When youโre working with binary data in Pythonโwhether thatโs image bytes, network payloads, or any in-memory binary streamโyou often need a file-like interface without touching the disk. Thatโs where BytesIO from the built-in io module comes in handy. It lets you treat a bytes buffer as if it were a file.
What Is BytesIO?
Module:io
Class:BytesIO
Purpose:
Provides an in-memory binary stream.
Acts like a file opened in binary mode ('rb'/'wb'), but data lives in RAM rather than on disk.
Ideal for testing code that expects a file-like object.
Safety
No temporary files cluttering up your filesystem.
Integration
Libraries that accept file-like objects (e.g., PIL, requests) will work with BytesIO.
Basic Examples
1. Writing Bytes to a Buffer
from io import BytesIO
# Create a BytesIO buffer
buffer = BytesIO()
# Write some binary data
buffer.write(b'Hello, \xF0\x9F\x98\x8A') # includes a smiley emoji in UTF-8
# Retrieve the entire contents
data = buffer.getvalue()
print(data) # b'Hello, \xf0\x9f\x98\x8a'
print(data.decode('utf-8')) # Hello, ๐
# Always close when done
buffer.close()
Marigold repurposes Stable Diffusion for dense prediction tasks such as monocular depth estimation and surface normal prediction, delivering a level of detail often missing even in top discriminative models.
Key aspects that make it great: – Reuses the original VAE and only lightly fine-tunes the denoising UNet – Trained on just tens of thousands of synthetic imageโmodality pairs – Runs on a single consumer GPU (e.g., RTX 4090) – Zero-shot generalization to real-world, in-the-wild images
Generate New Camera Angles Generate the Next Shot Use Any Style to Transfer to a Video Change Environments, Locations, Seasons and Time of Day Add Things to a Scene Remove Things from a Scene Change Objects in a Scene Apply the Motion of a Video to an Image Alter a Character’s Appearance Recolor Elements of a Scene Relight Shots Green Screen Any Object, Person or Situation
This demo is created for coders who are familiar with this awesome creative coding platform. You may quickly modify the code to work for video or to stipple your own Procssing drawings by turning them intoย PImageย and run the simulation. This demo code also serves as a reference implementation of my articleย Blue noise sampling using an N-body simulation-based method. If you are interested in 2.5D, you may mod the code to achieve what I discussed in this artist friendlyย article.
๐น Code Readability & Simplicity โ Use meaningful names, write short functions, follow SRP, flatten logic, and remove dead code. โ Clarity is a feature.
๐น Function & Class Design โ Limit parameters, favor pure functions, small classes, and composition over inheritance. โ Structure drives scalability.
๐น Testing & Maintainability โ Write readable unit tests, avoid over-mocking, test edge cases, and refactor with confidence. โ Test what matters.
๐น Code Structure & Architecture โ Organize by features, minimize global state, avoid god objects, and abstract smartly. โ Architecture isnโt just backend.
๐น Refactoring & Iteration โ Apply the Boy Scout Rule, DRY, KISS, and YAGNI principles regularly. โ Refactor like itโs part of development.
๐น Documentation & Comments โ Let your code explain itself. Comment why, not what, and document at the source. โ Good docs reduce team friction.
๐น Tooling & Automation โ Use linters, formatters, static analysis, and CI reviews to automate code quality. โ Let tools guard your gates.
๐น Final Review Practices โ Review, refactor nearby code, and avoid cleverness in the name of brevity. โ Readable code is better than smart code.
I ran Steamboat Willie (now public domain) through Flux Kontext to reimagine it as a 3D-style animated piece. Instead of going the polished route with something like W.A.N. 2.1 for full image-to-video generation, I leaned into the raw, handmade vibe that comes from converting each frame individually. It gave it a kind of stop-motion texture, imperfect, a bit wobbly, but full of character.
Our human-centric dense prediction model delivers high-quality, detailed (depth) results while achieving remarkable efficiency, running orders of magnitude faster than competing methods, with inference speeds as low as 21 milliseconds per frame (the large multi-task model on an NVIDIA A100). It reliably captures a wide range of human characteristics under diverse lighting conditions, preserving fine-grained details such as hair strands and subtle facial features. This demonstrates the model’s robustness and accuracy in complex, real-world scenarios.
The state of the art in human-centric computer vision achieves high accuracy and robustness across a diverse range of tasks. The most effective models in this domain have billions of parameters, thus requiring extremely large datasets, expensive training regimes, and compute-intensive inference. In this paper, we demonstrate that it is possible to train models on much smaller but high-fidelity synthetic datasets, with no loss in accuracy and higher efficiency. Using synthetic training data provides us with excellent levels of detail and perfect labels, while providing strong guarantees for data provenance, usage rights, and user consent. Procedural data synthesis also provides us with explicit control on data diversity, that we can use to address unfairness in the models we train. Extensive quantitative assessment on real input images demonstrates accuracy of our models on three dense prediction tasks: depth estimation, surface normal estimation, and soft foreground segmentation. Our models require only a fraction of the cost of training and inference when compared with foundational models of similar accuracy.
Maya blue is a highly unusual pigment because it is a mix of organic indigo and an inorganic clay mineral called palygorskite. ย
Echoing the color of an azure sky, the indelible pigment was used to accentuate everything from ceramics to human sacrifices in the Late Preclassic period (300 B.C. to A.D. 300).
A team of researchers led byย Dean Arnold, an adjunct curator of anthropology at the Field Museum in Chicago, determined that the key to Maya blue was actually a sacred incense called copal. By heating the mixture of indigo, copal and palygorskite over a fire, the Maya produced the unique pigment, he reported at the time.
In general, when light interacts with matter, a complicated light-matter dynamic occurs. This interaction depends on the physical characteristics of the light as well as the physical composition and characteristics of the matter.
That is, some of the incident light is reflected, some of the light is transmitted, and another portion of the light is absorbed by the medium itself.
A BRDF describes how much light is reflected when light makes contact with a certain material. Similarly, a BTDF (Bi-directional Transmission Distribution Function) describes how much light is transmitted when light makes contact with a certain material
It is difficult to establish exactly how far one should go in elaborating the surface model. A truly complete representation of the reflective behavior of a surface might take into account such phenomena as polarization, scattering, fluorescence, and phosphorescence, all of which might vary with position on the surface. Therefore, the variables in this complete function would be:
incoming and outgoing angle incoming and outgoing wavelength incoming and outgoing polarization (both linear and circular) incoming and outgoing position (which might differ due to subsurface scattering) time delay between the incoming and outgoing light ray
