OpenCV bitwise_or_1

bitwise_or_1 is bitwise_or_0. Same function, same inputs, same output - this pack makes a wrapper for every OpenCV overload it finds in the type stubs, cv2.bitwise_or is declared twice, and the generator never bothered deduplicating. If you've read the _0 page, you're done. If not, the short version follows.

The node computes the union of two inputs: a pixel is non-zero wherever either input is non-zero. On masks, that's the merge operation - combine several region masks into one by OR-ing them together. On images, it's the additive composite where bright wins.

The inputs

• src1 / src2 - two same-sized NPARRAYs.
• dst (optional) - out-parameter, leave unwired.
• mask (optional) - single-channel 8-bit limiter: OR only writes where the mask is non-zero; elsewhere the output keeps src1.
• Output: one nparray.

The gotchas, in two sentences

Scale matters: ComfyUI masks are float 0–1, OpenCV arrays are 8-bit 0–255, and this pack auto-converts nothing - convert before you OR or the union comes out murky. And everything here is NPARRAY-based, so Image2Nparray in and Nparrays2Image out are part of every workflow using it.

Install

Same as all its siblings - opencv-comfyui via ComfyUI Manager (search "opencv-comfyui"), or:

cd ComfyUI/custom_nodes
git clone https://github.com/geroldmeisinger/opencv-comfyui
pip install opencv-python-contrib

Restart, no models to fetch. The pack's one setup trap is a conflicting OpenCV install (Cannot import name 'guidedFilter' from 'cv2.ximgproc'), solved with a clean reinstall.

That's the whole page, honestly. The _0/_1 pairing is a recurring theme across this pack - every function with a MatLike/UMat overload ships as two identical nodes. It's noise, not a choice.