Separable convolution (cv::sepFilter2D). Split a 2D kernel into row/column 1D kernels; compute drops from k² to 2k — the right way to do large Gaussian-style filters.
Module：OpenCV filtering · Task type：opencv_sep_filter_2d
Ports
Inputs
| Port | Type | Required |
|---|---|---|
in | Image / cv::Mat | ✓ |
Outputs
| Port | Type | Required |
|---|---|---|
out | cv::Mat | ✓ |
Parameters
| 参数 | 类型 | 默认值 | 范围 / 选项 | 说明 |
|---|---|---|---|---|
kernel_size | int | 3 | [1, 31] (step 2) | kernel edge length in pixels; must be a positive odd number |
delta | float | 0 | [-255, 255] |
Example graph
A minimal runnable pipeline (read → process → write). Drag it into GraphStudio or open via --open; replace data/test.png with a real path.
{
"version": "2.0",
"tasks": [
{
"id": "read",
"type": "opencv_image_read",
"params": {
"file_path": "data/test.png"
}
},
{
"id": "node",
"type": "opencv_sep_filter_2d",
"params": {
"kernel_size": 3,
"delta": 0
}
},
{
"id": "save",
"type": "opencv_image_write",
"params": {
"file_path": "out_out.png"
}
}
],
"edges": [
{
"from": "read",
"from_port": "out",
"to": "node",
"to_port": "in"
},
{
"from": "node",
"from_port": "out",
"to": "save",
"to_port": "in"
}
]
}
Part of the GraphStudio node guide series; parameters reflect the latest release.