ARM GCC Lean: programming and debugging the Nordic NRF52

The nRF52832 is an ARM Cortex M4 chip with an impressive range of peripherals, including an on-chip 2.4 GHz wireless transceiver. Nordic supply a comprehensive SDK with plenty of source-code examples; they are fully compatible with the GCC compiler, but there is little information on how to program and debug a target system using open-source tools such as the GDB debugger, or the OpenOCD JTAG/SWD programmer.

This blog will show you how to compile, program and debug some simple examples using the GNU ARM toolchain; the target board is the NRF52832 Breakout from Sparkfun, and the programming is done via a Nordic development board, or OpenOCD on a Raspberry Pi. Compiling & debugging is with GCC and GDB, running on Windows or Linux.

Source files

All the source files are in an ‘nrf_test’ project on GitHub; if you have Git installed, change to a suitable project directory and enter:

git clone

Alternatively you can download a zipfile from github here. You’ll also need the nRF5 15.3.0 SDK from the Nordic web site. Some directories need to be copied from the SDK to the project’s nrf5_sdk subdirectory; you can save disk space by only copying components, external, integration and modules as shown in the graphic above.

Windows PC hardware

Cortex Debug Connection to a Nordic evaluation board.

The standard programming method advocated by Nordic is to use the Segger JLink adaptor that is incorporated in their evaluation boards, and the Windows nRF Command Line Tools (most notably, the nrfjprog utility) that can be downloaded from their Web site.

Connection between the evaluation board and target system can be a bit tricky; the Sparkfun breakout board has provision for a 10-way Cortex Debug Connector, and adding the 0.05″ pitch header does require reasonable soldering skills. However, when that has been done, a simple ribbon cable can be used to connect the two boards, with no need to change any links or settings from their default values.

One quirk of this arrangement is that the programming adaptor detects the 3.3V power from the target board in order to switch the SWD interface from the on-board nRF52 chip to the external device. This has the unfortunate consequence that if you forget to power up the target board, you’ll be programming the wrong device, which can be confusing.

The JLink adaptor isn’t the only programming option for Windows; you can use a Raspberry Pi with OpenOCD installed…

Raspberry Pi hardware

Raspberry Pi SWD interface (pin 1 is top right in this photo)

In a previous blog, I described the use of OpenOCD on the raspberry Pi; it can be used as a Nordic device programmer, with just 3 wires: ground, clock and data – the reset line isn’t necessary. The breakout board needs a 5 volt supply which could be taken from the RPi, but take care: accidentally connecting a 5V signal to a 3.3V input can cause significant damage.

Rasberry Pi SWD connections
NRF52832 breakout SWD connections

Install OpenOCD as described in the previous blog; I’ve included the RPi and SWD configuration files in the project openocd directory, so for the RPi v2+, run the commands:

cd nrf_test
sudo openocd -f openocd/rpi2.cfg -f openocd/nrf52_swd.cfg

The response should be..

BCM2835 GPIO config: tck = 25, tms = 24, tdi = 23, tdo = 22

Info : Listening on port 6666 for tcl connections
Info : Listening on port 4444 for telnet connections
Info : BCM2835 GPIO JTAG/SWD bitbang driver
Info : JTAG and SWD modes enabled
Info : clock speed 1001 kHz
Info : SWD DPIDR 0x2ba01477
Info : nrf52.cpu: hardware has 6 breakpoints, 4 watchpoints
Info : Listening on port 3333 for gdb connections

The DPIDR value of 0x2BA01477 is correct for the nRF52832 chip; if any other value appears, there is a problem: check the wiring.

Windows development tools

The recommended compiler toolset for the SDK files is gcc-arm-none-eabi, version 7-2018-q2-update, available here. This places the tools in the directory

C:\Program Files (x86)\GNU Tools Arm Embedded\7 2018-q2-update\bin

Check that this directory in included in your search path by opening a command window, and typing

arm-none-eabi-gcc  -v

If not found, close the window, add to the PATH environment variable, and retry.

You will also need to install Windows ‘make’ from here. At the time of writing, the version is 3.81, but I suspect most modern versions would work fine. As with GCC, check that it is included in your executable path by opening a new command window, and typing

make -v

Linux development tools

A Raspberry Pi 2+ is quite adequate for compiling and debugging the test programs.

Although RPi Linux already has an ARM compiler installed, the executable programs it creates are heavily dependant on the operating system, so we also need to install a cross-compiler: arm-none-eabi-gcc version 7-2018-q2-update. The easiest way to do this is to click on Add/Remove software in the Preferences menu, then search for arm-none-eabi. The correct version is available on Raspbian ‘Buster’, but probably not on earlier distributions.

The directory structure is the same as for Windows, with the SDK components, external, integration and modules directories copied into the nrf5_sdk subdirectory.

As with Windows, it is worth typing

arm-none-eabi-gcc  -v make sure the GCC executable is installed correctly.


This is in the nrf_test1 directory, and is as simple as you can get; it just flashes the blue LED at 1 Hz.

// Simple LED blink on nRF52832 breakout board, from

#include "nrf_gpio.h"
#include "nrf_delay.h"

// LED definitions
#define LED_PIN      7
#define LED_BIT      (1 << LED_PIN)

int main(void)

    while (1)
        NRF_GPIO->OUT ^= LED_BIT;

// EOF

An unusual feature of this CPU is that the I/O pins aren’t split into individual ports, there is just a single port with a bit number 0 – 31. That number is passed to an SDK function to initialise the LED O/P pin, and I could have used another SDK function to toggle the pin, but instead used an exclusive-or operation on the hardware output register.

The SDK delay function is implemented by performing dummy CPU operations, so isn’t particularly accurate.


For both platforms, the method is the same: change directory to nrf_test1, and type ‘make’; the response should be similar to:

Assembling ../nrf5_sdk/modules/nrfx/mdk/gcc_startup_nrf52.S
 Compiling ../nrf5_sdk/modules/nrfx/mdk/system_nrf52.c
 Compiling nrf_test1.c
 Linking build/nrf_test1.elf
    text    data     bss     dec     hex filename
..for Windows..
    1944     108      28    2080     820 build/nrf_test1.elf
..or for Linux..
    2536     112     172    2820     b04 build/nrf_test1.elf

If your compile-time environment differs from mine, it shouldn’t be difficult to change the Makefile definitions to match, but there are some points to note:

  • The main changeable definitions are towards the top of the file. Resist the temptation to rearrange CFLAGS or LNFLAGS, as this can create a binary image that crashes the target system.
  • You can add files to the SRC_FILES definition, they will be compiled and linked in; the order of the files isn’t significant, but I generally put gcc_startup_nrf52.S first, so Reset_Handler is at the start of the executable code. Similarly, INC_FOLDERS can be expanded to include any other folders with your .h files.
  • The task definitions toward the bottom of the file use the tab character for indentation. This is essential: if replaced with spaces, the build process will fail.
  • ELF, HEX and binary files are produced in the ‘build’ subdirectory; ELF is generally used with GDB, while HEX is required by the JLink flash programmer.
  • I’ve defined the jflash and ocdflash tasks, that do flash programming after the ELF target is built; you can add your own custom programming environment, using a similar syntax.
  • The makefile will re-compile any C source files after they are changed, but will not automatically detect changes to the ‘include’ files, or the makefile itself; when these are edited, it will be necessary to force a re-make using ‘make -B’.
  • If a new image won’t run on the target system, the most common reason is an un-handled exception, and it can be quite difficult to find the cause. So I’d recommend that you expand the code in relatively small steps, making it easier to backtrack if there is a problem.

Device programming

Having built the binary image, we need to program it into Flash memory on the target device. This can be done by:

  • JLink adaptor on an evaluation board (Windows PC only)
  • Directly driving OpenOCD (RPi only)
  • Using the GNU debugger GDB to drive OpenOCD (both platforms)

Device programming using JLink

Set up the hardware and install the Nordic nRF Command Line Tools as described above, then the nrfjflash utility can be used to program the target device with a hex file, e.g.

nrfjprog --program build/nrf_test1.hex --sectorerase
nrfjprog --reset

The second line resets the chip after programming, to start the program running. This is done via the SWD lines, a hardware reset line isn’t required; alternatively you can just power-cycle the target board.

The above commands have been included in the makefile, so if you enter ‘make jflash’, the programming commands will be executed after the binary image is built.

An additional usage of the JLink programmer is to restore the original Arduino bootloader, that was pre-installed on the Sparkfun board. To do this, you need to get hold of the softdevice and DFU files from the Sparkfun repository, combine them using the Nordic merge utility, then program the result using a whole-chip erase:

mergehex -m s132_nrf52_2.0.0_softdevice.hex sfe_nrf52832_dfu.hex -o dfu.hex
nrfjprog --program dfu.hex --chiperase
nrfjprog --reset 

Device programming using OpenOCD

OpenOCD can be used to directly program the target device, providing the image has been built on the Raspberry Pi, or the ELF file has been copied from the development system. Install and test OpenOCD as described in the Raspberry Pi Hardware section above (check the DPIDR value is correct), hit ctrl-C to terminate it, then enter the command:

sudo openocd -f ../openocd/rpi2.cfg -f ../openocd/nrf52_swd.cfg -c "program build/nrf_test1.elf verify reset exit"

The response should be similar to:

 ** Programming Started **
 Info : nRF52832-QFAA(build code: E0) 512kB Flash
 Warn : using fast async flash loader. This is currently supported
 Warn : only with ST-Link and CMSIS-DAP. If you have issues, add
 Warn : "set WORKAREASIZE 0" before sourcing nrf51.cfg/nrf52.cfg to disable it
 ** Programming Finished **
 ** Verify Started **
 ** Verified OK **
 ** Resetting Target **
 shutdown command invoked

Note the warnings: by default, OpenOCD uses a ‘fast async flash loader’ that achieves a significant speed improvement by effectively sending a write-only data stream. Unfortunately the Nordic chip occasionally takes exception to this, and returns a ‘wait’ response, which can’t be handled in fast async mode, so the programming fails – in my tests with small binary images, it does fail occasionally. As recommended in the above text, I’ve tried adding ‘set WORKAREASIZE 0’ to nrf52_swd.cfg (before ‘find target’), but this caused problems when using GDB. By the time you read this, the issue may well have been solved; if not, you might have to do some experimentation to get reliable programming.

