I'm building a cascade to detect electrical outlets. I've gone through the 3 or so available tutorials, which are helpful.
What I haven't found and am still unclear on is what are the ideal traits of the positive and negative images that would result in the most perfect object detection?
I have numerous questions like:
- Does the object need to comprise ALL of the positive image?
- Can there be variations of the object in the positive image ( I assume so because for faces we want to supply many different faces) ? For instance, provide a positive image with an outlet that has one outlet plugged in?
- Sizing: should I presize all of the images? Meaning should all of the positive and negative images be the same size?
- what about the distortion options
opencv_createsamplesprovides? I rotated 180ยบ but what about coloring? How do I want to recolor my images to achieve the best results? - Regarding this dataset of negative images here: should I size my positive images to the size of these negative images?
opencv_traincascadegave me numerous errors when training with these negative images, possibly because they were different sizes.
I think you can see my line of questioning, and I realized there are multiple questions here. It's just I haven't found information about what I should ideally provide to OpenCV for training. I think that I'm actually seeking better docs, because the docs don't really provide more information about the positive and negative images.
One more thing: I think because of the time it takes to train here, a trial-and-error approach to improving the training is not optimal. Plus there are many different variables as far as training that it would take weeks to optimize this on one's own.