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Monkey patching cv2 methods crashes own scripts #7285

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janzumsteg opened this issue Apr 5, 2022 · 4 comments · Fixed by #7287
Closed
1 of 2 tasks

Monkey patching cv2 methods crashes own scripts #7285

janzumsteg opened this issue Apr 5, 2022 · 4 comments · Fixed by #7287
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@janzumsteg
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janzumsteg commented Apr 5, 2022

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  • I have searched the YOLOv5 issues and found no similar bug report.

YOLOv5 Component

PyTorch Hub

Bug

The current master branch contains monkey patches of cv2.imread, cv2.imwrite and cv2.imshow (see

cv2.imread, cv2.imwrite, cv2.imshow = imread, imwrite, imshow # redefine
). When importing yolov5 (e.g. via torch.hub.load), the respective methods in my own scripts will also be overwritten without any hints. Moreover, the signature of cv2.imread changed with these monkey patches resulting in TypeError: imread() takes 1 positional argument but 2 were given.

Related PRs:

Environment

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Minimal Reproducible Example

import cv2
import torch

cv2.imread("path/to/image.png", cv2.IMREAD_COLOR)  # works
torch.hub.load("ultralytics/yolov5", "yolov5m", force_reload=True)
cv2.imread("path/to/image.png", cv2.IMREAD_COLOR)  # fails

Additional

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Are you willing to submit a PR?

  • Yes I'd like to help by submitting a PR!
@janzumsteg janzumsteg added the bug Something isn't working label Apr 5, 2022
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github-actions bot commented Apr 5, 2022

👋 Hello @janzumsteg, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

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Python>=3.7.0 with all requirements.txt installed including PyTorch>=1.7. To get started:

git clone https://github.com/ultralytics/yolov5  # clone
cd yolov5
pip install -r requirements.txt  # install

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@glenn-jocher
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@janzumsteg good news 😃! Your original issue may now be fixed ✅ in PR #7287. This PR updates the patched function to accept a second argument just like the original function, and the code to reproduce the error now runs without issue:

Screenshot 2022-04-05 at 11 48 40

To receive this update:

  • Gitgit pull from within your yolov5/ directory or git clone https://github.com/ultralytics/yolov5 again
  • PyTorch Hub – Force-reload model = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True)
  • Notebooks – View updated notebooks Open In Colab Open In Kaggle
  • Dockersudo docker pull ultralytics/yolov5:latest to update your image Docker Pulls

Thank you for spotting this issue and informing us of the problem. Please let us know if this update resolves the issue for you, and feel free to inform us of any other issues you discover or feature requests that come to mind. Happy trainings with YOLOv5 🚀!

@grzegorzk
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grzegorzk commented Mar 21, 2023

Hi @glenn-jocher, I have been playing with yolov5==7.0.11 and found that monkey-patched imread has different signature than the one provided by opencv. Your argument is called path while in opencv it's called filename. This is resulting in my other scripts that are executed after loading yolov5 to fail, even though they are working fine if I don't use yolov5.

>>> import cv2
>>> help(cv2.imread)
Help on built-in function imread:

imread(...)
    imread(filename[, flags]) -> retval
(...)

>>> import yolov5
>>> help(cv2.imread)
Help on function imread in module yolov5.utils.general:

imread(path, flags=1)

I proposed fix here

I would personally prefer if these methods are not monkey-patched each time I import yolov5 though, I can prepare a PR if this is something you would consider.

@glenn-jocher
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Hi @grzegorzk 👋, it's great to hear your feedback on the monkey-patched imread method. Your proposed fix in PR #11209 looks like a step in the right direction. I'll make sure to discuss this with the team to see if we can merge your changes.

Regarding the monkey-patching, I totally understand your concern. We are currently evaluating different options to reduce the impact of monkey-patching on user-defined scripts. Your willingness to submit a PR to help address this is much appreciated! We'd certainly be open to reviewing your solution. Thank you for your contribution and for helping make YOLOv5 even better 🙌!

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