> For the complete documentation index, see [llms.txt](https://docs.augelab.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.augelab.com/function-blocks/blocks-reference/image-transformations/analysis/mean-value-of-image.md).

# Mean Value of Image

This function block computes the average pixel intensity of an incoming image and displays the result as a number between 0 and 255. It is useful for quick quality checks (brightness), preprocessing decisions, and simple monitoring.

## 📥 Inputs <a href="#inputs" id="inputs"></a>

* `Image Any` The image whose mean pixel value will be computed.

## 📤 Outputs <a href="#outputs" id="outputs"></a>

* `Mean Value` The calculated mean pixel intensity (rounded), range 0–255.

## 🕹️ Controls <a href="#controls" id="controls"></a>

* `Mean value` A small on-block display that shows the current computed mean.\
  This block has no other user-editable controls.

## 🎨 Features <a href="#features" id="features"></a>

* Fast computation of the average pixel intensity for any image type.
* Live visual feedback via the on-block display.
* Works with color or grayscale images by averaging all channels/pixels into a single scalar value.

## 📝 Usage Instructions <a href="#usage" id="usage"></a>

1. Connect any image-producing block to the `Image Any` input.
2. Run the scenario. The block will compute the average intensity and update the `Mean value` display.
3. Read the numeric result from the `Mean Value` output for downstream logic or logging.

## 📊 Evaluation <a href="#evaluation" id="evaluation"></a>

When executed, the block calculates the mean across all pixels (and channels) of the provided image and returns a numeric value (rounded) that represents overall brightness/average intensity.

## 💡 Tips and Tricks <a href="#tips-and-tricks" id="tips-and-tricks"></a>

* Use `Blur` before this block to reduce the effect of small noisy fluctuations on the mean.
* Use `Image Threshold` or `Apply Mask` to compute mean only over foreground regions instead of the entire image.
* Use `Image ROI` or `Image ROI Select` to restrict the calculation to a region of interest (for localized brightness checks).
* Reduce processing cost on large images by adding `Image Resizer` prior to this block.
* Use `Show Image` alongside this block to visually confirm which frame the mean value refers to.
* Combine with `Image Memory` to freeze a sample image and repeatedly analyze the same frame for stable comparisons.

## 🛠️ Troubleshooting <a href="#troubleshooting" id="troubleshooting"></a>

* If the `Mean Value` is unexpected, verify the input image using `Show Image` to confirm correct source and content.
* If no value appears, ensure the image-producing block is actively providing valid image data. The block expects an image-type input.
* For inconsistent readings, try smoothing with `Blur` or cropping the area of interest with `Image ROI Select` before computing the mean.