Converting an OpenCV Image to Black and White

Question

How do you convert a grayscale OpenCV image to black and white? I see a similar question has already been asked, but I\'m using OpenCV 2.3, and the proposed solution no long

Answer 1

Pay attention, if you use cv.CV_THRESH_BINARY means every pixel greater than threshold becomes the maxValue (in your case 255), otherwise the value is 0. Obviously if your threshold is 0 everything becomes white (maxValue = 255) and if the value is 255 everything becomes black (i.e. 0).

If you don't want to work out a threshold, you can use the Otsu's method. But this algorithm only works with 8bit images in the implementation of OpenCV. If your image is 8bit use the algorithm like this:

cv.Threshold(im_gray_mat, im_bw_mat, threshold, 255, cv.CV_THRESH_BINARY | cv.CV_THRESH_OTSU);

No matter the value of threshold if you have a 8bit image.

Answer 2

Simply you can write the following code snippet to convert an OpenCV image into a grey scale image

import cv2
image = cv2.imread('image.jpg',0)
cv2.imshow('grey scale image',image)

Observe that the image.jpg and the code must be saved in same folder.

Note that:

• ('image.jpg') gives a RGB image
• ('image.jpg',0) gives Grey Scale Image.

Answer 3

Step-by-step answer similar to the one you refer to, using the new cv2 Python bindings:

1. Read a grayscale image

import cv2
im_gray = cv2.imread('grayscale_image.png', cv2.IMREAD_GRAYSCALE)

2. Convert grayscale image to binary

(thresh, im_bw) = cv2.threshold(im_gray, 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)

which determines the threshold automatically from the image using Otsu's method, or if you already know the threshold you can use:

thresh = 127
im_bw = cv2.threshold(im_gray, thresh, 255, cv2.THRESH_BINARY)[1]

3. Save to disk

cv2.imwrite('bw_image.png', im_bw)

Answer 4

Here's a two line code I found online that might be helpful for a beginner

# Absolute value of the 32/64
abs_image_in32_64 = np.absolute(image_in32_64)

image_8U = np.uint8(abs_image_in32_64)

Answer 5

Specifying CV_THRESH_OTSU causes the threshold value to be ignored. From the documentation:

Also, the special value THRESH_OTSU may be combined with one of the above values. In this case, the function determines the optimal threshold value using the Otsu’s algorithm and uses it instead of the specified thresh . The function returns the computed threshold value. Currently, the Otsu’s method is implemented only for 8-bit images.

This code reads frames from the camera and performs the binary threshold at the value 20.

#include "opencv2/core/core.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"

using namespace cv;

int main(int argc, const char * argv[]) {

    VideoCapture cap; 
    if(argc > 1) 
        cap.open(string(argv[1])); 
    else 
        cap.open(0); 
    Mat frame; 
    namedWindow("video", 1); 
    for(;;) {
        cap >> frame; 
        if(!frame.data) 
            break; 
        cvtColor(frame, frame, CV_BGR2GRAY);
        threshold(frame, frame, 20, 255, THRESH_BINARY);
        imshow("video", frame); 
        if(waitKey(30) >= 0) 
            break;
    }

    return 0;
}

Answer 6

Approach 1

While converting a gray scale image to a binary image, we usually use cv2.threshold() and set a threshold value manually. Sometimes to get a decent result we opt for Otsu's binarization.

I have a small hack I came across while reading some blog posts.

1. Convert your color (RGB) image to gray scale.
2. Obtain the median of the gray scale image.
3. Choose a threshold value either 33% above the median

Why 33%?

This is because 33% works for most of the images/data-set.

You can also work out the same approach by replacing median with the mean.

Approach 2

Another approach would be to take an x number of standard deviations (std) from the mean, either on the positive or negative side; and set a threshold. So it could be one of the following:

• th1 = mean - (x * std)
• th2 = mean + (x * std)

Note: Before applying threshold it is advisable to enhance the contrast of the gray scale image locally (See CLAHE).