Photos | Diverse Life of Chinatown Streets in 2023
A vibrant portrayal of a typical day in Chinatown, 2023. Countless people navigating the urban canvas, each with their own stories, destinations, and journeys. Amidst the crowd, the street is alive with a symphony of automobiles—cars, trucks, motorcycles, and even bicycles—all coexisting on this urban path. The cloudy sky overlooking this lively intersection adds a charming contrast to the bustling scene below.
BLIP-2 Description:
a street with a lot of people walking down itMetadata
Capture date:
Original Dimensions:
6000w x 4000h - (download 4k)
Usage
Dominant Color:
urban wheel rekognition_c storefront street 甜 lamp jeans plate heart town transportation outdoor footwear bag tire path motorcycle parking sky pedestrian cai city pickup sports truck automobile car buddha sidewalk building license bicycle intersection shoe handbag outdoors sweetheart walkway coupe tarmac cloudy 淵 balloon lot neighborhood alloy lantern pants 品 area downtown metropolis 心意 road spoke vehicle lane machine architecture light traffic car wheel shelter accessories sedan tea cocktai chinatown land
iso
100
metering mode
5
aperture
f/4
focal length
24mm
shutter speed
1/125s
camera make
Canon
camera model
lens model
overall
(45.41%)
curation
(50.00%)
highlight visibility
(4.51%)
behavioral
(90.73%)
failure
(-0.22%)
harmonious color
(5.41%)
immersiveness
(6.74%)
interaction
(1.00%)
interesting subject
(-49.39%)
intrusive object presence
(-3.91%)
lively color
(-6.02%)
low light
(17.38%)
noise
(-0.95%)
pleasant camera tilt
(-7.01%)
pleasant composition
(-60.99%)
pleasant lighting
(-9.10%)
pleasant pattern
(9.23%)
pleasant perspective
(17.65%)
pleasant post processing
(1.65%)
pleasant reflection
(-0.63%)
pleasant symmetry
(2.32%)
sharply focused subject
(0.29%)
tastefully blurred
(0.88%)
well chosen subject
(5.36%)
well framed subject
(-52.93%)
well timed shot
(4.32%)
all
(1.81%)
* 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.