Photos | Ordination Day Excitement
A crowd of 29 people gather outside the building, ready to celebrate Sarah Hagan's ordination. The blue sky and palm trees set the scene for a joyous occasion as cars and pedestrians share the busy sidewalk.
BLIP-2 Description:
a crowd of people standing outside of a buildingChronologically Adjacent
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Metadata
Capture date:
Original Dimensions:
5616w x 3744h - (download 4k)
Usage
Dominant Color:
tarmac path machine walkway wheel city urban furniture ordination sarah hagan intersection vehicle car coupe sky plant tree transportation bag accessories sidewalk tire area traffic outdoor street chair road arch glasses building footwear architecture crowd boulevard spoke wbtla automobile sports palm light metropolis pedestrian handbag walking wbtla_ordination land town femple alloy shoe
iso
100
metering mode
5
aperture
f/8
focal length
16mm
shutter speed
1/320s
camera make
Canon
camera model
lens model
date
2011-05-15T12:36:10.230000-07:00
tzoffset
-25200
tzname
America/Los_Angeles
overall
(42.43%)
curation
(50.00%)
highlight visibility
(4.51%)
behavioral
(90.74%)
failure
(-0.10%)
harmonious color
(4.05%)
immersiveness
(2.49%)
interaction
(1.00%)
interesting subject
(-28.59%)
intrusive object presence
(-6.96%)
lively color
(0.80%)
low light
(0.63%)
noise
(-0.66%)
pleasant camera tilt
(-8.36%)
pleasant composition
(-59.08%)
pleasant lighting
(1.84%)
pleasant pattern
(5.47%)
pleasant perspective
(20.09%)
pleasant post processing
(-1.93%)
pleasant reflection
(-5.67%)
pleasant symmetry
(0.90%)
sharply focused subject
(0.22%)
tastefully blurred
(-2.31%)
well chosen subject
(17.32%)
well framed subject
(-43.99%)
well timed shot
(0.35%)
all
(2.01%)
* 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-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.