Hieu Nguyen

Remove background with OpenCV

Jul 20 2018

Recently I need to work on a task to remove the background of certain images. I have some experience with OpenCV in my university, so I decide to try it on this task.


My favorite language is Ruby, so I try with ruby first. ruby-opencv seems like a good candidate: it’s only stale for about a year, which is not bad because opencv isn’t upgraded very often.

However, after install opencv using brew, I realized that newest version is 3.x while ruby-opencv only supports upto opencv 2.4.13. There is a branch which attempts to upgrade to opencv 3.2, but I see that it is still too unstable.

It does not discourage me much, though. A little search shows that opencv version 2.4.13 still has very good documentation, and I don’t need very advance feature anyway. I don’t know that the nightmare waiting in front of me, though.

Everything goes smoothy at first, I got this piece of code running after the first hour:

original_image = CvMat.load(path, 1) # Read the file.
gray = original_image.BGR2GRAY
image, _ = gray.threshold(100, 255, CV_THRESH_BINARY_INV, true)
contours = image.find_contours

mask = image.create_mask
mask.draw_contours!(contours, 0, 255, 1)

dst_image = original_image.and(original_image, mask)

It results in an image slightly different from original image, with correct grayscale and mask created.

(I do struggle a bit with find_contours method: the document says that I can pass in options such as mode: :tree, but in really, I must use mode: CV_RETR_TREE instead.)

For the rest of the day, I hopelessly fiddle with the code to make it work: I cannot choose the max contour to get the whole object, and bitwise operation work incorrectly compared to the document (hint: it does nothing).

Finally, I give up. I decide that it is much easier to use opencv directly instead of relying on wrapper.

Python opencv

With a few minutes I can convert the ruby code to python

import cv2
import numpy as np

## Read
originalImage = cv2.imread("sample.png")
grayscaleImage = cv2.cvtColor(originalImage, cv2.COLOR_BGR2GRAY)

## Threshold
_, threshedImage = cv2.threshold(grayscaleImage, 127, 255, cv2.THRESH_BINARY_INV|cv2.THRESH_OTSU)

## Find the contours with biggest area
_, contours, _ = cv2.findContours(threshedImage, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
maxContour = max(contours, key=cv2.contourArea)

## Create mask
mask = np.zeros(originalImage.shape[:2], np.uint8)
cv2.drawContours(mask, [maxContour], -1, 255, -1)

## Copy the croped image to a white canvas
destinationImage = np.zeros(originalImage.shape[:3], np.uint8)
height = originalImage.shape[:2][1]
destinationImage[:, 0:height] = (255, 255, 255)

locations = np.where(mask != 0)
destinationImage[locations[0], locations[1]] = originalImage[locations[0], locations[1]]

## Save
cv2.imwrite("result.png", destinationImage)

The only part that different is the part about copying the croped image to the white canvas. Because python opencv does not support copyTo like C++ lib, I have to reimplement it with the help of stackoverflow.

And finally, it’s done :D