The makefile includes the OpenOCD direct programming commands, just run ‘make ocdflash’.

Device programming using GDB and OpenOCD

The primary reason for using GDB is to debug the target program, but it can also serve as a programming front-end for OpenOCD. This method works with PC host, or directly on the RPi, as shown in the following diagram.

GDB OpenOCD debugging

In both cases we are using the GB ‘target remote’ command; on the development PC we have to specify the IP address of the RPi: for example, as shown above. If in doubt as to the address, it is displayed if you hover the cursor over the top-right network icon on the RPi screen. By default, OpenOCD only responds to local GDB requests, so the command ‘bindto’ must be added to the configuration. This means anyone on the network could gain control of OpenOCD, so use with care: consider the security implications.

Alternatively, the Raspberry Pi can host both GDB and OpenOCD, in which case the ‘localhost’ address is used, and there is no need for the additional ‘bindto’.

The commands for the PC-hosted configuration are:

# On the RPi:
  sudo openocd -f ../openocd/rpi2.cfg -f ../openocd/nrf52_swd.cfg -c "bindto"

# On the Windows PC:
arm-none-eabi-gdb -ex="target remote" build\nrf_test1.elf -ex "load" -ex "det" -ex "q"

The PC connects to the OpenOCD GDB remote server on port 3333, loads the file into the target flash memory, detaches from the connection, and exits. The response will be something like:

Loading section .text, size 0x790 lma 0x0
 Loading section .ARM.exidx, size 0x8 lma 0x790
 Loading section .data, size 0x6c lma 0x798
 Start address 0x2b4, load size 2052
 Transfer rate: 4 KB/sec, 684 bytes/write.
 Detaching from program: c:\Projects\nrf_test\nrf_test1\build\nrf_test1.elf, Remote target
 Ending remote debugging.

I have experienced occasional failures with the message “Error finishing flash operation”, in which case the command must be repeated; see my comments on the ‘fast async flash loader’ above.

The Rpi-hosted command sequence is similar:

# On the RPi (first terminal):
sudo openocd -f ../openocd/rpi2.cfg -f ../openocd/nrf52_swd.cfg

# On the RPi (second terminal):
gdb -ex="target remote localhost" build\nrf_test1.elf -ex "load" -ex "det" -ex "q" 

Note that the GDB programming cycle does not include a CPU reset, so to run the new program the target reset button must be pressed, or the board power-cycled.


There are many ways the first test program can be extended, I chose to add serial output (including printf), and also a timeout function based on the ARM systick timer, so the delay function doesn’t hog the CPU. The main loop is:

int main(void)
    uint32_t tix;

    mstimeout(&tix, 0);
    printf("\nNRF52 test\n");
    while (1)
        if (mstimeout(&tix, 500))
            NRF_GPIO->OUT ^= LED_BIT;

I encountered two obstacles; firstly, I ran out of time trying to understand how to create a non-blocking serial transmit routine using the SDK buffering scheme, so implemented a simple circular buffer that is polled for transmit characters in the main program loop.

The second obstacle was that the CPU systick is a 24-bit down-counter clocked at 64 MHz, which means that it wraps around every 262 milliseconds. So we can’t just use the counter value to check when 500 milliseconds has elapsed, it needs some creative coding to measure that length of time; with hindsight, it might have been better to use a conventional hardware timer.

To build the project just change directory to nrf_test2, and use ‘make’ as before. The source code is fairly self explanatory, but the following features are a bit unusual:

  • For printf serial output, the Arduino programming link on the 6-way connector can’t be used, so we have to select an alternative.
  • A remarkable feature of the UART is that we can choose any unused pin for I/O; the serial signals aren’t tied to specific pins. I’ve arbitrarily chosen I/O pin 15 for output, 14 for input.
  • The method of initialising the UART and the printf output is also somewhat unusual, in that it involves a ‘context’ structure with the overall settings, in addition to the configuration structure.

Viewing serial comms

Serial I/O pins used by nrf_test2
Raspberry Pi SWD and serial connections

The serial output from the target system I/O pin 15 is a 3.3V signal, that is compatible with the serial input pin 10 (BCM 15) on the RPi (TxD -> RxD). To enable this input, launch the Raspberry Pi Configuration utility, select ‘interfaces’, enable the serial port, disable the serial console, and reboot.

To view the serial data, you could install a comms program such as ‘cutecom’, or just enter the following command line in a terminal window (ctrl-C to exit):

stty -F /dev/ttyS0 115200 raw; cat /dev/ttyS0


We have already used GDB to program the target system, a similar setup can be used for debugging. Some important points:

  • You’ll be working with 2 binary images; one that is loaded into GDB, and another that has been programmed into the target, and these two images must be identical. If in doubt, you need to reprogram the target.
  • The .elf file that is loaded into GDB contains the binary image and debug symbols, i.e.the names and addresses of your functions & variables. You can load in a .hex file instead, but that has no symbolic information, so debugging will be very difficult.
  • Compiler optimisation is normally enabled (using the -O3 option) as it generates efficient code, but this code is harder to debug, since there isn’t a one-to-one correspondence between a line of source and a block of instructions. Disabling optimisation will make the code larger and slower, but easier to debug; to do this, comment out the OPTIMISE line in the makefile (by placing ‘#’ at the start) and rebuild using ‘make -B’
  • OpenOCD must be running on the Raspberry Pi, configured for SWD mode and the NRF52 processor (files rpi2.cfg and nrf52_swd.cfg). It will be fully remote-controlled from GDB, so won’t require any other files on the RPi.
  • GDB must be invoked in remote mode, with “target remote ADDR:3333” where ADDR is the IP address of the Raspberry Pi, or localhost if GDB and OpenOCD are running on the same machine.
  • GDB commands can be abbreviated providing there is no ambiguity, so ‘print’ can be shortened to ‘p’. Some commands can be repeated by hitting the Enter key, so if the last command was ‘step’, just hit Enter to do another step.

Here is a sample debugging session (user commands in bold):

# On the RPi:
sudo openocd -f ../openocd/rpi2.cfg -f ../openocd/nrf52_swd.cfg -c "bindto"

# On the PC, if RPi is at
arm-none-eabi-gdb -ex="target remote" build/nrf_test2.elf
Target system halts, current source line is shown

# Program binary image into target system
Loading section .text, size 0x215c lma 0x0
Loading section .log_const_data, size 0x10 lma 0x215c
..and so on..

# Print Program Counter (should be at reset handler)
p $pc
$1 = (void (*)()) 0x2b4 <Reset_Handler>

# Execute program (continue)

# Halt program: hit ctrl-C, target reports current location
Program received signal SIGINT, Interrupt.
 main () at nrf_test2.c:72
 72              poll_serial();

# Print millisecond tick count
p msticks
$3 = 78504

# Print O/P port value in hex
$4 = 0x8080

# Toggle LED pin on O/P port
set NRF_GPIO->OUT ^= 1<<7

# Restart the program from scratch, with breakpoint
set $pc=Reset_Handler
b putch
Breakpoint 1, putch (c=13) at nrf_test2.c:149
 149         int in=ser_txin+1;

# Single-step, and print a local variable
151         if (in >= SER_TX_BUFFLEN)
p in
$5 = 46

# Detach from remote, and exit

Next step

I guess the next step is to get wireless communications working, watch this space…

Copyright (c) Jeremy P Bentham 2019. Please credit if you use the information or software in here.

Raspberry Pi position detection using fiducial tags


What is a fiducial?

You may not have heard the word ‘fiducial’ before; outside the world of robotics (or electronics manufacture) it is little known. It refers to an easily-detected optical marker that is added to an object, so its position can be determined by an image-processing system.

It is similar to a 2-dimensional QR barcode, but has a much simpler structure, so can be detected at a distance; the tags in the photo above are only 12 mm (0.5 inch) in size, but I’ve successfully detected them in an HD image at a distance of 1.6 metres (over 5 feet).

The image analysis returns the x,y position of the tag centre, and the coordinates of its 4 corners, which can be used to highlight the tag outline in the camera image display; there is also a ‘goodness factor’ that indicates how well the tag has been matched; this can be used to filter out some spurious detections.

There isn’t just one type of fiducial; several organisations have developed their own formats. The type directly supported by OpenCV is known as ArUco, but I’ve opted for a rival system developed by the University of Michigan, called AprilTag. They have a full set of open-source software to generate & decode the tags; the decoder is written in C, with Python bindings, so can easily be integrated into a Raspberry Pi image processing system.

The AprilTag package has several tag ‘families’, that are characterised by two numbers; the number of data bits in a square, and the hamming distance between adjacent tags, e.g. 16h5 is a 4-by-4 data square, with a hamming distance of 5. The hamming distance is used to remove similar-looking tags that might easily be confused for each other, including rotations, so although 16h5 has 16 data bits, there are only 30 unique tags in that family.

I’m using 3 of the simpler families: 16h5, 25h9 and 36h11. Here are the tag values of 0 to 2 for each of them:


Generating Apriltag images

The original Apriltag generator¬†here¬†is written in Java, with the option of auto-generating C code. For simplicity, I’ve completely rewritten it in Python, with the option of outputting a bitmap (PNG/JPEG) or vector (SVG) file. The vector format allows us to generate tags with specific dimensions, that can accurately be reproduced by a low-cost laser printer.

To generate the tags, we need some ‘magic numbers’ that indicate which bits are set for a given tag. I got these numbers from the original Java code, for example has the lines:

public class Tag16h5 extends TagFamily
  public Tag16h5()
    super(16, 5, new long[] { 0x231bL, 0x2ea5L, 0x346aL etc..

