Photos | Literary Haven in Japan Town, 2023
A vibrant snapshot of the local bookstore in the heart of Japan Town, Skyela. This picture, taken on December 22, 2023, captures a bibliophile's paradise filled to the brim with volumes of knowledge and stories. Among the patrons, Tenzin Gyatso, an avid reader, immerses himself in this world of words. The bookstore's architectural beauty and unique decor, including glasses and artistic accessories, invite visitors to lose themselves in the magic of reading. Look closer and you'll spot three charming paintings and a memory-filled photo frame; a testament to the presence of art in all walks of life.
BLIP-2 Description:
a book store with many books on shelvesChronologically Adjacent
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Metadata
Capture date:
Original Dimensions:
6000w x 4000h - (download 4k)
Usage
Dominant Color:
iso
5000
metering mode
5
aperture
f/2.8
focal length
24mm
shutter speed
1/640s
camera make
Canon
camera model
lens model
date
2023-12-22T18:37:15.830000-08:00
tzoffset
-28800
tzname
America/Los_Angeles
overall
(53.32%)
curation
(50.00%)
highlight visibility
(4.35%)
behavioral
(70.33%)
failure
(-0.05%)
harmonious color
(6.69%)
immersiveness
(2.76%)
interaction
(1.00%)
interesting subject
(-7.17%)
intrusive object presence
(-0.83%)
lively color
(-12.70%)
low light
(11.89%)
noise
(-0.63%)
pleasant camera tilt
(-2.98%)
pleasant composition
(4.50%)
pleasant lighting
(6.51%)
pleasant pattern
(30.59%)
pleasant perspective
(39.43%)
pleasant post processing
(-7.57%)
pleasant reflection
(3.70%)
pleasant symmetry
(4.49%)
sharply focused subject
(1.61%)
tastefully blurred
(3.49%)
well chosen subject
(5.41%)
well framed subject
(-10.61%)
well timed shot
(-3.88%)
all
(9.35%)
* NOTE: Amazon Rekognition
detected a celebrity in this image using the
Celebrity Recognition API. The API isn't perfect, but it does give you the MatchConfidence which I display
next to the celebrity's name along with links _↗ to their info.
* WARNING: The title and caption of this image were generated by an AI LLM (gpt-4-0613
from
OpenAI)
based on a
BLIP-2 image-to-text labeling, tags,
location,
people
and album metadata from the image and are
potentially inaccurate, often hilariously so. If you'd like me to adjust anything,
just reach out.