2.waitKey(0)

This example finds a lot of garbage, and all rectangles (not just those with background)

EDIT: OpenСV duplicates rectangle contours. How can i cut off duplicates?

I just started to learn opencv. there is an urgent task. I tried to use canny filter, then findContours — ilya
– ilya, Commented Jun 13, 2017 at 13:51
Post your code (edit it into your post) - show us where it's going wrong. — asongtoruin
– asongtoruin, Commented Jun 13, 2017 at 13:52
if you are getting a lot of garbage it could be due to the canny thresholds that you are using, try tweaking them a little to get the edges of your rectangles. You may try removing the background noise or binarizing the image using a threshold in the grey image to remove impurities in the scanned image — api55
– api55, Commented Jun 13, 2017 at 14:07

Vardan Agarwal · Accepted Answer · 2019-01-14 13:23:14Z

1

You can use adaptive thresholding as a pre-processing step followed by gaussian blur before applying canny edge detection.

import cv2
import numpy as np

img = cv2.imread('image with boxes.jpg')
blur = cv2.GaussianBlur(img,(7,7),0)
gray = cv2.cvtColor(blur, cv2.COLOR_BGR2GRAY)
mask = cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2)
canny = cv2.Canny(mask, 30, 200)
(_, cnts, _) = cv2.findContours(canny, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for c in cnts:
    peri = cv2.arcLength(c, True)
    approx = cv2.approxPolyDP(c, 0.02 * peri, True)

    if len(approx) == 4:
        cv2.drawContours(img, [approx], -1, (0, 0, 255), 1)

cv2.imshow('result', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

As you can see no rectangles are allocated which do not have any background

answered Jan 14, 2019 at 13:23

Vardan Agarwal

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Comments

ZdaR · Accepted Answer · 2017-06-13 14:38:37Z

0

You are very close to the solution, while iterating the contours in for c in cnts: you are filtering contours on the basis of number of sides of polygon approximated from the contour, You just need to add filter for contour area as well in order to remove the smaller rectangles being detected, which can be done as:

for c in cnts:
    peri = cv2.arcLength(c, True)
    approx = cv2.approxPolyDP(c, 0.02 * peri, True)

    if len(approx) == 4 and cv2.contourArea(c) > 200:
        cv2.drawContours(image, [approx], -1, (0, 255, 0), 3)

answered Jun 13, 2017 at 14:38

ZdaR

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1 Comment

ilya Over a year ago

thx, all the trash cleaned, but the question remains how to not allocate rectangles with no background

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