A Low-Cost Real Color Picker Based on Arduino

Abstract

Color measurements have traditionally been linked to expensive and difficult to handle equipment. The set of mathematical transformations that are needed to transfer a color that we observe in any object that doesn't emit its own light (which is usually called a color-object) so that it can be displayed on a computer screen or printed on paper is not at all trivial. This usually requires a thorough knowledge of color spaces, colorimetric transformations and color management systems. The TCS3414CS color sensor (I2C Sensor Color Grove), a system for capturing, processing and color management that allows the colors of any non-self-luminous object using a low-cost hardware based on Arduino, is presented in this paper. Specific software has been developed in Matlab and a study of the linearity of chromatic channels and accuracy of color measurements for this device has been undertaken. All used scripts (Arduino and Matlab) are attached as supplementary material. The results show acceptable accuracy values that, although obviously do not reach the levels obtained with the other scientific instruments, for the price difference they present a good low cost option.

Full text

Sensors 2014, 14, 11943-11956; doi:10.3390/s140711943
sensors
ISSN 1424-8220
www.mdpi.com/journal/sensors
Article
A Low-Cost Real Color Picker Based on Arduino
Juan Enrique Agudo 1, Pedro J. Pardo 1,*, Héctor Sánchez 1, Ángel Luis Pérez 2 and
María Isabel Suero 2
1 University Center of Merida, University of Extremadura, Sta. Teresa de Jornet, 38,
Mérida 06800, Spain; E-Mails: jeagudo@unex.es (J.E.A.); sasah@unex.es (H.S.)
2 Physics Department, University of Extremadura, Avda. Elvas s/n, Badajoz 06006, Spain;
E-Mails: aluis@unex.es (A.L.P.); suero@unex.es (M.I.S.)
* Author to whom correspondence should be addressed; E-Mail: pjpardo@unex.es;
Tel.: +34-924-289-300 (ext. 82622); Fax: +34-924-301-212.
Received: 18 March 2014; in revised form: 2 July 2014 / Accepted: 3 July 2014 /
Published: 7 July 2014
Abstract: Color measurements have traditionally been linked to expensive and difficult to
handle equipment. The set of mathematical transformations that are needed to transfer a
color that we observe in any object that doesn’t emit its own light (which is usually called a
color-object) so that it can be displayed on a computer screen or printed on paper is not at
all trivial. This usually requires a thorough knowledge of color spaces, colorimetric
transformations and color management systems. The TCS3414CS color sensor (I2C Sensor
Color Grove), a system for capturing, processing and color management that allows the
colors of any non-self-luminous object using a low-cost hardware based on Arduino, is
presented in this paper. Specific software has been developed in Matlab and a study of the
linearity of chromatic channels and accuracy of color measurements for this device has
been undertaken. All used scripts (Arduino and Matlab) are attached as supplementary
material. The results show acceptable accuracy values that, although obviously do not
reach the levels obtained with the other scientific instruments, for the price difference they
present a good low cost option.
Keywords: color; colorimetry; Arduino; Matlab
OPEN ACCESS

