IPCV

IPCV is an Image Processing and Computer Vision toolbox for Scilab. It provides Scilab macros and native gateways for image I/O, filtering, morphology, feature detection, DNN inference, video/camera handling, object tracking, and other OpenCV-backed workflows.

The current development line is the OpenCV 5 and C++ migration of IPCV. The native implementation targets OpenCV/OpenCV contrib 5.0.0 and Scilab 2026.x, with the legacy gateway helpers replaced by category-based C++ source modules.

Highlights

• OpenCV 5 native backend with category-based C++ source files under src/cpp.
• Scilab gateway image exchange rewritten for the current Scilab API.
• DNN support updated for OpenCV 5, including ONNX examples and OpenCV Zoo model download support.
• OpenCV Zoo browser GUI: opencv_zoo_gui().
• Current OpenCV 5 migration tests are in tests/unit_tests; legacy 4.5 tests are kept only as an optional archive/reference set.
• Large DNN .onnx models are excluded from git and downloaded on demand.

Requirements

• Scilab 2026.x
• Windows build tools for native compilation
• OpenCV/OpenCV contrib 5.0.0 thirdparty build for the IPCV native gateway

For native builds on Windows, start Scilab from a Visual Studio native tools command prompt so the compiler environment is available.

Load IPCV

From Scilab:

exec("loader.sce", -1);

Then call IPCV functions directly, for example:

S = imread(fullpath(getIPCVpath() + "/images/baboon.png"));
imshow(S);

Build From Source

Build order:

1. Prepare compiler tools.
2. Build OpenCV/OpenCV contrib 5.0.0 into thirdparty.
3. Start Scilab from the compiler environment.
4. Run IPCV builder.sce.
5. Load IPCV and run a quick smoke test.

1. Prepare Compiler Tools

On Windows, install Visual Studio 2022 Build Tools or Visual Studio with the C++ workload. Make sure cmake, ninja, curl, and tar are available in the command prompt used for the build.

On Linux or macOS, install CMake, Ninja, a C/C++ compiler toolchain, curl, make, and common build utilities.

2. Build OpenCV 5 Thirdparty

IPCV expects a local OpenCV/OpenCV contrib 5.0.0 build under thirdparty. The helper scripts in thirdparty/build download OpenCV sources, configure CMake with Ninja, build opencv_world, and install headers/libraries into the platform-specific thirdparty folder.

For a detailed Windows and Linux thirdparty build guide, see thirdparty/build/README.txt.

Windows

Open a Visual Studio x64 native tools command prompt, then run:

cd thirdparty\build
build.bat

The script:

• Downloads OpenCV and OpenCV contrib 5.0.0.
• Applies the IPCV local MLAS skip patch for the OpenCV DNN Windows build.
• Configures OpenCV with -G Ninja.
• Builds and installs into thirdparty\Windows\%PROCESSOR_ARCHITECTURE%.
• Copies generated OpenCV DLL/import-library files into the local lib folder expected by IPCV.

Expected Windows layout after installation:

thirdparty/Windows/AMD64/include/
thirdparty/Windows/AMD64/lib/

Linux And macOS

Run:

cd thirdparty/build
./build.sh

The script first builds a local shared FFmpeg dependency, then downloads and builds OpenCV/OpenCV contrib 5.0.0.

Expected layout after installation:

thirdparty/Linux/<architecture>/
thirdparty/Darwin/<architecture>/

3. Start Scilab From The Compiler Environment

On Windows, keep using the Visual Studio x64 native tools command prompt and launch Scilab from there, for example:

"<SCILAB_INSTALL_DIR>\bin\WScilex.exe"

Using the native tools prompt matters because IPCV builds C++ gateways and must see the same MSVC compiler environment used by OpenCV.

On Linux or macOS, start Scilab from a shell where the compiler, CMake, and OpenCV thirdparty library paths are available.

4. Build IPCV

In Scilab, change to the IPCV root folder and run:

cd("<IPCV_ROOT>");
exec("builder.sce", -1);

The top-level builder compiles macros, native source code, gateways, help, loader, and cleaner files. The source build is organized around src/cpp/builder_cpp.sce, which builds the native libipcv_core library from category files such as ipcv_image_io.cpp, ipcv_filtering.cpp, ipcv_feature_detection.cpp, and ipcv_dnn.cpp.

Expected generated build outputs include:

src/cpp/libipcv_core.*
sci_gateway/cpp/gw_ipcv.*
loader.sce

5. Load And Smoke Test

After the build completes, load IPCV:

exec("loader.sce", -1);

Then run a small image I/O and display check:

S = imread(fullpath(getIPCVpath() + "/images/baboon.png"));
disp(size(S));
imshow(S);

For a focused command-line check, run a current unit test from tests/unit_tests, for example:

test_run(getIPCVpath(), "ipcv_image_io");

OpenCV Zoo Models

IPCV includes a Scilab browser GUI for OpenCV Zoo models:

opencv_zoo_gui();

The GUI can:

• Fetch available ONNX models from the official OpenCV Zoo.
• Group models by category.
• Show real Git LFS model sizes.
• Download a selected model.
• Download a selected model, generate a starter Scilab script, and open it in the Scilab editor.

Downloaded .onnx files are ignored by git. The default download folder is:

images/dnn

DNN Example

dnn_path = fullpath(getIPCVpath() + "/images/dnn/");
model_name = "image_classification_mobilenetv2_2022apr.onnx";
model_file = dnn_path + model_name;

if ~isfile(model_file) then
    model_url = "https://github.com/opencv/opencv_zoo/raw/main/models/image_classification_mobilenet/" + model_name;
    http_get(model_url, model_file, follow=%t, timeout=300);
end

net = dnn_readmodel(model_file, "", "onnx");
net = dnn_setpreferable(net, "opencv", "cpu");

S = imread(fullpath(getIPCVpath() + "/images/baboon.png"));
out = dnn_forward(net, S, [224, 224], [], 1 / 127.5, [127.5 127.5 127.5], 1, 0);

[score, index] = max(out);
disp([index, score]);
dnn_unloadmodel(net);

Tests

Current OpenCV 5 migration tests:

tests/unit_tests

These are the active tests for the C++/OpenCV 5 code path and are the default place for new regression coverage.

Legacy IPCV 4.5 tests are not part of the default 5.0.0 test run. If they are kept for migration comparison, keep them isolated under:

tests/unit_tests/4.5

That archive path is only for historical behavior checks and should not be mixed with the active OpenCV 5 .tst and .dia.ref files.

Run tests from Scilab after loading/building the toolbox as needed. For focused migration checks, run the relevant .tst file directly or use Scilab's test_run workflow.

Repository Layout

macros/              Scilab user-facing functions
sci_gateway/cpp/     Scilab gateway wrappers
src/cpp/             OpenCV-backed C++ implementation
help/en_US/          Help pages
demos/               Demo scripts
tests/unit_tests/    Current unit tests
thirdparty/          Local thirdparty build area
images/              Sample images and downloaded model location

Change History

Detailed release notes, migration notes, and the converted-function list are maintained in ChangeLog.txt.

License

GPL. See the repository license and source headers for details.