Which pixel counting method in OpenCV is more accurate for colored images?

Question

I have a sample image:

and I use different thresholding methods in order to count the number of pixels.

First Method is simple thresholding since on the source image I only have a one colored object against a white background.

    Mat image = imread("/$image_path", IMREAD_GRAYSCALE);
    Mat binary_image;
    threshold(image, binary_image, 120, 255, THRESH_BINARY);
    int TotalNumberOfPixels = binary_image.rows * binary_image.cols;
    int PixelCount = TotalNumberOfPixels - cv::countNonZero(binary_image);
    return PixelCount;

The second method is assuming I have an image with multiple colored objects (ie multiple colored marks) hence I need to filter and apply a red mask. I did it via:

Mat image2 = imread("/$image_path", IMREAD_COLOR);
Mat blurred, edge;
Mat bgrInv = ~image2;
Mat hsvIm;
Mat maskRed;    

cvtColor(bgrInv, hsvIm, COLOR_BGR2HSV);

inRange(hsvIm, Scalar(80, 70, 239), Scalar(100, 255, 255), maskRed);
imshow("Mask", maskRed);

//blur(maskRed, blurred, Size(3, 3));
//Canny(blurred, edge, 75, 200, 3);

cout << "Pixel Count: " << countNonZero(maskRed)<< endl;

The output for both methods are:

Method 1: 406
Method 2: 155

I will be operating on a colored image hence I was using the second method at first. But I do not know if it will be "accurate" or correct.

Here is the sample template that I am working on. Its basically a survey type template with minor colored blocks. With red circles as mark placeholders for easier pre/post processing.

Answer 1

Let's have a look at the outputs of your two methods.

This is binary_image from method #1:

You count - cumbersomely (is this proper English?) - the black pixels, which corresponds to your task to count the red pixels. (By the way, invert the threshold and just count the white pixels.)

This is edge from method #2:

You count the white pixels, but as you can see, this is only the outline of the initial red object. And this does NOT correspond to your original task.

So, given both methods as they are, the first is more "accurate", at the moment and for the given example. Nevertheless, you mentioned objects of different colors, so method #2 should be reworked to count the proper pixels.

Could you please give examples for images with multiple objects of different color?

Also, I edited your question. (The edit isn't reviewed, yet.) The image loading part is important, since I guess, in method #1 you used imread(..., IMREAD_GRAYSCALE) and imread(..., IMREAD_COLOR) in method #2.

Answer 2

I think all methods are not accurate if your image is blured (such as in JPEG format). But let's assume it's clear.

To count colored object pixels, we can count all colored pixels or count all RED pixels.

(1) Find the colored regions in HSV: How to detect colored patches in an image using OpenCV?

# count colored pixels in S(HSV)
def countColored(img):
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    h,s,v = cv2.split(hsv)
    num = np.sum(s>20)
    return num

(2) Find the Red regions: How to find the RED color regions using OpenCV?

def countRed(img):
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    h,s,v = cv2.split(hsv)
    mask1 = cv2.inRange(hsv, (0,50,20), (5,255,255))
    mask2 = cv2.inRange(hsv, (175,50,20), (180,255,255))
    mask = cv2.bitwise_or(mask1, mask2 )
    num = cv2.countNonZero(mask)
    return num

---

#!/usr/bin/python3
# 2019/02/28
import cv2
import numpy as np

def cvshow(img):
    cv2.imshow("OpenCV", img)
    cv2.waitKey();cv2.destroyAllWindows()

def countColored(img):
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    h,s,v = cv2.split(hsv)
    num = np.sum(s>20)
    return num

def countRed(img):
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    h,s,v = cv2.split(hsv)
    mask1 = cv2.inRange(hsv, (0,50,20), (5,255,255))
    mask2 = cv2.inRange(hsv, (175,50,20), (180,255,255))
    mask = cv2.bitwise_or(mask1, mask2 )
    num = cv2.countNonZero(mask)
    return num

if __name__ == "__main__":
    fpath = "fQipc.jpg"
    img = cv2.imread(fpath)
    num1 = countColored(img)
    num2 = countRed(img)
    print(num1, num2)
    # 643, 555