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1. Introduction
The world around us is perceived through the senses, and of these, sight is the one which
contributes more information to the human brain in its human-environment interface task. Sight gives
us information through light, its intensity and color. Despite the complex processing that is carried out
in human neurons from the moment the light reaches the rods and cones, this was the first human sense
to be mathematical modeled through its own space of representation and measurement [1]. In 1931, the
International Commission on Illumination defined the standard colorimetric observer [2] which sought
to represent the average human being in terms of color vision in order to determine and identify a color
based on a mathematical coordinates. Despite undergoing constant evolution since then, linked to the
constant advances in knowledge of human visual physiology and psychometrics, the origin of complex
colorimetric transformations performed to represent a color in an independent representation space are
still based on this colorimetric standard observer.
These complex colorimetric transformations and concepts such as standard observer or CIE 1931
space contrasts with the ease with which anyone used to working with digital devices that specify a
color by its RGB coordinates. However, this simplicity becomes difficult when these same people try
to reproduce a particular color in a different electronic device such as a monitor or printer. This is
because the spaces representing the digital color, RGB for monitors and digital cameras and CMYK
for printers, are dependent color spaces of the device, that is, the same RGB color generates a
simulation of a different color on a monitor depending on the primary colors that the monitor uses and
the specific configuration of brightness, contrast, gamma and color temperature.
To solve this problem, critical in some professions such as graphic designers, photographers, etc.
color management systems (CMS), as the name indicates, manage colorimetric transformations
necessary to obtain an accurate color reproduction when using colorimetric profiles on each device [3].
These colorimetric profiles are separate files associated with each device that are able to reproduce or
capture a color. They can also be integrated into the digital images generated by them. These files
specify color from a point of view independent from the device, employing independent color spaces
such as CIE 1931 or CIE Lab. These color management systems make it much easier but needs an
external device that allows for real measurements of radiation emitted or reflected in each case, such as
spectrophotometers or colorimeters.
Although there are many colorimeters on the market, that measure the color of any object in order
to “digitize” it and begin these colorimetric transformations, they are usually quite costly in time and
money. However, the development of modular low cost electronics platforms, such as Arduino [4],
mbed [5] or raspberry PI [6] which can be equipped with all kinds of sensors, opens the door to the use
of these devices for capturing colors.
This type of platform is spreading in research fields for low cost prototyping [7–11], allowing fast
and easy development and extensive configuration options that are sometimes limited in commercial
devices. Arduino [12] is a project whose aim is to provide simplicity in creating electronic projects.
This platform is one of the most famous electronic prototype development boards because of its ease
of use/programming and affordable price, leading to the so called democratizing technology [13] in
which users create their own technology devices through the DIY philosophy.

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The aim of this paper is to explore the possibilities of a prototype based on Arduino and the
TCS3414 color sensor as a color capturer of solid objects—what we have called color picker—and the
creation of software for management and control of color in a simple way. The results and the software
developed are provided as supplementary material.
This article is organized as follows: In Section 2, a theoretical introduction of different color spaces
is made, how they are generated and what their advantages and disadvantages are. In Section 3 the
prototype and its characteristics are presented. In Section 3, the experiments and device possibilities
are explained. In Section 4 we detail the calibration process and software development. Section 5
concludes with the work undertaken as well as the future outlook for this work.
2. Color Spaces
Kuehni [14] defines color spaces as three-dimensional geometric spaces with axes appropriately
defined so that symbols for all possible color human perceptions fit into them psychologically ordered.
In this space each color perception is represented as a point. From this definition, note each color that
humans can perceive is defined by a single point in this three-dimensional space and corresponding the
ordering of these points to the visual perception. This is important since uninitiated people in
colorimetry associate color with the wavelength of light radiation that reach the eye but, for example,
the same yellow color of the luminous radiation of a sodium lamp of 589 nm can be achieved by
adding in suitable proportions of two lights, one green 540 nm and one red 620 nm. Under this point of
view, the human visual system is trivariate and space of the electromagnetic radiation has infinite
variance. Furthermore, if the geometric distance between two colors represented in a vector space
corresponds to the color difference perceived by the human, the color space is said to be uniform.
2.1. Device-Independent Color Spaces
The first color space used as reference system universally accepted was XYZ tristimulus space,
standardized by CIE in 1931 [2]. This space arises directly from the sensitivity of the three types of
cones that are found in the human retina and various visual properties specified by mathematical laws
known as the Grassmann’s laws [15] and define it as Euclidean space. Since it was very difficult to
represent a three-dimensional space graphically, a two dimension diagram is known as chromaticity
diagram CIE 1931 xy (Figure 1) where chromaticity coordinates x,y are obtained by normalization
x = X/(X + Y + Z); y = Y/(X + Y + Z) allowing dispensed with the third coordinate z since
z = 1 − x − y. Furthermore, as in the definition of tristimulus space as the chromaticity diagram, took
advantage to introduce certain boundary conditions which eliminate the possibility to appear negative
coordinates and for illuminant equi-energy E had as chromaticity coordinates (x,y) = (0.33,0.33).
Despite the progress that led to the existence of the CIE XYZ color space, and its associated
chromaticity diagram (CIE1931 xy), this space does not have the second necessary property of a good
color space: the correspondence of visual perception in terms of distances (color differences), so that in
1976 another color space is developed, emanating from the first, called CIE LAB [16] in which this
aspect of the color differences as well as the independence of the target type used as illuminant
are improved.

