Calibrating Cameras

It is common to want to calibrate cameras for lens distortion.


First print out a standard chequerboard image, and take some photos of it from various angles. You can print out images, for example, here:

Here I assume these photos are in camera_cal/calibration*.jpg

Even if you ultimately want to calibrate the images from c++, python, javascript etc, the calibration itself can be done separately, in a more convenient language, producing a matrix that you can use pretty much anywhere.  In my case, I calibrate in a jypter notebook, but use the matrix in c++.

%matplotlib inline

import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
#%matplotlib qt

# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((6*9,3), np.float32)
objp[:,:2] = np.mgrid[0:9,0:6].T.reshape(-1,2)

# Arrays to store object points and image points from all the images.
objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.

# Make a list of calibration images
images = glob.glob('camera_cal/calibration*.jpg')

# Step through the list and search for chessboard corners
for fname in images:
img = cv2.imread(fname)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

# Find the chessboard corners
ret, corners = cv2.findChessboardCorners(gray, (9,6),None)

# If found, add object points, image points
if ret == True:

# Draw and display the corners
img = cv2.drawChessboardCorners(img, (9,6), corners, ret)

ret, camera_undistort_matrix, camera_undistort_dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)

fname = images[8]
img = cv2.imread(fname)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret, corners = cv2.findChessboardCorners(gray, (9,6),None)
img = cv2.drawChessboardCorners(img, (9,6), corners, ret)
dst = cv2.undistort(img, camera_undistort_matrix, camera_undistort_dist, None, camera_undistort_matrix)
img = np.concatenate((img, dst), axis=1)
cv2.imwrite("output_images/undistort_chessboard.png", img)


On the left is the original photo, and on the right is after applying the undistortion matrix warp.

import pickle
with open('camera_undistort_matrix.pkl', 'wb') as f:
pickle.dump((camera_undistort_matrix, camera_undistort_dist), f)
[[ 1.15777829e+03 0.00000000e+00 6.67113865e+02]
[ 0.00000000e+00 1.15282230e+03 3.86124659e+02]
[ 0.00000000e+00 0.00000000e+00 1.00000000e+00]]


img = cv2.imread("test_images/straight_lines1.jpg")
dst = cv2.undistort(img, camera_undistort_matrix, dist, None, camera_undistort_matrix)
img = np.concatenate((img,dst),axis=1)
img_rgb = cv2.cvtColor(np.asarray(img), cv2.COLOR_BGR2RGB)
cv2.imwrite("output_images/undistort_straight_lines1.png", img)

In the top left is the original image, in the top right is the undistorted image.  There is little difference to the eye, but the distortion lets us to now apply further distortions such as a perspective transform to provide an apparent “top-down view”.

It is also useful for applying other transforms key to stitching together images from multiple cameras.




Interactive dialogue on a webpage with React and Promises

Here’s the goal:

A way to make a webpage that lets you have an interactive dialogue by choosing options, like the old Interactive Fiction games.

The input file is very simple:

PRINT Hello!
WAIT ; This is a comment.  Wait for a key press or mouse click
PRINT I would like to ask you a question.
PRINTW Please don't be nervous. ; The 'W' means WAIT afterwards
PRINT Are you happy?
PRINT [0] Yes
PRINT [1] Not really...
    PRINTW Then I am miserable 😦

The challenge is to make a webpage that could read that input, and run it, producing the interactive output shown in the video above.

Perhaps have a think if you don’t know how you would implement this. It is perhaps not so obvious.

Here is the approach that I decided to take.  Note that I only wanted to spend a weekend on this, so I took various shortcuts that I wouldn’t recommend if writing for production:


  1. Read in the input file in, using javascript with a Tokenizer and Parser.
  2. Output javascript using the above Parser, effectively making a transpiler from the input language to javascript.  Call outside javascript functions to ‘PRINT’ etc.
  3. Use ‘eval’ to run the resulting transpiled code.  Yes, eval is evil and this is a case of don’t do what I do.
  4. Use Promises, via async and await, to ‘pause’ and allow interaction.  Implement ‘WAIT’, ‘PRINTW’ ‘INPUTINT’ etc functions in javascript with Promises, so that they only resolve when the user has clicked, typed a number etc.
  5. Display output by just appending to a list, and displaying that list in React.


1. Read the file in, using Javascript with a Tokenizer and Parser

I used jison.  Although the README blurb says that it is for Context Free Grammars, because it is based on BISON which is likewise, it does actually support context.  This was vital because the input file language is not actually a context free grammar.

2. Output javascript using the above Parser, effectively making a transpiler from the input language to javascript

The correct way to do this would be to create an Abstract Syntax Tree, however I didn’t want to take too long on this, and instead simply outputted javascript code as a string.

3. Use ‘eval’ to run the resulting transpiled code.

This is very frowned upon, but this was a weekend project, so….

There is one trick that I used here.  I wrap the entire program to be ‘eval’ed like:

async function() { ..... }()

This allows the program code inside to use async and await to wait for input, and the eval is returning a Promise.  One minor point – when we use eval to evaluate this, we want to catch errors that the Promise throws, to provide clear feedback to the user if there are problems.  E.g.

try {
    await eval(program);
} catch(e) { ... }

4. Use Promises, via async and await, to ‘pause’ and allow interaction.  Implement ‘WAIT’, ‘PRINTW’ ‘INPUTINT’ etc functions in javascript with Promises, so that they only resolve when the user has clicked, typed a number etc.

I used two layers of callbacks, to implement a poor-man’s publish and subscribe system.

