Applications for anamorphic lenses
Novel applications for anamorphic lenses in smart glasses. Anamorphic lenses preserve straightness of motion and lines (i.e., “linearity”). As a practical matter, existing computer vision models can be used with anamorphic images without re-training or intermediate conversion steps (unlike fisheye lenses). The contents of the present disclosure provide substantial improvements for applications that have different FOV requirements along different axis. Solutions for ergonomic hand placement (relative to gaze), non-square photosites, binning and eye-tracking are discussed throughout.
1 . A device comprising:
a camera module comprising:
an array of photosites;
an anamorphic lens characterized by an optical squeeze ratio that is different than 1, the anamorphic lens irremovably mounted in a fixed position relative to the array of photosites, where each photosite of the array of photosites is characterized by a reciprocal aspect ratio to the optical squeeze ratio; and
machine learning logic configured to:
receive and process light information comprising raw sensor data from the array of photosites; and
send a subset of the light information from the array of photosites to a processing device; and
the processing device comprising:
a processor; and
a non-transitory computer-readable medium comprising instructions that when executed by the processor, cause the processor to:
receive from the camera module the subset of the light information from the array of photosites; and
demosaic the subset of the light information to create a region of interest image.
2 . The device of claim 1 , where the device comprises a smart doorbell.
3 . The device of claim 1 , where the camera module is horizontally mounted within an access panel.
4 . The device of claim 1 , where the device comprises a smart car.
5 . The device of claim 4 , where the camera module is externally mounted to capture surroundings of the smart car.
6 . The device of claim 4 , where the camera module is internally mounted to capture a user interaction of a user of the smart car.
7 . The device of claim 1 , where the device comprises an Internet of Things (IOT) device.
8 . The device of claim 1 , where the device comprises a smart phone.
9 . A camera module comprising:
an array of photosites;
an anamorphic lens characterized by an optical squeeze ratio that is different than 1, the anamorphic lens irremovably mounted in a fixed position relative to the array of photosites, where each photosite of the array of photosites is characterized by a reciprocal aspect ratio to the optical squeeze ratio; and
a machine learning processor configured to:
receive and process light information from the array of photosites; and
send a subset of the light information from the array of photosites as an output of the camera module, where the light information and the subset of the light information comprises non-demosaiced image data.
10 . The camera module of claim 9 , further comprising a physical frame configured to support the anamorphic lens, where:
the anamorphic lens is characterized by a major axis in a fixed orientation relative to the array of photosites and a minor axis,
the major axis of the anamorphic lens is characterized by a first magnification,
the minor axis of the anamorphic lens is characterized by a second magnification, and
the first magnification is greater than the second magnification.
11 . The camera module of claim 10 , where:
the physical frame is configured to be used in a first orientation, and
the major axis of the anamorphic lens is vertically-oriented with respect to the first orientation of the physical frame.
12 . The camera module of claim 10 , where:
the physical frame is configured to be used in a first orientation, and
the major axis of the anamorphic lens is horizontally-oriented with respect to the first orientation of the physical frame.
13 . The camera module of claim 10 , where:
the physical frame is configured to be installed onto another device in a first fixed orientation, and
the major axis of the anamorphic lens is obliquely-oriented with respect to the first fixed orientation of the physical frame.
14 . The camera module of claim 9 , further comprising a color filter array characterized by the reciprocal aspect ratio to the optical squeeze ratio.
15 . The camera module of claim 9 , where the anamorphic lens is configured to optically squeeze light based on the optical squeeze ratio and the each photosite of the array of photosites is configured to optically un-squeeze light based on the reciprocal aspect ratio.
16 . A method of accessing a camera module, comprising:
causing the camera module to capture light information via an array of photosites that are irremovably mounted in a fixed position relative to an anamorphic lens, where the anamorphic lens is characterized by an optical squeeze ratio that is different than 1 and each photosite of the array of photosites is characterized by a reciprocal aspect ratio to the optical squeeze ratio; and
obtaining region-of-interest light information from the camera module, the region-of-interest light information determined based on a determination by a machine learning processor in the camera module configured to process the light information, where the region-of-interest light information comprises non-demosaiced image data.
17 . The method of claim 16 , further comprising obtaining, by a device accessing the camera module, neural network activations associated with the light information from the camera module.
18 . The method of claim 16 , where the camera module further comprises a Red Clear Clear Clear (RCCC) color filter array.
19 . The method of claim 16 , where the camera module further comprises a Bayer color filter array.
20 . The method of claim 19 , further comprising developing one or more pixels from the light information.