"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 ➶ 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 ➶ 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 ➶ curated by the OpenCV developers.
Once done, visit Switching from OpenCV ➶ to easily replace OpenCV APIs with suitable Gears ➶ 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 ➶
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 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 platfroms/OSes 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 ➶ Community channel.
How to contribute to VidGear development?⚓
Answer: See our Contribution Guidelines ➶
What OSes are supported by VidGear?⚓
Answer: See Supported Systems ➶
What Python versions are supported by VidGear?⚓
Answer: See Supported Python legacies ➶
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 ➶
"I Love using VidGear for my projects", How can I support it?⚓
Answer: See Helping VidGear ➶