Visual Regression Library for Robot Framework
Uses Imagemagick to Compare Images and create a diff image. Custom Report to view baseline, actual and diff images. View passed and failed tests. Blur regions (only for selenium) within a page to ignore comparison (helpful when there are dynamic elements like text etc in a page). Support SeleniumLibrary(tested) , Selenium2Library(tested) and AppiumLibrary(not tested).
- Install the
robotframework-eyes
library usingpip
:
pip install robotframework-eyes
- Install
Imagemagick
(for mac:brew install imagemagick
, linux:apt-get install imagemagick
)
-- Important Imagemagick7: Make sure that you check the Install Legacy Utilities (e.g. convert, compare) check mark in the installation process and that the directory to ImageMagick is in your PATH env variable. Please ensure that compare.exe is in your path env variable. If you still dont see diff images being generated, please downgrade to Imagemagick6
- Import the Library into your Robot test. E.g:
Library RobotEyes
- Call the
Open Eyes
keyword after opening the browser in your selenium test. - Use the
Capture Full Screen
andCapture Element
keywords to capture images. - Call the
Compare Images
keyword at the end of the test to compare all the images captured in the respective test. - Once done running the tests, report with name
visualReport.html
should be generated at the root of the project - You can manually generate the report by running the below command. Eg:
reportgen --baseline=<baseline image directory> --results=<output directory>
This guide contains the suggested steps to efficently integrate RobotEyes
library into your Robot Framework test development workflow.
It also serves as documentation to clarify how this library functions on a high level.
Keyword | Arguments | Comments |
---|---|---|
Open Eyes | lib, tolerance, template_id, cleanup | Ex open eyes lib=AppiumLibrary tolerance=5 cleanup=all_passed |
Capture Full Screen | tolerance, blur, radius, name, redact | Ex capture full screen tolerance=5 name=homepage blur=<array of locators> radius=50(thickness of blur) |
Capture Element | locator, tolerance, blur, radius, name, redact | |
Capture Mobile Element | locator, tolerance, blur, radius, name, redact | |
Scroll To Element | locator | Ex scroll to element id=user |
Compare Images | Compares all the images captured in the test with their respective base image | |
Compare Two Images | first, second, output, tolerance | Compares two images captured in the above steps. Takes image names, diff file name and tolerance as arguments Ex: Compare Two Images img1 img2 diff 10 |
This is only set when invoking open eyes
- all_passed
- This will cleanup diff and actual folders that passed
- diffs_passed
- Will only cleanup diffs that passed, leaving actuals in place
- actuals_passed
- Will only cleanup actuals that passed, leaving diffs in place
- None
- Won't do any image folder cleanups (default)
robot -d results -v images_dir:<baseline_images_directory> tests
If baseline image directory does not exist, RobotEyes will create it.
If baseline image(s) does not exist, RobotEyes will move the captured image into the baseline directory.
For example, when running tests the first time all captured images will be moved to baseline directory passed by you (images_dir)
Important It is mandatory to pass baseline image directory, absence of which will throw an exception.
The RobotEyes
library creates a visual_images
directory which will contain two additional directories, named actual
& diff
, respectively.
These directories are necessary for the library to function and are created by it at different stages of the test case (TC) development workflow.
The resulting directory structure created in the project looks as follows:
- visual_images/
- actual/
- name_of_tc1/
- img1.png
- img1.png.txt
- name_of_tc2/
- img1.png
- img1.png.txt
- name_of_tc3/
- img1.png
- img1.png.txt
- name_of_tc1/
- diff/
- name_of_tc1/
- img1.png
- name_of_tc2/
- img1.png
- name_of_tc3/
- img1.png
- name_of_tc1/
- actual/
Baseline images will be generated when tests are run the first time. Subsequent test runs will trigger comparison of actual and baseline images.
For example:
*** Settings ***
Library SeleniumLibrary
Library RobotEyes
*** Test Cases ***
Sample visual regression test case # Name of the example test case
Open Browser https://www.google.com/ chrome
Maximize Browser Window
Open Eyes SeleniumLibrary(AppiumLibrary) 5
Wait Until Element Is Visible id=lst-ib
Capture Full Screen
Compare Images
Close Browser
To compare the images, the following needs to exist in the TC's code:
- Library declaration:
Library RobotEyes
- The
Open Eyes
keyword after theOpen Browser
keyword. - Any of the image capture keywords. E.g
Capture Full Screen
- The
Compare Images
keyword after capturing the desired images.
For Example:
*** Settings ***
Library SeleniumLibrary
Library RobotEyes
*** Test Cases ***
Sample visual regression test case # Name of the example test case
Open Browser https://www.google.com/ chrome
Maximize Browser Window
Open Eyes SeleniumLibrary 5
Wait Until Element Is Visible id=lst-ib
Capture Full Screen
Compare Images
Close Browser
After the comparison is completed (i.e. the Compare Images
keyword in the TC is executed), a difference image will be generated and stored in the diff
directory.
