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:
architecture spoke arch urban plant building coupe wbtla chair footwear pedestrian boulevard sidewalk transportation walkway area tree road outdoor furniture femple intersection sky tarmac city wbtla_ordination bag shoe land sarah hagan street traffic machine glasses metropolis path tire wheel ordination car vehicle light accessories handbag walking town alloy sports automobile palm crowd
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.