I’ve copied the first 10 data entries from Tag16h5, 25h9 and 36h11 Java files:

tag16h5 =  16, 5,(0x231b,0x2ea5,0x346a,0x45b9,0x79a6,
tag25h9  = 25, 9,(0x155cbf1,0x1e4d1b6,0x17b0b68,0x1eac9cd,0x12e14ce,
tag36h11 = 36,11,(0xd5d628584,0xd97f18b49,0xdd280910e,0xe479e9c98,0xebcbca822,

If you need more than 10 different tags of a given family, just copy more data values.

In my code, a tag is created as a 2-dimensional Numpy array, where ‘0’ is a black square, and ‘1’ is white. The source data is a right-justified bit-stream, for example the above value of 231b hex is decoded as follows:

There is a 1-bit solid black frame around the data bits, and an (invisible) 1-bit white frame round that. The encoder steps are:

  • Calculate the number of data bits per row by taking the square root of the area
  • Load the data for the required tag as an 8-byte big-endian value, convert it to a linear array of byte values
  • Convert the byte array into bits, discard the unused left-most bits, and reshape into a square array
  • Add a black (0) frame around the array
  • Add a white (1) frame around the black frame
# Generate a tag with the given value, return a numpy array
def gen_tag(tag, val):
    area, minham, codes = tag
    side = int(math.sqrt(area))
    d = np.frombuffer(np.array(codes[val], ">i8"), np.uint8)
    bits = np.unpackbits(d)[-area:].reshape((-1,side))
    bits = np.pad(bits, 1, 'constant', constant_values=0)
    return np.pad(bits, 2, 'constant', constant_values=1)

We now have a numpy array with the desired binary pattern, that needs to be turned into a graphic.

Bitmap output

The extension on the output filename (.png, .jpg, .pgm, or .svg) determines the output file format. If a bitmap is required, Python Imaging Library (PIL, or the fork ‘pillow’) is used to convert the list of tag arrays into graphic objects. The binary bits only need to be multiplied by 255 to provide the full monochrome value, then are copied into the image. This creates one-pixel squares that are invisible without zooming, so the whole image is scaled up to a reasonable size.

# Save numpy arrays as a bitmap
def save_bitmap(fname, arrays):
    img ='L', (IMG_WD,IMG_HT), WHITE)
    for i,a in enumerate(arrays):
        t = Image.fromarray(a * WHITE)
        img.paste(t, (i*TAG_PITCH,0))
    img = img.resize((IMG_WD*SCALE, IMG_HT*SCALE)), FTYPE)

PGM output is an old uncompressed binary format, that is rarely encountered nowadays: it can be useful here because it is compatible with the standard apriltag_demo application, which I’ll be describing later.

Vector output

The vector (SVG) version uses the ‘svgwrite’ library, that can be installed using pip or pip3 as usual. The tag size is specified by setting the document and viewport sizes:

    SCALE     = 2
    DWG_SIZE  = "%umm"%(IMG_WD*SCALE),"%umm"%(IMG_HT*SCALE)
    VIEW_BOX  = "0 0 %u %s" % (IMG_WD, IMG_HT)

This means each square in the tag will be 2 x 2 mm, so 4 x 4 data bits plus a 1-bit black frame makes the visible tag size 12 x 12 mm.

The background is defined as white, so only the black squares need to be drawn; the numpy ‘where’ operator is used to return a list of bits that are zero.

# Save numpy arrays as a vector file
def save_vector(fname, arrays):
    dwg = svgwrite.Drawing(fname, DWG_SIZE, viewBox=VIEW_BOX, debug=False)
    for i,a in enumerate(arrays):
        g = dwg.g(stroke='none', fill='black')
        for dy,dx in np.column_stack(np.where(a == 0)):
            g.add(dwg.rect((i*TAG_PITCH + dx, dy), (1, 1)))

Each tag is defined as a separate SVG group, which is convenient if it has to be copy-and-pasted into another image. If you are unfamiliar with SVG, take a look at my blog on the subject.

Source code for Apriltag generator

The source code ( is compatible with Python 2.7 or 3.x, and can run on Windows or Linux. It requires numpy, svgwrite, and PIL/pillow to be installed using pip or pip3 as usual:

# Apriltag generator, from

import sys, math, numpy as np, svgwrite
from PIL import Image

filename  = 'test.svg'  # Default filename (.svg, .png, .jpeg or .pgm)
family    = 'tag16h5'   # Default tag family (see tag_families)
NTAGS     = 10          # Number of tags to create
TAG_PITCH = 10          # Spacing of tags
WHITE     = 255         # White colour (0 is black)

# First 10 values of 3 tag families
tag16h5 =  16, 5,(0x231b,0x2ea5,0x346a,0x45b9,0x79a6,
tag25h9  = 25, 9,(0x155cbf1,0x1e4d1b6,0x17b0b68,0x1eac9cd,0x12e14ce,
tag36h11 = 36,11,(0xd5d628584,0xd97f18b49,0xdd280910e,0xe479e9c98,0xebcbca822,
tag_families = {"tag16h5":tag16h5, "tag25h9":tag25h9, "tag36h11":tag36h11}

# Set up the graphics file, given filename and tag family
def set_graphics(fname, family):
    FTYPE = fname.split('.')[-1].upper()
    FTYPE = FTYPE.replace("PGM", "PPM").replace("JPG", "JPEG")
    IMG_HT = int(math.sqrt(family[0])) + 6

    # Vector definitions
    if FTYPE == "SVG":
        SCALE     = 2
        DWG_SIZE  = "%umm"%(IMG_WD*SCALE),"%umm"%(IMG_HT*SCALE)
        VIEW_BOX  = "0 0 %u %s" % (IMG_WD, IMG_HT)

    # Bitmap definitions
        SCALE = 10

# Generate a tag with the given value, return a numpy array
def gen_tag(tag, val):
    area, minham, codes = tag
    dim = int(math.sqrt(area))
    d = np.frombuffer(np.array(codes[val], ">i8"), np.uint8)
    bits = np.unpackbits(d)[-area:].reshape((-1,dim))
    bits = np.pad(bits, 1, 'constant', constant_values=0)
    return np.pad(bits, 2, 'constant', constant_values=1)

# Save numpy arrays as a bitmap
def save_bitmap(fname, arrays):
    img ='L', (IMG_WD,IMG_HT), WHITE)
    for i,a in enumerate(arrays):
        t = Image.fromarray(a * WHITE)
        img.paste(t, (i*TAG_PITCH,0))
    img = img.resize((IMG_WD*SCALE, IMG_HT*SCALE)), FTYPE)

# Save numpy arrays as a vector file
def save_vector(fname, arrays):
    dwg = svgwrite.Drawing(fname, DWG_SIZE, viewBox=VIEW_BOX, debug=False)
    for i,a in enumerate(arrays):
        g = dwg.g(stroke='none', fill='black')
        for dy,dx in np.column_stack(np.where(a == 0)):
            g.add(dwg.rect((i*TAG_PITCH + dx, dy), (1, 1)))

if __name__ == '__main__':
    opt = None
    for arg in sys.argv[1:]:    # Process command-line arguments..
        if arg[0]=="-":
            opt = arg.lower()
            if opt == '-f':     # '-f family': tag family
                family = arg
                filename = arg  # 'filename': graphics file  
            opt = None
    if family not in tag_families:
        print("Unknown tag family: '%s'" % family)
    tagdata = tag_families[family]
    set_graphics(filename, tagdata)
    print("Creating %s, file %s" % (family, filename))
    tags = [gen_tag(tagdata, n) for n in range(0, NTAGS)]
    if FTYPE == "SVG":
        save_vector(filename, tags)
        save_bitmap(filename, tags)

Decoding Apriltags

For the decoder, I’m using the standard Apriltag ‘C’ code, which includes a Python library, so no knowledge of the C programming language is required. The code is Linux-specific, so will run on the Raspberry Pi, but not on Windows unless you install the Microsoft ‘Windows Subsystem for Linux’, which can compile & run the text-based decoder, but sadly not the graphical display.

On the raspberry pi, I’m using the Raspbian Buster distribution; the Apriltag build process may not be compatible with older distributions. I’ve had no success building on a Pi Zero, due to the RAM size being too small, so had to compile on a larger board, and transfer the files across.

The commands to fetch and compile the code are:

sudo apt install cmake
cd ~
git clone
cd apriltag
cmake .
sudo make install
make apriltag_demo

The installation command returns an error with the Python library, but succeeds in installing the other application files.

You can now run my Python tag encoder, and feed the output into the demonstration decoder supplied in the Apriltag package, for example:

python3 -f tag16h5 test.jpg
apriltag_demo -f tag16h5 test.jpg

You should be rewarded with a swathe of text, such as:

loading test.jpg
 detection   0: id (16x 5)-0   , hamming 0, margin  203.350
 detection   1: id (16x 5)-1   , hamming 0, margin  246.072
 detection   2: id (16x 5)-2   , hamming 0, margin  235.426
 ..and so on..

The -0, -1, -2 sequence shows the decoded tag numbers, and the large ‘margin’ value indicates there is a high degree of confidence that the decode is correct. The time taken by the various decoder components is also displayed, which is useful if you’re trying to optimise the code.

If the decode fails, check that you’ve entered the tag family & filename correctly; the decoder application doesn’t accept JPEG files with a .jpeg extension, it has to be .jpg.

Python tag decoder

To use the Python library interface, you have to tell Python where to find the library file, for example at the command prompt:

export PYTHONPATH=${PYTHONPATH}:${HOME}/apriltag
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${HOME}/apriltag/lib

This can be a bit of a nuisance; a quick (but rather inefficient) alternative is to copy the ‘.so’ library file from the compiled Apriltag package into the current directory. For my current build, the command would be:

cp ~/apriltag/ .