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Figure 1. CIE 1931 chromaticity diagram.
This space has an associated chromaticity diagram which corresponds to a color circle
in a*b* plane centered at the origin (Figure 2).
Figure 2. CIE LAB 1976 color space.
2.2. Device-Dependent Color Spaces
Opposite to color spaces described above, there are color spaces associated with different electronic
devices such as RGB, associated with screens and cameras, and CMYK spaces associated with printers
and offset machines. These spaces are used to represent the colors that a device can display or capture
depending on source sensitivity curves for each device or primary stimulus depending on the case.
Therefore, it does not serve to communicate a color unequivocally by its own and must be connected
with independent color space through correct chromatic device characterization, with which this
paper deals.

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3. Prototype
In order to build the prototype, we have used an Arduino Uno with a shield Grove—Base Shield
V1.3 and a color sensor Grove—I2C Color Sensor (Figure 3a), the overall cost of the system is less
than 70$. Arduino Uno is the basic board within the existing Arduino family, based on the
ATmega328 chip. It has 14 digital input/output pins (of which six can be used as PWM outputs) and
six analog inputs. The ATmega328 included also supports I2C and SPI communication. It contains
everything needed to start developing your own prototype; simply connect it to a computer with a USB
cable and program it. Besides, a new prototype based on an Arduino Mini for a higher portability with
equal characteristics has been developed (Figure 3b).
Figure 3. Color picker prototype prototype (a) and portable version (b).
(a) (b)
The Grove system [17] developed by Seedstudio [18] consists of an Arduino shield and modules
with standardized connectors. The shield allows for easy connection of Arduino pinout from the Grove
modules. Each one comes with demo code and documentation to help you use it easily. The base shield
1.3 has 8 connectors for digital input/output, 4 analog input connectors and four I2C connectors.
The selected module, Grove‒I2C Color Sensor, uses the I2C serial protocol to communicate with
Arduino. This module contains a Color Light-to-Digital Converter TCS3414cs sensor from the
manufacturer ams AG [19]. According to the sensor manufacturer’s specifications, the TCS3414
digital color light sensor is designed to accurately derive the color chromaticity and the luminance
(intensity) of ambient light as well as provide a digital output with 16-bits of resolution. This device
includes an 8 × 2 array of filtered photodiodes, analog-to-digital converters and control functions on a
single integrated circuit. Of the 16 photodiodes, four have red filters, four have green filters, four have
blue filters, and four have no filter (clear). The applications suggested by the manufacturer are: Light
color temperature measurement, RGB LED Backlight control, RGB LED consistency control, Health
fitness, industrial process control or use in medical diagnostic equipment.
Spectral response curves of the four channels of the sensor can be seen in Figure 4. The device has
16-Bit Digital Output with I2C at 400 kHz, programmable Analog Gain and Integration Time
Supporting 1,000,000-to-1 Dynamic Range.

