---
title: Hemocytometer Ground Truth
emoji: 🎯
colorFrom: blue
colorTo: yellow
sdk: gradio
sdk_version: 5.35.0
app_file: app.py
pinned: false
---

# Hemocytometer ground truth

Annotation tool for building ground-truth counts of the 4×4 hemocytometer block.
Two tabs: **Single image** and **4 squares**.

## Single image

1. **Upload** one photo.
2. **Click the 4 corners** of the 4×4 block (any order). Drag a handle to adjust it.
3. **Segment.** The block is warped to an upright square (≤1024 px) and segmented with the
   fine-tuned hemocytometer Cellpose-SAM model.
4. **Correct the count** (see below).

## 4 squares

1. **Upload** the four square images at once. They are sorted by filename into Square 1–4.
2. **Set each block:** click its 4 corners, or press **⌖ Auto** and click the block centre
   to auto-detect the grid. Drag any handle to adjust.
3. **Segment all 4.** One GPU call per square, as in the main counter. The time from the
   click until all four squares are ready to annotate is recorded (end-to-end), along
   with the server's own time.
4. **Correct each square** on the large canvas below. Switch squares with the buttons or
   keys **1–4**; edits on each square are kept when you switch.
5. **Copy the results row** (or **Copy with header** the first time) and paste it into a
   spreadsheet. Columns: `timestamp, files, model_sq1, corrected_sq1, … model_sq4,
   corrected_sq4, mean_corrected, seg_time_s, server_time_s, min_size_px`.
   *Model* is what Cellpose counted (after min size and exclusion); *corrected* is your
   ground truth. The table also shows the count error, (model − corrected) / corrected.

## Correcting the count

- click a cell → remove it (false positive); click again → restore
- click empty space → add a dot (false negative)
- click a dot → delete it
- **Shift+click** → always add a dot, even on top of a cell (e.g. two cells merged into one mask)
- **Ctrl+Z / Ctrl+Shift+Z** undo / redo · **Space** (hold) hide outlines · **F** fit
- mouse wheel zooms, drag pans

The total is the count of detected objects, minus objects below the min size, minus
objects touching the left or bottom 1% exclusion zones, minus removed, plus added dots.

Everything after segmentation runs in the browser, so clicks are instant. The page
keeps nothing: copy the results row (or write the count down) before uploading the
next image.

## Files

`app.py` (server), `gt_canvas.js` (annotation canvas), `corner_canvas.js` (4-corner
canvases in the 4-squares tab), `grid_seeded.py` (grid auto-detection, a copy of the
main app's), `requirements.txt`.

Set hardware to **ZeroGPU** in the Space settings. To use local weights instead of the
Hub file, set `CPCC_LOCAL_MODEL=/path/to/weights.npy`.