PBM: Netpbm Test Image (P4)
A 256×256 fruit still-life image as binary Netpbm P4 (1-bit bitmap): the minimal, header-plus-raw-pixels family used across Unix imaging tools. For testing Netpbm parsers and conversion.
Rendered preview of the pbm file (8 KB). Download above for the original.
Specifications
- Format
- Netpbm P4
- Width
- 256
- Height
- 256
- Mode
- 1
- Depth
- 1-bit bitmap
- Encoding
- binary
- Alt Text
- A 256×256 fruit still-life image as binary Netpbm P4 (1-bit bitmap): the minimal, header-plus-raw-pixels family used across Unix imaging tools
- Alt Text Source
- description
Testing contract
Expected to pass- Scenario
- Exercise PBM: Netpbm Test Image (P4) in its raster formats workflow. A 256×256 fruit still-life image as binary Netpbm P4 (1-bit bitmap): the minimal, header-plus-raw-pixels family used across Unix imaging tools.
- Expected result
- Decoded geometry is 256×256 pixels in 1 mode; frame count is 1. Declared feature checks: mode=1; depth=1-bit bitmap.
What is a .pbm file?
PBM (Portable Bitmap) is the simplest Netpbm format, storing 1-bit black-and-white images as ASCII or packed binary bits after a short text header. It uses no compression and represents each pixel as a single on or off value. It is used for bilevel masks and toolchain intermediates.
How to use this file
Use an example PBM to test bilevel image parsing, bit-packing handling, and pipelines that produce or consume black-and-white masks.
How to use this file for testing
“PBM: Netpbm Test Image (P4)” is a deterministic Testaroo fixture for Conversion testing. The same content exported across many formats and linked as a group, so you can convert one and diff against the expected twin.
Documented properties for this file: 256×256 · binary · Netpbm P4. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.
Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such, expect parsers to fail loudly rather than silently accept them.
For image AI or enhancement tools, run the model or filter on this file and diff against the clean or ground-truth companion in the same group when available. Keep seeds and documented damage parameters in your evaluation notes so regressions are attributable.
Code examples
<img src="testcard.pbm" alt="Example image" width="640" loading="lazy">Generated by generation/images_formats2.py. Free for any use, no attribution required, license.
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