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Figure 4. Spectral responsivity of the four channels.
In order to control the device from Matlab we have created a simple command protocol via a serial
port in the Arduino Board. The TCS3414 sensor has a timing register that controls the synchronization
and integration time of the Analog-to-Digital-Conversor (ADC) channels. The Timing Register settings
apply to all four ADC channels and it has three working modes. The default mode is a free-running mode
in which one of the three internally-generated Nominal Integration Times (12, 100, 400 ms) is selected
for each conversion. The second mode is a manual start/stop integration mode through a serial bus
using ADC_Enable field in the control register. The third mode is based on the integration times preset
in the free-running mode but synchronized with an external signal, allowing synchronization measures
for pulsed sources.
The aim of the protocol therefore is controlling these operation modes and their different options.
This serial communication protocol is based on alphanumeric characters to allow its remote and
automatic execution to perform the checks that we will see in the following sections. The complete
code is attached as supplementary material.
The protocol is based on a string with capitalized characters separate by blank spaces. The first
character is always R to indicate that we will work remotely. Then you must specify the operating
mode, choosing between F for free-running, M for manual or A for automatic. Next, for the three
modes, the gain values (between 1 and 4) and the prescaling values (1 to 4) are fixed, always
separating individual values by a space. In the case of free-running and manual modes, the integration
time must be set: 12, 100, 400 ms, with a value between 1 and 3. In automatic mode, the system
sensor + Arduino calculates the integration time required to obtain a constant level of response in
the green digital channel. As an explanatory example, the “R F 1 1 3” sequence corresponds to
the free-running mode with gain 1, prescaler 1 and integration time 400 ms. The “R M 2 1 1” sequence
corresponds with manual mode with gain 2, prescaler 1 and integration time 12 ms.

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4. Experimental Procedure
In order to measure the potential of our low cost electronic system, in terms of color measurement
capabilities, we have undertaken a series of experiments and actions which are then explained.
4.1. Starting Setup
To test the capabilities of this system we have followed the manufacturer’s instructions, the library
and sample code provided by the manufacturer of the Grove module needed to run it using Arduino
and conducted the first color measurements using the calibration parameters provided by the
manufacturer’s example. In these color measurements, we observed a number of inconsistencies
between the measures obtained chromaticity and objects used as samples and when looking for the
source of these inconsistencies we detected an error in the address memory of one of the two bytes of
the red channel. Once this problem was fixed, we modified the sample code and the library and then
started to obtain reasonable results that allowed us to continue with the experiments.
4.2. Linearity in the Channels Response
One common source of error in the measurements provided by an electronic device is the
nonlinearity in the response of the digital channels that exist in the device. When this occurs, there are
solutions based on artificial neural network models generation that improve the color signals from the
sensor [20,21]. In this particular case, the TCS3414CS sensor has three channels, R, G, B and a fourth
channel that is called Clear in which the sensor is exposed to radiation without any filter except an IR
filtering component. To test the linearity of these channels an experimental device based on a video
projector connected to a computer that generates light simulations of different RGB values projected
onto the sensor. Also, together with the electronic device equipped with the sensor, a diffuse
reflectance pattern [22] was produced on which the chromaticity coordinates of the light stimulation
were measured using a Minolta CS-2000 tele-spectroradiometer controlled by the same computer.
Figure 5. Normalized digital count VS. Relative filtered radiation received for the four
channels (a) Red; (b) Green; (c) Blue; (d) Clear.
(a) (b)

