There is no virtual limit to the type of objects you can detect in your images and videos. However, to obtain an acceptable level of accuracy, you need a sufficiently large dataset, containing train images that are identical in size.
This would be a time consuming operation if we were to do it all by ourselves (which is entirely possible).
We can avail of ready-made datasets; there are a number of them freely downloadable from various sources:
The University of Illinois: http://l2r.cs.uiuc.edu/~cogcomp/Data/Car/CarData.tar.gz
Stanford University: http://ai.stanford.edu/~jkrause/cars/car_dataset.html
I'll be using the UIUC dataset in my example, but feel free to explore the Internet for other types of datasets.
Now, let's take a look at an example:
import cv2 import numpy as np from os.path import join datapath = "/home/d3athmast3r/dev/python/CarData/TrainImages/" def path(cls,i): return "%s/...