How to make a region of interest proposal from convolutional feature maps?
To implement region proposal you need two major parts:


The region proposal network that generates a set of candidate bounding boxes. It can be implemented simply as two convolutional layers to 1) predict the object presense and 2) predict offsets for the default (anchor bounding boxes)
The ROI pooling layer that provides a fixed-size feature vector for an arbitrary sized proposal.


Here is an implementation of Faster R-CNN in Keras, and here is a detailed explanation of the model and the code.

Here is implementations of the RPN, and here is implementation of the ROI pooling.