You can now run a simple Python console program to exercise the library. It uses Python OpenCV, which needs to be installed using ‘apt’; see this blog for more information. File

# Simple test of Apriltag decoding from

import cv2
from apriltag import apriltag

fname = 'test.jpg'
image = cv2.imread(fname, cv2.IMREAD_GRAYSCALE)
detector = apriltag("tag16h5")
dets = detector.detect(image)
for det in dets:
    print("%s: %6.1f,%6.1f" % (det["id"], det["center"][0], det["center"][1]))

You will need to run this under python3, as the Apriltag library isn’t compatible with Python 2.x. The output is somewhat uninspiring, just showing the tag value, and the x & y positions of its centre, but is sufficient to show the decoder is working:

0:   49.9,  49.9
1:  149.9,  49.8
2:  249.9,  49.9
..and so on..

Graphical display of detected tags

A better test is to take video from the Raspberry Pi camera, detect the value and position of the tags, and overlay that information onto the display. Here is the source code (

# Detect Apriltag fiducials in Raspbery Pi camera image
# From

import cv2
from apriltag import apriltag

TITLE      = "apriltag_view"  # Window title
TAG        = "tag16h5"        # Tag family
MIN_MARGIN = 10               # Filter value for tag detection
FONT       = cv2.FONT_HERSHEY_SIMPLEX  # Font for ID value
RED        = 0,0,255          # Colour of ident & frame (BGR)

if __name__ == '__main__':
    cam = cv2.VideoCapture(0)
    detector = apriltag(TAG)
    while cv2.waitKey(1) != 0x1b:
        ret, img =
        greys = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        dets = detector.detect(greys)
        for det in dets:
            if det["margin"] >= MIN_MARGIN:
                rect = det["lb-rb-rt-lt"].astype(int).reshape((-1,1,2))
                cv2.polylines(img, [rect], True, RED, 2)
                ident = str(det["id"])
                pos = det["center"].astype(int) + (-10,10)
                cv2.putText(img, ident, tuple(pos), FONT, 1, RED, 2)
        cv2.imshow(TITLE, img)

To test the code, create a tag16h5 file in SVG format:

 python3 -f tag16h5 test.svg

This vector file can be printed out using Inkscape, to provide an accurately-sized set of paper tags, or just displayed on the Raspberry Pi screen, by double-clicking in File Manager. Then run apriltag_view:


With the camera pointed at the screen, you can position the decoded images and original tags so they are both in view. Note that the camera doesn’t need to be at right-angles to the screen, the decoder can handle oblique images. The MIN_MARGIN value may need to be adjusted; it can be increased to suppress erroneous detections, but then some distorted tags may be missed.

To terminate the application, press the ESC key while the decoder display has focus.

The application is a bit slower than I’d like, with a noticeable lag on the image display, so the code needs to be optimised.

Copyright (c) Jeremy P Bentham 2019. Please credit this blog if you use the information or software in it.

Accurate position measurement using low-cost cameras and OpenCV

There are many ways to sense the position of an object, and they’re generally either expensive or low-resolution. Laser interferometers are incredibly accurate, but the complex optics & electronics make the price very high. Hand-held laser measures are quite cheap, but they use a time-of-flight measurement method which limits their resolution, as light travels at roughly 1 foot (300 mm) per nanosecond, and making sub-nanosecond measurements isn’t easy. Lidar (light-based radar) is currently quite expensive, and has similar constraints. Ultrasonic methods benefit from the fact that sound waves travel at a much slower speed; they work well in constrained environments, such as measuring the height of liquid in a tank, but multipath reflections are a problem if there is more than one object in view.

Thanks to the smartphone boom, high-resolution camera modules are quite cheap, and I’ve been wondering whether they could be used to sense the position of an object to a reasonable accuracy for everyday measurements (at least 0.5 mm or 0.02 inches).

To test the idea I’ve set up 2 low-cost webcams at right-angles, to sense the X and Y position of an LED. To give a reproducible setup, I’ve engraved a baseboard with 1 cm squares, and laser-cut a LED support, so I can accurately position the LED and see the result.

The webcams are Logitech C270, that can provide an HD video resolution of 720p (i.e. 1280 x 720 pixels). For image analysis I’ll be using Python OpenCV; it has a wide range of sophisticated software tools, that allow you to experiment with some highly advanced methods, but for now I’ll only be using a few basic functions.

The techniques I’m using are equally applicable to single-camera measurements, e.g. tracking the position of the sun in the sky.

Camera input

My camera display application uses PyQt and OpenCV to display camera images, and it is strongly recommended that you start with this, to prove that your cameras will work with the OpenCV drivers. It contains code that can be re-used for this application, so is imported as a module.

Since we’re dealing with multiple cameras and displays, we need a storage class to house the data.

import sys, time, threading, cv2, numpy as np
import cam_display as camdisp

IMG_SIZE    = 1280,720          # 640,480 or 1280,720 or 1920,1080
DISP_SCALE  = 2                 # Scaling factor for display image
DISP_MSEC   = 50                # Delay between display cycles
CAP_API     = cv2.CAP_ANY       # API: CAP_ANY or CAP_DSHOW etc...

# Class to hold capture & display data for a camera
class CamCap(object):
    def __init__(self, cam_num, label, disp):
        self.cam_num, self.label, self.display = cam_num, label, disp
        self.imageq = camdisp.Queue.Queue()
        self.pos = 0
        self.cap = cv2.VideoCapture(self.cam_num-1 + CAP_API)
        self.cap.set(cv2.CAP_PROP_FRAME_WIDTH, IMG_SIZE[0])
        self.cap.set(cv2.CAP_PROP_FRAME_HEIGHT, IMG_SIZE[1])

The main window of the GUI is subclassed from cam_display, with the addition of a second display area, and storage for the camera capture data:

# Main window
class MyWindow(camdisp.MyWindow):
    def __init__(self, parent=None):
        camdisp.MyWindow.__init__(self, parent)
        self.camcaps = []
        self.disp2 = camdisp.ImageWidget(self)
        self.capturing = True

On startup, 2 cameras are added to the window:

if __name__ == '__main__':
    app = camdisp.QApplication(sys.argv)
    win = MyWindow()
    win.camcaps.append(CamCap(2, 'x', win.disp))
    win.camcaps.append(CamCap(1, 'y', win.disp2))

As with cam_display, a separate thread is used to fetch data from the cameras:

    # Grab camera images (separate thread)
    def grab_images(self):
        while self.capturing:
            for cam in self.camcaps:
                if cam.cap.grab():
                    retval, image = cam.cap.retrieve(0)
                    if image is not None and cam.imageq.qsize() < 2:
                        time.sleep(DISP_MSEC / 1000.0)
                    print("Error: can't grab camera image")
                    self.capturing = False
        for cam in self.camcaps:

Image display

A timer event is used to fetch the image from the queue, convert it to RGB, do the image processing, and display the result.

    # Fetch & display camera images
    def show_images(self):
        for cam in self.camcaps:
            if not cam.imageq.empty():
                image = cam.imageq.get()
                if image is not None and len(image) > 0:
                    img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
                    cam.pos = colour_detect(img)
                    self.display_image(img, cam.display, DISP_SCALE)
    # Show position values given by cameras
    def show_positions(self, s=""):
        for cam in self.camcaps:
            s += "%s=%-5.1f " % (cam.label, cam.pos)

Image processing

We need to measure the horizontal (left-to-right) position of the LED for each camera. If the LED is brighter than the surroundings, this isn’t difficult; first we create a mask that isolates the LED from the background, then extract the ‘contour’ of the object with the background masked off. The contour is a continuous curve that marks the boundary between the object and the background; for the illuminated LED this will approximate to a circle. To find an exact position, the contour is converted to a true circle, which is drawn in yellow, and the horizontal position of the circle centre is returned.

LOWER_DET   = np.array([240,  0,  0])       # Colour limits for detection
UPPER_DET   = np.array([255,200,200])

# Do colour detection on image
def colour_detect(img):
    mask = cv2.inRange(img, LOWER_DET, UPPER_DET)
    ctrs = cv2.findContours(mask, cv2.RETR_TREE,
    if len(ctrs) > 0:
        (x,y),radius = cv2.minEnclosingCircle(ctrs[0])
        radius = int(radius), (int(x),int(y)), radius, (255,255,0), 2)
        return x
    return 0

This code is remarkably brief, and if you’re thinking that I may have taken a few short-cuts, you’d be right:

Colour detection: I’ve specified the upper and lower RGB values that are acceptable; because this is a red LED, the red value is higher than the rest, being between 240 and 255 (the maximum is 255). I don’t want to trigger on a pure white background so I’ve set the green and blue values between 0 and 200, so a pure white (255,255,255) will be rejected. This approach is a bit too simplistic; if the LED is too bright it can saturate the sensor and appear completely white, and conversely another bright light source can cause the camera’s auto-exposure to automatically reduce the image intensity, such that the LED falls below the required level. The normal defence against this is to use manual camera exposure, which can be adjusted to your specific environment. Also it might be worth changing the RGB colourspace to HSV for image matching; I haven’t yet tried this.

Multiple contours: the findContours function returns a list of contours, and I’m always taking the first of these. In a real application, there may be several contours in the list, and it will be necessary to check them all, to find the most likely – for example, the size of the circle to see if it is within an acceptable range.

However, the measurement method does show some very positive aspects:

Complex background: as you can see from the image at the top of this blog, it works well in a normal office environment – no need for a special plain-colour background.

No focussing: most optical applications require the camera to be focussed, but in this case there is no need. I’ve deliberately chosen a target distance of approximately 4 inches (100 mm) that results in a blurred image, but OpenCV is still able to produce an accurate position indication.

Sub-pixel accuracy: with regard to measurement accuracy, the main rule for the camera is obviously “the more pixels, the better”, but also OpenCV can compute the position to within a fraction of a pixel. My application displays the position (in pixels) to one decimal place; at 4 inches (100 mm) distance, the Logitech cameras’ field of view is about 3.6 inches (90 mm), so if the position can be measured within, say, 0.2 of a pixel, this would be a resolution of 0.0006 inch (0.015 mm).

