Photos | Christmas Cheer with Friends

Tang Lingsheng, Li Yu, Andy N, Alvaro V, and Kevin H strike a pose in front of a beautifully decorated Christmas tree. The group's festive clothing adds to the holiday spirit, and the cozy room and warm lighting create the perfect backdrop for a holiday photo.
BLIP-2 Description:
a group of people posing for a picture in front of a christmas treeMetadata
Capture date:
Original Dimensions:
4032w x 3024h - (download 4k)
Usage
Dominant Color:
building decorations photography jacket shoe christmas flower furniture handbag bag lighting toy mascot couch blouse book decoration andy potted shelter architecture indoors jeans kevin shirt library glasses yu publication li balloon sweater tang lingsheng plant pants accessories footwear coat portrait flooring room costume tree outdoors living alvaro v knitwear lamp hat floor fir christmas tree festival window
iso
80
metering mode
5
aperture
f/1.8
focal length
4mm
shutter speed
1/15s
camera make
Apple
camera model
lens model
overall
(44.78%)
curation
(70.05%)
highlight visibility
(5.88%)
behavioral
(70.45%)
failure
(-0.24%)
harmonious color
(1.91%)
immersiveness
(0.20%)
interaction
(2.00%)
interesting subject
(52.59%)
intrusive object presence
(-18.87%)
lively color
(0.52%)
low light
(11.35%)
noise
(-2.37%)
pleasant camera tilt
(-8.74%)
pleasant composition
(-20.04%)
pleasant lighting
(-38.67%)
pleasant pattern
(13.31%)
pleasant perspective
(3.67%)
pleasant post processing
(7.19%)
pleasant reflection
(2.03%)
pleasant symmetry
(0.63%)
sharply focused subject
(0.42%)
tastefully blurred
(-1.35%)
well chosen subject
(-39.55%)
well framed subject
(31.25%)
well timed shot
(1.86%)
all
(-0.45%)
* 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 with AI (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.