What is a dense feature detector?

In order to extract a meaningful amount of information from the images, we need to make sure our feature extractor extracts features from all parts of a given image. Consider the following image:

If you extract features using a feature extractor as we did in Chapter 5, Extracting Features from an Image, it will look like this:

If you used to use the cv2.FeaturetureDetector_create("Dense") detector, unfortunately, that was removed from OpenCV 3.2 onwards, so we would need to implement our own one iterating over the grid and obtaining the keypoints:

We can control the density as well. Let's make it sparse:

By doing this, we can make sure that every single part in the image is processed. Here is the code to do it:

import sys
import cv2 
import numpy as np 

class DenseDetector(): 
    def __init__(self, step_size=20, feature_scale=20, img_bound=20): 
        # Create a dense feature detector 
        self.initXyStep = step_size
        self.initFeatureScale = feature_scale...