Summary

Well, you have had a great start to your computer vision journey toward making a real self-driving car.

You learned about a very useful toolset called OpenCV with bindings for Python and NumPy. With these tools, you are now able to create and import images using methods such as imread(), imshow(), hconcat(), and vconcat(). You learned how to import and create video files, as well as capturing video from a webcam with methods such as VideoCapture() and VideoWriter(). Watch out Spielberg, there is a new movie-maker in town!

It was wonderful to be able to import images, but how do you start manipulating them to help your computer vision algorithms learn what features matter? You learned how to do this through methods such as flip(), blur(), GaussianBlur(), medianBlur(), bilateralFilter(), and convertScaleAbs(). Then, you learned how to annotate images for human consumption with methods such as rectangle() and putText().

Then came the real magic, where you learned how to take the images and do your first piece of real computer vision using HOG to detect pedestrians. You learned how to apply a sliding window to scan the detector over an image in various sized windows using the detectMultiScale() method, with parameters such as winStride, padding, scale, hitThreshold, and finalThreshold.

You had a lot of fun with all the new tools you learned for working with images. But there was something missing. How do I get these images on my self-driving car? To answer this, you learned about the camera and its basic terminology, such as resolution, FoV, focal length, aperture, DoF, dynamic range, ISO, frame rate, lens flare, and finally, lens distortion. Then, you learned the basic components that comprise a camera, namely the lens, aperture, and light-sensitive arrays. With these basics, you moved on to some considerations for choosing a camera for your application by learning about the strengths and weaknesses of a camera.

Armed with this knowledge, you boldly began to remove one of these weaknesses, lens distortion, with the tools you learned in OpenCV for distortion correction. You used methods such as findChessboardCorners(), calibrateCamera(), undistort(), and cornerSubPix() for this.

Wow, you are really on your way to being able to perceive the world in your self-driving application. You should take a moment and be proud of what you have learned so far. Maybe you can celebrate with a selfie and apply some of what you learned!

In the next chapter, you are going to learn about some of the basic signal types and protocols you are likely to encounter when trying to integrate sensors in your self-driving application.