Generating simulated image training data
Generating image training data for training an autonomous photography agent to learn the preferences and photo-taking styles of a given set of human users. The preferences and/or photo-taking styles include, but are not necessarily limited, to: (i) human users of a certain age group (such as toddlers or adults); (ii) profession of the photo-taker (professional photographer rather than a hobbyist); (iii) health status of the photo-taker; and/or (iv) travel status of the photo-taker (such as a tourist in a new city rather than a resident of a given city).
1. A computer-implemented method (CIM) comprising:
receiving a class information data set including information indicative of a set of personal characteristic parameter value range(s) for a class of humans, with the personal characteristic parameter value range(s) respectively corresponding to a plurality of personal characteristic parameters;
receiving a photographic images data set including: (i) a plurality of photographic images captured by human(s), and (ii) for each given photographic image, a set of personal characteristic parameter value(s) characterizing the photographer who captured the given photographic image;
selecting a plurality of training images from the plurality of photographic images, with the selection being based upon the personal characteristic parameter value range(s) and the personal characteristic parameter value(s) respectively associated with the photographic images;
generating human-emulating machine logic for controlling a photographic robot to capture human-emulating photographic images that emulate photographs that would tend to be taken by humans characterized by the personal characteristic parameter value range(s);
configuring the photographic robot with the human-emulating machine logic; and
capturing, by photographic robot, a first human-emulating photographic image under control of the human-emulating machine logic.
2. The CIM of claim 1 wherein the human-emulating machine logic causes the first photographic image to emulate a photograph that would tend to be taken by humans characterized by the personal characteristic parameter value range(s) of the humans.
3. The CIM of claim 1 wherein the metadata of the first human-emulating photographic image is used as a part of an image training data set to train an artificial intelligence (AI) system relating to training image data.
4. The CIM of claim 1 wherein metadata is created for the first human-emulating photographic image.
5. The CIM of claim 4 wherein the metadata includes information indicative of: (i) the time that the human-emulating photographic image was taken, and (ii) the location where the human-emulating photographic image was taken.
6. The CIM of claim 4 wherein the metadata includes information indicative of whether the human-emulating photographic image is an accurate reflection of the photo-taking quality of the humans characterized by the personal characteristic parameter value range(s) of the humans.
7. A computer program product (CPP) comprising:
a machine readable storage device; and
computer code stored on the machine readable storage device, with the computer code including instructions and data to cause a processor(s) set to perform operations including the following:
receiving a class information data set including information indicative of a set of personal characteristic parameter value range(s) for a class of humans, with the personal characteristic parameter value range(s) respectively corresponding to a plurality of personal characteristic parameters,
receiving a photographic images data set including: (i) a plurality of photographic images captured by human(s), and (ii) for each given photographic image, a set of personal characteristic parameter value(s) characterizing the photographer who captured the given photographic image,
selecting a plurality of training images from the plurality of photographic images, with the selection being based upon the personal characteristic parameter value range(s) and the personal characteristic parameter value(s) respectively associated with the photographic images,
generating human-emulating machine logic for controlling a photographic robot to capture human-emulating photographic images that emulate photographs that would tend to be taken by humans characterized by the personal characteristic parameter value range(s),
configuring the photographic robot with the human-emulating machine logic, and
capturing, by photographic robot, a first human-emulating photographic image under control of the human-emulating machine logic.
8. The CPP of claim 7 wherein the human-emulating machine logic causes the first photographic image to emulate a photograph that would tend to be taken by humans characterized by the personal characteristic parameter value range(s) of the humans.
9. The CPP of claim 7 wherein the metadata of the first human-emulating photographic image is used as a part of an image training data set to train an artificial intelligence (AI) system relating to training image data.
10. The CPP of claim 7 wherein metadata is created for the first human-emulating photographic image.
11. The CPP of claim 10 wherein the metadata includes information indicative of: (i) the time that the human-emulating photographic image was taken, and (ii) the location where the human-emulating photographic image was taken.
12. The CPP of claim 10 wherein the metadata includes information indicative of whether the human-emulating photographic image is an accurate reflection of the photo-taking quality of the humans characterized by the personal characteristic parameter value range(s) of the humans.
13. A computer system (CS) comprising:
a processor(s) set;
a machine readable storage device; and
computer code stored on the machine readable storage device, with the computer code including instructions and data to cause the processor(s) set to perform operations including the following:
receiving a class information data set including information indicative of a set of personal characteristic parameter value range(s) for a class of humans, with the personal characteristic parameter value range(s) respectively corresponding to a plurality of personal characteristic parameters,
receiving a photographic images data set including: (i) a plurality of photographic images captured by human(s), and (ii) for each given photographic image, a set of personal characteristic parameter value(s) characterizing the photographer who captured the given photographic image,
selecting a plurality of training images from the plurality of photographic images, with the selection being based upon the personal characteristic parameter value range(s) and the personal characteristic parameter value(s) respectively associated with the photographic images,
generating human-emulating machine logic for controlling a photographic robot to capture human-emulating photographic images that emulate photographs that would tend to be taken by humans characterized by the personal characteristic parameter value range(s),
configuring the photographic robot with the human-emulating machine logic, and
capturing, by photographic robot, a first human-emulating photographic image under control of the human-emulating machine logic.
14. The CS of claim 13 wherein the human-emulating machine logic causes the first photographic image to emulate a photograph that would tend to be taken by humans characterized by the personal characteristic parameter value range(s) of the humans.
15. The CS of claim 13 wherein the metadata of the first human-emulating photographic image is used as a part of an image training data set to train an artificial intelligence (AI) system relating to training image data.
16. The CS of claim 13 wherein metadata is created for the first human-emulating photographic image.
17. The CS of claim 16 wherein the metadata includes information indicative of: (i) the time that the human-emulating photographic image was taken, and (ii) the location where the human-emulating photographic image was taken.
18. The CS of claim 16 wherein the metadata includes information indicative of whether the human-emulating photographic image is an accurate reflection of the photo-taking quality of the humans characterized by the personal characteristic parameter value range(s) of the humans.