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Figure 5. Cont.
(c) (d)
This measuring instrument has a spectral measurement range between 380 and 780 nm and accuracy
in the measurement of CIE 1931 xy chromaticity of x = 0.0015 y = 0.001. Using proprietary software
developed in Visual Basic and automatic control of a light stimulation generator, the test device
measurement and reference measurement device, we conducted a study of the linearity of the RGB
channels of the color sensor as graphically displayed in Figure 5. In all the channels, the adjustment in
the linearity obtained exceeds Pearson’s correlation coefficient of R > 0.99.
4.3. Influence of Integration Mode on the Fluctuation of the Measurement
As we mentioned, the TCS3414cs sensor has three working modes for controlling the color capture.
In order to test how to use the sensor, several series of measurements were performed to check
the operation of the first two modes, the third one is not applicable in our case. In the case of the
free-running mode an integration time of 400 ms is set so the simulation used does not saturate
the sensor with this integration time. In the second case, an algorithm is designed, that along with two
previous measurements, calculates the integration time needed to maintain the sensor at 80% of its
maximum response. With these two run modes the calibration procedure was performed, including a
measure of the white reference target (in this case the white of a video projector), touring the RGB
channels of the projector from 0 to 255 in steps of 5 units and performing measurements of 50 RGB
values randomly generated.
Through this calibration process a 3 × 3 matrix is obtained that relates the RGB values provided by
the sensor with XYZ tristimulus values needed to specify the color in a separate space, in this case the
CIE 1931. As a method of obtaining this matrix the Microsoft Excel Solver tool was used, using the
average chromaticity error of the measurements as an optimization parameter, excluding the random
types which have been left to assess the accuracy of the measurements. Similarly, we used a second
optimization parameter based on the absolute error of the XYZ tristimulus values. Table 1 shows the
summary of the errors obtained on 50 random values measured in the two operating modes and with
the two optimization methods, although in the case of the dynamic integration time, always looking for
a same level of response does not make sense for the second type of adjustment.

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Table 1. Average chromaticity and tristimulus error obtained by two optimization methods.
Optimization Parameter Lower Average Chromaticity Error Lower Average Tristimulus Error
Integration Time Chromaticity Error Relative Tristimulus Error Chromaticity Error Relative Tristimulus Error
Internal fixed 0.005 0.03 0.03 0.04
Manually fixed 0.07 N.A. 0.07 N.A
In light of Table 1, the optimum integration method for this type of device is the free-running mode
based on the internal clock of the sensor that ensures less error in chromaticity and tristimulus values.
Therefore the final experiment has been conducted in this mode. The error in chromaticity obtained is
usually greater than the error provided by commercially produced scientific measuring instruments but
in line with what is expected of such low cost devices.
5. Results and Discussion
In the light of the preliminary results, the lowest average error in chromaticity measured is 0.005 units
in the CIE 1931 chromaticity diagram. This means that dedicating this device to measuring correlated
color temperature CCT, as indicated by the manufacturer’s data sheet, would not be an optimal use for
as in these types of measurements chromaticity accuracy is crucial. However, these results place this
device in a position to be used as a good color meter, especially in relation to non-self-luminous solid
objects, by taking advantage of the high power white LED that can be used as light source
incorporated into the Grove model. These color measures could also do with any imaging device based
on CCD or CMOS sensors if they provide access to raw images and have a good stability and linearity.
It would also be necessary to include a light source that would use this device to measure colors by
reflection, like Arduino based prototype presented here, and prepare a carrier fixed to keep the distance
and angle between source, sample and sensor. These requirements are supported by medium-high
range mobile phones of latest generation but its integration with the software for subsequent
colorimetric transformations require some knowledge of more advanced programming and little
changes in the mobile phone hardware. In our case, we have designed specific software based on
matlab that automates the complex set of colorimetric transformations that are needed once the color is
captured (picked) until it is displayed on the screen of a digital device. This code is also attached as
supplementary material so that it can be used or modified by users.
5.1. Colorimetric Transformations
The colorimetric transformations are necessary in order to display a color captured from a real
object on the screen of a digital device with the most accurate appearance begin with the transformation
of RGB color space captured by the sensor to XYZ space by calibrating the sensor. Afterwards we
need to transform the tristimulus values obtained with the device’s light source to appropriate values
for a reference illuminant such as D65. This step attempts to alleviate capture errors as far as possible
when using a different source to the reference illuminant, but inevitably introduces an error. Then they
have to be converted from the XYZ values for the D65 illuminant to CIE Lab space values, as they are
widely used due to their greater uniformity. Finally an RGB coordinates transformation to the output
device needs to be undertaken and then the color can be displayed. In this last step the characteristics