Also, a text file will be created containing the result of the comparison between the RMSE (root mean squared error) of the diff
image and the tolerance set by the user.
After that, the regular Robot Framework report will raise a failure if the comparison fails.
*** Settings ***
Library SeleniumLibrary
Library RobotEyes
*** Variables ***
@{blur} id=body css=#SIvCob
*** Test Cases ***
Sample visual regression test case # Name of the example test case
Open Browser https://www.google.com/ chrome
Maximize Browser Window
Open Eyes SeleniumLibrary 5
Wait Until Element Is Visible id=lst-ib
# Below, the optional arguments are the tolerance to override global value, the regions to blur in the image and
# the thickness of the blur (radius of Gaussian blur applied to the regions)
Capture Full Screen 10(tolerance) ${blur} 50
Capture Element id=hplogo
Compare Images
Close Browser
You can run plain non web/mobile image comparison tests as well. Here is an example:
*** Settings ***
Library RobotEyes
*** Test Cases ***
Plain image comparison test case # Name of the example test case
Open Eyes lib=none 5
Compare Two Images ref=oldsearchpage.png actual=newsearchpage.png output=diffimage.png tolerance=5
You need to place images to compare within two folders and provide their path while running the tests.
robot -d results -v images_dir:<reference_directory> -v actual_dir:<actual_directory> Tests/nonwebtest.robot
Important Do not run non web tests and web/mobile tests together. This will result in errors during report creation.
When writing a data driven Template Test, you need to provide a unique template_id in order to uniquely save images for each test.
*** Settings ***
Library SeleniumLibrary
Library RobotEyes
*** Test Cases ***
Sample test
[Template] Sample keyword
https://www.google.com/ 0
https://www.google.com/ 1
https://www.google.com/ 2
*** Keywords ***
Sample keyword
[Arguments] ${url} ${uid}
open browser ${url} chrome
open eyes SeleniumLibrary template_id=${uid}
sleep 3
capture element id=hplogo
capture element id=body 50
compare images
close browser
Tolerance is the allowed dissimilarity between images. If comparison difference is more than tolerance, the test fails.
You can pass tolerance globally to the open eyes
keyword. Ex Open Eyes lib=SeleniumLibrary tolerance=5
.
Additionally you can override global tolerance by passing it to Capture Element
, Capture Fullscreen
keywords.
Ex:
Capture Element <locator> tolerance=10 blur=${locators}
Tolerance should range between 1 to 100
You can also blur out unwanted elements (dynamic texts etc) from image to ignore them from comparison. This can help in getting more accurate test results. You can pass a list of locators or a single locator as argument to Capture Element
and Capture Full Screen
keywords.
Ex: Capture Element <locator> blur=id=test
@{blur} id=body css=#SIvCob
Capture Element <locator> blur=${blur}
Capture Full Screen blur=${blur}
If blurring elements does not serve your purpose, you can redact elements from images. Simply pass a list of locators that you want to redact as argument to the capture keywords.
Ex: Capture Element <locator> redact=id=test
@{redact} id=body css=#SIvCob
Capture Element <locator> redact=${redact}
Capture Full Screen redact=${redact}
Basic report should be autogenerated after execution (not supported for pabot). Alternatively, you can generate report by running the following command.
reportgen --baseline=<baseline image folder> --results=<results folder>
Important: If you want to remotely view the report on Jenkins, you might need to update the CSP setting, Refer: https://wiki.jenkins.io/display/JENKINS/Configuring+Content+Security+Policy#ConfiguringContentSecurityPolicy-HTMLPublisherPlugin
Robot Eyes generates a report automatically after all tests have been executed. However a more interactive and intuitive flask based report is available.
You can view passed and failed tests and also use this feature to move acceptable actual images to baseline directory.
Run eyes server like this. eyes --baseline=<baseline image directory> --results=<outputdir>(leave empty if output is at project root)
You can move selected images in a testcase by selecting images and clicking on "Baseline Images" button.
You can also move all images of test cases by selecting the test cases you want to baseline and clicking on "Baseline Images" button.
Note: You need to have gevent library installed in the machine to be able to use eyes server.
Visual tests can be executed in parallel using pabot to increase the speed of execution. Generate the report using reportgen --baseline=<baseline images folder> --results=<results folder>
after running the tests.
Adirala Shiva Contributed in creating a robotmetrics inspired reporting for RobotEyes.
DiegoSanchezE Added major improvements in the ReadMe.
Priya Contributes by testing and finding bugs/improvements before every release.
Ciaran Doheny Actively testing and suggesting improvements.
If you find this library useful, you can support me by doing the following:
- Star the repository.
- Make a donation via Paypal. You can request for features and I will prioritise them for you.
For any issue, feature request or clarification feel free to raise an issue in github or email me at [email protected]