Need to test your LEDs position, operation and color in production?
meet the LED Recognition API and example code
New!!!LED Recognition software API or application
Evaluate LEDs operation and color recognition in a board utilizing our tester application.
(We can also consult for the right camera with lens)
Fast automated LED recognition tester:
We developed a LED tester solution includes camera, lens and API software with example code in LabVIEW and CVI
The API includes four functionalities:
Open/Close camera (can work on images without camera)
Color learning – Learn the software the LED color configuration and location. Also use as a live LED color status
Test: Perform fast LED color test and log results to a csv file
Can be use as a standalone LED tester as well!
How Does LED Color Lights Detectors?
- Open the camera or use static images
- Learn the system the LED configuration in the new board. After one-time learning you can use the configuration to test many similar boards and even a full batch
- In Learn function the LEDs are live shown
- Test – fast LED test and log result to a csv result file
API Option to Run
- Live image from camera
- Image from a path ( without a camera with existing images)
- Using JSON configuration file New and easy to use board
- working with existing JSON file (CVS file)
LED Color Bulb Matching Concepts
Color matching is performed in three steps:
- Regions in the image containing the color information should be provided to the application.
So called Region of interest ROI .
- The machine vision software learns a reference color distribution.
- The software compares color information from other images to the reference image and returns a score as an indicator of similarity.
ROI could be defined manually, or they can be the output of some other machine vision tool, such as pattern matching used to locate the components to be inspected.
Learning Color Distribution
The machine vision software learns a color distribution by generating a color spectrum.
The color spectrum is a one-dimensional representation of the three-dimensional color information in an image
The machine vision software then generates a color spectrum based on the provided information.
The color spectrum becomes the basis of comparison during the matching phase.
Comparing LED Color Distributions
During the matching phase, the color spectrum obtained from or region in the target image is compared to the reference color spectrum taken during the learning step. A match score is computed based on the similarity between these two-color spectrums using the Manhattan distance between two vectors. A fuzzy membership weighting function is applied to both the color spectrums before computing the distance between them. The weighting function compensates for some errors that may occur during the binning process in the color space.
The fuzzy color comparison approach provides a robust and accurate quantitative match score. The match score, ranging from 0 to 1000, defines the similarity between the color spectrums. A score of zero represents no similarity between the color spectrums, while a score of 1000 represents a perfect match.
Source Code Deliveries
To minimize the efforts of integration to the existing tester application Source Code will be provided as an Application Programming Interface (API) with an example of how to use it.
It will simplify programming by abstracting the underlying implementation and only exposing objects or actions the developer needs. The high-level functions that the user would put on their block diagrams would typically be set to Public while the lower level functions will be set to Private. This will make the API more user-friendly and intuitive since the high-level tasks will be what the user focuses on.