Of course these figures are purely theoretical, and the resolution will be much reduced in a real-world application, but all the same, it does suggest the technique may be capable of achieving quite good accuracy, at relatively low cost.

Single camera

With minor modifications, the code can be used in a single-camera application, e.g. tracking the position of the sun in the sky.

The code scans all the cameras in the ‘camcaps’ list, so will automatically adapt if there is only one.

The colour_detect function currently returns the horizontal position only; this can be changed to return the vertical as well. The show_positions method can be changed to display both of the returned values from the single camera.

Then you just need a wide-angle lens, and a suitable filter to stop the image sensor being overloaded. Sundial, anyone?

Source code

The ‘campos’ source code is available here, and is compatible with Windows and Linux, Python 2.7 and 3.x, PyQt v4 and v5. It imports my cam_display application, and I strongly recommended that you start by running that on its own, to check compatibility. If it fails, read the Image Capture section of that blog, which contains some pointers that might be of help.

Copyright (c) Jeremy P Bentham 2019. Please credit this blog if you use the information or software in it.

PC / RPi camera display using PyQt and OpenCV

OpenCV is an incredibly powerful image-processing tool, but it can be difficult to know where to start – how do you grab an image from a camera, and display it in a user-friendly GUI? This post describes such an application, that runs unmodified on a PC or Raspberry Pi, Windows or Linux, Python 2.7 or 3.x, and PyQt v4 or v5.


On Windows, the OpenCV and PyQt5 libraries can be installed using pip:

pip install numpy opencv-python PyQt5

If pip isn’t available, you should be able to run the module from the command line by invoking Python, e.g. for Python 3:

py -3 -m pip install numpy opencv-python PyQt5

Installing on a Raspberry Pi is potentially a lot more complicated; it is generally recommended to install from source, and for opencv-python, this is a bit convoluted. Fortunately there is a simpler option, if you don’t mind using versions that are a few years old, namely to load the binary image from the standard repository, e.g.

sudo apt update
sudo apt install python3-opencv python3-pyqt5 

At the time of writing, the most recent version of Raspbian Linux is ‘buster’, and that has OpenCV 3.2, which is quite usable. The previous ‘stretch’ distribution has python-opencv version 2.4, which is a bit too old: my code isn’t compatible with it.

With regard to cameras, all the USB Webcams I’ve tried have worked fine on Windows without needing to have any extra driver software installed; they also work on the Raspberry Pi, as well as the standard Pi camera with the ribbon-cable interface.

PyQt main window

Being compatible with PyQt version 4 and 5 requires some boilerplate code to handle the way some functions have been moved between libraries:

import sys, time, threading, cv2
    from PyQt5.QtCore import Qt
    pyqt5 = True
    pyqt5 = False
if pyqt5:
    from PyQt5.QtCore import QTimer, QPoint, pyqtSignal
    from PyQt5.QtWidgets import QApplication, QMainWindow, QTextEdit, QLabel
    from PyQt5.QtWidgets import QWidget, QAction, QVBoxLayout, QHBoxLayout
    from PyQt5.QtGui import QFont, QPainter, QImage, QTextCursor
    from PyQt4.QtCore import Qt, pyqtSignal, QTimer, QPoint
    from PyQt4.QtGui import QApplication, QMainWindow, QTextEdit, QLabel
    from PyQt4.QtGui import QWidget, QAction, QVBoxLayout, QHBoxLayout
    from PyQt4.QtGui import QFont, QPainter, QImage, QTextCursor
    import Queue as Queue
    import queue as Queue

The main window is subclassed from PyQt, with a simple arrangement of a menu bar, video image, and text box:

class MyWindow(QMainWindow):
    text_update = pyqtSignal(str)

    # Create main window
    def __init__(self, parent=None):
        QMainWindow.__init__(self, parent)

        self.central = QWidget(self)
        self.textbox = QTextEdit(self.central)
        self.textbox.setMinimumSize(300, 100)
        sys.stdout = self
        print("Camera number %u" % camera_num)
        print("Image size %u x %u" % IMG_SIZE)
        if DISP_SCALE > 1:
            print("Display scale %u:1" % DISP_SCALE)

        self.vlayout = QVBoxLayout()        # Window layout
        self.displays = QHBoxLayout()
        self.disp = ImageWidget(self)    
        self.label = QLabel(self)

        self.mainMenu = self.menuBar()      # Menu bar
        exitAction = QAction('&Exit', self)
        self.fileMenu = self.mainMenu.addMenu('&File')

There is a horizontal box layout called ‘displays’, that seems to be unnecessary as it only has one display widget in it. This is intentional, since much of my OpenCV experimentation requires additional displays to show the image processing in action; this can easily be done by creating more ImageWidgets, and adding them to the ‘displays’ layout.

Similarly, there is a redundant QLabel below the displays, which isn’t currently used, but is handy for displaying static text below the images.

Text display

It is convenient to redirect the ‘print’ output to the text box, rather than appearing on the Python console. This is done using the ‘text_update’ signal that was defined above:

    # Handle sys.stdout.write: update text display
    def write(self, text):
    def flush(self):

    # Append to text display
    def append_text(self, text):
        cur = self.textbox.textCursor()     # Move cursor to end of text
        s = str(text)
        while s:
            head,sep,s = s.partition("\n")  # Split line at LF
            cur.insertText(head)            # Insert text at cursor
            if sep:                         # New line if LF
        self.textbox.setTextCursor(cur)     # Update visible cursor

The use of a signal means that print() calls can be scattered about the code, without having to worry about which thread they’re in.

Image capture

A separate thread is used to capture the camera images, and put them in a queue to be displayed. The camera may produce images faster than they can be displayed, so it is necessary to check how many images are already in the queue; if more than 1, the new image is discarded. This prevents a buildup of unwanted images.

IMG_SIZE    = 1280,720          # 640,480 or 1280,720 or 1920,1080
CAP_API     = cv2.CAP_ANY       # or cv2.CAP_DSHOW, etc...
EXPOSURE    = 0                 # Non-zero for fixed exposure

# Grab images from the camera (separate thread)
def grab_images(cam_num, queue):
    cap = cv2.VideoCapture(cam_num-1 + CAP_API)
    cap.set(cv2.CAP_PROP_FRAME_WIDTH, IMG_SIZE[0])
    cap.set(cv2.CAP_PROP_FRAME_HEIGHT, IMG_SIZE[1])
    if EXPOSURE:
        cap.set(cv2.CAP_PROP_AUTO_EXPOSURE, 0)
        cap.set(cv2.CAP_PROP_EXPOSURE, EXPOSURE)
        cap.set(cv2.CAP_PROP_AUTO_EXPOSURE, 1)
    while capturing:
        if cap.grab():
            retval, image = cap.retrieve(0)
            if image is not None and queue.qsize() < 2:
                time.sleep(DISP_MSEC / 1000.0)
            print("Error: can't grab camera image")

The choice of image size will depend on the camera used; all cameras support VGA size (640 x 480 pixels), more modern versions the high-definition standards of 720p (1280 x 720) or 1080p (1920 x 1080).

The camera number refers to the position in the list of cameras collected by the operating system; I’ve defined the first camera as number 1, but the OpenCV call defines the first as 0, so the number has to be adjusted.

The same parameter is also used to define the capture API setting; by default this is ‘any’, which usually works well; my Windows 10 system defaults to the MSMF (Microsoft Media Foundation) backend, while the Raspberry Pi defaults to Video for Linux (V4L). Sometimes you may need to force a particular API to be used, for example, I have a Logitech C270 webcam that works fine on Windows 7, but fails on Windows 10 with an ‘MSMF grab error’. Forcing the software to use the DirectShow API (using the cv2.CAP_DSHOW option) fixes the problem.

If you want to check which backend is being used, try:

print("Backend '%s'" % cap.getBackendName())

Unfortunately this only works on the later revisions of OpenCV.

Manual exposure setting can be a bit hit-and-miss, depending on the camera and API you are using; the default is automatic operation, and setting EXPOSURE non-zero (e.g. to a value of -3) generally works, however it can be difficult to set a webcam back to automatic operation: sometimes I’ve had to use another application to do this. So it is suggested that you keep auto-exposure enabled if possible.

[Supplementary note: it seems that these parameter values aren’t standardised across the backends. For example, the CAP_PROP_AUTO_EXPOSURE value in my source code is correct for the MSMF backend; a value of 1 enables automatic exposure, 0 disables it. However, the V4L backend on the Raspberry Pi uses the opposite values: automatic is 0, and manual is 1. So it looks like my code is incorrect for Linux. I haven’t yet found any detailed documentation for this, so had to fall back on reading the source code, namely the OpenCV videoio ‘cap’ files such as cap_msmf.cpp and cap_v4l.cpp.]

Image display

The camera image is displayed in a custom widget:

# Image widget
class ImageWidget(QWidget):
    def __init__(self, parent=None):
        super(ImageWidget, self).__init__(parent)
        self.image = None

    def setImage(self, image):
        self.image = image

    def paintEvent(self, event):
        qp = QPainter()
        if self.image:
            qp.drawImage(QPoint(0, 0), self.image)

A timer event is used to trigger a scan of the image queue. This contains images in the camera format, which must be converted into the PyQt display format:

DISP_SCALE  = 2                 # Scaling factor for display image

    # Start image capture & display
    def start(self):
        self.timer = QTimer(self)           # Timer to trigger display
                    self.show_image(image_queue, self.disp, DISP_SCALE))
        self.capture_thread = threading.Thread(target=grab_images, 
                    args=(camera_num, image_queue))
        self.capture_thread.start()         # Thread to grab images

    # Fetch camera image from queue, and display it
    def show_image(self, imageq, display, scale):
        if not imageq.empty():
            image = imageq.get()
            if image is not None and len(image) > 0:
                img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
                self.display_image(img, display, scale)

    # Display an image, reduce size if required
    def display_image(self, img, display, scale=1):
        disp_size = img.shape[1]//scale, img.shape[0]//scale
        disp_bpl = disp_size[0] * 3
        if scale > 1:
            img = cv2.resize(img, disp_size, 
        qimg = QImage(, disp_size[0], disp_size[1], 
                      disp_bpl, IMG_FORMAT)

This demonstrates the power of OpenCV; with one function call we convert the image from BGR to RGB format, then another is used to resize the image using cubic interpolation. Finally a PyQt function is used to convert from OpenCV to PyQt format.

