I have worked on the problem of image classification using Bag of features (BoF)and SVM. I did it using C++ and OpenCV but I am sure that you can get similar methods for python too.

Concept:

Create BoF Dictionary:

1. Take one image from your training samples.
2. Extract SIFT keypoints
3. Extract SIFT descriptors
4. Use k-means clustering to cluster the descriptors
5. Create BoF dictionary (Refer the link which I have mentioned below)

Training:

1. Load your BoF dictionary
2. Initialize your BoF instance with the above BoF dictionary
3. Take one image from your training samples.
4. Extract SIFT keypoints
5. Find BoF descriptors for the extracted keypoints.
6. Use this BoF descriptor for the SVM learning
7. Do the above steps for all the training images
8. You will get a SVM classifier files in .xml form...save it

Testing:

1. Load your SVM classifier
2. Initialize your SVM classifier with the .xml file which your created above
3. Capture image
4. Find BoF descriptor for the captured image using your dictionary
5. Use those BoF descriptor to classify it using SVM

You can refer this article

Install opencv-python (which is the official pre-built OpenCV package for Python) by issuing the following command:

pip install opencv-python

I found this.

OpenCV on wheels.

Unofficial OpenCV packages for Python.

Installation was painless for Ubuntu 16.04:

pip3 install opencv-python

Check the installation:

python3
Python 3.5.2 (default, Nov 17 2016, 17:05:23) 
[GCC 5.4.0 20160609] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import cv2
>>> cv2.__version__
'3.2.0'