# Calculate Mean: different result for masked image vs ROI

I have a weird problem where my average gradient magnitude result is different if I use a mask as opposed to creating a new Mat of that small ROI. I'll explain the 2 different ways I do this and 2 different average gradient magnitude results I get. I thought I should get the same average gradient magnitude result?

Scenario: Image A is my source/original image of a landscape. I want to get the average gradient magnitude in the region A

`(10,100), (100,100), (100,150), (10,150)`

.

**Technique 1:**

- Create a ROI `Mat`

that just shows region A. So its dimensions are `90`

by `50`

.

- Perform `cv::Sobel()`

, `cv::magnitude()`

then `cv::meanStdDev()`

- My average gradient magnitude result is `11.34`

.

**Technique 2:**

- Create a new `Mat`

that is a mask. The mat is the same dimensions as Image A and has a white area where Region A is. Then create a new Mat that just shows that region of Image A and the rest of the Mat is black - hopefully this makes sense.

- Perform `cv::Sobel()`

, `cv::magnitude()`

(but use the mask) then `cv::meanStdDev()`

- My average gradient magnitude result is `43.76`

.

Why the different result?

Below is my code:

```
static Mat backupSrc;
static Mat curSrc;
// Technique 1
void inspectRegion(const Point& strt, const Point& end) {
curSrc = Mat(backupSrc.size(), CV_8UC3);
cvtColor(backupSrc, curSrc, CV_GRAY2RGB);
Rect region = Rect(strt, end);
Mat regionImg = Mat(curSrc, region);
// Calculate the average gradient magnitude/strength across the image
Mat dX, dY, mag;
Sobel(regionImg, dX, CV_32F, 1, 0);
Sobel(regionImg, dY, CV_32F, 0, 1);
magnitude(dX, dY, mag);
Scalar sMMean, sMStdDev;
meanStdDev(mag, sMMean, sMStdDev);
double magnitudeMean = sMMean[0];
double magnitudeStdDev = sMStdDev[0];
rectangle(curSrc, region, { 0 }, 1);
printf("[Gradient Magnitude Mean: %.3f, Gradient Magnitude Std Dev: %.3f]\n", magnitudeMean, magnitudeStdDev);
}
// Technique 2
void inspectRegion(const std::vector<Point>& pnts) {
curSrc = Mat(backupSrc.size(), CV_8UC3);
cvtColor(backupSrc, curSrc, CV_GRAY2RGB);
std::vector<std::vector<Point>> cPnts;
cPnts.push_back(pnts);
Mat mask = Mat::zeros(curSrc.rows, curSrc.cols, CV_8UC1);
fillPoly(mask, cPnts, { 255 });
Mat regionImg;
curSrc.copyTo(regionImg, mask);
// Calculate the average gradient magnitude/strength across the image
Mat dX, dY, mag;
Sobel(regionImg, dX, CV_32F, 1, 0);
Sobel(regionImg, dY, CV_32F, 0, 1);
magnitude(dX, dY, mag);
Scalar sMMean, sMStdDev;
meanStdDev(mag, sMMean, sMStdDev, mask);
double magnitudeMean = sMMean[0];
double magnitudeStdDev = sMStdDev[0];
polylines(curSrc, pnts, true, { 255 }, 3);
printf("[Gradient Magnitude Mean: %.3f, Gradient Magnitude Std Dev: %.3f]\n", magnitudeMean, magnitudeStdDev);
}
```

**Edit**: someone suggested to me to erode the mask before calling `meanStdDev`

(with a kernel of 3x3). Doing this brings technique 2 result much closer - 11.97. But is there a way to make this exactly accurate? Ie, produce the same result as technique 1?