opencv resource_error ai_generated true

cv::error: Out of memory in function 'cv::detail::MultiBandBlender::feed'

ID: opencv/image-stitching-out-of-memory

Also available as: JSON · Markdown · 中文
85%Fix Rate
87%Confidence
1Evidence
2024-11-05First Seen

Version Compatibility

VersionStatusIntroducedDeprecatedNotes
4.7.0 active — — —
4.8.0 active — — —
4.9.0 active — — —

Root Cause

The image stitching pipeline ran out of memory when trying to blend large images, typically due to high resolution or too many images in the panorama.

generic

中文

图像拼接管线在尝试混合大图像时内存耗尽，通常是由于分辨率过高或全景图中的图像数量过多。

Official Documentation

https://docs.opencv.org/4.x/d8/d19/tutorial_stitcher.html

Workarounds

  1. 90% success Reduce image resolution before stitching: cv::resize each image to 50% using INTER_AREA, then run stitching. For example: cv::resize(img, img, cv::Size(), 0.5, 0.5, cv::INTER_AREA). 
    Reduce image resolution before stitching: cv::resize each image to 50% using INTER_AREA, then run stitching. For example: cv::resize(img, img, cv::Size(), 0.5, 0.5, cv::INTER_AREA).
  2. 80% success Use the 'scans' or 'affine' warper type in cv::Stitcher::create which uses less memory than the default 'spherical' warper. 
    Use the 'scans' or 'affine' warper type in cv::Stitcher::create which uses less memory than the default 'spherical' warper.

中文步骤

  1. Reduce image resolution before stitching: cv::resize each image to 50% using INTER_AREA, then run stitching. For example: cv::resize(img, img, cv::Size(), 0.5, 0.5, cv::INTER_AREA).
  2. Use the 'scans' or 'affine' warper type in cv::Stitcher::create which uses less memory than the default 'spherical' warper.

Dead Ends

Common approaches that don't work:

  1. Increasing the system swap space without reducing image size 70% fail

    Swap space does not help with GPU memory or large CPU allocations; the blender still fails due to insufficient RAM.

  2. Switching to CUDA backend without checking GPU memory limits 85% fail

    GPU memory is often more limited than system RAM; this can cause out-of-memory errors faster.