sima-vision 26.0.4

sima-vision segment                        # background blurred, instances sharp
sima-vision segment --keep-classes person  # only people stay sharp
sima-vision segment --anonymise            # the other way round: blur the people
sima-vision segment --no-blur              # a plain overlay, nothing blurred

sima-vision fall
sima-vision fall --classes person --confirm 2.0

The frame is the one detect draws: same palette, same boxes, same captions. A confirmed fall changes one thing, the class the box is labelled with, so a person reading person 0.93 a second earlier now reads FALL 0.93. Each fall is also printed on the console and counted in the run summary.

A confirmed fall re-arms only after sustained recovery.

sima-vision ret-analytics
sima-vision ret-analytics --source shopping-trolley-items.mp4
sima-vision ret-analytics --classes bottle banana apple

Displays detected trolley items with boxes, names and confidence, and writes retail.mp4. Uses the same source, model and recording flags as detect. The white side menu uses a table with Qty, Item, Unit $, and Total $ columns, larger text, alternating rows, and an estimated basket total. Each product row is bold and has its own consistent color. Products enter the menu after three consecutive detections and stay in first-added order. Quantities increase only after a higher simultaneous count is confirmed for three frames. Brief misses never remove rows or reduce counts, and repeated frames never add purchases. The menu retains confirmed items for the run; removals are not inferred, and new items hidden behind existing ones cannot be counted reliably. All model classes are displayed by default; --classes optionally filters them. The default sample is shopping-trolley-items.mp4, fetched automatically. The default model recognizes COCO categories such as bottle, banana, apple, and orange. Product-specific recognition needs a trained model and matching labels. Point the camera at the trolley so the displayed detections represent its contents.

The optional retail.classes config setting selects item names or ids; an empty list displays all model classes.

Prices are rough per-item US estimates, not live quotes. Produce estimates use US retail listings as a reference. Unpriced classes show price N/A and are excluded from the known subtotal. Defaults include cherry ($0.10 each), cantaloupe ($3.00 each), banana ($0.20 each), cucumber ($0.75 each), bread ($2.00 per loaf), snack ($1.50 per small packet), orange ($1.00 each), avocados ($1.00 each), egg ($0.25 each), and milk ($4.00 per gallon container). Both avocado and avocados labels are supported. Override estimates for your products in config.yaml:

retail:
  classes: []
  prices:
    banana: 0.20
    tomato: 0.25
    apple: 1.00

The COCO model does not have a tomato class; tomato detection requires a model trained for tomatoes and a matching labels file.

If an automatically discovered config.yaml contains fall settings without a retail section, retail uses its own sample and output paths and prints a warning. CLI flags still win; an explicit --config uses the named file as written.

# --- PowerShell, as Administrator -------------------------------------------
wsl --install -d Ubuntu
wsl -l -v                             # want: Ubuntu, Running, 2

# Install Docker Desktop, then Settings > Resources > WSL integration > Ubuntu.
# Without that toggle docker works in PowerShell and is missing inside Ubuntu.

wsl -d Ubuntu                         # everything below is Ubuntu. exit comes back

# --- Ubuntu ------------------------------------------------------------------
docker run hello-world                # must print "Hello from Docker!"

sudo apt update && sudo apt install -y python3-venv python3-pip
python3 -m venv ~/sima                # Ubuntu refuses a bare pip install
source ~/sima/bin/activate            # new shells start outside it. Look for (sima)
pip install sima-cli
sima-cli login
sima-cli install ghcr:sima-neat/sdk:v2.0.0    # several GB, once per machine

cd /path/to/your/model                # the folder holding best.pt
sima-cli sdk setup --workspace .      # . is what gets mounted. 15-20 minutes
activate-model-compiler

# --- Model SDK shell ---------------------------------------------------------
pip install "sima-vision[compile]"    # ultralytics, onnx, onnxsim
sima-vision compile best.pt           # ONNX, bfloat16, tessellate, ELF. 10-15 minutes
                                      # -> build/best_mpk.tar.gz

# --- on the DevKit -----------------------------------------------------------
sima-vision push build/best_mpk.tar.gz
sima-vision detect --model best_mpk.tar.gz
sima-vision detect --model https://example.com/my-model.tar.gz   # a URL works too