Photos | Oriental Calligraphy on Wooden Building

A glimpse of the beautiful oriental writings on the wooden walls of a historic building in Osaka, Japan. The intricate detail on this wooden plaque tells the story of the building's ancient heritage and the meaning that lies within.
BLIP-2 Description:
a wooden building with oriental writing on the wallMetadata
Capture date:
Original Dimensions:
640w x 480h - (download 4k)
Usage
Dominant Color:
Location:
architecture 御 道喜 calligraphy 千百 示 奉祝 building 过 年 心 symbol whiskey bar plywood 事業 temple 后 始 kita furniture 管 敖 来 flooring text handwriting wood 书 境内 art japan 大 plaque altar 即 平成 prayer church osaka_whiskey_bar cross nakanoshima 慰 落 hardwood 整 shrine trip 十四 当 意 公 於 神徳 floor 共 の 神 monastery archaeology osaka
flash fired
true
iso
223
metering mode
5
aperture
f/2.9
focal length
6mm
shutter speed
1/30s
camera make
OLYMPUS OPTICAL CO.,LTD
camera model
date
2003-01-03T23:05:26-08:00
tzoffset
-28800
tzname
America/Los_Angeles
overall
(20.15%)
curation
(50.00%)
highlight visibility
(2.44%)
behavioral
(70.21%)
failure
(-1.12%)
harmonious color
(-1.06%)
immersiveness
(0.37%)
interaction
(1.00%)
interesting subject
(-77.10%)
intrusive object presence
(-4.27%)
lively color
(-17.63%)
low light
(32.52%)
noise
(-8.79%)
pleasant camera tilt
(-14.20%)
pleasant composition
(-74.51%)
pleasant lighting
(-40.67%)
pleasant pattern
(10.94%)
pleasant perspective
(-13.12%)
pleasant post processing
(1.82%)
pleasant reflection
(-1.57%)
pleasant symmetry
(1.10%)
sharply focused subject
(0.68%)
tastefully blurred
(-7.53%)
well chosen subject
(-3.10%)
well framed subject
(-31.42%)
well timed shot
(-1.17%)
all
(-8.69%)
* NOTE: This image was scaled up from its original size using an AI model called GFP-GAN (Generative Facial Prior), which is a
Generative adversartial network that can be used to repair (or upscale in this case) photos, sometimes the results are a little...
weird.
* WARNING: The title and caption of this image were generated by an AI LLM (gpt-3.5-turbo-0301
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.