OpenCV HaarTraining Positive images background

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Just wanted to clarify some things:

1. I use many positive images but almost all of them are synthetic (a simple picture of black and white downloaded from Internet) and only few of them I created using a camera. Is this a mistake and should I always make photos or use opencv__annotation?

2. You say that I don't need to crop an image. But there are many sources that say that we need to reduce an image as much as possible to contain only the object we want to recognize (because all the other information is treated as positive as well and it worsens the training result)? So, as I understand, the best way is to restrict the image to contain only the object and do nothing if there's some portion of background left, isn't it?

2: yea, but you do that by e.g. selecting the closest fitting bounding box in the annotation tool.

And what about 1 - should I stop using all those synthetic images?

we have no idea, what your images are, or what you're trying to detect

Synthetic images help if you have large datasets and you want to generate conditions that do not occur often, as seen in many deep learning pipelines. However, cascades work on fairly small datasets, but only if the data used for training is actually meaningful. The artificial tweaking is not good in that case. In my experience of using the cascades for 4 years, using valid actual naturally occuring data works better in 99% of all applications I made based on them.