General FAQs¶
"I'm new to Python Programming or its usage in OpenCV Library", How to use vidgear in my projects?¶
Answer: Before using vidgear, It's recommended to first go through the following dedicated blog sites and learn how OpenCV-Python syntax works (with examples):
-
PyImageSearch.com âž¶ is the best resource for learning OpenCV and its Python implementation. Adrian Rosebrock provides many practical OpenCV techniques with tutorials, code examples, blogs, and books at PyImageSearch.com. Highly recommended!
-
learnopencv.com âž¶ Maintained by OpenCV CEO Satya Mallick. This blog is for programmers, hackers, engineers, scientists, students, and self-starters interested in Computer Vision and Machine Learning.
-
There's also the official OpenCV Tutorials âž¶ curated by the OpenCV developers.
Once done, visit Switching from OpenCV âž¶ to easily replace OpenCV APIs with suitable Gears âž¶ in your project. All the best!
If you run into any trouble or have any questions, then refer our Help section.
"VidGear is using Multi-threading, but Python is notorious for its poor performance in multithreading?"¶
Answer: Refer vidgear's Threaded-Queue-Mode âž¶
ModuleNotFoundError: No module named 'vidgear.gears'. 'vidgear' is not a package?¶
Answer: This error means you either have a file named vidgear.py
in your python path or you've named your python script vidgear.py
. Replace vidgear
name with anything else to fix this error.
How to log to a file in VidGear?¶
Answer: VidGear provides exclusive VIDGEAR_LOGFILE
environment variable to enable logging to a file while logging is enabled (i.e. logging=True
) on respective Gear. You just have to set directory pathname (automatically creates vidgear.log
file) or a log file pathname itself as value for this environment variable. This can be done on various Operating Systems as follows:
Remember enabling this logging to a file will completely disable any output on the terminal.
Can I perform Deep Learning task with VidGear?¶
Answer: VidGear is a powerful Video Processing library (similar to OpenCV, FFmpeg, etc.) that can read, write, process, send & receive a sequence of video-frames in an optimized manner. But for Deep Learning or Machine Learning tasks, you have to use a third-party library. That being said, all VidGear's APIs can be used with any third-party Library(such as PyTorch, Tensorflow, etc.) that can leverage the overall performance if you're processing video/audio streams/frames in your application with Deep Learning tasks. Also, it eases the workflow since you have to write way fewer lines of code to read/store/process output videos.
Can I ask my question directly without raising an issue?¶
Answer: Yes, please join our Gitter âž¶ Community channel.
How to contribute to VidGear development?¶
Answer: See our Contribution Guidelines âž¶
What OSes are supported by VidGear?¶
Answer: See Supported Systems âž¶
What Python versions are supported by VidGear?¶
Answer: See Supported Python legacies âž¶
Can I include VidGear in my project commercially or not?¶
Answer: Yes, you can, but strictly under the Terms and Conditions given in VidGear License âž¶
"I Love using VidGear for my projects", How can I support it?¶
Answer: See Helping VidGear âž¶