HTML <​canvas> testing with Selenium and OpenCV

from: https://www.linkedin.com/pulse/html-canvas-testing-selenium-opencv-maciej-kusz

Since HTML <canvas> become more and more popular for creating interactive content on any web page like games (especially since Adobe Flash technology is dying), there is a big problem with testing it using pure Selenium. If you have never seen <canvas> you may be wondering why? Mostly because <canvas> (like old Flash element) is seen in DOM structure just like element without any content even if there is a complex game inside, eg.

<canvas id="myCanvas" width="200" height="100"></canvas>

Using just Selenium you will be only able to locate <canvas> element and get its position, size and some state, like isElementVisible, etc., but you will not be able to see what's inside and test internal behavior.

What can we do to test HTML <canvas>?

Since the <canvas> element is a container for graphics elements (with additional logic written in JavaScript) we can try to perform manual mouse actions using Selenium Action Chains. We have there a few useful action, like:

• click
• move_by_offset

By combining only those 2 actions you will be able to click any button inside <canvas>element. But you will face 2 big problems:

1. What are (x, y) coordinates of the center of button to be clicked?
2. What is the current state of the game?

Get (x, y) coordinates the button center

We can approach this problem from 2 different directions:

1. Prepare static (x, y) coordinate of the button center inside <canvas> element and use move_to_element_with_offset from Selenium Action Chain
2. Get button center dynamically

Point 1 is quite easy to prepare using any graphics editing tool and we will not talk about it (going and easy path is not the way we follow at XCaliber, especially when the path is short and ends with a cliff). Reason for it is quite easy: we will need to implement dynamic method if we want to know a state of the game.

So how we can obtain button center coordinates dynamically?

We "just" need to "see" what's happening inside the <canvas> element. You can think: "easy to say, harder to do", but you will see that it's not that hard.

The best approach to "just see" problem is to use computer vision. Since Python has a very good binding for widely use the library called OpenCV, we can use it to solve this problem. In short, OpenCV is an image processing tool that will allow us to see what's happening inside <canvas> element.

In my previous article about Page Object Pattern, I have described how to prepare the object for XPath element locator. Let's use the same approach for a graphical locator.

Graphical locator

import cv2
import numpy
from io import BytesIO
from PIL import Image

class GraphicalLocator(object):

    def __init__(self, img_path):
        self.locator = img_path
        # x, y position in pixels counting from left, top corner
        self.x = None
        self.y = None
        self.img = cv2.imread(img_path)
        self.height = self.img.shape[0]
        self.width = self.img.shape[1]
        self.threshold = None

    @propertydef center_x(self):return self.x + int(self.width / 2) \
               if self.x and self.width else None

    @propertydef center_y(self):return self.y + int(self.height / 2) \
               if self.y and self.height else None

    def find_me(self, drv):# Clear last found coordinates
        self.x = self.y = None
        # Get current screenshot of a web page
        scr = drv.get_screenshot_as_png()
        # Convert img to BytesIO
        scr = Image.open(BytesIO(scr))
        # Convert to format accepted by OpenCV
        scr = numpy.asarray(scr, dtype=numpy.float32).astype(numpy.uint8)
        # Convert image from BGR to RGB format
        scr = cv2.cvtColor(scr, cv2.COLOR_BGR2RGB)

        # Image matching works only on gray images
        # (color conversion from RGB/BGR to GRAY scale)
        img_match = cv2.minMaxLoc(
            cv2.matchTemplate(cv2.cvtColor(scr, cv2.COLOR_RGB2GRAY),
                              cv2.cvtColor(self.img, cv2.COLOR_BGR2GRAY),
                              cv2.TM_CCOEFF_NORMED))

        # Calculate position of found element
        self.x = img_match[3][0]
        self.y = img_match[3][1]

        # From full screenshot crop part that matches template image
        scr_crop = scr[self.y:(self.y + self.height),
                   self.x:(self.x + self.width)]

        # Calculate colors histogram of both template# and matching images and compare them
        scr_hist = cv2.calcHist([scr_crop], [0, 1, 2], None,
                                [8, 8, 8], [0, 256, 0, 256, 0, 256])
        img_hist = cv2.calcHist([self.img], [0, 1, 2], None,
                                [8, 8, 8], [0, 256, 0, 256, 0, 256])
        comp_hist = cv2.compareHist(img_hist, scr_hist,
                                    cv2.HISTCMP_CORREL)

        # Save treshold matches of: graphical image and image histogram
        self.threshold = {'shape': round(img_match[1], 2),'histogram': round(comp_hist, 2)}

        # Return image with blue rectangle around match
        return cv2.rectangle(scr, (self.x, self.y),
                             (self.x + self.width, self.y + self.height),
                             (0, 0, 255), 2)

　　

The code above should be self-explained. The main reason for this is to provide the same interface as for XPath element locator. Having above you can do something like this if you want to test if given image is present on a web page:

img_check = GraphicalLocator("/path/to/image.png")
img_check.find_me(webdriver_instance)

Problem with above code is that it can give you false positive matches.

How to defend against false positive matches?

Take a look at GraphicalLocator object and its threshold attribute. It contains 2 values:

1. The threshold for image shape match is telling us how similar both images are (the one you are looking for and found one). If value equals 1 then images shapes are identical.
2. The threshold for image colors histogram match is telling us how similar colors of both images are. If value equals 1 then images colors histograms are identical.

Why do we need those 2 thresholds? Take a look at pictures below:

Both images present the same button, but in 2 different states (enabled and disabled). When you will try to find the first image and the second one will be present, shape threshold will be set to 1. It's happened because OpenCV image matching algorithm works on grey scaled images. In grey scale, both images shape is the same. Because of that, when you want to be sure that image you are looking for, is an image you can see, you need to check not only if the shape is identical but also colors of the images are the same. It's why there is also color histogram threshold calculated during image finding. This way code for checking is image present, should lock like this:

img_check = GraphicalLocator("/path/to/img.png")
img_check.find_me(webdriver_instance)

is_found = True if img_check.threshold['shape'] >= 0.8 and \
                   img_check.threshold['histogram'] >= 0.4 else False

Values of thresholds to compare should be chosen during some experiments (those are working for me).

How to click?

Now the best part. Just take a look at this code snippet:

from selenium.webdriver.common.action_chains import ActionChains

img_check = GraphicalLocator("/path/to/img.png")
img_check.find_me(webdriver_instance)

is_found = True if img_check.threshold['shape'] >= 0.8 and \
                   img_check.threshold['histogram'] >= 0.4 else False

if is_found:
    action = ActionChains(webdriver_instance)
    action.move_by_offset(img_check.center_x, img_check.center_y)
    action.click()
    action.perform()

Conclusion

As you can see it's not so hard to check if the image is visible in the <canvas> element and click on it. Extending this approach with allow you check the current state of the game, because of state checking will be based on visibility or invisibility of some elements.

PS. It also works with Flash elements ;)