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of the color management systems that are usually responsible for managing color in electronic devices
and used as a reference for RGB space is taken into account [23]. Figure 6 summarizes graphically the
transformations needed.
Figure 6. Colorimetric transformations.
5.2. Calibration
To simplify the calibration process used in preliminary experiments and adapted to the new
objective of measuring solid colors, we have designed a calibration process based on the most widely
recognized color card, the X-Rite Color Checker [24].This color card consists of 18 chromatic samples
and 6 achromatic, and their chromaticities are provided by the manufacturer under the illuminant
reference D65. Figure 7 shows a picture of the color chart with the values provided by the manufacturer
for the first sample.
Figure 7. Chromatic chart ColorChecker and chromatic values in CIE Lab space and sRGB.
No. Number sRGB CIE L*a*b*
R G B L* a* b*
1. Dark skin 115 82 68 37.986 13.555 14.059
Anyone who possesses this color card only needs to measure the RGB response of each sample
using the Arduino serial communication and enter the coordinates of the RGB color rows in a text file.
To facilitate the process of obtaining the calibration matrix, we have implemented a matlab script
called Calibration along with necessary supporting files that generate this matrix and store it in native
matlab format. All of this code is attached as supplementary material.

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5.3. Color Management Software
In order to automate the complex process of color capture and subsequent colorimetric transformations
we have implemented a matlab script along with a graphical interface. Once the calibration process is
completed by placing the color sensor on the object and clicking on the “pick color” button, the
program takes care of setting the native RGB coordinates of the measuring instrument, transforming
them to an independent color space, either CIE 1931 or CIE Lab, presenting the chosen chromaticity
diagram and calculating the RGB color coordinates within the sRGB standard color space. The sRGB
color space is the most widely used and is delimited by the chromaticity of the primaries used by a
triangle in the CIE 1931 diagram. The chromaticity of the white reference is also recorded, in this case
the illuminant D65. In Figure 8, shows the triangle with all reproducible chromaticities in a display
that complies with this standard.
Figure 8. GUI of the software implemented in Matlab.
We have also added two more options, selectable by a drop-down control, in which an image is
displayed with an altered color profile embedded so that if a software application does not perform,
the color management appears in green tones whereas if management is correct, the color appears in
reddish tones, typical of a sunset. This image is also provided as supplementary material as it is
considered very useful for detecting their use by CMS graphics applications.

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To complement the software, a matlab script can export the captured color to a file in Adobe COlor
file (ACO), which allows the graphic palette of the software to be imported into Adobe Photoshop and,
if it is out of the sRGB colour triangle, transforms it to the closest reproducible color.
6. Conclusions and Outlook
The possibilities that modular electronics offers are endless and, in a large part, this diversity is
based on the existence of very different types of sensors, actuators, etc., their ease of use and low cost.
In this case, the combination of Arduino, the Grove system and a color sensor accompanied by a high
power white LED, can capture the color of any readily homogeneous solid object and display it on the
screen of an electronic device in the most reliable way possible. The results indicate that this system of
color measurement has similar accuracy to other electronic devices, however without the accuracy of
purely scientific instruments, but enough for a non-professional user. This shows that today valid
research prototypes can perform for very low cost, although without the accuracy of commercial ones,
providing an alternative for limited budgets.
In addition, as all the components are open hardware and the software is open source anyone can
use and/or adapt the work presented in this paper following the philosophy of open knowledge.
Moreover, this accuracy could be improved if a white light source LED that simulates the spectrum of
the illuminant D65 is used as reference white in most colorimetric transformations.
Acknowledgments
This work was supported by the grant GRU10102 of the Regional Government of the Junta de
Extremadura, and partially financed by the European Regional Development Fund.
Author Contributions
Juan Enrique Agudo and Héctor Sánchez designed and built the two prototypes and developed the
Arduino code. Pedro J. Pardo developed the software on Matlab that connect with the device and the
serial communication. Ángel Luis Pérez and María Isabel Suero performed the different test with the
prototype. Finally, all the authors collaborated on the manuscript preparation and redaction.
Conflicts of Interest
The authors declare no conflict of interest.
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