So the transpiler turns:



await wait();

And the wait() function is implemented as:

async function wait() {
  await new Promise(
    resolve => cb_setClickListener(() => resolve())

So we subscribe to a click listener via the callback ‘cb_setClickListener’ and then resolve the promise (and thus resume running the program) when the click is published.

Inside the React page, we now listen for clicks and publish it to the callback:

<App onclick={
  () => this.state.clickListener &&

And likewise for keypresses.  (Note, I’ve simplified the code here a bit.  In the real code, I pass the keypressed etc, so that INPUTINT can listen to a particular key).

5. Display output by just appending to a list, and displaying that list in React.

The ‘printLine’ function was implemented like:

function printLine(str) {
  const newLine = <div>{str}</div>;
  this.setState({displayLines: [...displayLines, newLine]})

One extra detail – if the string starts with a number in a bracket like: “[0] Yes”, then I output a link that publishes that number like:

<div className="clickable" onClick={
    () => this.state.keyPressedListener &&

This way, when presented with a choice, the user can just click a link instead.   I maintain two separate output lists, so that I can disable the links once we are done with them.

Conclusion and notes

It worked very nicely! I further extended this to support variables, entering strings, showing pictures, and so on.

The language is actually a real language, but very niche and used almost exclusively by Japanese. It hasn’t seen much acceptance or use outside of Japan.

Machine Translated renaming of files

I needed to rename a whole load of files that were in Japanese.  So I wrote a python program that translates the filename using google translate.

It’s not at all fancy, just run it and pass the filenames to translate and rename.


$ ls
$ sudo pip3 install googletrans
$ こんにちは世界.png
こんにちは世界.png -> Hello_World.png
$ ls

import sys, re, os
from googletrans import Translator

translator = Translator()

sourceLanguage = 'ja'
destLanguage = 'en'

# Set to false to actually rename the files
dryRun = True

def translate_and_rename(filename):
    filenameSplit = filename.rsplit('.',1)
    translated = translator.translate(filenameSplit[0], src=sourceLanguage, dest=destLanguage).text
    translated = re.sub( '[^a-zA-Z0-9.]+', '_', translated).strip().title()
    if len(filenameSplit) > 1:
        translated += '.' + filenameSplit[1]
    if filename == translated:
        print(filename, ' (unchanged)')
        print(filename, " -> ", translated)
        if not dryRun:
            os.rename(filename, translated)

def main(argv):
    if len(argv) == 1:
        print("Need to pass filenames to translate and rename")

    for x in argv[1:]:
    if dryRun:
        print("  Dry run only - no actual changes made  ")
        print("Edit this file and set DryRun to True")

if __name__ == "__main__":

TypeScript + lodash map and filter

I love TypeScript.  I use it whenever I can.  That said, sometimes it can be…  interesting.  Today, out of the blue, I got the typescript error in code that used to work:

[06:53:30]  typescript: src/mycode.ts, line: 57 
            Property 'video' does not exist on type 'number | (<U>(callbackfn: (value: Page, index: number, 
            array: Page[]) => U, thisA...'. Property 'video' does not exist on type 'number'. 


The code looks like:

return _.chain(pages)
        .filter((s, sIdx) => || s.videoEmbedded)
        .map((s, sIdx) => {
            if ( { ... }

Can you spot the ‘error’?

The problem is that || s.videoEmbedded isn’t returning a boolean. It’s return a truthy value, but not a boolean. And the lodash typescript developers made a change 1 month ago that meant that filter() would only accept booleans, not any truthy value. And the lodash typescript developers are finding that fixing this becomes very complicated and complex. See the full conversation here:

(Open issue at time of writing. Please leave me feedback or message me if you see this bug get resolved)

The workaround/fix is to just make sure it’s a boolean. E.g. use !! or Boolean(..) or:

return _.chain(pages)
        .filter((s, sIdx) => !== null || s.videoEmbedded !== null )
        .map((s, sIdx) => {
            if ( { ... }

Robot face

I played about with making a robot face.  I combined a Ultrasonic Sensor (HC-SR04) and NeoPixel leds.  The leds reflect the distance – one pixel per 10 of cm.

I’m wondering if I can do SLAM – Simultaneous Location And Mapping  with a single (or a few) very-cheap ultrasonic sensor. Well, the answer is almost certainly no, but I’m very curious to see how far I can get.


I don’t like PID control


I’ve implemented PID control far too many times now, and I’ve never been that happy with the results.

It is simple to implement, but tricky to tune.  My thick control theory book says that 95% of commercial in-use PID systems are sub-optimally tuned.  And I can believe it.

I’ve been recently playing with other systems, and currently I really like Model Predictive Control.  It does require some significant compute power, which was a big problem when it was invented, but not so much any more.   (As a side point, I’d like to implement MPC in tensorflow.  That would be cool, and could probably be done in a weekend).

I wrote a PID control system for a self driving car.  It is dumbly auto-tuned, so to speak, by a simple twiddle method.  It tests out a particular set of PID values for one minute, resets the track, then picks a new set of PID values based on the best seen values, changed slightly.

The results are…  very underwhelming.


The same driving but using Model Predictive Control, below, starts to look a lot nicer.  For reference, the PID control took a total of two days to implement, and the MPC took a total of three days.   The MPC version also has an additional complication – there’s a simulated delay of 100ms.

But I’m very pleased with the results.  It’s a bit jerky here due to random latency variations, made worse by the screen recording software.  I’ve got some ideas on trying to solve that by timestamping, but I don’t think I’ll have time to work on it.

I would love to try applying this MPC system to a Quadcopter.  I’ve written quite a bit previously about latency problems with PID in a quadcopter:  (In this video, the ufo-shaped quadcopter is attempting to move to, and maintain, a fixed position above the chair.  Ignore the blue man 🙂 )