Implement calibrated camera images to produce a bird's eye view

[Ezward]

We suggest a 120 degree to 160 degree camera lens for Donkeycar so the car, which is close to the ground, can still see both sides of the track. Such a lens creates a distorted image. This is generally ok in that the neural network can still produce good predictions. However, it has a number of disadvantages in other use cases;

• It hampers efforts to create more general model using images from many users because they may have different camera lenses and each camera has different inherent distortions.
• It makes it difficult to use an image to figure out the relative position of an obstacle because distances and angles are distorted.

Project:

• Implement a utility to walk the user through getting the necessary image samples of a checkerboard and then calculate the camera calibration matrix. Ideally it automatically snaps the images so the user does not have to press a button for each image, then it allows the user to decide if any images should be dropped and it more images should be taken, until they are happy.
• Note that this problem has been solved many times so we are not looking for a brand new implementation. There are many open source projects that have code to do this; we want to use that code and integrate it into donkeycar like the donkey createcar or donkey calibration command; so add a new command like donkey camera-calibration and save the user's calibration in their mycar folder.
• Once we can save calibrations, write a part that will use the calibration to correct the camera image. It's run() method will take the uncalibrated image and then use the calibration parameters to correct it.
• The corrected image should have an bird's eye view; see Issue #910 and jump to the section entitled A Neural Network using 2D Lidar data. That project needs a bird's eye view image so it can merge it with 2D lidar data. The bird's eye view is useful in other ways; it can be used to estimate the angle and distance from an obstacle for instance.
• Once we have a part that will produce bird's eye view images, integrate it into the deep learning template so we can create an autopilot using bird's eye view images.

[cfox570]

This might be helpful in the solution. https://docs.opencv.org/3.4.0/d9/dab/tutorial_homography.html#tutorial_homography_Demo3

[Ezward]

See notes on Issue https://github.com/autorope/donkeycar/issues/97