# Pupil Capture screen surface

This small Python program fills the primary screen and displays four unique
`tag36h11` AprilTags in its corners. It has no third-party dependencies; the
standard Python installation used on Windows normally includes Tkinter.

## Run

Close or move anything important from the primary screen, then run:

```powershell
python show_apriltag_surface.py
```

Press `Esc` or `Q` to close it. For a preview in a normal resizable window:

```powershell
python show_apriltag_surface.py --windowed
```

By default, every marker is flush with both adjacent screen edges. Useful
options include `--tag-size 240`, `--margin 32`, and
`--tag-ids 10 11 12 13`. The requested size is rounded down to a multiple of
10 pixels so that the source grid is always scaled without interpolation.

## Define the surface in Pupil Capture

1. Start this program and look at the screen through the headset.
2. Enable **Surface Tracker** in Pupil Capture.
3. Confirm all four markers are outlined/detected in the World window.
4. Select **Add surface**, name it (for example, `screen`), then freeze the
   scene and edit the surface corners to match the usable screen area.
5. Keep this program running whenever the surface should remain trackable.

If detection is unreliable, increase `--tag-size`, reduce glare, and ensure
the world camera can see the markers. Do not reuse any of these four marker IDs
elsewhere in the camera's view.

For the next integration step, enable Pupil Capture's **Network API** plugin
and keep **Surface Tracker** active. Surface-relative gaze is broadcast on a
topic beginning with `surfaces.`; its normalized `(x, y)` coordinates use the
bottom-left as `(0, 0)` and the top-right as `(1, 1)`.

## Live gaze visualization

Set the `screen` surface Width to `2560` and Height to `1440`, keep Surface
Tracker and Network API enabled, and install the two network dependencies once:

```powershell
python -m pip install -r requirements.txt
```

Then close the marker-only program and run:

```powershell
python visualize_surface_gaze.py
```

This program displays the same four corner tags, subscribes to
`surfaces.screen`, draws a red gaze dot, and prints rows containing Pupil
timestamp, screen x, screen y, and confidence. Coordinates are converted to
the Windows top-left origin. It connects to `127.0.0.1:50020` by default;
use `--host 192.168.0.47` only when this script runs on another computer.

The displayed point passes through a 5-sample median filter, exponential
smoothing, and a 3-pixel dead zone. Terminal rows contain timestamp, raw x,
raw y, filtered x, filtered y, and confidence. For a steadier but slower point:

```powershell
python visualize_surface_gaze.py --smoothing 0.10 --median-window 7
```

For a more responsive point, use `--smoothing 0.30 --median-window 3`.

## Guided gaze exercises

With Pupil Capture configured the same way, run:

```powershell
python gaze_exercises.py
```

Press Space to begin and between blocks. The sequence contains 10 three-second
steady fixations, 10 rapid target acquisitions, 20 two-color selective-attention
trials, and three 10-second smooth-pursuit paths. A live yellow dot shows the
raw gaze when the program is run with `--cursor`; it is hidden by default. No
median or smoothing filter is applied, and every received sample that meets the
`--confidence` threshold is written to a timestamped CSV in `results/`, including
the target, gaze coordinates, pixel error, and whether gaze was on target. During
every color trial, green is the target and red is the distractor.

Visualize the newest result file with:

```powershell
python visualize_results.py
```

Or select a particular run with `python visualize_results.py results\file.csv`.
The script opens a six-panel dashboard, prints summary statistics, and saves a
`*_dashboard.png` beside the source CSV. Use `--no-show` to only save the PNG.