Machine vision system and interactive graphical user interfaces related thereto
Machine vision devices may be configured to automatically connect to a remote management server (e.g., a “cloud”-based management server), and may offload and/or communicate images and analyses to the remote management server via wired or wireless communications. The machine vision devices may further communicate with the management server, user computing devices, and/or human machine interface devices, e.g., to provide remote access to the machine vision device, provide real-time information from the machine vision device, receive configurations/updates, provide interactive graphical user interfaces, and/or the like.
1. A computer system comprising:
a computer readable storage medium having program instructions embodied therewith; and
one or more processors configured to execute the program instructions to cause the computer system to:
generate data useable for rendering a graphical user interface;
receive, via the graphical user interface, a selection of at least a first feature and a second feature of a first image;
receive, via the graphical user interface, a selection of a trigger;
determine configuration data based at least in part on the first feature and the trigger; and
provide the configuration data to a machine vision system for execution by the machine vision system.
2. The computer system of claim 1 , wherein the first image is received from the machine vision system, and wherein the machine vision system is remote from the computer system and in secure communications with the computer system.
3. The computer system of claim 1 , wherein the one or more processors are configured to execute the program instructions to further cause the computer system to:
receive, via the graphical user interface, a selection of a second feature of the first image, wherein the second feature is spatially located relative to the first feature in the first image,
wherein the configuration data is determined further based on the second feature.
4. The computer system of claim 1 , wherein the one or more processors are configured to execute the program instructions to further cause the computer system to:
receive, via the graphical user interface, one or more adjustments to be applied to images acquired by the machine vision system.
5. The computer system of claim 4 , wherein the one or more processors are configured to execute the program instructions to further cause the computer system to:
receive, via the graphical user interface, a selection of a tolerance threshold associated with the first feature.
6. The computer system of claim 1 , wherein the one or more processors are configured to execute the program instructions to further cause the computer system to:
receive, via secure communications and from the machine vision system, one or more additional images;
process the one or more additional images based on the configuration data; and
provide, via the graphical user interface, an evaluation of the one or more additional images.
7. The computer system of claim 6 , wherein the evaluation comprises an indication of whether or not the first feature was found in each of the one or more additional images.
8. The computer system of claim 1 , wherein the trigger comprises an indication of a threshold number of at least one of: images passing evaluation based on the configuration data, images not passing evaluation based on the configuration data, or images not being readable based on the configuration data.
9. The computer system of claim 1 , wherein the one or more processors are configured to execute the program instructions to further cause the computer system to:
receive, via secure communications and from the machine vision system, a live feed of image data acquired by the machine vision system; and
display, in the graphical user interface, the live feed of image data.
10. The computer system of claim 1 , wherein the one or more processors are configured to execute the program instructions to further cause the computer system to:
access, from a data store separate from the machine vision system, one or more images and associated analysis data provided by the machine vision system;
generate aggregate information based at least in part on: the one or more images and associated analysis data, and identifiers and/or configurations associated with the one or more images and associated analysis data; and
provide, via the graphical user interface, the aggregate information, wherein the aggregate information is partitioned based at least in part on the identifiers and/or configurations associated with the one or more images and associated analysis data.
11. A computer-implemented method comprising:
by one or more processors executing program instructions:
generating data useable for rendering a graphical user interface;
receiving, via the graphical user interface, a selection of at least a first feature and a second feature of a first image;
receiving, via the graphical user interface, a selection of a trigger;
determining configuration data based at least in part on the first feature and the trigger; and
providing the configuration data to a machine vision system for execution by the machine vision system.
12. The computer-implemented method of claim 11 , wherein the first image is received from the machine vision system.
13. The computer-implemented method of claim 11 further comprising:
by the one or more processors executing program instructions:
receiving, via the graphical user interface, a selection of a second feature of the first image, wherein the second feature is spatially located relative to the first feature in the first image,
wherein the configuration data is determined further based on the second feature.
14. The computer-implemented method of claim 11 further comprising:
by the one or more processors executing program instructions:
receiving, via the graphical user interface, one or more adjustments to be applied to images acquired by the machine vision system.
15. The computer-implemented method of claim 14 further comprising:
by the one or more processors executing program instructions:
receiving, via the graphical user interface, a selection of a tolerance threshold associated with the first feature.
16. The computer-implemented method of claim 11 further comprising:
by the one or more processors executing program instructions:
receiving, via secure communications and from the machine vision system, one or more additional images;
processing the one or more additional images based on the configuration data; and
providing, via the graphical user interface, an evaluation of the one or more additional images.
17. The computer-implemented method of claim 16 , wherein the evaluation comprises an indication of whether or not the first feature was found in each of the one or more additional images.
18. The computer-implemented method of claim 11 , wherein the trigger comprises an indication of a threshold number of at least one of: images passing evaluation based on the configuration data, images not passing evaluation based on the configuration data, or images not being readable based on the configuration data.
19. The computer-implemented method of claim 11 further comprising:
by the one or more processors executing program instructions:
receiving, via secure communications and from the machine vision system, a live feed of image data acquired by the machine vision system; and
displaying, in the graphical user interface, the live feed of image data.
20. The computer-implemented method of claim 11 further comprising:
by the one or more processors executing program instructions:
accessing, from a data store separate from the machine vision system, one or more images and associated analysis data provided by the machine vision system;
generating aggregate information based at least in part on: the one or more images and associated analysis data, and identifiers and/or configurations associated with the one or more images and associated analysis data; and
providing, via the graphical user interface, the aggregate information, wherein the aggregate information is partitioned based at least in part on the identifiers and/or configurations associated with the one or more images and associated analysis data.