Running the application

Make sure you’re using the Python version that has the OpenCV and PyQt installed, e.g. for the Raspberry Pi:


There is an optional argument that can be used if there are multiple cameras; the default first camera is number 1.

On Linux, some USB Webcams cause a constant stream of JPEG format errors to be printed on the console, complaining about extraneous bytes in the data. There is some discussion online as to the cause of the error, and the cure seems to involve rebuilding the libraries from source; I’m keen to avoid that, so used the simple workaround of suppressing the errors by redirecting STDERR to null:

python3 2> /dev/null

Fortunately this workaround is only needed with some USB cameras; the standard Raspberry Pi camera with the CSI ribbon-cable interface works fine.

Source code

Full source code is available here.

For a more significant OpenCV application, take a look at this post.

Copyright (c) Jeremy P Bentham 2019. Please credit this blog if you use the information or software in it.

Python WebSocket programming

Real-time display in a Web browser, using data pushed from a server.


A basic Web interface has a simple request/response format; the browser requests a Web page, and the server responds with that item. The browser’s request may contain parameters to customise the request, but the requests always come from the browser (i.e. ‘client pull’) rather than the server sending data of its own accord (‘server push’).

As browser applications became more sophisticated, there was a need for general-purpose communication channel between server and browser, so if the server has dynamic data (e.g. constantly fluctuating stock price) it can immediately be ‘pushed’ to the client for display. This is achieved by various extensions to the underlying Web transfer protocol (HTTP), and the latest version of the protocol (HTTP/2) has full support for multiple data streams, but I’ll start by creating a minimal application using a simpler HTTP extension that is compatible with all modern browsers, namely Websockets.

What is a socket?

A socket is a logical endpoint for TCP/IP communications, consisting of an IP address and a port number. On servers, the port number implicitly refers to the service you require from that server; for example, an HTTP Web page request is normally sent to to port 80, or port 443 for the secure version HTTPS.

However, there is no law that says HTTP transactions have to be on port 80; if you are running your own local Web server, you may well have set it up to respond on port 8080, since this is easier than using port 80: port numbers below 1024 are generally for use by the operating system, not user-space programs. You can tell the browser to access a specific port on the server by appending a colon and its number to the Web address.

An additional complication is that communications over the Internet have to get past firewalls, most of which are programmed to block communications on unknown port numbers. For the time being I’ll assume that we are using a private local network, so port 8000 will be fine for the Web server, and port 8001 for the Websocket server. In case you wondered, there is no real rationale behind these numbers; anything above 1023 would do.


The protocol starts with a normal HTTP request from browser to Websocket server, but it contains an ‘upgrade’ header to change the connection from HTTP to Websocket (WS). If the server agrees to the change, the connection becomes a transparent data link between client & server, without the usual restrictions on HTTP content.

So the elements we need for a simple demonstration are:

  • Web (HTTP) server
  • Websocket (WS) server
  • Web browser
  • Web page with JavaScript code for a Websocket client

This may sound rather complicated, but the reality is really quite easy, as I’ll show below.

Web & Websocket servers

It is tempting to think of combining the Web & Websocket servers into a single entity, but in reality there are two very different requirements; the Web server churns out largely-static pages fetched from disk, while the Websocket server contains application-specific code to organise the flow of non-standard data across the network.

So the solution I’ve adopted is to keep the two servers separate. The simplest possible Web server is included within Python as standard, you just need to run:

# For python 2.7:
  python -m SimpleHTTPServer
# ..or for python3:
  python3 -m http.server

This makes all the files in your current directory visible in the browser, so you can just click on an HTML file to run it. A word of warning: this is can be a major security risk, as an attacker could potentially manipulate the URL to access other information on your system; use with caution.

Next, the Websocket server: there are a few Python libraries containing the protocol negotiation; I’ve chosen SimpleWebSocketServer, which can be installed with ‘pip’ as usual. A minimum of code is needed to make a functioning server (file:

# Websocket demo, from

import signal, sys
from SimpleWebSocketServer import WebSocket, SimpleWebSocketServer

PORTNUM = 8001

# Websocket class to echo received data
class Echo(WebSocket):

    def handleMessage(self):
        print("Echoing '%s'" %

    def handleConnected(self):

    def handleClose(self):

# Handle ctrl-C: close server
def close_server(signal, frame):

if __name__ == "__main__":
    print("Websocket server on port %s" % PORTNUM)
    server = SimpleWebSocketServer('', PORTNUM, Echo)
    signal.signal(signal.SIGINT, close_server)

Web page

The browser has a built-in Websocket client, so the Web page just needs to provide:

  • Buttons to open & close the Websocket connection
  • A display of connection status, and Websocket data
  • Some Javascript to link the buttons & display to the Websocket client
  • A data source, that will be echoed back by the Python server

Once the Web page has been received and displayed, the user will click a ‘connect’ button to contact the Websocket server. However, the client needs to know the address of the server in order to make the connection; we could just ask the user to fill in a text box with the value, but it is much nicer for the client to work this out, based on the Web server’s address.


Javascript provides a variable that has the current IP address and port number, as shown above.

  // Client for Python SimpleWebsocketServer
  const portnum = 8001;
  var host, server, connected = false;

  // Display the given text
  function display(s)
    document.myform.text.value += s;
    document.myform.text.scrollTop = document.myform.text.scrollHeight;

  // Initialisation
  function init()
    host = ? String( : "unknown";
    host = host.replace("", "localhost");
    server = host.replace(/:\d*\b/, ":" + portnum);
    document.myform.text.value = "Host " + host + "\n";
    window.setInterval(timer_tick, 1000);

We use a regular expression to match the Web server port number, and change it to the Websocket server port, on the assumption that the two are hosted at the same IP address. There is also some code to handle the special case of an IP address This address is used by a client, when it is running on the same system as the servers; it should be synonymous with ‘localhost’ but Windows seems to make a distinction between the two, so it is necessary to make a substitution.

Starting and stopping the Websocket connection is relatively straightforward:

  // Open a Websocket connection
  function connect()
    var url = "ws://" + server + "/";
    display("Opening websocket " + url + "\n");
    websock = new WebSocket(url);
    websock.onopen    = function(evt) {sock_open(evt)};
    websock.onclose   = function(evt) {sock_close(evt)};
    websock.onmessage = function(evt) {sock_message(evt)};
    websock.onerror   = function(evt) {sock_error(evt)};
    connected = true;
  // Close a Websocket connection
  function disconnect()
    connected = false;

Once open, we can send data using a simple function call, and handle incoming data using the callback.

  // Timer tick handler
  function timer_tick()
    if (connected)

  // Display incoming data
  function sock_message(evt)

The resulting display shows the data that has been echoed back by the server:


Web page source

This is the complete source to the Web page (file: websock.html).

<!DOCTYPE html>
<meta charset="utf-8"/>
<title>WebSocket Test</title>
<script language="javascript" type="text/javascript">

  // Client for Python SimpleWebsocketServer
  const portnum = 8001;
  var host, server, connected = false;

  // Display the given text
  function display(s)
    document.myform.text.value += s;
    document.myform.text.scrollTop = document.myform.text.scrollHeight;

  // Initialisation
  function init()
    host = ? String( : "unknown";
    host = host.replace("", "localhost");
    server = host.replace(/:\d*\b/, ":" + portnum);
    document.myform.text.value = "Host " + host + "\n";
    window.setInterval(timer_tick, 1000);

  // Open a Websocket connection
  function connect()
    var url = "ws://" + server + "/";
    display("Opening websocket " + url + "\n");
    websock = new WebSocket(url);
    websock.onopen    = function(evt) {sock_open(evt)};
    websock.onclose   = function(evt) {sock_close(evt)};
    websock.onmessage = function(evt) {sock_message(evt)};
    websock.onerror   = function(evt) {sock_error(evt)};
    connected = true;
  // Close a Websocket connection
  function disconnect()
    connected = false;

  // Timer tick handler
  function timer_tick()
    if (connected)

  // Display incoming data
  function sock_message(evt)

  // Handlers for other Websocket events
  function sock_open(evt)
  function sock_close(evt)
  function sock_error(evt)
    display("Socket error\n");

  // Do initialisation when page is loaded
  window.addEventListener("load", init, false);

<form name="myform">
  <h2>Websocket test</h2>
  <textarea name="text" rows="10" cols="60">
  <input type="button" value="Connect" onClick="connect();">
  <input type="button" value="Disconnect" onClick="disconnect();">

Running the demonstration

To run the demonstration, open 2 console windows on the server, and change to a suitable working directory containing the HTML and Python files websock.html and In the first window, run the Web server of your choice; you can just run the built-in Python server:

# For python 2.7:
  python -m SimpleHTTPServer
# ..or for python3:
  python3 -m http.server

..but this is relatively insecure, so is only suitable for an isolated private network.

In the second console window, run the ‘’ application; the console should report:

Websocket server on port 8001

Now run a browser on any convenient system, and enter the address of the server, including the Web server port number after a colon, e.g.

You should now see the home page of the Web server; if you are using the built-in Python server, there should be a list of files in the current directory. Click on websock.html, then the connect button; an asterisk should appear every second, having been generated by the Javascript client, and echoed back by the Websocket server. To stop the test, click the disconnect button.

In the next post, I will show how this technique can be expanded to provide a graphical real-time display of server data, watch this space…

Copyright (c) Jeremy P Bentham 2019. Please credit this blog if you use the information or software in it.

Web display for OpenOCD



In my reporta project, I used a PyQt program to drive an FTDI adapter, producing a graphical display of the CPU’s internals: a real-time animation showing the I/O states, that doesn’t require any additional programming on the target system.

This post aims to produce a more powerful version, namely:

  • Use a Raspberry Pi as the interface to the CPU (SWD or JTAG)
  • Allow remote diagnosis, by using network communications
  • Use standard web browser graphics in place of PyQt

There are many advantages to using the Web browser as a display tool; most importantly, there is no need to install extra software on the display system; you can even use your smartphone as a display device. Wireless communication between the data acquisition & display can be really useful when working on real-world industrial systems, which are often in cramped and inaccessible locations.

First we have to create the display graphic, and I’m using Scalable Vector Graphics (SVG). Since everything is drawn on-the-fly from 2-dimensional x,y positions, it automatically resizes from large to small screens, which is important for mobile devices.

Scalable Vector Graphics

In a previous post, I created some simple graphics in SVG; now I need to draw something that looks like my demonstration target system, with a pushbutton, seven-segment display and ‘blue pill’ STM32F103 CPU module:


My previous PyQt display looked like this:


..but we can do better than that! My first idea was to create the SVG graphics in Inkscape, then add Javascript code to animate them. The problem with this approach is the very large number of tools & settings in Inkscape; it is easy to create something that looks really good visually, but is extremely difficult (or impossible) to animate. So it is much easier to create the SVG from scratch using the Python ‘svgwrite’ library; the display elements can be structured so as to make animation easy.


The background component is a solderless breadboard, with holes at 0.1 inch pitch. This can be created in SVG using a ‘pattern’:

import svgwrite
PIN_PITCH   = 10
PIN_SIZE    = 2

# Create maximised SVG drawing
def create_svg(fname, size):
    return svgwrite.Drawing(fname,
            width="100%", height="100%",
            viewBox=("0 0 %u %u" % size),

# Add a breadboard background pattern
def add_breadboard(dwg, pos=(XPAD,TPAD), size=BB_SIZE):
    dots = svgwrite.pattern.Pattern(width=TILE_SIZE[0], height=TILE_SIZE[1],
                                    id="dots", patternUnits="userSpaceOnUse")
    dots.add(dwg.rect((0,0), TILE_SIZE, fill="#f0f0f0"))
    dots.add(, 1, fill="white"))
    dwg.add(dwg.rect(pos, size, stroke="darkgray", fill="url(#dots)", filter="url(#shadow)"))

dwg = create_svg(FNAME, DWG_SIZE)

A single light grey tile of 10 x 10 units is defined, with a small white dot in the middle. This is used to fill a full-size rectangle; the SVG interpreter automatically duplicates the tile to fill the area.

You may wonder why I have chosen to make the holes 10 units apart, instead of redefining the SVG coordinate system so they are 0.1 units apart, to match the real-world value. The reason is that I’ve found the 10-unit convention to be much more convenient, as it allows positioning to be done with integer values, and the default line width of 1 unit looks fine, so doesn’t need to be modified.

CPU module


A blue rectangle is created, with red ‘pins’ that can be animated to show the I/O on/off status. A list is used to define the pins and their I/O functions:

# STM32F103 'blue pill' board pinout, starting top left
BOARD_PINS=("GND", "GND", "3V3", "NRST","PB11","PB10","PB1", "PB0", "PA7", "PA6",
            "PA5", "PA4", "PA3", "PA2", "PA1", "PA0", "PC15","PC14","PC13","VBAT",
            "PB12","PB13","PB14","PB15","PA8", "PA9", "PA10","PA11","PA12","PA15",
            "PB3", "PB4", "PB5", "PB6", "PB7", "PB8", "PB9", "5V",  "GND", "3V3")

CSS styles are used to define the box colour and pin size. The pin text is also defined, using a ‘writing mode’ of top-to-bottom, which produces the vertical labels.

STYLES = """
    .cpu_style     {stroke:darkblue; stroke-width:1; fill:#b0c0e0}
    .pin_style     {stroke:red; stroke-width:1; fill:red}
    .pin_text      {font-size:6px; writing-mode:tb; font-family:Arial}


It is then just a question of iterating across the pins, drawing them and the optional text labels; these are optional so the same code can be used to draw the (unlabeled) seven-segment display pins.

# Add a dual-in-line part
def add_dil_part(dwg, pos, row_pitch, idents, label=False, style="part_style"):
    g = Group(transform="translate"+str(pos), class_="pin_text")
    row_pins = len(idents) / 2
    g.add(dwg.rect((0,0), (row_pins*PIN_PITCH, row_pitch), class_=style))
    for n, ident in enumerate(idents):
        pos = pin_pos(n, row_pins, PIN_PITCH/2,
                      (PIN_PITCH/2, row_pitch-PIN_PITCH/2))
        g.add(, PIN_SIZE/2, class_="pin_style", id=ident))
        if label:
            pos = pin_pos(n, row_pins, PIN_PITCH/2,
                          (PIN_PITCH, row_pitch-PIN_PITCH*2.5))
            g.add(svgwrite.text.Text(ident, pos))

add_dil_part(dwg, (100,30), PIN_PITCH*7, BOARD_PINS, True, "cpu_style")

A ‘group’ is used to house the complete part, so it can be styled and positioned as a single item.

Seven-segment display


The same dual-in-line code is used to draw the component base; the pins aren’t actually visible in the real part, but have been included as a handy on/off status indication.

The display segments are drawn using a list of points, arranged so that the line drawing is sequential; this is transformed by the ‘zip’ function into a list of start & end points for each line:

# Dimensions of 7-seg display
D7W,D7H,D7L = 20,20,2       # X and Y seg length, and X-direction lean

# Segment endpoints in the order FABCDEG (for continous drawing)
SEG_LINES   = ((D7L, D7H), (D7L*2,0),(D7L*2+D7W,0),(D7L+D7W,D7H),
               (D7W,D7H*2),(0,D7H*2),(D7L,D7H),    (D7L+D7W,D7H))

# Idents for the display pins, starting top left
DISP_PINS = ("PB11","PB10","GND", "PB1", "PB0",
             "PB12","PB13","GND", "PB14","PB15")

# Idents for the segments, in order ABCDEFGH
SEG_PINS =  ("PB1", "PB0", "PB14","PB13",

STYLES = """
    .seg_stroke    {stroke:#00a000; stroke-width:5; stroke-linecap:round}

# Add 7 display segments
def add_disp_segs(dwg, pos):
    g = Group(transform="translate"+str(pos), class_="seg_stroke")
    lines = zip(SEG_LINES[:-1], SEG_LINES[1:])
    for n, line in enumerate(lines):
        g.add(dwg.line(*line, id=SEG_PINS[n]))


A simple square-plus circle gives an approximation to the real button. The square has slightly rounded corners, using the ‘rx’ parameter.

PB_SIZE = 20

# Add a pushbutton
def add_pb(dwg, pos, ident, size=PB_SIZE, fill="darkred"):
    g = Group(transform="translate"+str(pos))
    g.add(dwg.rect((0,0), (size,size), rx=2, fill=fill, opacity=0.8))
    g.add(,size/2), size/2, fill=fill, id=ident))

Drop shadow

Adding a drop shadow to a component is a simple way of creating a 3-dimensional effect.


Confusingly, there are two ways this effect can be achieved; using a CSS definition, or an SVG filter. The CSS method is simpler (since the CSS functionality is a subset of the SVG functionality) but doesn’t work on all browsers, so I’ve used the SVG method instead.

The filter definition consists of a series of steps, with an input and an output; the steps I’ve used are:

  • Get the alpha (i.e. the monochrome) values of the image, and offset by 2 units
  • Add Gaussian blur to the offset image
  • Combine the original image with the offset & blurred image
# Define a shadow filter
def define_shadow(dwg):
    f = dwg.defs.add(dwg.filter(id="shadow", x=0, y=0, width="150%", height="150%"))
    f.feOffset(in_="SourceAlpha", result="AlphaOset", dx="2", dy="2")
    f.feGaussianBlur(in_="AlphaOset", result="AlphaBlur", stdDeviation=2)
    f.feBlend(in_="SourceGraphic", in2="AlphaBlur", mode="normal")

# Add filter to a rectangle, e.g. for the breadboard:
    dwg.add(dwg.rect(pos, size, stroke="darkgray", fill="url(#dots)", filter="url(#shadow)"))

Note the appending of an underscore to ‘in’. This is necessary to avoid a Python syntax error; it is stripped off when the SVG output file is written

Control button, and text display

We need some method of controlling the data connection between the browser and Web server, also displaying the current status. This is achieved by adding an area at the bottom of the graphic.


The ‘connect’ button is drawn as a group, containing a rounded-corner rectangle, and text.

CTRL_SIZE    = 60,19

STYLES = """
    .ctrl_style    {stroke:black; stroke-width:0.5;
                    font-size:9px; font-family:Arial; text-anchor:middle}

# Add a pushbutton control
def add_ctrl_button(dwg, pos, ident, text, onclick, size=CTRL_SIZE, fill="palegreen"):
    g = Group(transform="translate"+str(pos), onclick=onclick, class_="ctrl_style")
    g.add(dwg.rect((0,0), size, rx=5, fill=fill))
    g.add(svgwrite.text.Text(text, (size[0]/2,12), fill="black", id=ident))

add_ctrl_button(dwg, (20,133), "button1", "Connect", "click_handler()")

The ‘onclick’ parameter will trigger the given JavaScript function when the button is clicked, e.g.

var connected=0;
function click_handler()
    if (connected)

The status display consists of 2 lines of text; there is no need for scrolling, so the lines are tagged individually:


STYLES = """
    .textbox_style {stroke-width:1.0; stroke:lightgray; fill:none}
    .text_style    {font-size:8px; font-family:Courier}

# Add a text area
def add_textbox(dwg, pos, size=TEXTBOX_SIZE):
    g = Group(transform="translate"+str(pos))
    g.add(dwg.rect((0,0), size, class_="textbox_style"))
    g.add(svgwrite.text.Text("Line1", (5,8), class_="text_style", id="text1"))
    g.add(svgwrite.text.Text("Line2", (5,17), class_="text_style", id="text2"))

Updating the text in Javascript just requires the ‘textContent’ to be set, e.g.:

// Connect to host
    function connect()
        text2.textContent = "Connected";
        button1.textContent = "Disconnect";
        connected = 1;

    // Disconnect from host
    function disconnect()
        text2.textContent = "Disconnected";
        button1.textContent = "Connect";
        connected = 0;


To be concluded…

The next blog will describe how Raspberry Pi OpenOCD data is used to animate the graphics. It will include a link to the full source code.

Copyright (c) Jeremy P Bentham 2019. Please credit this blog if you use the information or software in it.

3D design with Python and FreeCAD

3D CAD packages can be hard work; there is a lot to learn, which can be a major problem for an infrequent user such as myself.

Most packages support some form of scripting, so why not program my complete design from scratch, without touching the GUI? FreeCAD is a (free) 3D design package, with a comprehensive Python interface, so seems to be ideal…


This is simple in theory, but a bit tricky in practice; I’ll spare you the many frustrating false-starts I’ve made, and describe some simple ways of producing 3D objects from scratch in Python. This is very much a work-in-progress, but hopefully will provide some useful pointers if you’re a Python programmer doing occasional 3D design.

The examples here have been tested with FreeCAD v0.16, and the current version 0.18

Running Python code

There are several ways of running a Python script in FreeCAD:

1. Entering commands at the console

In the FreeCAD Python console window, try entering:

import Part
box = Part.makeBox(4, 3, 2)

The result is a bit underwhelming; all you can see is the bottom left-hand corner of a square. If you want to see the box in its full 3-D glory, either use the GUI controls to change the viewpoint, or add the following 2 lines:

2. Executing as a Macro

Click on Macro then Macros… and you are given a list of Python macro files that can be executed. They are stored in the default location for scripts; you can alter this to a directory of your choosing, by changing the ‘user macros location’ in the dialog box. If you make that change, it is necessary to exit & re-enter FreeCAD for the change to take effect.

If FreeCAD encounters a problem with your script, it will generally give a sensible error message, however very occasionally a script can corrupt the internals of the program, so it fails to respond in the usual manner. Hence, if you are experiencing problems with previously-good code that suddenly doesn’t work, it is worth restarting FreeCAD in case this fixes the problem.

3. Using the FreeCAD editor

You can load a Python script into FreeCAD using the normal GUI File Open. You are then presented with a nice-looking editor window, into which you can paste one of the examples from this blog. When complete, the file can be run by pressing ctrl-F6. Unfortunately, there are some subtle differences when executing a file in this manner, as opposed to the other methods, see my usage of the recompute() function in the later code examples.

4. Executing a file from the FreeCAD Python console

You can directly execute a file by entering a command at the Python console, e.g.



Design methodology

Like many 3D CAD packages, FreeCAD uses the Constructive Solid Geometry (CSG) method, where the final design is built up by adding (fusing) elements together, and subtracting (cutting) one element from another. Simple 3D  objects (cube, cylinder etc.) can be created with a single line of code:

# Simple test of FreeCAD Part scripting, from

from FreeCAD import Vector
import Part

if FreeCAD.ActiveDocument:
doc = FreeCAD.newDocument("Unnamed")
plate = Part.makeBox(50, 30, 2)
verticals = [edge for edge in plate.Edges if edge.BoundBox.ZLength]
plate = plate.makeFillet(5, verticals)
cyl = Part.makeCylinder(10, 2, Vector(20, 15, 0))
plate = plate.cut(cyl)
doc.addObject("Part::Feature", "plate").Shape = plate

The result is a plate with rounded corners, and a large off-centre hole:


However the code is more than a single line, so some explanations are in order:

Lines 6-8 remove a previous unnamed document, and create a new one. This means that every time you run the script you get a new clean document to display the result. As a safeguard, if you currently have a named document open, the script will error out

Line 9 creates a square plate, size 50 x 30 units, and 2 units thick.

Lines 10 & 11 create the rounded corners (‘fillets’ in FreeCAD terminology), with a radius of 5 units. The makeFillet method requires a list of edges, and we only want to modify the vertical edges, so the list of edges is filtered by checking the z-dimension length is non-zero.

Lines 12 & 13 create a cylinder with 10 units radius and 2 units high, offset from the origin using a ‘Vector’ object, which defines a position in 3-dimensional (x,y,z) space. The plate is then cut with the cylinder, creating a hole 20 units in diameter.

Line 14 adds the resulting object to the current document; without this step, the object won’t be visible.

Lines 15 & 16 set a perspective view, and adjust the zoom level so the part fits in the display space.

The ‘labels and attributes’ on the left-hand side of the screen show there is only one defined object, named ‘plate’; if you click on that you can modify its placement (i.e. its position and orientation) but none of the other design parameters.


FreeCAD has various sets of software tools, divided up into ‘workbenches’. This division isn’t just for the GUI, it also applies to scripting; for example, the above Python code used tools imported from the Part workbench. There is some overlap between workbenches, so there can be other ways of creating the same object, for example starting with 2-dimensional sketches in the Draft workbench:

# Simple test of FreeCAD Draft scripting, from

from FreeCAD import Vector
import Draft

if FreeCAD.ActiveDocument:
doc = FreeCAD.newDocument("Unnamed")

rect = Draft.makeRectangle(50, 30, face=True)
rect.FilletRadius = 5
rect.ViewObject.Visibility = False
plate = doc.addObject("Part::Extrusion", "plate")
plate.Base, plate.Dir = rect, Vector(0, 0, 2)

circ = Draft.makeCircle(10, face=True)
rotation = App.Rotation(Vector(0,0,0), 0)
circ.Placement = App.Placement(Vector(20, 15, 0), rotation)
circ.ViewObject.Visibility = False
cyl = doc.addObject("Part::Extrusion", "cylinder")
cyl.Base, cyl.Dir = circ, Vector(0, 0, 2)


cutplate = doc.addObject("Part::Cut", "cutplate")
cutplate.Base, cutplate.Tool = plate, cyl


To turn the 2-dimensional x-y sketches into 3-dimensional objects, they are extruded in the z-plane. The Draft workbench has no extrusion capability, so this is done by adding extrusion objects to the document. It is important to set the original 2-dimensional sketches as transparent (set visibility false), otherwise they will form a thin layer that obscures the cutout.

I must explain the last 5 lines of code, where I set the viewing mode, cut the plate with the cylinder, then do a recompute. This is a workaround for some minor issues I found in the current FreeCAD versions, which may well be fixed by now:

  • If the view mode is set after doing the cut, you don’t get the correct perspective view.
  • If the final recompute is omitted, everything works fine when executed as a standalone script, but when the same script is executed from the edit screen, the document is blank; none of the objects are visible.

Apart from these issues, the graphical end result looks exactly the same as with the Part workbench, but note the collection of nested objects in the left-hand window. This is CSG in action; the cut plate is derived from a plate and a cylinder, which in turn are derived from a rectangle and circle.


An advantage of this object hierarchy is that the design parameters can be changed within the GUI; for example, try changing the circle radius in the attributes window from 10 to 14 units, hit F5 to recompute, and the hole diameter will increase to 28 units. The primary focus of this blog is to use scripting rather than the GUI, but it can be useful to manually change a few parameters, and instantly see the result.

An interesting quirk of this hierarchy is that the cut plate has effectively taken possession of the circle and cylinder, since they are its sub-objects. This raises the question: what happens if we cut 2 plates with the same cylinder, which of the plates will own the cylinder? Let’s try it out:

# Simple test of FreeCAD CSG hierarchy, from

from FreeCAD import Vector
import Draft

if FreeCAD.ActiveDocument:
doc = FreeCAD.newDocument("Unnamed")

rect = Draft.makeRectangle(50, 30, face=True)
rect.FilletRadius = 5
rect.ViewObject.Visibility = False
plate = doc.addObject("Part::Extrusion", "plate")
plate.Base, plate.Dir = rect, Vector(0, 0, 2)

plate2 = doc.addObject("Part::Extrusion", "plate2")
plate2.Base, plate2.Dir = rect, Vector(0, 0, 2)
rotation = App.Rotation(Vector(0,0,0), 0)
plate2.Placement = App.Placement(Vector(0, 0, 10), rotation)

circ = Draft.makeCircle(10, face=True)
circ.Placement = App.Placement(Vector(20, 15, 0), rotation)
circ.ViewObject.Visibility = False
cyl = doc.addObject("Part::Extrusion", "cylinder")
cyl.Base, cyl.Dir = circ, Vector(0, 0, 12)


cutplate = doc.addObject("Part::Cut", "cutplate")
cutplate.Base, cutplate.Tool = plate, cyl

cutplate2 = doc.addObject("Part::Cut", "cutplate2")
cutplate2.Base, cutplate2.Tool = plate2, cyl


..and the answer to the question is..


FreeCAD v0.18 has duplicated the single cylinder, so it appears twice in the hierarchy. Even though they appear to be separate, these 2 circles & cylinders are actually linked; if you change the radius of one circle, both hole sizes change. The trick is to note that the duplicate objects have the same name; if they were separate items, FreeCAD would have automatically renamed the second one by adding a numeric suffix.

FreeCAD v0.16 does not duplicate the parts, the circle & cylinder only appear once under cutplate2, so caution is needed when working with the older version.

Copyright (c) Jeremy P Bentham 2019. Please credit this blog if you use the information or software in it.